merge: 合并远程 ft_wyt 分支，解决冲突

backend/app/api/v1/endpoints/data_process.py

@@ -35,7 +35,12 @@ from fastapi.responses import StreamingResponse
 from psycopg.rows import dict_row
 
 from app.core.auth import filter_accessible_resource_ids, get_current_user, is_admin
+
 from app.core.logging import get_structured_logger
+
+from app.core.config import get_settings
+from app.db.platform_store import get_platform_store
+
 from app.modules.data_process.algorithms import (
     ParsedText,
     canonical_record_json,
@@ -61,6 +66,10 @@ from app.modules.data_process.document_chunking import (
     chunk_semantic_text,
     merge_short_chunks,
 )
+from app.modules.data_process.evaluation import (
+    evaluate_result_record,
+    reevaluate_edited_record,
+)
 from app.modules.data_process.generation import generate_model_records
 from app.modules.data_process.office_preview import (
     MAX_XLSX_PREVIEW_ROWS,
@@ -82,6 +91,8 @@ from app.modules.data_process.store import (
     new_id,
     repeat_task_id,
 )
+from app.modules.storage.minio_store import get_object_storage
+from app.modules.storage.policy import should_store_in_minio
 from app.schemas.data_process import (
     DataProcessRegenerateRequest,
     DataProcessRepeatRequest,
@@ -97,6 +108,7 @@ from app.schemas.data_process import (
     PreviewItemUpdate,
     ProcessType,
     PublishRequest,
+    ResultBatchEvaluateRequest,
     ResultBatchRegenerateRequest,
     ResultRegenerateRequest,
     ResultUpdate,
@@ -221,9 +233,38 @@ def _commit_source_batch(
     staged: list[StagedSourceObject],
 ) -> list[dict[str, Any]]:
     storage.publish(staged)
+    storage_object_ids: list[str] = []
     try:
+        # The source reference remains in the task schema for compatibility,
+        # while storage_objects provides the authoritative MinIO index.
+        if get_settings().minio_enabled:
+            for item in prepared:
+                reference = str(item.get("storage_object_id") or "")
+                if not reference.startswith("minio://"):
+                    continue
+                object_key = storage.object_key(reference)
+                metadata = get_object_storage().stat(object_key)
+                object_row = get_platform_store().create_storage_object({
+                    "resource_type": "data_process_source",
+                    "resource_id": task_id,
+                    "version_id": str(item.get("id") or new_id("dpsf")),
+                    "bucket": get_object_storage().bucket,
+                    "object_key": object_key,
+                    "file_name": item.get("name"),
+                    "content_type": (item.get("metadata") or {}).get("content_type", "application/octet-stream"),
+                    "byte_size": metadata.get("byte_size") or item.get("raw_size") or 0,
+                    "checksum_sha256": item.get("checksum_sha256"),
+                    "status": "available",
+                    "created_by": item.get("created_by"),
+                })
+                storage_object_ids.append(str(object_row["id"]))
         return store.add_source_files(task_id, prepared)
     except Exception:
+        for object_id in storage_object_ids:
+            try:
+                get_platform_store().update_storage_object(object_id, {"status": "deleted"})
+            except Exception:
+                pass
         for item in staged:
             try:
                 storage.delete(item.reference)
@@ -771,6 +812,25 @@ def _run_generation(
                 duplicate_count=duplicate_count,
                 error_count=error_count,
             )
+            result_bytes = "".join(
+                structured_json_dumps(item) + "\n" for item in accepted
+            ).encode("utf-8")
+            if should_store_in_minio(len(result_bytes)):
+                result_key = f"data-process/{task_id}/results/{generation_run_id}.jsonl"
+                uploaded = get_object_storage().put_bytes(result_key, result_bytes, "application/jsonl")
+                get_platform_store().create_storage_object({
+                    "resource_type": "data_process_result",
+                    "resource_id": task_id,
+                    "version_id": generation_run_id,
+                    "bucket": uploaded["bucket"],
+                    "object_key": result_key,
+                    "file_name": f"{generation_run_id}.jsonl",
+                    "content_type": "application/jsonl",
+                    "byte_size": len(result_bytes),
+                    "checksum_sha256": hashlib.sha256(result_bytes).hexdigest(),
+                    "status": "available",
+                    "created_by": (store.get_task(task_id) or {}).get("created_by"),
+                })
             logger.info(
                 "data process generation completed task_id=%s generation_run_id=%s "
                 "output_count=%s filtered_count=%s duplicate_count=%s error_count=%s "
@@ -930,7 +990,7 @@ def _repeat_file_copies(
         source = store.get_source_file(source_task_id, old_file_id, include_content=True)
         new_file_id = new_id("dpsf")
         old_reference = str(source.get("storage_object_id") or "")
-        if old_reference.startswith("local://data-process/"):
+        if old_reference.startswith(("local://data-process/", "minio://data-process/")):
             staged_object = storage.stage_copy(
                 batch_id=batch_id,
                 source_reference=old_reference,
@@ -2044,12 +2104,13 @@ def update_result(
                 or 20
             ),
         )
-        quality = score_quality(
+        # 编辑后内容已变化：重算规则与语义层，旧的评审分不再可信直接丢弃。
+        update["quality_score"] = reevaluate_edited_record(
             merged,
-            min_output_length=minimum,
             source_content=source_content,
+            previous_quality=current.get("quality_score"),
+            min_output_length=minimum,
         )
-        update["quality_score"] = asdict(quality)
         result = store.update_result(
             task_id,
             result_id,
@@ -2094,11 +2155,6 @@ def restore_result(
                 or 20
             ),
         )
-        quality = score_quality(
-            restored,
-            min_output_length=minimum,
-            source_content=source_content,
-        )
         restored = store.update_result(
             task_id,
             result_id,
@@ -2108,7 +2164,12 @@ def restore_result(
                 "output": restored["output"],
                 "chosen": restored["chosen"],
                 "rejected": restored["rejected"],
-                "quality_score": asdict(quality),
+                "quality_score": reevaluate_edited_record(
+                    restored,
+                    source_content=source_content,
+                    previous_quality=current.get("quality_score"),
+                    min_output_length=minimum,
+                ),
                 "expected_updated_at": current.get("updated_at"),
             },
         )
@@ -2271,6 +2332,46 @@ def _safe_regeneration_error(exc: Exception) -> str:
     return re.sub(r"\s+", " ", str(exc)).strip()[:500] or "result regeneration failed"
 
 
+def _evaluate_result_in_place(
+    task_id: str,
+    current: dict[str, Any],
+    source_content: str,
+    config: dict[str, Any],
+    evaluation_model: dict[str, Any] | None,
+    store: DataProcessStore,
+    *,
+    expected_updated_at: str,
+    model_client: httpx.Client | None = None,
+    minimum: int = 20,
+) -> dict[str, Any]:
+    """评测单条结果并落库；复用逐结果互斥锁避免与重生成并发写冲突。"""
+
+    result_id = str(current["id"])
+    with _claim_result_regeneration(task_id, result_id):
+        quality = evaluate_result_record(
+            {
+                "instruction": current.get("instruction"),
+                "input": current.get("input"),
+                "output": current.get("output"),
+                "chosen": current.get("chosen"),
+                "rejected": current.get("rejected"),
+            },
+            source_content=source_content,
+            model=evaluation_model,
+            config=config,
+            client=model_client,
+            min_output_length=minimum,
+        )
+        return store.update_result(
+            task_id,
+            result_id,
+            {
+                "quality_score": quality,
+                "expected_updated_at": expected_updated_at,
+            },
+        )
+
+
 @router.post("/{task_id}/results/regenerate-batch")
 def regenerate_results_batch(
     task_id: str,
@@ -2444,6 +2545,186 @@ def regenerate_results_batch(
         )
 
 
+@router.post("/{task_id}/results/evaluate-batch")
+def evaluate_results_batch(
+    task_id: str,
+    payload: ResultBatchEvaluateRequest,
+    store: DataProcessStore = Depends(get_data_process_store),
+) -> dict[str, Any]:
+    """对一批结果执行三层质量评测（规则+语义+评审），允许部分成功。"""
+
+    started_at = time.perf_counter()
+    batch_id = new_id("dpeb")
+    with api_errors():
+        task = store.get_task(task_id)
+        if task.get("status") == "running":
+            raise ConflictError("data process task is running")
+        if task.get("output_dataset_id"):
+            raise InvalidStateError("published results cannot be evaluated")
+        config = task.get("config") or {}
+        evaluation_model: dict[str, Any] | None = None
+        model_id = _value(config, "generation_model_id", "generationModelId", None)
+        if model_id:
+            try:
+                evaluation_model = store.get_generation_model(str(model_id))
+            except NotFoundError:
+                logger.warning(
+                    "data process evaluation model unavailable, judge layer "
+                    "skipped task_id=%s model_id=%s",
+                    task_id,
+                    model_id,
+                )
+        evaluation_config = {
+            **config,
+            "output_type": str(
+                _value(config, "output_type", "outputType", "standard")
+            ).strip().lower(),
+        }
+        minimum = max(
+            1,
+            int(_value(config, "min_output_length", "minOutputLength", 20) or 20),
+        )
+
+        prepared: list[tuple[int, dict[str, Any], str, str]] = []
+        failures: list[tuple[int, dict[str, str]]] = []
+        for index, requested in enumerate(payload.items):
+            try:
+                current = store.get_result(task_id, requested.result_id)
+                if requested.expected_updated_at != str(current.get("updated_at") or ""):
+                    raise ConflictError("data process result was modified by another request")
+                source_content = ""
+                preview_id = current.get("preview_item_id")
+                if preview_id:
+                    preview = store.get_preview_item(task_id, str(preview_id))
+                    source_content = str(
+                        preview.get("edited_content")
+                        or preview.get("original_content")
+                        or ""
+                    )
+                prepared.append(
+                    (index, current, source_content, requested.expected_updated_at)
+                )
+            except ConflictError as exc:
+                failures.append((index, {
+                    "result_id": requested.result_id,
+                    "code": "conflict",
+                    "message": _safe_regeneration_error(exc),
+                }))
+            except (NotFoundError, InvalidStateError) as exc:
+                failures.append((index, {
+                    "result_id": requested.result_id,
+                    "code": "skipped",
+                    "message": _safe_regeneration_error(exc),
+                }))
+
+        logger.info(
+            "data process result batch evaluation started batch_id=%s task_id=%s "
+            "requested=%s prepared=%s judge_enabled=%s",
+            batch_id,
+            task_id,
+            len(payload.items),
+            len(prepared),
+            evaluation_model is not None,
+        )
+        successes: list[tuple[int, dict[str, Any]]] = []
+        if prepared:
+            try:
+                from app.modules.data_process.algorithms.embedding import (
+                    semantic_embedding_model,
+                )
+
+                semantic_embedding_model()
+            except Exception:
+                logger.warning(
+                    "data process semantic embedding unavailable, semantic layer "
+                    "will be skipped batch_id=%s",
+                    batch_id,
+                )
+            request_timeout = _result_regeneration_timeout(config)
+            model_timeout = httpx.Timeout(
+                request_timeout,
+                connect=min(10.0, request_timeout),
+            )
+            model_limits = httpx.Limits(
+                max_connections=RESULT_REGENERATION_CONCURRENCY,
+                max_keepalive_connections=RESULT_REGENERATION_CONCURRENCY,
+            )
+            with httpx.Client(timeout=model_timeout, limits=model_limits) as model_client, \
+                    ThreadPoolExecutor(
+                        max_workers=min(RESULT_REGENERATION_CONCURRENCY, len(prepared)),
+                        thread_name_prefix="data-result-evaluation",
+                    ) as executor:
+                futures = {
+                    executor.submit(
+                        _evaluate_result_in_place,
+                        task_id,
+                        current,
+                        source_content,
+                        evaluation_config,
+                        evaluation_model,
+                        store,
+                        expected_updated_at=expected_updated_at,
+                        model_client=model_client if evaluation_model else None,
+                        minimum=minimum,
+                    ): (index, str(current["id"]), time.perf_counter())
+                    for index, current, source_content, expected_updated_at in prepared
+                }
+                for future in as_completed(futures):
+                    index, result_id, item_started_at = futures[future]
+                    try:
+                        evaluated = future.result()
+                        successes.append((index, evaluated))
+                        outcome = "succeeded"
+                    except ConflictError as exc:
+                        outcome = "conflict"
+                        failures.append((index, {
+                            "result_id": result_id,
+                            "code": outcome,
+                            "message": _safe_regeneration_error(exc),
+                        }))
+                    except Exception as exc:
+                        outcome = "evaluation_failed"
+                        failures.append((index, {
+                            "result_id": result_id,
+                            "code": outcome,
+                            "message": _safe_regeneration_error(exc),
+                        }))
+                    logger.info(
+                        "data process result batch evaluation item finished "
+                        "batch_id=%s task_id=%s result_id=%s outcome=%s duration_ms=%.2f",
+                        batch_id,
+                        task_id,
+                        result_id,
+                        outcome,
+                        (time.perf_counter() - item_started_at) * 1000,
+                    )
+
+        success_items = [item for _, item in sorted(successes, key=lambda pair: pair[0])]
+        failure_items = [item for _, item in sorted(failures, key=lambda pair: pair[0])]
+        duration_ms = (time.perf_counter() - started_at) * 1000
+        logger.info(
+            "data process result batch evaluation completed batch_id=%s task_id=%s "
+            "succeeded=%s failed=%s duration_ms=%.2f",
+            batch_id,
+            task_id,
+            len(success_items),
+            len(failure_items),
+            duration_ms,
+        )
+        return ok(
+            {
+                "batch_id": batch_id,
+                "total": len(payload.items),
+                "succeeded": len(success_items),
+                "failed": len(failure_items),
+                "duration_ms": round(duration_ms, 2),
+                "items": success_items,
+                "failures": failure_items,
+            },
+            "data process results evaluated",
+        )
+
+
 @router.post("/{task_id}/results/{result_id}/regenerate")
 def regenerate_result(
     task_id: str,

backend/app/api/v1/endpoints/platform.py

@@ -23,8 +23,9 @@ from app.core.audit import audit_log, AuditActions
 from app.core.op_log import op_log, OpModule, OpAction
 from app.db.platform_store import get_platform_store
 from app.modules.compute_gateway.client import ComputeNodeClient
-from app.modules.compute_gateway.sync import fetch_eval_result_content, poll_compute_jobs_once
+from app.modules.compute_gateway.sync import _archive_node_directory, fetch_eval_result_content, poll_compute_jobs_once
 from app.modules.storage.minio_store import ObjectStorageError, get_object_storage
+from app.modules.storage.policy import should_store_in_minio
 
 router = APIRouter()
 _LOGIN_FAILURES: dict[str, list[float]] = {}
@@ -57,6 +58,33 @@ def _select_first_online_node(store: Any) -> dict[str, Any] | None:
     return None
 
 
+def _normalize_gpu_indices(payload: dict[str, Any], *, allow_primary: bool = True) -> list[int]:
+    """Normalize all frontend GPU selection shapes to sorted integer indexes."""
+    raw = payload.get("gpu_indices")
+    if raw is None:
+        raw = payload.get("gpus")
+    if raw is None and allow_primary and payload.get("gpu_id") is not None:
+        raw = [payload.get("gpu_id")]
+    if raw is None or raw == "":
+        return []
+    if isinstance(raw, str):
+        raw = [item.strip() for item in raw.split(",") if item.strip()]
+    if not isinstance(raw, (list, tuple, set)):
+        raw = [raw]
+    result: set[int] = set()
+    for item in raw:
+        if isinstance(item, str) and ":" in item:
+            item = item.rsplit(":", 1)[-1]
+        try:
+            index = int(item)
+        except (TypeError, ValueError) as exc:
+            raise ValueError(f"invalid GPU index: {item}") from exc
+        if index < 0:
+            raise ValueError("GPU index must be non-negative")
+        result.add(index)
+    return sorted(result)
+
+
 async def _wait_for_object_storage() -> None:
     """Wait for MinIO before starting a resource task."""
     settings = get_settings()
@@ -73,6 +101,106 @@ async def _wait_for_object_storage() -> None:
         await asyncio.sleep(max(1, settings.storage_check_interval_seconds))
 
 
+def _store_json_snapshot(
+    store: Any,
+    resource_type: str,
+    resource_id: str,
+    version_id: str,
+    object_key: str,
+    payload: dict[str, Any],
+    created_by: str | None = None,
+) -> dict[str, Any]:
+    """Persist non-secret task/model parameters as an auditable MinIO snapshot."""
+    sanitized = {
+        key: value
+        for key, value in payload.items()
+        if key not in {"api_key", "secret_key", "password", "token", "access_token"}
+    }
+    raw = json.dumps(sanitized, ensure_ascii=False, sort_keys=True, default=str).encode("utf-8")
+    # Task/evaluation payloads are already persisted in PostgreSQL. Avoid an
+    # extra MinIO round trip for small non-secret parameter snapshots.
+    if not should_store_in_minio(len(raw), content_type="application/json", file_format="json"):
+        return {}
+    uploaded = get_object_storage().put_bytes(object_key, raw, "application/json")
+    return store.create_storage_object({
+        "resource_type": resource_type,
+        "resource_id": resource_id,
+        "version_id": version_id,
+        "bucket": uploaded["bucket"],
+        "object_key": object_key,
+        "file_name": Path(object_key).name,
+        "content_type": "application/json",
+        "byte_size": len(raw),
+        "checksum_sha256": hashlib.sha256(raw).hexdigest(),
+        "status": "available",
+        "created_by": created_by,
+    })
+
+
+def _dataset_file_bytes(store: Any, file_id: str) -> bytes:
+    """Return the canonical dataset bytes, lazily indexing legacy DB content."""
+    row = store.dataset_file(file_id)
+    if not get_settings().minio_enabled:
+        return str(row.get("content") or "").encode("utf-8")
+
+    storage_object = None
+    object_id = str(row.get("storage_object_id") or "")
+    if object_id:
+        try:
+            storage_object = store.storage_object(object_id)
+        except KeyError:
+            storage_object = None
+    if storage_object and storage_object.get("status") == "available":
+        try:
+            return get_object_storage().get_bytes(storage_object["object_key"])
+        except Exception as exc:  # noqa: BLE001 - expose storage outage to callers
+            raise RuntimeError(f"dataset object is unavailable in MinIO: {exc}") from exc
+
+    # Compatibility migration for files created before MinIO was enabled.
+    raw = str(row.get("content") or "").encode("utf-8")
+    if not raw:
+        raise RuntimeError(f"dataset file has no MinIO object or legacy content: {file_id}")
+    # Small files intentionally remain database-backed. They can still be
+    # copied to a compute node directly when a task needs them.
+    if not should_store_in_minio(len(raw), file_format=row.get("file_format")):
+        return raw
+    object_key = (
+        f"datasets/{row['dataset_id']}/versions/"
+        f"{row.get('active_version_id') or row['id']}/{Path(str(row.get('name') or row['id'])).name}"
+    )
+    uploaded = get_object_storage().put_bytes(object_key, raw, "application/octet-stream")
+    created = store.create_storage_object({
+        "resource_type": "dataset",
+        "resource_id": str(row["dataset_id"]),
+        "version_id": str(row.get("active_version_id") or row["id"]),
+        "bucket": uploaded["bucket"],
+        "object_key": object_key,
+        "file_name": row.get("name"),
+        "content_type": "application/octet-stream",
+        "byte_size": len(raw),
+        "checksum_sha256": hashlib.sha256(raw).hexdigest(),
+        "status": "available",
+    })
+    store.link_dataset_file_storage_object(str(row["id"]), created["id"])
+    return raw
+
+
+def _dataset_version_bytes(store: Any, file_id: str, version_id: str) -> bytes:
+    row = store.dataset_file(file_id)
+    try:
+        version = next(item for item in store.file_versions(file_id)["versions"] if item["id"] == version_id)
+    except StopIteration as exc:
+        raise KeyError(version_id) from exc
+    object_id = str(version.get("storage_object_id") or "")
+    if get_settings().minio_enabled and object_id:
+        try:
+            obj = store.storage_object(object_id)
+            return get_object_storage().get_bytes(obj["object_key"])
+        except KeyError:
+            pass
+    return _dataset_file_bytes(store, file_id)
+
+
 def _select_eval_node(store: Any, preferred_node_id: str | None = None) -> dict[str, Any] | None:
     """Select the compute node for an eval job.
 
@@ -103,18 +231,63 @@ async def _prepare_resource_on_node(store: Any, resource_type: str, resource_id:
     if not get_settings().minio_enabled or not resource_id:
         return None
     objects = store.storage_objects_for_resource(resource_type, resource_id)
+    if not objects and resource_type in {"model", "trained_model"}:
+        resource = None
+        if resource_type == "model":
+            try:
+                resource = store.model(resource_id)
+            except KeyError:
+                try:
+                    resource = store.model_by_name(resource_id)
+                except KeyError:
+                    resource = next((item for item in store.models() if item.get("path") == resource_id), None)
+        else:
+            resource = next(
+                (item for item in store.trained_models() if item.get("id") == resource_id or item.get("name") == resource_id),
+                None,
+            )
+        source_path = str(
+            (resource or {}).get("path")
+            or (resource or {}).get("merged_path")
+            or (resource or {}).get("artifact_dir")
+            or ""
+        )
+        resolved_id = str((resource or {}).get("id") or resource_id)
+        if source_path and resolved_id:
+            client = ComputeNodeClient(node["api_base_url"], timeout=900)
+            await _archive_node_directory(
+                store,
+                client,
+                node,
+                source_path,
+                resource_type,
+                resolved_id,
+                "legacy-import",
+                f"models/{resolved_id}" if resource_type == "model" else f"trained_models/{resolved_id}",
+            )
+            resource_id = resolved_id
+            objects = store.storage_objects_for_resource(resource_type, resource_id)
     if not objects:
         return None
     client = ComputeNodeClient(node["api_base_url"], timeout=900)
     root_name = "trained_models" if resource_type in {"trained_model", "model_artifact"} else f"{resource_type}s"
     for obj in objects:
+        object_key = str(obj["object_key"])
+        marker = f"{root_name}/{resource_id}/versions/"
+        relative_name = Path(str(obj.get("file_name") or object_key)).name
+        if marker in object_key:
+            suffix = object_key.split(marker, 1)[1]
+            if "/" in suffix:
+                suffix = suffix.split("/", 1)[1]
+            if suffix:
+                relative_name = suffix
         await client.prepare_cache({
             "resource_id": resource_id,
             "version_id": obj["version_id"],
-            "download_url": get_object_storage().presigned_get(obj["object_key"]),
+            "download_url": get_object_storage().presigned_get(object_key),
             "checksum_sha256": obj.get("checksum_sha256") or "",
             "byte_size": obj.get("byte_size") or 0,
-            "relative_path": f"{root_name}/{resource_id}/{Path(str(obj.get('file_name') or obj['object_key'])).name}",
+            "relative_path": f"{root_name}/{resource_id}/{relative_name}",
         })
     return f"/data/yg-ft/{root_name}/{resource_id}"
 
@@ -370,8 +543,23 @@ async def _submit_fine_tune_task(store: Any, payload: dict[str, Any]) -> dict[st
         if not preflight["valid"]:
             errors = "; ".join(preflight.get("errors") or ["preflight failed"])
             raise RuntimeError(f"preflight failed: {errors}")
-        payload = {**payload, "compute_node_id": preflight["node"]["id"]}
+        prepared_job_payload = preflight.get("job_payload") or {}
+        payload = {
+            **payload,
+            "compute_node_id": preflight["node"]["id"],
+            "prepared_base_model_path": prepared_job_payload.get("model_name_or_path") or payload.get("prepared_base_model_path"),
+        }
     task = store.start_task(payload)
+    if get_settings().minio_enabled:
+        _store_json_snapshot(
+            store,
+            "fine_tune",
+            str(task["id"]),
+            str(task["id"]),
+            f"training/{task['id']}/versions/{task['id']}/training-config.json",
+            task,
+            task.get("created_by"),
+        )
     if get_settings().compute_mode == "simulator":
         return task
     node, job_payload = store.build_compute_job_payload(task["id"])
@@ -427,6 +615,20 @@ async def _fine_tune_preflight_with_job_payload(
     if get_settings().minio_enabled and get_settings().compute_mode != "simulator":
         try:
             await _wait_for_object_storage()
+            base_model_path = str(job_payload.get("model_name_or_path") or job_payload.get("base_model") or "")
+            base_model_id = str(job_payload.get("base_model_id") or job_payload.get("model_id") or "")
+            base_model = None
+            if base_model_id:
+                try:
+                    base_model = store.model(base_model_id)
+                except KeyError:
+                    base_model = None
+            if base_model is None:
+                base_model = next((item for item in store.models() if item.get("path") == base_model_path), None)
+            if base_model:
+                prepared_model = await _prepare_resource_on_node(store, "model", str(base_model["id"]), node)
+                if prepared_model:
+                    job_payload = {**job_payload, "base_model": prepared_model, "model_name_or_path": prepared_model}
         except Exception as exc:  # noqa: BLE001 - preflight exposes node storage failure
             sync_errors.append(f"shared storage health check failed: {exc}")
     if get_settings().compute_mode == "simulator":
@@ -1076,8 +1278,9 @@ async def merge_model(payload: dict[str, Any] = Body(...), current_user: dict =
 @router.get("/dataset-manage/preview/{file_id}")
 async def dataset_preview(file_id: str) -> dict[str, Any]:
     try:
-        row = get_platform_store().dataset_file(file_id)
+        store = get_platform_store()
-        return ok({"content": row["content"]})
+        content = _dataset_file_bytes(store, file_id).decode("utf-8", errors="replace")
+        return ok({"content": content})
     except KeyError:
         raise fail(404, "dataset file not found")
 
@@ -1106,7 +1309,8 @@ async def dataset_version_content(file_id: str, version_id: str) -> dict[str, An
         version = next((item for item in versions if item["id"] == version_id), None)
         if not version:
             raise KeyError(version_id)
-        return ok({"version": version, "content": row["content"]})
+        content = _dataset_version_bytes(get_platform_store(), file_id, version_id)
+        return ok({"version": version, "content": content.decode("utf-8", errors="replace")})
     except KeyError:
         raise fail(404, "dataset version not found")
 
@@ -1197,7 +1401,7 @@ async def _sync_training_dataset_to_compute_node(
 ) -> list[dict[str, Any]]:
     if get_settings().minio_enabled:
         files = store.training_dataset_files(dataset_id)
-        object_by_resource_name: dict[tuple[str, str], dict[str, Any]] = {}
+        object_by_resource_name: dict[tuple[str, str, str], dict[str, Any]] = {}
         resource_ids = {str(dataset_id)} | {
             str(item.get("dataset_id"))
             for item in files
@@ -1206,18 +1410,19 @@ async def _sync_training_dataset_to_compute_node(
         for resource_id in resource_ids:
             for obj in store.storage_objects_for_resource("dataset", resource_id):
                 file_name = Path(str(obj.get("file_name") or obj.get("object_key") or "")).name
-                object_by_resource_name[(resource_id, file_name)] = obj
+                object_by_resource_name[(resource_id, file_name, str(obj.get("version_id") or ""))] = obj
         results: list[dict[str, Any]] = []
         client = ComputeNodeClient(node["api_base_url"])
         for item in files:
             target_name = Path(str(item.get("name") or f"{item['id']}.jsonl")).name
             item_dataset_id = str(item.get("dataset_id") or dataset_id)
-            obj = object_by_resource_name.get((item_dataset_id, target_name))
-            if not obj and item.get("content"):
-                # 兼容 MinIO 接入前已经发布的数据处理数据集：
-                # 预检时用数据库正文补建对象，避免要求用户重新处理数据集。
-                raw = str(item.get("content") or "").encode("utf-8")
             version_id = str(item.get("active_version_id") or item["id"])
+            obj = object_by_resource_name.get((item_dataset_id, target_name, version_id))
+            if not obj and item.get("content"):
+                # 兼容 MinIO 接入前已经发布的数据处理数据集。大文件补建
+                # MinIO 对象，小文件直接从数据库正文同步到目标节点。
+                raw = str(item.get("content") or "").encode("utf-8")
+                if should_store_in_minio(len(raw)):
                     object_key = f"datasets/{item_dataset_id}/versions/{version_id}/{target_name}"
                     uploaded = get_object_storage().put_bytes(object_key, raw, "application/jsonl")
                     obj = store.create_storage_object({
@@ -1233,8 +1438,30 @@ async def _sync_training_dataset_to_compute_node(
                         "status": "available",
                     })
                     store.link_dataset_file_storage_object(str(item["id"]), obj["id"])
+                else:
+                    result = await client.upload_file(
+                        target_name,
+                        raw,
+                        f"datasets/{dataset_id}/{target_name}",
+                        resource_type="dataset",
+                        resource_id=dataset_id,
+                    )
+                    store.upsert_resource_replica(
+                        node["id"], "dataset", dataset_id, str(result.get("local_path") or "")
+                    )
+                    results.append({
+                        "node_id": node["id"],
+                        "node_code": node.get("code"),
+                        "file_id": item.get("id"),
+                        "name": target_name,
+                        "local_path": result.get("local_path"),
+                        "byte_size": result.get("byte_size"),
+                        "checksum_sha256": result.get("checksum_sha256"),
+                        "storage_backend": "database",
+                    })
+                    continue
             if not obj:
-                raise RuntimeError(f"dataset file is not available in MinIO: {target_name}")
+                raise RuntimeError(f"dataset file is not available: {target_name}")
             url = get_object_storage().presigned_get(obj["object_key"])
             result = await client.prepare_cache({
                 "resource_id": dataset_id,
@@ -1250,7 +1477,13 @@ async def _sync_training_dataset_to_compute_node(
                 dataset_id,
                 str(result.get("local_path") or ""),
             )
-            results.append({**result, "file_id": item.get("id"), "name": target_name, "node_id": node["id"]})
+            results.append({
+                **result,
+                "file_id": item.get("id"),
+                "name": target_name,
+                "node_id": node["id"],
+                "storage_backend": "minio",
+            })
         return results
     if not dataset_id:
         raise RuntimeError("train_dataset_id is required")
@@ -1316,7 +1549,11 @@ async def upload_dataset_files(
             created_file = store.add_dataset_file(conn, dataset_id, file.filename or "upload.jsonl", content)
             created.append(created_file)
             pending_sync.append((created_file["id"], created_file["name"], raw))
-            if get_settings().minio_enabled:
+            if should_store_in_minio(
+                len(raw),
+                content_type=file.content_type,
+                file_format=Path(created_file["name"]).suffix,
+            ):
                 object_key = f"datasets/{dataset_id}/versions/{created_file.get('active_version_id') or created_file['id']}/{Path(created_file['name']).name}"
                 uploaded = get_object_storage().put_bytes(object_key, raw, file.content_type or "application/octet-stream")
                 storage_object = get_platform_store().create_storage_object({
@@ -1360,7 +1597,10 @@ async def download_dataset(dataset_id: str, current_user: dict = Depends(get_cur
             full_file = store.dataset_file(str(item["id"]))
         except KeyError:
             continue
-        files.append({**item, "content": full_file.get("content") or ""})
+        files.append({
+            **item,
+            "content": _dataset_file_bytes(store, str(item["id"])).decode("utf-8", errors="replace"),
+        })
     if not files:
         raise fail(404, "dataset has no downloadable files")
 
@@ -1401,8 +1641,12 @@ async def download_dataset(dataset_id: str, current_user: dict = Depends(get_cur
 
 @router.get("/dataset-manage/download/{dataset_id}/{file_id}")
 async def download_dataset_file(dataset_id: str, file_id: str, version_id: str | None = Query(default=None)) -> PlainTextResponse:
-    row = get_platform_store().dataset_file(file_id)
+    store = get_platform_store()
-    return PlainTextResponse(row["content"], media_type="text/plain")
+    row = store.dataset_file(file_id)
+    if str(row.get("dataset_id")) != str(dataset_id):
+        raise fail(404, "dataset file not found")
+    content = _dataset_version_bytes(store, file_id, version_id) if version_id else _dataset_file_bytes(store, file_id)
+    return PlainTextResponse(content.decode("utf-8", errors="replace"), media_type="text/plain")
 
 
 @router.get("/dataset-manage")
@@ -1545,10 +1789,10 @@ async def start_fine_tune(
         if node_id and gpu_indices:
             if not store.check_gpu_access(current_user["id"], node_id, gpu_indices):
                 raise fail(403, "无权使用所选 GPU，请联系管理员分配")
-    # 记录创建者
         if node_id and not gpu_indices:
             payload["allowed_gpu_indices"] = store.assigned_gpu_indexes(current_user["id"], node_id)
-        payload["strict_node_selection"] = bool(node_id)
+    # 页面明确选择节点时，调度器必须保持节点约束；否则可能落到其它节点。
+    payload["strict_node_selection"] = bool(payload.get("compute_node_id") or payload.get("node_id"))
     payload.setdefault("created_by", current_user.get("id"))
     try:
         return ok(await _submit_fine_tune_task(store, payload))
@@ -1679,6 +1923,42 @@ async def fine_tune_diagnostics(task_id: str) -> dict[str, Any]:
     )
 
 
+@router.get("/fine-tune/{task_id}/gpu-status")
+async def fine_tune_gpu_status(task_id: str, current_user: dict[str, Any] = Depends(get_current_user)) -> dict[str, Any]:
+    """Return live GPU metrics for the task's selected node and cards."""
+    store = get_platform_store()
+    try:
+        task = store.task(task_id)
+    except KeyError:
+        raise fail(404, "fine tune task not found")
+    if not has_resource_access("fine-tune", task_id, current_user, "read"):
+        raise fail(403, "no permission to access this task")
+    node = _node_for_task(task)
+    selected = set(_normalize_gpu_indices({"gpus": task.get("gpus") or []}, allow_primary=False))
+    if not node:
+        return ok({"source": "unavailable", "items": [], "selected_gpus": sorted(selected)})
+    try:
+        if get_settings().compute_mode == "simulator":
+            live_items = store.gpus()
+        else:
+            live_items = await ComputeNodeClient(node["api_base_url"]).gpu_resources()
+        items = []
+        for item in live_items:
+            index = int(item.get("gpu_index", item.get("id", -1)))
+            if selected and index not in selected:
+                continue
+            items.append({
+                **item,
+                "id": index,
+                "node_id": node["id"],
+                "node_code": node.get("code"),
+                "node_name": node.get("name"),
+            })
+        return ok({"source": "compute", "items": items, "selected_gpus": sorted(selected)})
+    except Exception as exc:  # noqa: BLE001 - let UI retain last good snapshot
+        return ok({"source": "unavailable", "items": [], "selected_gpus": sorted(selected), "error": str(exc)})
+
+
 @router.put("/fine-tune/{task_id}")
 @op_log(module=OpModule.FINE_TUNE, action=OpAction.UPDATE, target_type="fine_tune", target_name_param="task_id")
 async def update_fine_tune(task_id: str, payload: dict[str, Any] = Body(...), current_user: dict = Depends(get_current_user)) -> dict[str, Any]:
@@ -1828,15 +2108,35 @@ async def model_eval_detail(task_id: str, current_user: dict = Depends(get_curre
 async def model_eval_start(payload: dict[str, Any] = Body(...), current_user: dict = Depends(get_current_user)) -> dict[str, Any]:
     """Start an evaluation task: submit eval job to compute node."""
     store = get_platform_store()
+    try:
+        gpu_indices = _normalize_gpu_indices(payload)
+    except ValueError as exc:
+        raise fail(400, str(exc))
+    if not gpu_indices:
+        raise fail(400, "请选择至少一张 GPU")
     # 1. Create eval task record
     payload.setdefault("created_by", current_user.get("id"))
     task = store.create_eval_task({**payload, "status": "pending"})
+    if get_settings().minio_enabled:
+        _store_json_snapshot(
+            store,
+            "eval",
+            str(task["id"]),
+            str(task["id"]),
+            f"evaluations/{task['id']}/versions/{task['id']}/evaluation-config.json",
+            payload,
+            current_user.get("id"),
+        )
 
     # 2. Resolve model path (supports both regular models and trained models)
     model_id = str(payload.get("model_id", ""))
     model_path = ""
     adapter_path = payload.get("adapter_path", "")
     model_node_id = ""
+    ds_files: list[dict[str, Any]] = []
+    model_resource_type = "model"
+    model_resource_id = model_id
+    adapter_resource_id = ""
     try:
         db_model = store.model(model_id)
         model_path = db_model.get("path", "")
@@ -1845,6 +2145,9 @@ async def model_eval_start(payload: dict[str, Any] = Body(...), current_user: di
         # Try trained_models table (IDs prefixed with tm_)
         trained = next((m for m in store.trained_models() if m["id"] == model_id), None)
         if trained:
+            model_resource_type = "trained_model" if trained.get("merged") else "model"
+            model_resource_id = trained.get("id") or model_id
+            adapter_resource_id = trained.get("id") or ""
             model_node_id = trained.get("compute_node_id") or ""
             merged_path = trained.get("merged_path", "")
             base_path = trained.get("base_model_path", "")
@@ -1858,6 +2161,9 @@ async def model_eval_start(payload: dict[str, Any] = Body(...), current_user: di
                     adapter_path = merged_path
             else:
                 model_path = merged_path or base_path
+            if model_resource_type == "model":
+                base_model = next((item for item in store.models() if item.get("path") == base_path), None)
+                model_resource_id = str((base_model or {}).get("id") or base_path)
     if not model_path:
         store.update_eval_task(task["id"], {"status": "failed", "error": "model not found or no path"})
         return ok({"task_id": task["id"], "status": "failed", "error": "model not found or no path"})
@@ -1930,6 +2236,33 @@ async def model_eval_start(payload: dict[str, Any] = Body(...), current_user: di
         store.update_eval_task(task["id"], {"status": "failed", "error": message})
         return ok({"task_id": task["id"], "status": "failed", "error": message})
 
+    if get_settings().minio_enabled and get_settings().compute_mode != "simulator":
+        try:
+            prepared_model = await _prepare_resource_on_node(store, model_resource_type, model_resource_id, node)
+            if prepared_model:
+                model_path = prepared_model
+            if adapter_resource_id and model_resource_type == "model":
+                prepared_adapter = await _prepare_resource_on_node(store, "trained_model", adapter_resource_id, node)
+                if prepared_adapter:
+                    adapter_path = prepared_adapter
+            dataset_sync = await _sync_training_dataset_to_compute_node(store, node, dataset_id)
+            if dataset_sync:
+                dataset_path = str(dataset_sync[0].get("local_path") or dataset_path)
+        except Exception as exc:
+            store.update_eval_task(task["id"], {"status": "failed", "error": f"MinIO resource preparation failed: {exc}"})
+            return ok({"task_id": task["id"], "status": "failed", "error": str(exc)})
+
+    node_gpus = {
+        int(item.get("id", item.get("gpu_index", -1))): item
+        for item in store.gpus()
+        if item.get("node_id") == node["id"]
+    }
+    unavailable = [index for index in gpu_indices if node_gpus.get(index, {}).get("status") != "idle"]
+    if unavailable:
+        message = f"selected GPU is not idle on compute node {node.get('code')}: {unavailable}"
+        store.update_eval_task(task["id"], {"status": "failed", "error": message})
+        return ok({"task_id": task["id"], "status": "failed", "error": message})
+
     # 6. Build eval job payload
     output_dir = f"/data/yg-ft/outputs/{task['id']}"
     job_payload = {
@@ -1943,7 +2276,9 @@ async def model_eval_start(payload: dict[str, Any] = Body(...), current_user: di
         "output_dir": output_dir,
         "basic_metrics": payload.get("basic_metrics", {}),
         "dimension": dimension_cfg,
-        "gpus": [int(payload.get("gpu_id", 0))],
+        "gpu_id": gpu_indices[0],
+        "gpu_indices": gpu_indices,
+        "gpus": gpu_indices,
         "temperature": payload.get("temperature", 0.1),
         "max_new_tokens": payload.get("max_new_tokens", 512),
         "compute_node_id": node["id"],
@@ -1991,7 +2326,10 @@ async def model_eval_delete(task_id: str, current_user: dict = Depends(get_curre
     pending = _require_approval_or_admin("eval", task_id, current_user, f"删除评测任务 {task_id}")
     if pending:
         return pending
+    try:
         get_platform_store().delete_eval_task(task_id)
+    except KeyError:
+        raise fail(404, "eval task not found")
     return ok({"deleted": task_id})
 
 
@@ -2247,6 +2585,15 @@ async def model_compare_load(task_id: str, current_user: dict = Depends(get_curr
                 "model_name_or_path": model_path,
                 "template": item.get("template", "qwen"),
             }
+            try:
+                item_gpu_indices = _normalize_gpu_indices(item)
+            except ValueError as exc:
+                loaded_models.append({**item, "status": "error", "error": str(exc)})
+                continue
+            if not item_gpu_indices:
+                loaded_models.append({**item, "status": "error", "error": "no GPU selected"})
+                continue
+            load_payload["gpu_indices"] = item_gpu_indices
             if item.get("adapter_path"):
                 load_payload["adapter_name_or_path"] = item["adapter_path"]
             if get_settings().compute_mode == "simulator":
@@ -2255,14 +2602,28 @@ async def model_compare_load(task_id: str, current_user: dict = Depends(get_curr
             # 只派发：HTTP 响应成功即视为已接受（节点会异步加载），loaded 字段忽略
             item_dispatched = False
             errors = []
-            for node in _candidate_online_nodes(store, preferred_node_id):
+            candidate_nodes = _candidate_online_nodes(store, preferred_node_id)
+            if preferred_node_id:
+                candidate_nodes = candidate_nodes[:1]
+            for node in candidate_nodes:
                 try:
+                    node_gpu_map = {
+                        int(gpu.get("id", gpu.get("gpu_index", -1))): gpu
+                        for gpu in store.gpus()
+                        if gpu.get("node_id") == node["id"]
+                    }
+                    unavailable = [
+                        index for index in item_gpu_indices
+                        if node_gpu_map.get(index, {}).get("status") != "idle"
+                    ]
+                    if unavailable:
+                        raise RuntimeError(f"selected GPU is not idle on compute node {node.get('code')}: {unavailable}")
                     if get_settings().minio_enabled:
                         await _wait_for_object_storage()
                     client = ComputeNodeClient(node["api_base_url"])
                     await client.inference_load(load_payload)
-                    store.mark_inference_loaded(node["id"])
+                    store.mark_inference_loaded(node["id"], item_gpu_indices)
-                    loaded_models.append({**item, "status": "starting", "node_id": node["id"], "node_name": node.get("name")})
+                    loaded_models.append({**item, "gpu_indices": item_gpu_indices, "gpus": item_gpu_indices, "status": "starting", "node_id": node["id"], "node_name": node.get("name")})
                     item_dispatched = True
                     break
                 except Exception as exc:  # noqa: BLE001 - try next candidate node
@@ -2365,7 +2726,7 @@ async def model_chat_local_preload(payload: dict[str, Any] = Body(...)) -> dict[
         # 计算节点现在异步加载：HTTP 接受（loading/ready）即视为派发成功
         result = await client.inference_load(payload)
         if result.get("loaded") or result.get("status") in {"loading", "ready"}:
-            store.mark_inference_loaded(node["id"])
+            store.mark_inference_loaded(node["id"], _normalize_gpu_indices(payload))
         return ok(result)
     except Exception as exc:
         return ok({"loaded": False, "error": str(exc)})
@@ -2445,7 +2806,7 @@ async def model_chat_trained_preload(payload: dict[str, Any] = Body(...), curren
         # 计算节点现在异步加载：HTTP 接受（loading/ready）即视为派发成功
         result = await client.inference_load({**payload, "compute_node_id": node["id"]})
         if result.get("loaded") or result.get("status") in {"loading", "ready"}:
-            store.mark_inference_loaded(node["id"])
+            store.mark_inference_loaded(node["id"], _normalize_gpu_indices(payload))
         return ok(result)
     except Exception as exc:
         return ok({"loaded": False, "error": str(exc)})

backend/app/core/audit.py

@@ -61,6 +61,7 @@ def audit_log(
                     detail = _build_detail(detail_template, kwargs)
                     _record_audit(
                         action=action,
+                        actor_id=_extract_actor_id(kwargs),
                         target_type=target_type,
                         target_id=target_id,
                         detail=detail,
@@ -87,6 +88,7 @@ def audit_log(
                     detail = _build_detail(detail_template, kwargs)
                     _record_audit(
                         action=action,
+                        actor_id=_extract_actor_id(kwargs),
                         target_type=target_type,
                         target_id=target_id,
                         detail=detail,
@@ -136,6 +138,7 @@ def _build_detail(template: str, kwargs: dict) -> str:
 
 def _record_audit(
     action: str,
+    actor_id: Optional[str],
     target_type: str,
     target_id: Optional[str],
     detail: str,
@@ -149,6 +152,7 @@ def _record_audit(
         store = get_platform_store()
         store.record_audit(
             action=action,
+            actor_id=actor_id,
             target_type=target_type or None,
             target_id=target_id,
             detail=f"{detail} trace_id={trace_id} duration_ms={duration_ms:.1f}" if detail else f"trace_id={trace_id} duration_ms={duration_ms:.1f}",
@@ -157,6 +161,15 @@ def _record_audit(
         logger.error("写入审计日志失败 action=%s", action, exc_info=True)
 
 
+def _extract_actor_id(kwargs: dict) -> Optional[str]:
+    """从 FastAPI 注入的当前用户中提取操作人 ID。"""
+    for key in ("current_user", "user"):
+        value = kwargs.get(key)
+        if isinstance(value, dict) and value.get("id"):
+            return str(value["id"])
+    return None
+
+
 # ==================== 预定义的审计操作常量 ====================
 
 class AuditActions:

backend/app/core/config.py

@@ -68,6 +68,9 @@ class Settings:
     minio_secret_key: str = os.getenv("MINIO_SECRET_KEY", "minioadmin")
     minio_bucket: str = os.getenv("MINIO_BUCKET", "yg-ft-resources")
     minio_secure: bool = _bool_env("MINIO_SECURE", False)
+    # Small text/data files stay inline in PostgreSQL to avoid unnecessary
+    # MinIO round trips. Larger files remain the shared canonical objects.
+    minio_inline_max_bytes: int = _int_env("MINIO_INLINE_MAX_BYTES", 256 * 1024)
     storage_wait_seconds: int = _int_env("STORAGE_WAIT_SECONDS", 300)
     storage_check_interval_seconds: int = _int_env("STORAGE_CHECK_INTERVAL_SECONDS", 10)
     compute_service_token: str = os.getenv("COMPUTE_SERVICE_TOKEN", "")

backend/app/db/platform_store.py

@@ -454,15 +454,21 @@ class PlatformStore:
         self.ensure_seed_data()
         # Track which compute nodes have an active inference model loaded
         self._inference_nodes: set[str] = set()
+        self._inference_gpu_indexes: dict[str, set[int]] = {}
         self._last_runtime_refresh = 0.0
 
     # ── inference node tracking ────────────────────────────────────
 
-    def mark_inference_loaded(self, node_id: str) -> None:
+    def mark_inference_loaded(self, node_id: str, gpu_indexes: list[int] | None = None) -> None:
         self._inference_nodes.add(node_id)
+        if gpu_indexes is not None:
+            self._inference_gpu_indexes[node_id] = {int(item) for item in gpu_indexes}
+        else:
+            self._inference_gpu_indexes.pop(node_id, None)
 
     def mark_inference_unloaded(self, node_id: str) -> None:
         self._inference_nodes.discard(node_id)
+        self._inference_gpu_indexes.pop(node_id, None)
 
     def is_inference_loaded(self, node_id: str) -> bool:
         return node_id in self._inference_nodes
@@ -545,6 +551,16 @@ class PlatformStore:
                     "last_error": "TEXT",
                 },
             )
+            self._ensure_columns(
+                conn,
+                "storage_objects",
+                {"metadata": "TEXT NOT NULL DEFAULT '{}'"},
+            )
+            self._ensure_columns(
+                conn,
+                "model_artifacts",
+                {"storage_object_id": "TEXT", "storage_backend": "TEXT NOT NULL DEFAULT 'minio'"},
+            )
             schema_dir = Path(__file__).with_name("sql")
             for extra in (
                 "002_governance.sql",
@@ -556,7 +572,17 @@ class PlatformStore:
                 if extra_path.exists():
                     conn.executescript(extra_path.read_text(encoding="utf-8"))
             # data_convert_tasks 表补充 created_by 字段（用于数据隔离）
-            self._ensure_columns(conn, "data_convert_tasks", {"created_by": "TEXT"})
+            self._ensure_columns(
+                conn,
+                "data_convert_tasks",
+                {
+                    "created_by": "TEXT",
+                    "storage_backend": "TEXT NOT NULL DEFAULT 'minio'",
+                    "output_storage_object_id": "TEXT",
+                    "output_content": "TEXT",
+                },
+            )
+            self._ensure_columns(conn, "eval_tasks", {"report_storage_object_id": "TEXT"})
             # 修复历史数据：将 data_convert_tasks.created_by 回填到关联的 datasets 记录
             try:
                 conn.execute("""
@@ -1254,6 +1280,13 @@ class PlatformStore:
                 raise KeyError(artifact_id)
             return {**dict(row), "metadata": json_loads(row["metadata"], {})}
 
+    def link_model_artifact_storage_object(self, artifact_id: str, storage_object_id: str) -> None:
+        with self.connect() as conn:
+            conn.execute(
+                "UPDATE model_artifacts SET storage_object_id=?, storage_backend='minio' WHERE id=?",
+                (storage_object_id, artifact_id),
+            )
+
     def model_lineage(self, model_id: str) -> dict[str, Any]:
         with self.connect() as conn:
             parents = conn.execute(
@@ -2221,7 +2254,7 @@ class PlatformStore:
                         (str(validation_dataset_id),),
                     ).fetchall(),
                 ]
-        model_path = (model and model.get("path")) or task.get("model_name_or_path") or base_model_id
+        model_path = task.get("prepared_base_model_path") or (model and model.get("path")) or task.get("model_name_or_path") or base_model_id
         dataset_metadata = json_loads(dataset.get("metadata"), {}) if dataset else {}
         if not dataset or dataset.get("type") != "train" or dataset_metadata.get(
             "dataset_split"
@@ -2482,12 +2515,18 @@ class PlatformStore:
 
     def eval_tasks(self) -> list[dict[str, Any]]:
         with self.connect() as conn:
-            rows = conn.execute("SELECT * FROM eval_tasks ORDER BY create_time DESC").fetchall()
+            # 评测任务采用软删除，普通列表不得再次返回已删除记录。
+            rows = conn.execute(
+                "SELECT * FROM eval_tasks WHERE deleted_at IS NULL ORDER BY create_time DESC"
+            ).fetchall()
             return [self._enrich_eval_payload(conn, self._json_payload_row(row)) for row in rows]
 
     def eval_task(self, task_id: str) -> dict[str, Any]:
         with self.connect() as conn:
-            row = conn.execute("SELECT * FROM eval_tasks WHERE id=?", (task_id,)).fetchone()
+            row = conn.execute(
+                "SELECT * FROM eval_tasks WHERE id=? AND deleted_at IS NULL",
+                (task_id,),
+            ).fetchone()
             if not row:
                 raise KeyError(task_id)
             payload = self._enrich_eval_payload(conn, self._json_payload_row(row))
@@ -2558,7 +2597,13 @@ class PlatformStore:
 
     def delete_eval_task(self, task_id: str) -> None:
         with self.connect() as conn:
-            conn.execute("UPDATE eval_tasks SET deleted_at=?, deleted_by=? WHERE id=?", (utcnow(), "system", task_id))
+            result = conn.execute(
+                "UPDATE eval_tasks SET deleted_at=?, deleted_by=? "
+                "WHERE id=? AND deleted_at IS NULL RETURNING id",
+                (utcnow(), "system", task_id),
+            )
+            if not result.fetchone():
+                raise KeyError(task_id)
 
     def running_eval_tasks(self) -> list[dict[str, Any]]:
         """Return eval tasks that have been submitted to a compute node and are still running."""
@@ -2766,7 +2811,35 @@ class PlatformStore:
             "SELECT gpu_index FROM gpu_allocations WHERE node_id=? AND status IN ('allocated','running')",
             (node_id,),
         ).fetchall()
-        return {int(row["gpu_index"]) for row in rows}
+        active = {int(row["gpu_index"]) for row in rows}
+        # Evaluation jobs use the same Compute ProcessManager GPU lock but do
+        # not have fine-tune allocation rows; derive their selected cards here
+        # so a training task cannot race onto an evaluation GPU.
+        for row in conn.execute(
+            "SELECT payload FROM eval_tasks WHERE status IN ('syncing','queued','running')"
+        ).fetchall():
+            payload = json_loads(row["payload"], {})
+            if payload.get("compute_node_id") != node_id:
+                continue
+            selected = payload.get("gpu_indices") or payload.get("gpus")
+            if selected is None and payload.get("gpu_id") is not None:
+                selected = [payload.get("gpu_id")]
+            active.update(int(item) for item in selected or [])
+        # Loaded inference models also reserve only their selected cards.
+        active.update(self._inference_gpu_indexes.get(node_id, set()))
+        for row in conn.execute("SELECT payload FROM compare_tasks").fetchall():
+            payload = json_loads(row["payload"], {})
+            load_status = payload.get("load_status") or {}
+            if isinstance(load_status, str):
+                load_status = json_loads(load_status, {})
+            for item in load_status.get("loaded_models") or []:
+                if item.get("node_id") != node_id or item.get("status") not in {"starting", "ready", "running"}:
+                    continue
+                selected = item.get("gpu_indices") or item.get("gpus")
+                if selected is None and item.get("gpu_id") is not None:
+                    selected = [item.get("gpu_id")]
+                active.update(int(gpu) for gpu in selected or [])
+        return active
 
     def _node_gpu_indexes(self, conn: PgConnection, node: dict[str, Any]) -> set[int]:
         rows = conn.execute("SELECT gpu_index FROM gpus WHERE node_id=?", (node["id"],)).fetchall()
@@ -3000,17 +3073,18 @@ class PlatformStore:
                 """
                 INSERT INTO storage_objects
                 (id, resource_type, resource_id, version_id, bucket, object_key, file_name,
-                 content_type, checksum_sha256, byte_size, status, created_by, create_time)
+                 content_type, checksum_sha256, byte_size, status, metadata, created_by, create_time)
-                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
+                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
                 ON CONFLICT (resource_type, resource_id, version_id, object_key)
                 DO UPDATE SET file_name=EXCLUDED.file_name, content_type=EXCLUDED.content_type,
                     checksum_sha256=EXCLUDED.checksum_sha256, byte_size=EXCLUDED.byte_size,
-                    status=EXCLUDED.status, created_by=EXCLUDED.created_by
+                    status=EXCLUDED.status, metadata=EXCLUDED.metadata, created_by=EXCLUDED.created_by
                 """,
                 (
                     object_id, payload["resource_type"], payload["resource_id"], payload["version_id"],
                     payload["bucket"], payload["object_key"], payload.get("file_name"), payload.get("content_type"),
                     payload.get("checksum_sha256"), int(payload.get("byte_size") or 0), payload.get("status", "pending"),
+                    json_dumps(payload.get("metadata") or {}),
                     payload.get("created_by"), payload.get("create_time") or utcnow(),
                 ),
             )
@@ -3049,6 +3123,13 @@ class PlatformStore:
             ).fetchall()
             return [dict(row) for row in rows]
 
+    def storage_object(self, object_id: str) -> dict[str, Any]:
+        with self.connect() as conn:
+            row = conn.execute("SELECT * FROM storage_objects WHERE id=?", (object_id,)).fetchone()
+            if not row:
+                raise KeyError(object_id)
+            return dict(row)
+
     def update_storage_object(self, object_id: str, payload: dict[str, Any]) -> dict[str, Any]:
         allowed = {"status", "checksum_sha256", "byte_size", "content_type"}
         fields = {key: value for key, value in payload.items() if key in allowed}
@@ -3195,6 +3276,9 @@ class PlatformStore:
             # 推理模型占用算力节点同样计入：优先从 compare_tasks 持久化状态派生
             # （重启后仍准确），并用内存标记兜底（直接 preload 的模型无 compare 记录）
             inference_node_ids = set(self._inference_nodes)
+            inference_gpu_indexes: dict[str, set[int]] = {
+                node_id: set(indexes) for node_id, indexes in self._inference_gpu_indexes.items()
+            }
             for ctr in conn.execute("SELECT payload FROM compare_tasks").fetchall():
                 ls = json_loads(ctr["payload"], {}).get("load_status") or {}
                 if isinstance(ls, str):
@@ -3204,8 +3288,13 @@ class PlatformStore:
                         ls = {}
                 for m in ls.get("loaded_models") or []:
                     if m.get("status") in {"ready", "running"} and m.get("node_id"):
-                        inference_node_ids.add(m["node_id"])
+                        node_id = m["node_id"]
-            for nid in inference_node_ids:
+                        selected = m.get("gpu_indices") or m.get("gpus")
+                        if selected:
+                            inference_gpu_indexes.setdefault(node_id, set()).update(int(item) for item in selected)
+                        else:
+                            inference_node_ids.add(node_id)
+            for nid in set(inference_node_ids) | set(inference_gpu_indexes):
                 running_map[nid] = running_map.get(nid, 0) + 1
             rows = conn.execute("SELECT * FROM compute_nodes ORDER BY scheduler_weight DESC, code").fetchall()
             return [
@@ -3434,6 +3523,9 @@ class PlatformStore:
             # 推理模型占用的节点：优先从 compare_tasks 持久化状态派生（重启后仍准确），
             # 内存标记兜底（直接 preload 的模型无 compare 记录）
             inference_node_ids = set(self._inference_nodes)
+            inference_gpu_indexes: dict[str, set[int]] = {
+                node_id: set(indexes) for node_id, indexes in self._inference_gpu_indexes.items()
+            }
             for ctr in conn.execute("SELECT payload FROM compare_tasks").fetchall():
                 ls = json_loads(ctr["payload"], {}).get("load_status") or {}
                 if isinstance(ls, str):
@@ -3443,7 +3535,12 @@ class PlatformStore:
                         ls = {}
                 for m in ls.get("loaded_models") or []:
                     if m.get("status") in {"ready", "running"} and m.get("node_id"):
-                        inference_node_ids.add(m["node_id"])
+                        node_id = m["node_id"]
+                        selected = m.get("gpu_indices") or m.get("gpus")
+                        if selected:
+                            inference_gpu_indexes.setdefault(node_id, set()).update(int(item) for item in selected)
+                        else:
+                            inference_node_ids.add(node_id)
             items = []
             for row in rows:
                 task = next(
@@ -3459,7 +3556,10 @@ class PlatformStore:
                         t
                         for t in eval_running
                         if t.get("compute_node_id") == row["node_id"]
-                        and row["gpu_index"] == (int(t["gpu_id"]) if t.get("gpu_id") is not None else -1)
+                        and row["gpu_index"] in {
+                            int(item)
+                            for item in (t.get("gpu_indices") or t.get("gpus") or ([t["gpu_id"]] if t.get("gpu_id") is not None else []))
+                        }
                     ),
                     None,
                 )
@@ -3468,7 +3568,8 @@ class PlatformStore:
                     eval_task is not None and eval_task.get("status") in {"syncing", "queued"}
                 )
                 # Also mark GPU as busy if an inference model is loaded on this node
-                if row["node_id"] in inference_node_ids and not busy:
+                inference_on_gpu = row["node_id"] in inference_node_ids or row["gpu_index"] in inference_gpu_indexes.get(row["node_id"], set())
+                if inference_on_gpu and not busy:
                     busy = True
                     reserved = False
                 memory_used = round(row["memory_total_gb"] * (0.72 if busy else 0.18 if reserved else 0.04), 1)
@@ -3704,6 +3805,8 @@ class PlatformStore:
         actor_id: str | None = None,
         action: str | None = None,
         target_type: str | None = None,
+        target_id: str | None = None,
+        keyword: str | None = None,
         start_time: str | None = None,
         end_time: str | None = None,
         limit: int = 50,
@@ -3726,6 +3829,13 @@ class PlatformStore:
         if target_type:
             clauses.append("target_type=?")
             params.append(target_type)
+        if target_id:
+            clauses.append("target_id=?")
+            params.append(target_id)
+        if keyword:
+            clauses.append("(target_id LIKE ? OR detail LIKE ?)")
+            pattern = f"%{keyword}%"
+            params.extend([pattern, pattern])
         if start_time:
             clauses.append("time>=?")
             params.append(start_time)

backend/app/db/sql/000_full_init.sql

@@ -111,6 +111,8 @@ CREATE TABLE IF NOT EXISTS model_artifacts (
     path TEXT NOT NULL,
     size_bytes BIGINT NOT NULL DEFAULT 0,
     checksum_sha256 TEXT,
+    storage_object_id TEXT,
+    storage_backend TEXT NOT NULL DEFAULT 'minio',
     metadata TEXT NOT NULL,
     compute_job_id TEXT,
     create_time TEXT NOT NULL
@@ -303,6 +305,7 @@ CREATE TABLE IF NOT EXISTS storage_objects (
     checksum_sha256 TEXT,
     byte_size BIGINT NOT NULL DEFAULT 0,
     status TEXT NOT NULL DEFAULT 'pending',
+    metadata TEXT NOT NULL DEFAULT '{}',
     created_by TEXT,
     create_time TEXT NOT NULL,
     UNIQUE (resource_type, resource_id, version_id, object_key)
@@ -630,6 +633,9 @@ ALTER TABLE dataset_files ADD COLUMN IF NOT EXISTS metadata TEXT NOT NULL DEFAUL
 ALTER TABLE dataset_files ADD COLUMN IF NOT EXISTS created_at TIMESTAMPTZ NOT NULL DEFAULT now();
 ALTER TABLE dataset_files ADD COLUMN IF NOT EXISTS updated_at TIMESTAMPTZ NOT NULL DEFAULT now();
 ALTER TABLE dataset_files ADD COLUMN IF NOT EXISTS deleted_at TIMESTAMPTZ;
+ALTER TABLE model_artifacts ADD COLUMN IF NOT EXISTS storage_object_id TEXT;
+ALTER TABLE model_artifacts ADD COLUMN IF NOT EXISTS storage_backend TEXT NOT NULL DEFAULT 'minio';
+ALTER TABLE storage_objects ADD COLUMN IF NOT EXISTS metadata TEXT NOT NULL DEFAULT '{}';
 
 -- ---- 数据处理任务 / 源文件 / 预览 / 结果 ----
 
@@ -860,12 +866,17 @@ CREATE TABLE IF NOT EXISTS data_convert_tasks (
     input_count INTEGER NOT NULL DEFAULT 0,
     output_count INTEGER NOT NULL DEFAULT 0,
     error_message TEXT,
+    output_content TEXT,
     create_time TEXT NOT NULL DEFAULT (to_char(now(), 'YYYY-MM-DD"T"HH24:MI:SS.MS"Z"')),
     update_time TEXT NOT NULL DEFAULT (to_char(now(), 'YYYY-MM-DD"T"HH24:MI:SS.MS"Z"')),
     deleted_at TIMESTAMPTZ
 );
 CREATE INDEX IF NOT EXISTS idx_data_convert_tasks_status ON data_convert_tasks(status);
 CREATE INDEX IF NOT EXISTS idx_data_convert_tasks_create_time ON data_convert_tasks(create_time DESC);
+ALTER TABLE data_convert_tasks ADD COLUMN IF NOT EXISTS storage_backend TEXT NOT NULL DEFAULT 'minio';
+ALTER TABLE data_convert_tasks ADD COLUMN IF NOT EXISTS output_storage_object_id TEXT;
+ALTER TABLE data_convert_tasks ADD COLUMN IF NOT EXISTS output_content TEXT;
+ALTER TABLE eval_tasks ADD COLUMN IF NOT EXISTS report_storage_object_id TEXT;
 
 -- ============================================================================
 -- 七、种子数据：初始管理员 / 操作员

backend/app/modules/compute_gateway/client.py

@@ -240,6 +240,10 @@ class ComputeNodeClient:
     async def inference_status(self) -> dict[str, Any]:
         return await self._request("GET", "/inference/status", timeout=INFERENCE_STATUS_TIMEOUT)
 
+    async def gpu_resources(self) -> list[dict[str, Any]]:
+        """Read live per-GPU metrics from this compute node."""
+        return await self.gpus()
+
     async def inference_unload(self) -> dict[str, Any]:
         return await self._request("POST", "/inference/unload", json_data={}, timeout=INFERENCE_UNLOAD_TIMEOUT)
 

backend/app/modules/compute_gateway/sync.py

@@ -2,15 +2,75 @@ from __future__ import annotations
 
 import json
 import time
+from pathlib import Path
 from typing import Any
 
 from app.db.platform_store import get_platform_store
+from app.core.config import get_settings
 from app.modules.compute_gateway.client import ComputeNodeClient
+from app.modules.storage.minio_store import get_object_storage
 
 # starting 状态允许的最大轮询次数（约 40 * 3s ≈ 2 分钟），超过即判定节点不可达
 MAX_STARTING_ATTEMPTS = 40
 
 
+async def _archive_node_directory(
+    store: Any,
+    client: ComputeNodeClient,
+    node: dict[str, Any],
+    source_path: str,
+    resource_type: str,
+    resource_id: str,
+    version_id: str,
+    object_prefix: str,
+) -> list[dict[str, Any]]:
+    """Archive a completed node directory to MinIO, preserving subdirectories."""
+    data_root = Path(str(node.get("data_root") or "/data/yg-ft")).resolve()
+    source = Path(source_path).resolve()
+    try:
+        relative_root = source.relative_to(data_root).as_posix()
+    except ValueError as exc:
+        raise RuntimeError(f"artifact path is outside compute data root: {source_path}") from exc
+    queue = [relative_root]
+    archived: list[dict[str, Any]] = []
+    while queue:
+        relative = queue.pop(0)
+        listing = await client.list_files(root="data", relative_path=relative)
+        for item in listing.get("items") or []:
+            item_relative = str(item.get("relative_path") or "")
+            if item.get("type") == "directory":
+                queue.append(item_relative)
+                continue
+            path = str(item.get("path") or "")
+            if not path:
+                continue
+            try:
+                relative_file = Path(item_relative).relative_to(Path(relative_root)).as_posix()
+            except ValueError:
+                relative_file = Path(str(item.get("name") or Path(path).name)).name
+            object_key = f"{object_prefix}/{version_id}/{relative_file}"
+            upload_url = get_object_storage().presigned_put(object_key)
+            result = await client.upload_file_to_url(path, upload_url, object_key)
+            metadata = get_object_storage().stat(object_key)
+            archived.append(
+                store.create_storage_object(
+                    {
+                        "resource_type": resource_type,
+                        "resource_id": resource_id,
+                        "version_id": version_id,
+                        "bucket": get_object_storage().bucket,
+                        "object_key": object_key,
+                        "file_name": relative_file,
+                        "content_type": "application/octet-stream",
+                        "byte_size": metadata.get("byte_size") or result.get("byte_size") or 0,
+                        "checksum_sha256": result.get("checksum_sha256") or "",
+                        "status": "available",
+                    }
+                )
+            )
+    return archived
+
+
 def _node_for_task(task: dict[str, Any]) -> dict[str, Any] | None:
     return next((node for node in get_platform_store().compute_nodes() if node["id"] == task.get("compute_node_id")), None)
 
@@ -73,7 +133,8 @@ async def reconcile_inference_loads(store: Any) -> list[dict[str, Any]]:
             if node_status == "ready":
                 item["status"] = "ready"
                 item.pop("error", None)
-                store.mark_inference_loaded(node["id"])
+                selected_gpus = item.get("gpu_indices") or item.get("gpus")
+                store.mark_inference_loaded(node["id"], selected_gpus)
             elif node_status == "error":
                 item["status"] = "error"
                 item["error"] = status.get("error") or "model load failed on compute node"
@@ -138,7 +199,38 @@ async def poll_compute_jobs_once() -> dict[str, Any]:
                     job["log_snippet"] = str(last_logs.get("content") or "")[:8192]
                 except Exception:
                     pass
-            synced.append(store.apply_compute_job(task["id"], job))
+            updated_task = store.apply_compute_job(task["id"], job)
+            if (
+                get_settings().minio_enabled
+                and job.get("status") == "completed"
+                and job.get("output_dir")
+            ):
+                trained_model = next(
+                    (
+                        item
+                        for item in store.trained_models()
+                        if item.get("name")
+                        == (task.get("output_model_name") or f"{task.get('name')}-lora")
+                    ),
+                    None,
+                )
+                if trained_model:
+                    archived = await _archive_node_directory(
+                        store,
+                        client,
+                        node,
+                        str(job["output_dir"]),
+                        "trained_model",
+                        str(trained_model["id"]),
+                        str(job.get("id") or task.get("compute_job_id") or task["id"]),
+                        f"trained_models/{trained_model['id']}",
+                    )
+                    artifacts = store.model_artifacts(str(trained_model["id"]))
+                    if archived and artifacts:
+                        store.link_model_artifact_storage_object(
+                            str(artifacts[0]["id"]), str(archived[0]["id"])
+                        )
+            synced.append(updated_task)
         except Exception as exc:  # noqa: BLE001 - keep polling other jobs
             failed.append({"task_id": task["id"], "error": str(exc)})
     standalone_synced: list[dict[str, Any]] = []
@@ -150,6 +242,30 @@ async def poll_compute_jobs_once() -> dict[str, Any]:
         try:
             job = await ComputeNodeClient(node["api_base_url"]).get_job(record["id"])
             standalone_synced.append(store.sync_model_merge_job(record["id"], job))
+            if get_settings().minio_enabled and job.get("status") == "completed" and job.get("output_dir"):
+                payload = (store.compute_job(record["id"]).get("payload") or {})
+                trained_model_id = str(payload.get("trained_model_id") or payload.get("model_name") or "")
+                if trained_model_id:
+                    trained_model = next(
+                        (item for item in store.trained_models() if item.get("id") == trained_model_id or item.get("name") == trained_model_id),
+                        None,
+                    )
+                    if trained_model:
+                        archived = await _archive_node_directory(
+                            store,
+                            ComputeNodeClient(node["api_base_url"], timeout=900),
+                            node,
+                            str(job["output_dir"]),
+                            "trained_model",
+                            str(trained_model["id"]),
+                            str(job.get("id") or record["id"]),
+                            f"trained_models/{trained_model['id']}",
+                        )
+                        artifacts = store.model_artifacts(str(trained_model["id"]))
+                        if archived and artifacts:
+                            store.link_model_artifact_storage_object(
+                                str(artifacts[0]["id"]), str(archived[0]["id"])
+                            )
         except Exception as exc:  # noqa: BLE001 - keep polling other jobs
             failed.append({"job_id": record["id"], "error": str(exc)})
 
@@ -174,6 +290,17 @@ async def poll_compute_jobs_once() -> dict[str, Any]:
                 except Exception:
                     pass
             store.apply_eval_job_result(eval_task["id"], job, result_content)
+            if get_settings().minio_enabled and job.get("status") == "completed" and job.get("output_dir"):
+                await _archive_node_directory(
+                    store,
+                    client,
+                    node,
+                    str(job["output_dir"]),
+                    "eval",
+                    str(eval_task["id"]),
+                    str(job.get("id") or eval_task.get("compute_job_id") or eval_task["id"]),
+                    f"evaluations/{eval_task['id']}",
+                )
             # 评测 GPU 占用由 eval_tasks 状态派生，无需维护推理内存标记
             eval_synced += 1
         except Exception as exc:  # noqa: BLE001

backend/app/modules/data_convert/router.py

@@ -1,17 +1,21 @@
 from __future__ import annotations
 
 import json
+import hashlib
 import os
 from pathlib import Path
 from typing import Any
 
 from fastapi import APIRouter, Body, Depends, File, UploadFile
-from fastapi.responses import FileResponse
+from fastapi.responses import FileResponse, Response
 
 from app.api.v1.endpoints.platform import ok, fail
 from app.core.auth import get_current_user, is_admin
+from app.core.config import get_settings
 from app.core.op_log import op_log, OpModule, OpAction
 from app.db.platform_store import get_platform_store, new_id
+from app.modules.storage.minio_store import get_object_storage
+from app.modules.storage.policy import should_store_in_minio
 
 
 router = APIRouter(prefix="/data-convert", tags=["data-convert"])
@@ -56,6 +60,142 @@ def _output_dir(task_id: str) -> Path:
     return _task_dir(task_id) / "output"
 
 
+def _minio_enabled() -> bool:
+    return bool(get_settings().minio_enabled)
+
+
+def _input_object_key(task_id: str, name: str) -> str:
+    return f"data-convert/{task_id}/input/{Path(name).name}"
+
+
+def _output_object_key(task_id: str, name: str) -> str:
+    return f"data-convert/{task_id}/output/{Path(name).name}"
+
+
+def _task_objects(task_id: str) -> list[dict[str, Any]]:
+    return get_platform_store().storage_objects_for_resource("data_convert", task_id)
+
+
+def _register_object(
+    task_id: str,
+    *,
+    version_id: str,
+    object_key: str,
+    file_name: str,
+    content_type: str,
+    content: bytes,
+    created_by: str | None,
+) -> dict[str, Any]:
+    storage = get_object_storage()
+    uploaded = storage.put_bytes(object_key, content, content_type)
+    return get_platform_store().create_storage_object(
+        {
+            "resource_type": "data_convert",
+            "resource_id": task_id,
+            "version_id": version_id,
+            "bucket": uploaded["bucket"],
+            "object_key": object_key,
+            "file_name": file_name,
+            "content_type": content_type,
+            "byte_size": len(content),
+            "checksum_sha256": hashlib.sha256(content).hexdigest(),
+            "status": "available",
+            "created_by": created_by,
+        }
+    )
+
+
+def _input_objects(task_id: str) -> list[dict[str, Any]]:
+    prefix = f"data-convert/{task_id}/input/"
+    return sorted(
+        [item for item in _task_objects(task_id) if str(item.get("object_key") or "").startswith(prefix)],
+        key=lambda item: str(item.get("file_name") or item.get("object_key") or ""),
+    )
+
+
+def _output_object(task: dict[str, Any]) -> dict[str, Any] | None:
+    key = _output_object_key(task["id"], _safe_output_filename(task.get("output_filename")))
+    return next((item for item in _task_objects(task["id"]) if item.get("object_key") == key), None)
+
+
+def _read_output(task: dict[str, Any]) -> bytes | None:
+    if _minio_enabled():
+        item = _output_object(task)
+        if item:
+            return get_object_storage().get_bytes(item["object_key"])
+        inline = task.get("output_content")
+        return str(inline).encode("utf-8") if inline is not None else None
+    path = _task_output_path(task)
+    return path.read_bytes() if path.exists() else None
+
+
+def _convert_from_minio(task: dict[str, Any], created_by: str | None) -> tuple[int, int, bytes]:
+    output_name = _safe_output_filename(task.get("output_filename"))
+    output_lines: list[str] = []
+    input_count = 0
+    output_count = 0
+    for item in _input_objects(task["id"]):
+        input_count += 1
+        raw = get_object_storage().get_bytes(item["object_key"])
+        data = json.loads(raw.decode("utf-8"))
+        if isinstance(data, list):
+            records = data
+        elif isinstance(data, dict):
+            records = [data]
+        else:
+            raise ValueError(f"JSON must be object or array: {item.get('file_name')}")
+        for record in records:
+            output_lines.append(json.dumps(record, ensure_ascii=False) + "\n")
+            output_count += 1
+    output = "".join(output_lines).encode("utf-8")
+    store = get_platform_store()
+    with store.connect() as conn:
+        if should_store_in_minio(len(output)):
+            output_object = _register_object(
+                task["id"],
+                version_id="output",
+                object_key=_output_object_key(task["id"], output_name),
+                file_name=output_name,
+                content_type="application/jsonl",
+                content=output,
+                created_by=created_by,
+            )
+            conn.execute(
+                "UPDATE data_convert_tasks SET output_storage_object_id=%s, output_content=NULL, storage_backend='minio' WHERE id=%s",
+                (output_object["id"], task["id"]),
+            )
+        else:
+            conn.execute(
+                "UPDATE data_convert_tasks SET output_storage_object_id=NULL, output_content=%s, storage_backend='database' WHERE id=%s",
+                (output.decode("utf-8"), task["id"]),
+            )
+    return input_count, output_count, output
+
+
+def _convert_from_local(task: dict[str, Any]) -> tuple[int, int, bytes]:
+    input_dir = _input_dir(task["id"])
+    output_dir = _output_dir(task["id"])
+    input_dir.mkdir(parents=True, exist_ok=True)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    output_path = _task_output_path(task)
+    output_path.unlink(missing_ok=True)
+    input_count = 0
+    output_count = 0
+    with output_path.open("w", encoding="utf-8") as output_file:
+        for json_file in sorted(input_dir.iterdir()):
+            if not json_file.is_file() or not json_file.name.lower().endswith(".json"):
+                continue
+            input_count += 1
+            data = json.loads(json_file.read_text(encoding="utf-8"))
+            records = data if isinstance(data, list) else [data] if isinstance(data, dict) else None
+            if records is None:
+                raise ValueError(f"JSON must be object or array: {json_file.name}")
+            for record in records:
+                output_file.write(json.dumps(record, ensure_ascii=False) + "\n")
+                output_count += 1
+    return input_count, output_count, output_path.read_bytes()
+
+
 @router.get("")
 def list_tasks(
     page: int = 1,
@@ -109,7 +249,8 @@ def create_task(
             "VALUES (%s, %s, %s, %s, %s)",
             (task_id, name, description, output_filename, user_id),
         )
-    # 创建目录
+    # MinIO 是正式存储；本地目录只在关闭 MinIO 的旧兼容模式下创建。
+    if not _minio_enabled():
         _input_dir(task_id).mkdir(parents=True, exist_ok=True)
         _output_dir(task_id).mkdir(parents=True, exist_ok=True)
     return ok(_get_task(task_id))
@@ -123,9 +264,15 @@ def get_task(
     task = _get_task(task_id)
     if not task:
         raise fail(404, "task not found")
-    # 附加输入文件列表
+    # 附加输入文件列表；旧任务没有对象记录时继续读取本地兼容目录。
-    input_dir = _input_dir(task_id)
     files = []
+    if _minio_enabled():
+        files = [
+            {"name": item.get("file_name") or Path(item["object_key"]).name, "size": item.get("byte_size") or 0}
+            for item in _input_objects(task_id)
+        ]
+    else:
+        input_dir = _input_dir(task_id)
         if input_dir.exists():
             for f in sorted(input_dir.iterdir()):
                 if f.is_file():
@@ -146,16 +293,26 @@ async def upload_source_files(
         raise fail(404, "task not found")
     if task["status"] not in ("pending", "uploaded"):
         raise fail(400, "task is not editable")
-    input_dir = _input_dir(task_id)
-    input_dir.mkdir(parents=True, exist_ok=True)
     staged = []
     for upload in files:
         name = Path(upload.filename or "input.json").name
         if not name.lower().endswith(".json"):
             raise fail(415, f"only JSON files are supported: {name}")
-        target = input_dir / name
         content = await upload.read()
-        target.write_bytes(content)
+        if _minio_enabled():
+            _register_object(
+                task_id,
+                version_id=f"input-{hashlib.sha256(name.encode('utf-8')).hexdigest()[:16]}",
+                object_key=_input_object_key(task_id, name),
+                file_name=name,
+                content_type=upload.content_type or "application/json",
+                content=content,
+                created_by=task.get("created_by") or current_user.get("id"),
+            )
+        else:
+            input_dir = _input_dir(task_id)
+            input_dir.mkdir(parents=True, exist_ok=True)
+            (input_dir / name).write_bytes(content)
         staged.append({"name": name, "size": len(content)})
     store = get_platform_store()
     # 标记上传完成
@@ -166,30 +323,12 @@ async def upload_source_files(
         )
     # 自动转换并导入数据集
     try:
-        output_dir = _output_dir(task_id)
+        if _minio_enabled():
-        output_dir.mkdir(parents=True, exist_ok=True)
+            input_count, output_count, output = _convert_from_minio(
-        output_path = _task_output_path(task)
+                task, task.get("created_by") or current_user.get("id")
-        # 清空旧输出（如果重新上传）
+            )
-        if output_path.exists():
-            output_path.unlink()
-        input_count = 0
-        output_count = 0
-        for json_file in sorted(input_dir.iterdir()):
-            if not json_file.is_file() or not json_file.name.lower().endswith(".json"):
-                continue
-            input_count += 1
-            with open(json_file, "r", encoding="utf-8") as f:
-                data = json.load(f)
-            if isinstance(data, list):
-                records = data
-            elif isinstance(data, dict):
-                records = [data]
         else:
-                raise ValueError(f"JSON must be object or array: {json_file.name}")
+            input_count, output_count, output = _convert_from_local(task)
-            with open(output_path, "a", encoding="utf-8") as f:
-                for record in records:
-                    f.write(json.dumps(record, ensure_ascii=False) + "\n")
-                    output_count += 1
         with store.connect() as conn:
             conn.execute(
                 "UPDATE data_convert_tasks SET status='completed', "
@@ -197,12 +336,12 @@ async def upload_source_files(
                 (input_count, output_count, task_id),
             )
         # 自动导入数据集
-        content = output_path.read_text(encoding="utf-8")
+        content = output.decode("utf-8")
         size_bytes = len(content.encode("utf-8"))
         dataset = store.create_dataset({
             "name": task["name"],
             "type": "train",
-            "storage_type": "local",
+            "storage_type": "minio" if should_store_in_minio(size_bytes) else ("database" if _minio_enabled() else "local"),
             "source": "upload",
             "task_id": task_id,
             "size": f"{size_bytes} B",
@@ -212,7 +351,20 @@ async def upload_source_files(
         })
         dataset_id = dataset["id"]
         with store.connect() as conn:
-            store.add_dataset_file(conn, dataset_id, _safe_output_filename(task.get("output_filename")), content)
+            dataset_file = store.add_dataset_file(conn, dataset_id, _safe_output_filename(task.get("output_filename")), content)
+        if should_store_in_minio(len(output)):
+            output_name = _safe_output_filename(task.get("output_filename"))
+            object_key = f"datasets/{dataset_id}/versions/{dataset_file.get('active_version_id') or dataset_file['id']}/{output_name}"
+            uploaded = get_object_storage().put_bytes(object_key, output, "application/jsonl")
+            storage_object = store.create_storage_object({
+                "resource_type": "dataset", "resource_id": dataset_id,
+                "version_id": dataset_file.get("active_version_id") or dataset_file["id"],
+                "bucket": uploaded["bucket"], "object_key": object_key,
+                "file_name": output_name, "content_type": "application/jsonl",
+                "byte_size": len(output), "checksum_sha256": hashlib.sha256(output).hexdigest(),
+                "status": "available", "created_by": task.get("created_by") or current_user.get("id"),
+            })
+            store.link_dataset_file_storage_object(dataset_file["id"], storage_object["id"])
         return ok({
             "staged_files": staged,
             "auto_converted": True,
@@ -248,28 +400,10 @@ def run_convert(
             (task_id,),
         )
     try:
-        input_dir = _input_dir(task_id)
+        if _minio_enabled():
-        output_dir = _output_dir(task_id)
+            input_count, output_count, _ = _convert_from_minio(task, task.get("created_by") or current_user.get("id"))
-        output_dir.mkdir(parents=True, exist_ok=True)
-        output_path = _task_output_path(task)
-        input_count = 0
-        output_count = 0
-        for json_file in sorted(input_dir.iterdir()):
-            if not json_file.is_file() or not json_file.name.lower().endswith(".json"):
-                continue
-            input_count += 1
-            with open(json_file, "r", encoding="utf-8") as f:
-                data = json.load(f)
-            if isinstance(data, list):
-                records = data
-            elif isinstance(data, dict):
-                records = [data]
         else:
-                raise ValueError(f"JSON must be object or array: {json_file.name}")
+            input_count, output_count, _ = _convert_from_local(task)
-            with open(output_path, "a", encoding="utf-8") as f:
-                for record in records:
-                    f.write(json.dumps(record, ensure_ascii=False) + "\n")
-                    output_count += 1
         # 更新任务状态
         with store.connect() as conn:
             conn.execute(
@@ -297,11 +431,15 @@ def download_result(
         raise fail(404, "task not found")
     if task["status"] != "completed":
         raise fail(400, "task is not completed")
-    output_path = _task_output_path(task)
+    output = _read_output(task)
-    if not output_path.exists():
+    if output is None:
         raise fail(404, "output file not found")
+    if _minio_enabled():
+        return Response(content=output, media_type="application/octet-stream", headers={
+            "Content-Disposition": f"attachment; filename={_safe_output_filename(task.get('output_filename'))}"
+        })
     return FileResponse(
-        str(output_path),
+        str(_task_output_path(task)),
         media_type="application/octet-stream",
         filename=_safe_output_filename(task.get("output_filename")),
     )
@@ -319,10 +457,10 @@ def import_as_dataset(
         raise fail(404, "task not found")
     if task["status"] != "completed":
         raise fail(400, "task is not completed")
-    output_path = _task_output_path(task)
+    output = _read_output(task)
-    if not output_path.exists():
+    if output is None:
         raise fail(404, "output file not found")
-    content = output_path.read_text(encoding="utf-8")
+    content = output.decode("utf-8")
     dataset_name = str(payload.get("name") or task["name"]).strip()
     description = str(payload.get("description") or f"由数据类型转换任务 {task_id} 导入").strip()
     size_bytes = len(content.encode("utf-8"))
@@ -331,7 +469,7 @@ def import_as_dataset(
     dataset = store.create_dataset({
         "name": dataset_name,
         "type": "train",
-        "storage_type": "local",
+        "storage_type": "minio" if should_store_in_minio(size_bytes) else ("database" if _minio_enabled() else "local"),
         "source": "upload",
         "task_id": task_id,
         "size": f"{size_bytes} B",
@@ -341,7 +479,20 @@ def import_as_dataset(
     })
     dataset_id = dataset["id"]
     with store.connect() as conn:
-        store.add_dataset_file(conn, dataset_id, _safe_output_filename(task.get("output_filename")), content)
+        dataset_file = store.add_dataset_file(conn, dataset_id, _safe_output_filename(task.get("output_filename")), content)
+    if should_store_in_minio(len(output)):
+        output_name = _safe_output_filename(task.get("output_filename"))
+        object_key = f"datasets/{dataset_id}/versions/{dataset_file.get('active_version_id') or dataset_file['id']}/{output_name}"
+        uploaded = get_object_storage().put_bytes(object_key, output, "application/jsonl")
+        storage_object = store.create_storage_object({
+            "resource_type": "dataset", "resource_id": dataset_id,
+            "version_id": dataset_file.get("active_version_id") or dataset_file["id"],
+            "bucket": uploaded["bucket"], "object_key": object_key,
+            "file_name": output_name, "content_type": "application/jsonl",
+            "byte_size": len(output), "checksum_sha256": hashlib.sha256(output).hexdigest(),
+            "status": "available", "created_by": task.get("created_by") or (current_user.get("id") if current_user else None),
+        })
+        store.link_dataset_file_storage_object(dataset_file["id"], storage_object["id"])
     return ok({"dataset_id": dataset_id, "name": dataset_name})
 
 
@@ -360,7 +511,15 @@ def delete_task(
             "UPDATE data_convert_tasks SET deleted_at=NOW() WHERE id=%s",
             (task_id,),
         )
-    # 清理文件
+    if _minio_enabled():
+        for item in _task_objects(task_id):
+            try:
+                get_object_storage().delete(item["object_key"])
+                store.update_storage_object(item["id"], {"status": "deleted"})
+            except Exception:
+                pass
+    else:
+        # 旧兼容数据仍清理本地目录。
         import shutil
         task_dir = _task_dir(task_id)
         if task_dir.exists():

backend/app/modules/data_process/algorithms/embedding.py

@@ -0,0 +1,24 @@
+"""数据处理算法 - 本地语义嵌入模型共享单例。"""
+
+from __future__ import annotations
+
+import os
+from functools import lru_cache
+from typing import Any
+
+
+@lru_cache(maxsize=1)
+def semantic_embedding_model() -> Any:
+    """加载本地嵌入模型，供语义分块与语义质量评分共用。
+
+    模型可在部署环境覆盖；默认模型体积较小且适合中英文语义判断。
+    返回 LlamaIndex BaseEmbedding，通过 ``get_text_embedding`` 使用。
+    """
+
+    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+
+    return HuggingFaceEmbedding(
+        model_name=os.getenv("DATA_PROCESS_EMBEDDING_MODEL", "BAAI/bge-small-zh-v1.5"),
+        device=os.getenv("DATA_PROCESS_EMBEDDING_DEVICE", "cpu"),
+        trust_remote_code=False,
+    )

backend/app/modules/data_process/algorithms/parsers/office.py

@@ -7,12 +7,13 @@ import re
 import unicodedata
 import zipfile
 import xml.etree.ElementTree as ET
-from collections.abc import Mapping, Sequence
+from collections.abc import Iterator, Mapping, Sequence
 from pathlib import PurePosixPath
 from typing import Any
 from urllib.parse import unquote, urlsplit
 
 from docx import Document
+from docx.oxml.ns import qn
 from docx.oxml.table import CT_Tbl
 from docx.oxml.text.paragraph import CT_P
 from docx.table import Table
@@ -121,6 +122,21 @@ def _validate_office_archive(raw: bytes, file_format: TextFormat) -> None:
     except zipfile.BadZipFile as exc:
         raise ValueError(f"invalid {file_format.upper()} file: not an Office ZIP package") from exc
 
+def iter_document_blocks(parent: Any) -> Iterator[Any]:
+    """按文档顺序产出正文段落与表格，并下钻 SDT 内容控件。
+
+    Word 的目录、复选框等内容控件包在 ``w:sdt`` 元素里，只遍历 body
+    直接子级会把这些段落整段丢掉。
+    """
+
+    for child in parent.iterchildren():
+        if isinstance(child, (CT_P, CT_Tbl)):
+            yield child
+        elif child.tag == qn("w:sdt"):
+            content = child.find(qn("w:sdtContent"))
+            if content is not None:
+                yield from iter_document_blocks(content)
+
 def _extract_docx_text(raw: bytes) -> str:
     _validate_office_archive(raw, "docx")
     try:
@@ -130,7 +146,7 @@ def _extract_docx_text(raw: bytes) -> str:
 
     parts: list[str] = []
     total = 0
-    for child in document.element.body.iterchildren():
+    for child in iter_document_blocks(document.element.body):
         if isinstance(child, CT_P):
             total = _append_bounded_text(parts, Paragraph(child, document).text, total)
             continue

backend/app/modules/data_process/algorithms/quality.py

@@ -4,6 +4,7 @@ from __future__ import annotations
 
 import hashlib
 import json
+import math
 import re
 import unicodedata
 from collections import Counter
@@ -341,3 +342,87 @@ def score_quality(
         flags=tuple(flags),
         fingerprint=fingerprint,
     )
+
+
+def _cosine_similarity(left: Sequence[float], right: Sequence[float]) -> float:
+    if not left or not right or len(left) != len(right):
+        return 0.0
+    dot = math.fsum(a * b for a, b in zip(left, right))
+    norm_left = math.sqrt(math.fsum(a * a for a in left))
+    norm_right = math.sqrt(math.fsum(b * b for b in right))
+    if not norm_left or not norm_right:
+        return 0.0
+    return dot / (norm_left * norm_right)
+
+
+def semantic_quality_scores(
+    record: Mapping[str, Any],
+    *,
+    source_content: str = "",
+    embed_model: Any = None,
+) -> dict[str, Any] | None:
+    """用本地嵌入向量计算语义相关性（0-100）。
+
+    返回 ``question_answer``（问题↔答案）、``answer_source``（答案↔来源，
+    无来源时缺省）与 ``overall``；嵌入模型不可用时返回 None 降级，不阻断流程。
+    """
+
+    try:
+        if embed_model is None:
+            from .embedding import semantic_embedding_model
+
+            embed_model = semantic_embedding_model()
+        if embed_model is None:
+            return None
+
+        question = normalize_text(
+            " ".join(
+                str(record.get(field) or "")
+                for field in ("instruction", "input")
+            )
+        )
+        answer = normalize_text(
+            str(record.get("output") or "") or str(record.get("chosen") or "")
+        )
+        source = normalize_text(source_content)
+        texts = [text for text in {question, answer, source} if text]
+        if not texts:
+            return None
+        vectors = {text: embed_model.get_text_embedding(text) for text in texts}
+    except Exception:
+        return None
+
+    scores: dict[str, Any] = {}
+    if question and answer:
+        scores["question_answer"] = round(
+            100 * max(0.0, _cosine_similarity(vectors[question], vectors[answer])), 2
+        )
+    if answer and source:
+        scores["answer_source"] = round(
+            100 * max(0.0, _cosine_similarity(vectors[answer], vectors[source])), 2
+        )
+    if not scores:
+        return None
+    scores["overall"] = round(sum(scores.values()) / len(scores), 2)
+    return scores
+
+
+def composite_overall(
+    *,
+    rule: float | None,
+    semantic: float | None = None,
+    judge: float | None = None,
+) -> float:
+    """三层加权组合：规则 35% + 语义 20% + 评审 45%，缺失层自动重归一。"""
+
+    if rule is None:
+        rule = 0.0
+    if judge is not None and semantic is not None:
+        overall = rule * 0.35 + semantic * 0.20 + judge * 0.45
+    elif semantic is not None:
+        overall = rule * 0.60 + semantic * 0.40
+    elif judge is not None:
+        overall = rule * 0.55 + judge * 0.45
+    else:
+        overall = rule
+    return round(max(0.0, min(100.0, overall)), 2)

backend/app/modules/data_process/document_chunking.py

@@ -18,12 +18,19 @@ from llama_index.core.base.embeddings.base import BaseEmbedding
 from llama_index.core.node_parser import SemanticSplitterNodeParser, SentenceSplitter
 
 from app.modules.data_process.algorithms import normalize_text
+from app.modules.data_process.algorithms.embedding import semantic_embedding_model
 
 ChunkMethod = Literal["layout_hybrid", "semantic", "fixed"]
 
 _PAGE_FURNITURE = re.compile(
     r"(?m)^\s*(?:第\s*\d+\s*页\s*共\s*\d+\s*页|[-—–]?\s*\d+\s*[/／]\s*\d+\s*[-—–]?)\s*$"
 )
+# Docling 的 markdown 序列化会给列表项补上自动编号，而 Word 的编号存放在
+# numbering.xml 中，python-docx 抽取的正文不含这些编号；紧凑匹配前剥掉
+# 行首编号，否则带列表的切片会整体定位失败。
+_LIST_MARKER_PREFIX = re.compile(
+    r"(?m)^[ \t>]*(?:(?:\d{1,3}[.)])+|\([a-zA-Z0-9]{1,3}\)|[a-zA-Z][.)]|[-*+•·])[ \t]+"
+)
 _COMPACT_CHARACTER = re.compile(r"[\w\u3400-\u4dbf\u4e00-\u9fff]", re.UNICODE)
 _CONVERTER_LOCK = threading.Lock()
 
@@ -149,18 +156,6 @@ def chunk_fixed_text(
     return _text_chunks(text, chunk_size=chunk_size, chunk_overlap=chunk_overlap)
 
 
-@lru_cache(maxsize=1)
-def _semantic_embedding_model() -> BaseEmbedding:
-    # 模型可在部署环境覆盖；默认模型体积较小且适合中英文语义边界判断。
-    from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-
-    return HuggingFaceEmbedding(
-        model_name=os.getenv("DATA_PROCESS_EMBEDDING_MODEL", "BAAI/bge-small-zh-v1.5"),
-        device=os.getenv("DATA_PROCESS_EMBEDDING_DEVICE", "cpu"),
-        trust_remote_code=False,
-    )
-
-
 def chunk_semantic_text(
     text: str,
     *,
@@ -175,7 +170,7 @@ def chunk_semantic_text(
     if not normalized:
         return []
     splitter = SemanticSplitterNodeParser.from_defaults(
-        embed_model=embed_model or _semantic_embedding_model(),
+        embed_model=embed_model or semantic_embedding_model(),
         breakpoint_percentile_threshold=breakpoint_percentile_threshold,
         buffer_size=1,
         sentence_splitter=_sentence_chunks,
@@ -193,6 +188,7 @@ def chunk_semantic_text(
         if start is None:
             start = _locate_text(normalized, content, 0)
         if start is None:
+            result.append(_unlocated_chunk(content))
             continue
         if len(_tokenizer().encode(content)) <= chunk_size:
             result.append(_make_text_chunk(normalized, start, start + len(content)))
@@ -203,6 +199,7 @@ def chunk_semantic_text(
                 chunk_overlap=chunk_overlap,
             ):
                 if child.source_start is None or child.source_end is None:
+                    result.append(_unlocated_chunk(child.original_content))
                     continue
                 result.append(
                     _make_text_chunk(
@@ -235,6 +232,7 @@ def _nodes_to_chunks(nodes: list[Any], source_text: str) -> list[DocumentChunk]:
             if start is None:
                 start = _locate_text(source_text, content, 0)
         if start is None:
+            chunks.append(_unlocated_chunk(content))
             continue
         end = start + len(content)
         chunks.append(_make_text_chunk(source_text, start, end))
@@ -247,6 +245,20 @@ def _locate_text(source: str, content: str, start: int) -> int | None:
     return position if position >= 0 else None
 
 
+def _unlocated_chunk(content: str) -> DocumentChunk:
+    """正文在源文本中定位失败时保底保留切片，只放弃行号信息。"""
+
+    return DocumentChunk(
+        original_content=content,
+        contextualized_content=content,
+        source_start=None,
+        source_end=None,
+        source_start_line=None,
+        source_end_line=None,
+        token_count=len(_tokenizer().encode(content)),
+    )
+
+
 def _make_text_chunk(source: str, start: int, end: int) -> DocumentChunk:
     content = source[start:end]
     return DocumentChunk(
@@ -316,6 +328,14 @@ def _compact_with_offsets(value: str) -> tuple[str, list[int]]:
     return "".join(compact), offsets
 
 
+def _expand_to_line_boundaries(source_text: str, start: int, end: int) -> tuple[int, int]:
+    while start > 0 and source_text[start - 1] not in "\r\n":
+        start -= 1
+    while end < len(source_text) and source_text[end] not in "\r\n":
+        end += 1
+    return start, end
+
+
 def _project_layout_span(
     source_text: str,
     content: str,
@@ -324,21 +344,79 @@ def _project_layout_span(
     source_offsets: list[int],
     compact_start: int,
 ) -> tuple[int | None, int | None, int]:
-    compact_content, _ = _compact_with_offsets(content)
+    for candidate in (content, _LIST_MARKER_PREFIX.sub("", content)):
+        compact_content, _ = _compact_with_offsets(candidate)
         if len(compact_content) < 4:
-        return None, None, compact_start
+            continue
         position = compact_source.find(compact_content, compact_start)
         if position < 0:
             position = compact_source.find(compact_content)
         if position < 0:
+            continue
+        start, end = _expand_to_line_boundaries(
+            source_text,
+            source_offsets[position],
+            source_offsets[position + len(compact_content) - 1] + 1,
+        )
+        # 重复内容回退匹配可能命中已消费的更早位置，游标只进不退，
+        # 避免后续切片跟着错位。
+        return start, end, max(compact_start, position + len(compact_content))
+    return _project_layout_span_by_anchors(
+        source_text,
+        content,
+        compact_source=compact_source,
+        source_offsets=source_offsets,
+        compact_start=compact_start,
+    )
+
+
+def _project_layout_span_by_anchors(
+    source_text: str,
+    content: str,
+    *,
+    compact_source: str,
+    source_offsets: list[int],
+    compact_start: int,
+) -> tuple[int | None, int | None, int]:
+    """按行锚点顺序匹配，容忍切片里插入的重复表头等非连续内容。"""
+
+    segments = [
+        compact
+        for compact in (
+            _compact_with_offsets(line)[0]
+            for line in _LIST_MARKER_PREFIX.sub("", content).split("\n")
+        )
+        if len(compact) >= 6
+    ]
+    if not segments:
         return None, None, compact_start
-    start = source_offsets[position]
+    total = sum(len(segment) for segment in segments)
-    end = source_offsets[position + len(compact_content) - 1] + 1
+
-    while start > 0 and source_text[start - 1] not in "\r\n":
+    def match_from(cursor: int) -> tuple[list[tuple[int, int]], int]:
-        start -= 1
+        matched: list[tuple[int, int]] = []
-    while end < len(source_text) and source_text[end] not in "\r\n":
+        position = cursor
-        end += 1
+        for segment in segments:
-    return start, end, position + len(compact_content)
+            found = compact_source.find(segment, position)
+            if found < 0:
+                continue
+            matched.append((found, found + len(segment)))
+            position = found + len(segment)
+        return matched, sum(end - start for start, end in matched)
+
+    matched, covered = match_from(compact_start)
+    if covered * 2 < total:
+        retried, retry_covered = match_from(0)
+        if retry_covered > covered:
+            matched, covered = retried, retry_covered
+    # 覆盖不足一半时宁可不定位，也不能给出错误的行号。
+    if not matched or covered * 2 < total:
+        return None, None, compact_start
+    start, end = _expand_to_line_boundaries(
+        source_text,
+        source_offsets[matched[0][0]],
+        source_offsets[matched[-1][1] - 1] + 1,
+    )
+    return start, end, max(compact_start, matched[-1][1])
 
 
 def chunk_layout_document(

backend/app/modules/data_process/evaluation.py

@@ -0,0 +1,313 @@
+"""数据处理 - 生成结果的多层质量评测。
+
+三层体系：规则层（确定性规则分）+ 语义层（本地嵌入向量）+ 评审层
+（复用生成模型按 rubric 打分的 LLM-as-judge）。任一层失败自动降级，
+评测永远返回可用结果，不阻断调用方流程。
+"""
+
+from __future__ import annotations
+
+import logging
+from collections.abc import Mapping
+from dataclasses import asdict
+from datetime import UTC, datetime
+from typing import Any
+
+import httpx
+
+from .algorithms import normalize_text, score_quality
+from .algorithms.quality import composite_overall, semantic_quality_scores
+from .generation import (
+    ModelGenerationError,
+    _is_retryable_generation_error,
+    _json_payload,
+    _message_content,
+    chat_completions_url,
+)
+
+logger = logging.getLogger(__name__)
+
+# 送入评审提示词的来源正文上限，避免超长切片挤占评分输出空间。
+_MAX_JUDGE_SOURCE_CHARS = 6000
+
+_JUDGE_DIMENSIONS: dict[str, tuple[str, ...]] = {
+    "standard": (
+        "faithfulness",
+        "correctness",
+        "clarity",
+        "completeness",
+        "alignment",
+    ),
+    "reasoning": (
+        "faithfulness",
+        "correctness",
+        "clarity",
+        "completeness",
+        "alignment",
+        "reasoning_validity",
+    ),
+    "dpo": (
+        "clarity",
+        "chosen_quality",
+        "rejected_quality",
+        "preference_reasonableness",
+        "faithfulness",
+    ),
+}
+
+_DIMENSION_LABELS: dict[str, str] = {
+    "faithfulness": "忠实度",
+    "correctness": "正确性",
+    "clarity": "问题清晰度",
+    "completeness": "回答完整性",
+    "alignment": "指令对齐",
+    "reasoning_validity": "推理有效性",
+    "chosen_quality": "chosen 回答质量",
+    "rejected_quality": "rejected 回答质量",
+    "preference_reasonableness": "偏好区分合理性",
+}
+
+_DIMENSION_RULES: dict[str, str] = {
+    "faithfulness": "忠实度：答案的全部陈述是否被参考资料支持，没有编造、没有引入资料之外的信息；未提供参考资料时按答案内部自洽性评估",
+    "correctness": "正确性：答案中的事实、概念与计算是否正确",
+    "clarity": "问题清晰度：问题是否清晰、自包含、无歧义，脱离上下文也能理解",
+    "completeness": "回答完整性：答案是否充分、直接地回应了问题的全部要点",
+    "alignment": "指令对齐：答案的形式与范围是否符合问题的要求（如格式、语言、范围限定）",
+    "reasoning_validity": "推理有效性：思维链步骤是否逻辑连贯、无跳步或循环论证，结论是否由推理过程自然得出",
+    "chosen_quality": "chosen 回答质量：更优回答的正确性、完整性与表述质量",
+    "rejected_quality": "rejected 回答质量：较差回答是否仍具备基本可读性，使对比训练有意义",
+    "preference_reasonableness": "偏好区分合理性：chosen 是否明显优于 rejected，且优劣差异与问题直接相关",
+}
+
+
+def _judge_system_prompt(output_type: str) -> str:
+    dimensions = _JUDGE_DIMENSIONS[output_type]
+    rules = "\n".join(f"- {_DIMENSION_RULES[name]}" for name in dimensions)
+    scores_schema = ", ".join(f'"{name}": 1-5' for name in dimensions)
+    return (
+        "你是大模型训练数据质量评审员。严格依据用户消息中的【参考资料】评审这条训练数据，逐维度按 1-5 分打分：\n"
+        f"{rules}\n"
+        "评分锚点：5 分=完全符合维度描述；3 分=基本符合但有明显不足；1 分=严重不符合。\n"
+        "忠实度只依据参考资料与公认常识判断，无法得到支持的陈述必须扣分；不要因为答案冗长而加分。\n"
+        "只输出一个 JSON 对象，不要输出 JSON 之外的任何文字。\n"
+        '输出格式：{"scores": {' + scores_schema + '}, "reason": "一句话总评", "issues": ["具体问题，没有则为空数组"]}'
+    )
+
+
+def _judge_user_prompt(record: Mapping[str, Any], source_content: str) -> str:
+    source = normalize_text(source_content)[:_MAX_JUDGE_SOURCE_CHARS] or "（无参考资料）"
+    instruction = normalize_text(str(record.get("instruction") or "")) or "（空）"
+    input_text = normalize_text(str(record.get("input") or ""))
+    sections = [f"【参考资料】\n{source}", f"【问题】\n{instruction}"]
+    if input_text:
+        sections.append(f"【输入】\n{input_text}")
+    if record.get("chosen") or record.get("rejected"):
+        sections.append(f"【更优回答 chosen】\n{normalize_text(str(record.get('chosen') or '')) or '（空）'}")
+        sections.append(f"【较差回答 rejected】\n{normalize_text(str(record.get('rejected') or '')) or '（空）'}")
+    else:
+        output = normalize_text(str(record.get("output") or ""))
+        sections.append(f"【回答】\n{output or '（空）'}")
+    return "\n\n".join(sections)
+
+
+def _validated_judge_payload(payload: Any, output_type: str) -> dict[str, Any]:
+    if not isinstance(payload, Mapping):
+        raise ModelGenerationError("评审响应不是 JSON 对象")
+    raw_scores = payload.get("scores")
+    if not isinstance(raw_scores, Mapping):
+        raise ModelGenerationError("评审响应缺少 scores 对象")
+    expected = _JUDGE_DIMENSIONS[output_type]
+    scores: dict[str, float] = {}
+    for name in expected:
+        value = raw_scores.get(name)
+        if isinstance(value, bool) or not isinstance(value, (int, float)):
+            raise ModelGenerationError(f"评审响应缺少维度 {name} 的有效分数")
+        scores[name] = round(max(1.0, min(5.0, float(value))), 1)
+    issues = payload.get("issues")
+    if not isinstance(issues, list):
+        issues = []
+    issues = [str(item)[:200] for item in issues if str(item).strip()][:8]
+    reason = normalize_text(str(payload.get("reason") or ""))[:300]
+    return {
+        "scores": scores,
+        "overall": round(sum(scores.values()) / len(scores) * 20, 2),
+        "reason": reason,
+        "issues": issues,
+    }
+
+
+def _judge_record(
+    record: Mapping[str, Any],
+    source_content: str,
+    *,
+    model: Mapping[str, Any],
+    config: Mapping[str, Any],
+    client: httpx.Client | None,
+) -> dict[str, Any] | None:
+    output_type = str(config.get("output_type") or "standard").strip().lower()
+    if output_type not in _JUDGE_DIMENSIONS:
+        output_type = "standard"
+    endpoint = chat_completions_url(str(model.get("api_url") or ""))
+    model_name = str(model.get("online_model_name") or model.get("name") or "").strip()
+    if not model_name:
+        raise ModelGenerationError("generation model name is required")
+    temperature = 0.1
+    max_tokens = max(256, min(2048, int(config.get("max_tokens", 1024) or 1024)))
+    timeout = max(1.0, min(120.0, float(config.get("request_timeout_seconds", 60) or 60)))
+    retries = max(0, min(5, int(config.get("generation_retries", 2) or 2)))
+    headers = {"Content-Type": "application/json"}
+    api_key = str(model.get("api_key") or "").strip()
+    if api_key:
+        headers["Authorization"] = f"Bearer {api_key}"
+
+    request_payload: dict[str, Any] = {
+        "model": model_name,
+        "messages": [
+            {"role": "system", "content": _judge_system_prompt(output_type)},
+            {"role": "user", "content": _judge_user_prompt(record, source_content)},
+        ],
+        "temperature": temperature,
+        "max_tokens": max_tokens,
+    }
+    if bool(config.get("json_mode", False)):
+        request_payload["response_format"] = {"type": "json_object"}
+
+    owns_client = client is None
+    http_client = client or httpx.Client(timeout=timeout)
+    try:
+        last_error: Exception | None = None
+        for _ in range(retries + 1):
+            try:
+                response = http_client.post(endpoint, headers=headers, json=request_payload)
+                response.raise_for_status()
+                body = response.json()
+                if not isinstance(body, Mapping):
+                    raise ModelGenerationError("model response body must be a JSON object")
+                judged = _validated_judge_payload(
+                    _json_payload(_message_content(body)),
+                    output_type,
+                )
+                judged["model"] = model_name
+                judged["output_type"] = output_type
+                return judged
+            except Exception as exc:
+                last_error = exc
+                if not _is_retryable_generation_error(exc):
+                    break
+        raise ModelGenerationError(f"质量评审调用失败: {last_error}")
+    finally:
+        if owns_client:
+            http_client.close()
+
+
+def evaluate_result_record(
+    record: Mapping[str, Any],
+    *,
+    source_content: str = "",
+    model: Mapping[str, Any] | None = None,
+    config: Mapping[str, Any] | None = None,
+    client: httpx.Client | None = None,
+    embed_model: Any = None,
+    min_output_length: int = 20,
+) -> dict[str, Any]:
+    """对一条生成结果执行三层评测，返回可直接落库的 quality_score 字典。
+
+    规则层字段保持原样平铺（向后兼容既有读取方）；新增 ``semantic``、
+    ``judge``、``layers``、``evaluated`` 与组合 ``overall``。
+    """
+
+    config_dict = dict(config or {})
+    rule = score_quality(
+        record,
+        min_output_length=min_output_length,
+        source_content=source_content,
+    )
+    quality: dict[str, Any] = asdict(rule)
+
+    semantic = semantic_quality_scores(
+        record,
+        source_content=source_content,
+        embed_model=embed_model,
+    )
+    judge: dict[str, Any] | None = None
+    if model is not None:
+        try:
+            judge = _judge_record(
+                record,
+                source_content,
+                model=model,
+                config=config_dict,
+                client=client,
+            )
+        except Exception as exc:
+            logger.warning(
+                "data process judge evaluation degraded: %s",
+                exc,
+            )
+
+    layers = {
+        "rule": rule.overall,
+        "semantic": semantic.get("overall") if semantic else None,
+        "judge": judge.get("overall") if judge else None,
+    }
+    quality.update(
+        semantic=semantic,
+        judge=judge,
+        layers=layers,
+        evaluated=True,
+        evaluated_at=datetime.now(UTC).isoformat(),
+        overall=composite_overall(
+            rule=layers["rule"],
+            semantic=layers["semantic"],
+            judge=layers["judge"],
+        ),
+    )
+    return quality
+
+
+def reevaluate_edited_record(
+    record: Mapping[str, Any],
+    *,
+    source_content: str = "",
+    previous_quality: Mapping[str, Any] | None = None,
+    embed_model: Any = None,
+    min_output_length: int = 20,
+) -> dict[str, Any]:
+    """手动编辑/恢复后重算规则与语义层，丢弃已过期的评审层。
+
+    编辑会改变内容，旧的评审分不再可信；规则与语义层本地重算零成本。
+    ``evaluated`` 标记沿用原值，保证已评测过的结果编辑后仍有可用分数。
+    """
+
+    rule = score_quality(
+        record,
+        min_output_length=min_output_length,
+        source_content=source_content,
+    )
+    quality: dict[str, Any] = asdict(rule)
+    semantic = semantic_quality_scores(
+        record,
+        source_content=source_content,
+        embed_model=embed_model,
+    )
+    previous = dict(previous_quality or {})
+    evaluated = bool(previous.get("evaluated"))
+    layers = {
+        "rule": rule.overall,
+        "semantic": semantic.get("overall") if semantic else None,
+        "judge": None,
+    }
+    quality.update(
+        semantic=semantic,
+        judge=None,
+        layers=layers,
+        evaluated=evaluated,
+        evaluated_at=(
+            datetime.now(UTC).isoformat() if evaluated else None
+        ),
+        overall=composite_overall(
+            rule=layers["rule"],
+            semantic=layers["semantic"],
+        ),
+    )
+    return quality

backend/app/modules/data_process/office_preview.py

@@ -11,6 +11,7 @@ import re
 from typing import Any
 
 from docx import Document
+from docx.oxml.ns import qn
 from docx.oxml.table import CT_Tbl
 from docx.oxml.text.paragraph import CT_P
 from docx.table import Table
@@ -27,6 +28,7 @@ from app.modules.data_process.algorithms import (
     _xlsx_sheet_merge_ranges,
     normalize_text,
 )
+from app.modules.data_process.algorithms.parsers.office import iter_document_blocks
 
 MAX_DOCX_PREVIEW_BLOCKS = 2_000
 MAX_XLSX_PREVIEW_ROWS = 200
@@ -49,12 +51,21 @@ def _docx_alignment(paragraph: Paragraph) -> str:
 
 def _docx_heading_level(paragraph: Paragraph) -> int | None:
     style = paragraph.style
-    if style is None:
+    style_name = str(style.name or "") if style is not None else ""
-        return None
+    style_id = str(style.style_id or "") if style is not None else ""
-    style_name = str(style.name or "")
-    style_id = str(style.style_id or "")
     match = re.search(r"(?:heading|标题)\s*([1-6])", f"{style_name} {style_id}", re.IGNORECASE)
-    return int(match.group(1)) if match else None
+    if match:
+        return int(match.group(1))
+    # Word 的目录和导航窗格依据大纲级别识别标题；未套标题样式但带
+    # outlineLvl 的段落（如手工排版的编号小节）同样是标题。
+    outline = paragraph._p.find(f"{qn('w:pPr')}/{qn('w:outlineLvl')}")
+    if outline is not None:
+        value = outline.get(qn("w:val"))
+        if value is not None and value.isdigit():
+            level = int(value)
+            if 0 <= level <= 5:
+                return level + 1
+    return None
 
 
 def build_docx_preview(raw: bytes) -> dict[str, Any]:
@@ -84,7 +95,7 @@ def build_docx_preview(raw: bytes) -> dict[str, Any]:
         has_source_content = True
         return text, start, source_cursor
 
-    for child in document.element.body.iterchildren():
+    for child in iter_document_blocks(document.element.body):
         if rendered_blocks >= MAX_DOCX_PREVIEW_BLOCKS:
             truncated = True
             break

backend/app/modules/data_process/storage.py

@@ -1,4 +1,4 @@
-"""数据处理原始源文件的受控本地对象存储。"""
+"""数据处理源文件的受控暂存与分层对象存储。"""
 
 from __future__ import annotations
 
@@ -13,6 +13,9 @@ from pathlib import Path, PurePosixPath
 from typing import Iterable, Iterator
 from urllib.parse import quote, unquote, urlsplit
 
+from app.core.config import get_settings
+from app.modules.storage.minio_store import get_object_storage
+
 
 class DataProcessStorageError(ValueError):
     """本地对象引用或文件系统状态不安全。"""
@@ -68,7 +71,7 @@ def _safe_basename(value: str) -> str:
 
 
 class LocalDataProcessStorage:
-    """只允许访问配置根目录下的版本化原始文件。"""
+    """Stage locally, but publish and read authoritative source files from MinIO."""
 
     def __init__(self, root: str | os.PathLike[str] | Path | None = None) -> None:
         configured = Path(root) if root is not None else _configured_storage_root()
@@ -132,10 +135,7 @@ class LocalDataProcessStorage:
             f"v{version}",
             basename,
         )
-        reference = (
+        reference = self._reference(task_id, source_file_id, version, basename)
-            "local://data-process/"
-            f"{task_id}/{source_file_id}/v{version}/{quote(basename, safe='')}"
-        )
         staged = StagedSourceObject(reference, temporary_path, relative_path)
         self._issued_staged_objects[temporary_path] = staged
         return staged
@@ -168,6 +168,18 @@ class LocalDataProcessStorage:
             expected_task_id=expected_source_task_id,
             expected_source_file_id=expected_source_file_id,
         )
+        if self._is_minio_reference(source_reference):
+            content = self.read(source_reference)
+            if content is None:
+                raise DataProcessStorageError("original source object is not available")
+            return self.stage_bytes(
+                batch_id=batch_id,
+                task_id=task_id,
+                source_file_id=source_file_id,
+                version=version,
+                name=basename,
+                content=content,
+            )
         descriptor, source_info = self._open_read_descriptor(source_relative)
         os.close(descriptor)
 
@@ -193,10 +205,7 @@ class LocalDataProcessStorage:
             f"v{version}",
             basename,
         )
-        reference = (
+        reference = self._reference(task_id, source_file_id, version, basename)
-            "local://data-process/"
-            f"{task_id}/{source_file_id}/v{version}/{quote(basename, safe='')}"
-        )
         staged = StagedSourceObject(reference, temporary_path, relative_path)
         self._issued_staged_objects[temporary_path] = staged
         return staged
@@ -212,14 +221,22 @@ class LocalDataProcessStorage:
                     raise DataProcessStorageError("duplicate staged source object")
                 seen_temporary_paths.add(item._temporary_path)
             for item in staged:
+                if self._is_minio_reference(item.reference):
+                    content = item._temporary_path.read_bytes()
+                    get_object_storage().put_bytes(
+                        self.object_key(item.reference),
+                        content,
+                        "application/octet-stream",
+                    )
+                elif not item.reference.startswith("db://data-process/"):
                     final_path = self._path_for_relative(item._relative_path)
                     self._ensure_directory(final_path.parent)
                     if final_path.exists() or final_path.is_symlink():
                         raise DataProcessStorageError("source storage object already exists")
                     os.link(item._temporary_path, final_path, follow_symlinks=False)
+                    self._fsync_directory(final_path.parent)
                 published.append(item)
                 item._temporary_path.unlink()
-                self._fsync_directory(final_path.parent)
         except Exception:
             for item in reversed(published):
                 try:
@@ -258,7 +275,13 @@ class LocalDataProcessStorage:
             raise first_error
 
     def read(self, reference: str) -> bytes | None:
-        """读取 local 引用；旧 ``db://`` 对象返回 ``None`` 由数据库正文兜底。"""
+        """Read a MinIO object or legacy local reference."""
+
+        if self._is_minio_reference(reference):
+            try:
+                return get_object_storage().get_bytes(self.object_key(reference))
+            except Exception as exc:  # noqa: BLE001 - normalize object-not-found for callers
+                raise DataProcessStorageError("source storage object does not exist") from exc
 
         relative_path = self._relative_from_reference(reference)
         if relative_path is None:
@@ -276,6 +299,11 @@ class LocalDataProcessStorage:
     ) -> int | None:
         """返回受控 local 对象大小；旧 ``db://`` 对象没有原始文件。"""
 
+        if self._is_minio_reference(reference):
+            try:
+                return int(get_object_storage().stat(self.object_key(reference)).get("byte_size") or 0)
+            except Exception as exc:  # noqa: BLE001
+                raise DataProcessStorageError("source storage object does not exist") from exc
         relative_path = self._relative_from_reference(reference)
         if relative_path is None:
             return None
@@ -301,6 +329,15 @@ class LocalDataProcessStorage:
     ) -> Iterator[bytes]:
         """按范围流式读取原始文件，避免 PDF 预览把大文件整体载入内存。"""
 
+        if self._is_minio_reference(reference):
+            content = self.read(reference) or b""
+            if start < 0 or expected_size != len(content) or start > expected_size:
+                raise DataProcessStorageError("source object size does not match metadata")
+            remaining = expected_size - start if length is None else length
+            if remaining < 0 or start + remaining > expected_size:
+                raise DataProcessStorageError("invalid source byte range")
+            yield content[start : start + remaining]
+            return
         relative_path = self._relative_from_reference(reference)
         if relative_path is None:
             raise DataProcessStorageError("original source object is not available")
@@ -337,6 +374,12 @@ class LocalDataProcessStorage:
     ) -> bool:
         """校验 local 引用归属；旧 ``db://`` 引用无需文件系统处理。"""
 
+        if self._is_minio_reference(reference):
+            self._assert_minio_owner(reference, expected_task_id, expected_source_file_id)
+            return True
+        if str(reference or "").startswith("db://data-process/"):
+            self._assert_database_owner(reference, expected_task_id, expected_source_file_id)
+            return True
         relative_path = self._relative_from_reference(reference)
         if relative_path is None:
             return False
@@ -382,6 +425,19 @@ class LocalDataProcessStorage:
     ) -> bool:
         """删除受控 local 对象；旧 ``db://`` 引用保持不变。"""
 
+        if self._is_minio_reference(reference):
+            if (expected_task_id is None) != (expected_source_file_id is None):
+                raise DataProcessStorageError("both expected storage owner fields are required")
+            if expected_task_id is not None and expected_source_file_id is not None:
+                self._assert_minio_owner(reference, expected_task_id, expected_source_file_id)
+            get_object_storage().delete(self.object_key(reference))
+            return True
+        if str(reference or "").startswith("db://data-process/"):
+            if (expected_task_id is None) != (expected_source_file_id is None):
+                raise DataProcessStorageError("both expected storage owner fields are required")
+            if expected_task_id is not None and expected_source_file_id is not None:
+                self._assert_database_owner(reference, expected_task_id, expected_source_file_id)
+            return False
         relative_path = self._relative_from_reference(reference)
         if relative_path is None:
             return False
@@ -426,7 +482,7 @@ class LocalDataProcessStorage:
         if reference.startswith("db://"):
             return None
         parsed = urlsplit(reference)
-        if parsed.scheme != "local" or parsed.netloc != "data-process":
+        if parsed.scheme not in {"local", "minio"} or parsed.netloc != "data-process":
             raise DataProcessStorageError("unsupported source storage reference")
         if parsed.query or parsed.fragment or "\\" in parsed.path:
             raise DataProcessStorageError("unsafe source storage reference")
@@ -460,6 +516,39 @@ class LocalDataProcessStorage:
         basename = _safe_basename(decoded[3])
         return PurePosixPath(task_id, source_file_id, f"v{version}", basename)
 
+    @staticmethod
+    def _is_minio_reference(reference: str) -> bool:
+        return str(reference or "").startswith("minio://data-process/")
+
+    @staticmethod
+    def _reference(task_id: str, source_file_id: str, version: int, basename: str) -> str:
+        scheme = "minio" if get_settings().minio_enabled else "local"
+        return f"{scheme}://data-process/{task_id}/{source_file_id}/v{version}/{quote(basename, safe='')}"
+
+    @staticmethod
+    def object_key(reference: str) -> str:
+        parsed = urlsplit(reference)
+        if parsed.scheme != "minio" or parsed.netloc != "data-process":
+            raise DataProcessStorageError("reference is not a MinIO source object")
+        return "data-process/" + parsed.path.lstrip("/")
+
+    def _assert_minio_owner(self, reference: str, task_id: str, source_file_id: str) -> None:
+        relative = self._relative_from_reference(reference)
+        if relative is None:
+            raise DataProcessStorageError("invalid MinIO source reference")
+        self._assert_expected_owner(relative, expected_task_id=task_id, expected_source_file_id=source_file_id)
+
+    @staticmethod
+    def _assert_database_owner(reference: str, task_id: str, source_file_id: str) -> None:
+        parsed = urlsplit(reference)
+        parts = parsed.path.lstrip("/").split("/")
+        if parsed.netloc != "data-process" or len(parts) != 3:
+            raise DataProcessStorageError("invalid database source reference")
+        expected_task_id = _safe_component(task_id, "expected task id")
+        expected_source_file_id = _safe_component(source_file_id, "expected source file id")
+        if tuple(parts[:2]) != (expected_task_id, expected_source_file_id) or parts[2] != "v1":
+            raise DataProcessStorageError("source storage object owner mismatch")
+
     def _path_for_relative(self, relative_path: PurePosixPath) -> Path:
         if relative_path.is_absolute() or any(
             part in {"", ".", ".."} for part in relative_path.parts
@@ -479,6 +568,19 @@ class LocalDataProcessStorage:
             raise DataProcessStorageError("invalid staged source object")
         if self._issued_staged_objects.get(item._temporary_path) is not item:
             raise DataProcessStorageError("staged source object was not issued by this storage")
+        if item.reference.startswith("db://data-process/"):
+            parsed = urlsplit(item.reference)
+            parts = parsed.path.lstrip("/").split("/")
+            expected = item._relative_path.parts[:3]
+            if (
+                parsed.netloc != "data-process"
+                or parsed.query
+                or parsed.fragment
+                or len(parts) != 3
+                or tuple(parts) != expected
+            ):
+                raise DataProcessStorageError("staged source object reference mismatch")
+        else:
             expected_relative = self._relative_from_reference(item.reference)
             if expected_relative is None or expected_relative != item._relative_path:
                 raise DataProcessStorageError("staged source object reference mismatch")

backend/app/modules/data_process/store/base.py

@@ -247,14 +247,19 @@ def _source_storage_descriptor(
         or f"db://data-process/{task_id}/{file_id}/v1"
     )
     expected_local_prefix = f"local://data-process/{task_id}/{file_id}/v1/"
+    expected_minio_prefix = f"minio://data-process/{task_id}/{file_id}/v1/"
     expected_database_reference = f"db://data-process/{task_id}/{file_id}/v1"
     if storage_object_id.startswith(expected_local_prefix) and len(storage_object_id) > len(
         expected_local_prefix
     ):
         storage_backend = "local"
+    elif storage_object_id.startswith(expected_minio_prefix) and len(storage_object_id) > len(
+        expected_minio_prefix
+    ):
+        storage_backend = "minio"
     elif storage_object_id == expected_database_reference:
         storage_backend = "database"
-    elif storage_object_id.startswith(("local://data-process/", "db://data-process/")):
+    elif storage_object_id.startswith(("local://data-process/", "minio://data-process/", "db://data-process/")):
         raise DataProcessStoreError("source storage object owner mismatch")
     else:
         raise DataProcessStoreError("unsupported source storage object reference")

backend/app/modules/data_process/store/datasets.py

@@ -11,6 +11,7 @@ import psycopg
 
 from app.core.config import get_settings
 from app.modules.storage.minio_store import get_object_storage
+from app.modules.storage.policy import should_store_in_minio
 
 from .base import (
     StoreBase,
@@ -168,7 +169,11 @@ class DatasetsMixin:
                 split_name: assignments.count(split_name) for split_name in split_order
             }
             split_specs: list[dict[str, Any]] = []
-            use_minio = bool(get_settings().minio_enabled)
+            # Explicit local is retained for old callers/tests that request the
+            # legacy backend; all normal platform requests default to MinIO.
+            allow_minio = bool(get_settings().minio_enabled) and str(
+                payload.get("storage_type") or "minio"
+            ).lower() != "local"
             for split_name in split_order:
                 split_records = [
                     (source_row, record)
@@ -193,12 +198,15 @@ class DatasetsMixin:
                         "storage_object_id": (
                             f"db://data-process/{task_id}/{file_id}/v1"
                         ),
+                        "store_in_minio": allow_minio and should_store_in_minio(
+                            len(raw), content_type="application/jsonl", file_format="jsonl"
+                        ),
                     }
                 )
             source_result_ids = [row["id"] for row in rows]
             common_metadata = {
                 "source": "data_process",
-                "storage_backend": "minio" if use_minio else "database",
+                "storage_backend": "minio" if any(spec["store_in_minio"] for spec in split_specs) else "database",
                 "source_task_id": task_id,
                 "output_type": _task_output_type(task),
                 "reasoning_detail": _task_reasoning_detail(task),
@@ -273,7 +281,7 @@ class DatasetsMixin:
                     split_name = str(spec["split"])
                     dataset_id = dataset_ids[split_name]
                     storage_object_id = str(spec["storage_object_id"])
-                    if use_minio:
+                    if spec["store_in_minio"]:
                         file_name = f"{base_dataset_name}.{split_name}.jsonl"
                         object_key = f"datasets/{dataset_id}/versions/{spec['version_id']}/{file_name}"
                         uploaded = get_object_storage().put_bytes(
@@ -356,7 +364,7 @@ class DatasetsMixin:
                             (
                                 dataset_name,
                                 dataset_types[split_name],
-                                "minio" if use_minio else (payload.get("storage_type") or "local"),
+                        "minio" if spec["store_in_minio"] else "database",
                                 f"{len(spec['raw'])} B",
                                 len(spec["raw"]),
                                 len(spec["records"]),
@@ -386,7 +394,7 @@ class DatasetsMixin:
                                 dataset_id,
                                 dataset_name,
                                 dataset_types[split_name],
-                                "minio" if use_minio else (payload.get("storage_type") or "local"),
+                                "minio" if spec["store_in_minio"] else "database",
                                 task_id,
                                 task_id,
                                 f"{len(spec['raw'])} B",
@@ -405,7 +413,11 @@ class DatasetsMixin:
                             ),
                         ).fetchone()
 
-                    file_metadata = {**dataset_metadata, "file_split": split_name}
+                    file_metadata = {
+                        **dataset_metadata,
+                        "file_split": split_name,
+                        "storage_backend": "minio" if spec["store_in_minio"] else "database",
+                    }
                     version = {
                         "id": spec["version_id"],
                         "version_no": 1,

backend/app/modules/data_process/store/source_files.py

@@ -137,7 +137,7 @@ class SourceFilesMixin:
                             payload["record_count"],
                             payload["file_format"],
                             payload["checksum_sha256"],
-                            payload["content"],
+                            "" if str(storage_object_id or "").startswith("minio://") else payload["content"],
                             str(payload["content"])[:2000],
                             json_dumps(metadata_payload),
                             task.get("tenant_id"),
@@ -223,7 +223,17 @@ class SourceFilesMixin:
             ).fetchone()
         if not row:
             raise NotFoundError("source file not found")
-        return _decode_row(row) or {}
+        decoded = _decode_row(row) or {}
+        # New source files keep only a preview in PostgreSQL. Load the
+        # authoritative body from MinIO on demand for existing processing code.
+        reference = str(decoded.get("storage_object_id") or "")
+        if include_content and not decoded.get("content") and reference.startswith("minio://"):
+            from app.modules.data_process.storage import get_data_process_storage
+
+            decoded["content"] = (get_data_process_storage().read(reference) or b"").decode(
+                "utf-8", errors="replace"
+            )
+        return decoded
 
     def source_content_window(
         self, task_id: str, file_id: str, offset: int, limit: int

backend/app/modules/storage/minio_store.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 from datetime import timedelta
 from functools import lru_cache
 from io import BytesIO
+from collections.abc import Iterator
 from typing import Any
 
 from minio import Minio
@@ -53,6 +54,64 @@ class MinioObjectStorage:
         except S3Error as exc:
             raise ObjectStorageError(str(exc)) from exc
 
+    def get_bytes(self, object_key: str) -> bytes:
+        """Read an object through the backend for small API responses and workers."""
+        self._ensure_enabled()
+        self.ensure_bucket()
+        response = None
+        try:
+            response = self.client.get_object(self.bucket, object_key)
+            return response.read()
+        except S3Error as exc:
+            raise ObjectStorageError(str(exc)) from exc
+        finally:
+            if response is not None:
+                response.close()
+                response.release_conn()
+
+    def iter_bytes(self, object_key: str, chunk_size: int = 256 * 1024) -> Iterator[bytes]:
+        """Stream an object without loading the complete file into memory."""
+        self._ensure_enabled()
+        self.ensure_bucket()
+        response = None
+        try:
+            response = self.client.get_object(self.bucket, object_key)
+            while True:
+                chunk = response.read(chunk_size)
+                if not chunk:
+                    break
+                yield chunk
+        except S3Error as exc:
+            raise ObjectStorageError(str(exc)) from exc
+        finally:
+            if response is not None:
+                response.close()
+                response.release_conn()
+
+    def list_objects(self, prefix: str) -> list[dict[str, Any]]:
+        self._ensure_enabled()
+        self.ensure_bucket()
+        try:
+            return [
+                {
+                    "object_key": item.object_name,
+                    "byte_size": item.size or 0,
+                    "etag": item.etag,
+                    "last_modified": item.last_modified.isoformat() if item.last_modified else None,
+                }
+                for item in self.client.list_objects(self.bucket, prefix=prefix, recursive=True)
+            ]
+        except S3Error as exc:
+            raise ObjectStorageError(str(exc)) from exc
+
+    def delete(self, object_key: str) -> None:
+        self._ensure_enabled()
+        self.ensure_bucket()
+        try:
+            self.client.remove_object(self.bucket, object_key)
+        except S3Error as exc:
+            raise ObjectStorageError(str(exc)) from exc
+
     def put_bytes(self, object_key: str, content: bytes, content_type: str = "application/octet-stream") -> dict[str, Any]:
         self._ensure_enabled()
         self.ensure_bucket()

backend/app/modules/storage/policy.py

@@ -0,0 +1,54 @@
+"""Storage placement rules shared by dataset and data-processing flows."""
+
+from __future__ import annotations
+
+from app.core.config import get_settings
+
+
+_INLINE_TEXT_FORMATS = {
+    "txt", "text", "md", "markdown", "json", "jsonl", "csv", "tsv",
+    "yaml", "yml", "xml", "html", "text/plain", "application/json",
+    "application/jsonl", "text/csv",
+}
+
+
+def should_store_in_minio(
+    size_bytes: int | None,
+    *,
+    content_type: str | None = None,
+    file_format: str | None = None,
+) -> bool:
+    """Return whether a file is large enough to use the shared object store.
+
+    Small files remain inline in PostgreSQL so page previews and metadata reads
+    do not pay an object-storage round trip.  MinIO is still mandatory for
+    large files when it is enabled.
+    """
+
+    if not get_settings().minio_enabled:
+        return False
+    try:
+        size = max(0, int(size_bytes or 0))
+    except (TypeError, ValueError):
+        size = 0
+    if size > get_settings().minio_inline_max_bytes:
+        return True
+    # Binary office/document files remain in MinIO even when small because
+    # their original bytes cannot be safely represented by a text DB column.
+    normalized_format = str(file_format or "").strip().lower().lstrip(".")
+    normalized_type = str(content_type or "").strip().lower().split(";", 1)[0]
+    if normalized_format or normalized_type:
+        return not (
+            normalized_format in _INLINE_TEXT_FORMATS
+            or normalized_type in _INLINE_TEXT_FORMATS
+            or normalized_type.startswith("text/")
+        )
+    return False
+
+
+def storage_backend_for_size(size_bytes: int | None, *, requested: str | None = None) -> str:
+    """Return ``minio`` or ``database`` for a managed file."""
+
+    if str(requested or "").strip().lower() == "local":
+        return "database"
+    return "minio" if should_store_in_minio(size_bytes) else "database"

backend/app/modules/system/router.py

@@ -56,13 +56,15 @@ def audit_logs(
     actor_id: str | None = Query(default=None, description="操作人 ID"),
     action: str | None = Query(default=None, description="动作类型"),
     target_type: str | None = Query(default=None, description="目标类型"),
+    target_id: str | None = Query(default=None, description="目标 ID"),
+    keyword: str | None = Query(default=None, description="目标 ID 或详情关键字"),
     start_time: str | None = Query(default=None, description="ISO8601 起始时间"),
     end_time: str | None = Query(default=None, description="ISO8601 结束时间"),
     limit: int = Query(default=50, ge=1, le=200),
     offset: int = Query(default=0, ge=0),
     current_user: dict = Depends(get_current_user),
 ) -> dict:
-    """审计日志查询：按租户/项目/操作人/动作/目标类型/时间范围分页过滤。"""
+    """审计日志查询：按组织、操作人、动作、资源、关键字和时间范围分页过滤。"""
     if not is_admin(current_user):
         from app.api.v1.endpoints.platform import fail
         raise fail(403, "admin permission required")
@@ -73,6 +75,8 @@ def audit_logs(
         actor_id=actor_id,
         action=action,
         target_type=target_type,
+        target_id=target_id,
+        keyword=keyword,
         start_time=start_time,
         end_time=end_time,
         limit=limit,
@@ -88,6 +92,8 @@ def audit_logs_export(
     actor_id: str | None = Query(default=None, description="操作人 ID"),
     action: str | None = Query(default=None, description="动作类型"),
     target_type: str | None = Query(default=None, description="目标类型"),
+    target_id: str | None = Query(default=None, description="目标 ID"),
+    keyword: str | None = Query(default=None, description="目标 ID 或详情关键字"),
     start_time: str | None = Query(default=None, description="ISO8601 起始时间"),
     end_time: str | None = Query(default=None, description="ISO8601 结束时间"),
     current_user: dict = Depends(get_current_user),
@@ -103,6 +109,8 @@ def audit_logs_export(
         actor_id=actor_id,
         action=action,
         target_type=target_type,
+        target_id=target_id,
+        keyword=keyword,
         start_time=start_time,
         end_time=end_time,
         limit=10000,

backend/app/schemas/data_process.py

@@ -384,6 +384,26 @@ class ResultBatchRegenerateRequest(BaseModel):
         return self
 
 
+class ResultBatchEvaluateItem(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    result_id: str = Field(min_length=1, max_length=100)
+    expected_updated_at: str = Field(min_length=1, max_length=100)
+
+
+class ResultBatchEvaluateRequest(BaseModel):
+    model_config = ConfigDict(extra="forbid")
+
+    items: list[ResultBatchEvaluateItem] = Field(min_length=1, max_length=50)
+
+    @model_validator(mode="after")
+    def validate_unique_results(self) -> ResultBatchEvaluateRequest:
+        result_ids = [item.result_id for item in self.items]
+        if len(result_ids) != len(set(result_ids)):
+            raise ValueError("result_id values must be unique")
+        return self
+
+
 class DatasetSplit(BaseModel):
     model_config = ConfigDict(extra="forbid")
 

backend/tests/test_data_process_algorithms.py

@@ -9,6 +9,8 @@ from decimal import Decimal
 
 import pytest
 from docx import Document
+from docx.oxml import parse_xml
+from docx.oxml.ns import nsdecls, qn
 from openpyxl import Workbook
 from pptx import Presentation
 from pptx.util import Inches
@@ -38,6 +40,7 @@ from app.modules.data_process.algorithms import (
     stable_split_assignments,
     structured_json_dumps,
 )
+from app.modules.data_process.office_preview import build_docx_preview
 
 
 def _pdf_page_texts(*texts: str) -> tuple[PdfPageText, ...]:
@@ -318,6 +321,74 @@ def test_parse_pdf_docx_xlsx_and_pptx() -> None:
     assert parsed_pptx.records == ()
 
 
+def _docx_with_sdt_bytes() -> bytes:
+    """构造带 SDT 目录内容控件的 docx，段落顺序为正文、SDT、正文。"""
+
+    document = Document()
+    document.add_paragraph("正文开头。")
+    sdt = parse_xml(
+        "<w:sdt %s><w:sdtPr><w:id w:val='1'/></w:sdtPr>"
+        "<w:sdtContent><w:p><w:r><w:t>目录条目 第一章 概述</w:t></w:r></w:p>"
+        "</w:sdtContent></w:sdt>" % nsdecls("w")
+    )
+    body = document.element.body
+    sect_pr = body.find(qn("w:sectPr"))
+    if sect_pr is not None:
+        sect_pr.addprevious(sdt)
+    else:
+        body.append(sdt)
+    document.add_paragraph("正文结尾。")
+    output = io.BytesIO()
+    document.save(output)
+    return output.getvalue()
+
+
+def test_docx_extraction_and_preview_include_sdt_content() -> None:
+    raw = _docx_with_sdt_bytes()
+
+    parsed = parse_text_content(raw, filename="toc.docx")
+    assert "目录条目 第一章 概述" in parsed.text
+    assert (
+        parsed.text.index("正文开头。")
+        < parsed.text.index("目录条目 第一章 概述")
+        < parsed.text.index("正文结尾。")
+    )
+
+    preview = build_docx_preview(raw)
+    paragraph_texts = [
+        block["text"] for block in preview["blocks"] if block["type"] == "paragraph"
+    ]
+    assert "目录条目 第一章 概述" in paragraph_texts
+    # 预览偏移必须与正文抽取规则一致，否则前端定位会错位。
+    sdt_block = next(
+        block
+        for block in preview["blocks"]
+        if block.get("text") == "目录条目 第一章 概述"
+    )
+    assert parsed.text[sdt_block["source_start"] : sdt_block["source_end"]] == (
+        "目录条目 第一章 概述"
+    )
+
+
+def test_docx_preview_detects_outline_level_headings() -> None:
+    """未套标题样式但设了大纲级别的段落（Word 目录按此收录）也按标题渲染。"""
+
+    document = Document()
+    document.add_heading("一级标题", level=1)
+    plain = document.add_paragraph("4.2.1 数据管理")
+    p_pr = plain._p.get_or_add_pPr()
+    p_pr.append(parse_xml("<w:outlineLvl %s w:val='2'/>" % nsdecls("w")))
+    document.add_paragraph("普通正文段落。")
+    output = io.BytesIO()
+    document.save(output)
+
+    preview = build_docx_preview(output.getvalue())
+    blocks = {b["text"]: b for b in preview["blocks"] if b["type"] == "paragraph"}
+    assert blocks["一级标题"]["heading_level"] == 1
+    assert blocks["4.2.1 数据管理"]["heading_level"] == 3
+    assert blocks["普通正文段落。"]["heading_level"] is None
+
+
 def test_xlsx_record_locators_distinguish_sheets_rows_and_duplicate_records() -> None:
     workbook = Workbook()
     first = workbook.active

backend/tests/test_data_process_api.py

@@ -1691,6 +1691,223 @@ def test_batch_result_regeneration_rejects_locked_tasks_before_model_call(
     assert model_calls == 0
 
 
+def _prepare_evaluation_task(
+    client: TestClient,
+    store: Any,
+    tmp_path: Path,
+    *,
+    config: dict[str, Any] | None = None,
+) -> str:
+    task_id = client.post(
+        "/modelTF/data-process",
+        json={
+            "name": "数据评测",
+            "process_type": "structured",
+            "config": config or {"generation_model_id": "model-1", "output_type": "standard"},
+        },
+    ).json()["data"]["id"]
+    store.tasks[task_id].update(
+        status="completed",
+        progress=100,
+        workflow_step="results",
+        results_confirmed=False,
+    )
+    store.models["model-1"] = {
+        "id": "model-1",
+        "online_model_name": "test-model",
+        "api_url": "https://model.example/v1",
+        "api_key": "secret",
+    }
+    store.previews[task_id] = [
+        {
+            "id": "preview-1",
+            "status": "original",
+            "original_content": "申请编号用于唯一标识一笔报销申请。",
+            "edited_content": "申请编号用于唯一标识一笔报销申请。",
+        },
+        {
+            "id": "preview-2",
+            "status": "original",
+            "original_content": "联系电话用于联系申请人。",
+            "edited_content": "联系电话用于联系申请人。",
+        },
+    ]
+    store.results[task_id] = [
+        {
+            "id": "result-1",
+            "preview_item_id": "preview-1",
+            "instruction": "申请编号有什么作用？",
+            "input": "",
+            "output": "申请编号用于唯一标识一笔报销申请。",
+            "original_instruction": "申请编号有什么作用？",
+            "original_input": "",
+            "original_output": "申请编号用于唯一标识一笔报销申请。",
+            "status": "valid",
+            "error": None,
+            "split": "train",
+            "quality_score": {},
+            "updated_at": "2026-08-19T09:00:00Z",
+        },
+        {
+            "id": "result-2",
+            "preview_item_id": "preview-2",
+            "instruction": "联系电话有什么作用？",
+            "input": "",
+            "output": "联系电话用于联系申请人。",
+            "original_instruction": "联系电话有什么作用？",
+            "original_input": "",
+            "original_output": "联系电话用于联系申请人。",
+            "status": "valid",
+            "error": None,
+            "split": "train",
+            "quality_score": {},
+            "updated_at": "2026-08-19T09:00:01Z",
+        },
+    ]
+    return task_id
+
+
+def test_results_can_be_evaluated_in_batch_with_partial_success(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    client, store, _ = make_client(tmp_path)
+    task_id = _prepare_evaluation_task(client, store, tmp_path)
+    evaluation_calls: list[dict[str, Any]] = []
+
+    def fake_evaluate(record: dict[str, Any], **kwargs: Any) -> dict[str, Any]:
+        evaluation_calls.append({"record": deepcopy(record), "kwargs": {k: v for k, v in kwargs.items() if k != "client"}})
+        return {
+            "overall": 88.0,
+            "completeness": 100.0,
+            "length": 100.0,
+            "readability": 100.0,
+            "relevance": 90.0,
+            "duplicate": 100.0,
+            "is_valid": True,
+            "flags": [],
+            "fingerprint": "fp",
+            "semantic": {"question_answer": 80.0, "answer_source": 90.0, "overall": 85.0},
+            "judge": {"scores": {"faithfulness": 5}, "overall": 90.0},
+            "layers": {"rule": 92.0, "semantic": 85.0, "judge": 90.0},
+            "evaluated": True,
+        }
+
+    monkeypatch.setattr(data_process_endpoint, "evaluate_result_record", fake_evaluate)
+    response = client.post(
+        f"/modelTF/data-process/{task_id}/results/evaluate-batch",
+        json={
+            "items": [
+                {"result_id": "result-1", "expected_updated_at": "2026-08-19T09:00:00Z"},
+                # 乐观锁版本不匹配：该条应按冲突失败，另一条仍成功。
+                {"result_id": "result-2", "expected_updated_at": "2026-08-18T00:00:00Z"},
+            ],
+        },
+    )
+
+    assert response.status_code == 200
+    data = response.json()["data"]
+    assert data["total"] == 2
+    assert data["succeeded"] == 1
+    assert data["failed"] == 1
+    assert [item["id"] for item in data["items"]] == ["result-1"]
+    assert data["failures"][0]["result_id"] == "result-2"
+    assert data["failures"][0]["code"] == "conflict"
+
+    assert len(evaluation_calls) == 1
+    assert evaluation_calls[0]["record"]["instruction"] == "申请编号有什么作用？"
+    assert evaluation_calls[0]["kwargs"]["model"]["online_model_name"] == "test-model"
+    assert evaluation_calls[0]["kwargs"]["source_content"] == "申请编号用于唯一标识一笔报销申请。"
+
+    stored = store.results[task_id][0]["quality_score"]
+    assert stored["evaluated"] is True
+    assert stored["layers"]["judge"] == 90.0
+    assert store.results[task_id][1]["quality_score"] == {}
+
+
+def test_evaluation_without_generation_model_skips_judge_layer(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    client, store, _ = make_client(tmp_path)
+    task_id = _prepare_evaluation_task(client, store, tmp_path, config={"output_type": "standard"})
+    seen_models: list[Any] = []
+
+    def fake_evaluate(record: dict[str, Any], **kwargs: Any) -> dict[str, Any]:
+        seen_models.append(kwargs.get("model"))
+        return {
+            "overall": 70.0, "is_valid": True, "flags": [],
+            "semantic": None, "judge": None,
+            "layers": {"rule": 70.0, "semantic": None, "judge": None},
+            "evaluated": True,
+        }
+
+    monkeypatch.setattr(data_process_endpoint, "evaluate_result_record", fake_evaluate)
+    response = client.post(
+        f"/modelTF/data-process/{task_id}/results/evaluate-batch",
+        json={"items": [{"result_id": "result-1", "expected_updated_at": "2026-08-19T09:00:00Z"}]},
+    )
+
+    assert response.status_code == 200
+    assert response.json()["data"]["succeeded"] == 1
+    # 任务未配置生成模型时，评审层收到的 model 必须是 None。
+    assert seen_models == [None]
+
+
+def test_evaluation_rejects_running_task(
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    client, store, _ = make_client(tmp_path)
+    task_id = _prepare_evaluation_task(client, store, tmp_path)
+    store.tasks[task_id]["status"] = "running"
+    evaluation_calls = 0
+
+    def fake_evaluate(*args: Any, **kwargs: Any) -> dict[str, Any]:
+        nonlocal evaluation_calls
+        evaluation_calls += 1
+        return {"overall": 0, "is_valid": True, "flags": []}
+
+    monkeypatch.setattr(data_process_endpoint, "evaluate_result_record", fake_evaluate)
+    response = client.post(
+        f"/modelTF/data-process/{task_id}/results/evaluate-batch",
+        json={"items": [{"result_id": "result-1", "expected_updated_at": "2026-08-19T09:00:00Z"}]},
+    )
+
+    assert response.status_code == 409
+    assert evaluation_calls == 0
+
+
+def test_result_update_preserves_evaluation_layers_and_drops_stale_judge(
+    tmp_path: Path,
+) -> None:
+    client, store, _ = make_client(tmp_path)
+    task_id = _prepare_evaluation_task(client, store, tmp_path)
+    store.results[task_id][0]["quality_score"] = {
+        "overall": 90.0,
+        "is_valid": True,
+        "flags": [],
+        "semantic": {"overall": 85.0},
+        "judge": {"overall": 92.0},
+        "layers": {"rule": 90.0, "semantic": 85.0, "judge": 92.0},
+        "evaluated": True,
+    }
+
+    response = client.put(
+        f"/modelTF/data-process/{task_id}/results/result-1",
+        json={"output": "人工修正后的答案：申请编号唯一标识一笔报销申请。"},
+    )
+
+    assert response.status_code == 200
+    stored = store.results[task_id][0]["quality_score"]
+    # 手动编辑后：规则+语义重算，评审分丢弃，evaluated 标记保留。
+    assert stored["evaluated"] is True
+    assert stored["judge"] is None
+    assert stored["layers"]["judge"] is None
+    assert stored["layers"]["rule"] is not None
+    assert stored["overall"] >= 0
+
+
 def test_preview_build_replaces_only_selected_files_and_reports_file_counts(
     tmp_path: Path,
 ) -> None:

backend/tests/test_data_process_evaluation.py

@@ -0,0 +1,284 @@
+"""数据评测模块（三层质量评分）的单元测试。"""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+import httpx
+import pytest
+
+from app.modules.data_process.algorithms.quality import (
+    composite_overall,
+    semantic_quality_scores,
+)
+from app.modules.data_process.evaluation import (
+    _JUDGE_DIMENSIONS,
+    _judge_system_prompt,
+    _validated_judge_payload,
+    evaluate_result_record,
+    reevaluate_edited_record,
+)
+from app.modules.data_process.generation import ModelGenerationError
+
+RECORD = {
+    "instruction": "申请编号有什么作用？",
+    "input": "",
+    "output": "申请编号用于唯一标识一笔报销申请，便于跟踪审批状态。",
+}
+SOURCE = "报销系统中，申请编号用于唯一标识一笔报销申请，并支持跟踪审批状态。"
+
+
+class _FakeEmbedModel:
+    """按关键词返回固定向量，模拟语义嵌入。"""
+
+    def get_text_embedding(self, text: str) -> list[float]:
+        if "作用" in text or "编号" in text and "？" in text:
+            return [0.9, 0.1, 0.0]
+        if "申请编号" in text:
+            return [0.85, 0.2, 0.0]
+        return [0.0, 0.1, 0.9]
+
+
+class _FailingEmbedModel:
+    def get_text_embedding(self, text: str) -> list[float]:
+        raise RuntimeError("embedding unavailable")
+
+
+class _FakeResponse:
+    def __init__(self, payload: dict[str, Any]):
+        self._payload = payload
+
+    def raise_for_status(self) -> None:
+        return None
+
+    def json(self) -> dict[str, Any]:
+        return self._payload
+
+
+class _FakeClient:
+    def __init__(self, content: str):
+        self._content = content
+        self.calls: list[dict[str, Any]] = []
+
+    def post(self, endpoint: str, headers: Any = None, json: Any = None) -> _FakeResponse:
+        self.calls.append({"endpoint": endpoint, "payload": json})
+        return _FakeResponse({
+            "choices": [{"message": {"content": self._content}, "finish_reason": "stop"}],
+        })
+
+    def close(self) -> None:
+        return None
+
+
+class _RaisingClient:
+    def post(self, endpoint: str, headers: Any = None, json: Any = None) -> _FakeResponse:
+        raise httpx.ConnectError("model endpoint unreachable")
+
+    def close(self) -> None:
+        return None
+
+
+def _judge_content(scores: dict[str, float], **extra: Any) -> str:
+    return json.dumps({"scores": scores, "reason": "总体可靠", "issues": [], **extra})
+
+
+def test_judge_system_prompt_covers_rubric_dimensions() -> None:
+    standard = _judge_system_prompt("standard")
+    for name in _JUDGE_DIMENSIONS["standard"]:
+        assert name in standard
+    assert "1-5" in standard
+
+    dpo = _judge_system_prompt("dpo")
+    assert "chosen_quality" in dpo
+    assert "preference_reasonableness" in dpo
+
+    reasoning = _judge_system_prompt("reasoning")
+    assert "reasoning_validity" in reasoning
+
+
+def test_validated_judge_payload_converts_scores_to_overall() -> None:
+    judged = _validated_judge_payload(
+        {
+            "scores": {
+                "faithfulness": 5,
+                "correctness": 4,
+                "clarity": 4,
+                "completeness": 3,
+                "alignment": 4,
+            },
+            "reason": "答案可靠",
+            "issues": ["回答略冗长"],
+        },
+        "standard",
+    )
+
+    assert judged["overall"] == round((5 + 4 + 4 + 3 + 4) / 5 * 20, 2)
+    assert judged["issues"] == ["回答略冗长"]
+    assert judged["reason"] == "答案可靠"
+
+
+def test_validated_judge_payload_clamps_out_of_range_scores() -> None:
+    judged = _validated_judge_payload(
+        {
+            "scores": {
+                "faithfulness": 9,
+                "correctness": 4,
+                "clarity": 4,
+                "completeness": 0,
+                "alignment": 4,
+            },
+        },
+        "standard",
+    )
+
+    assert judged["scores"]["faithfulness"] == 5.0
+    assert judged["scores"]["completeness"] == 1.0
+
+
+@pytest.mark.parametrize(
+    "scores",
+    [
+        {"faithfulness": 5, "correctness": 4, "clarity": 4, "completeness": 3},
+        {
+            "faithfulness": 5,
+            "correctness": 4,
+            "clarity": "high",
+            "completeness": 3,
+            "alignment": 4,
+        },
+    ],
+)
+def test_validated_judge_payload_rejects_incomplete_scores(scores: dict[str, Any]) -> None:
+    with pytest.raises(ModelGenerationError):
+        _validated_judge_payload({"scores": scores}, "standard")
+
+
+def test_semantic_quality_scores_uses_cosine_similarity() -> None:
+    scores = semantic_quality_scores(
+        RECORD,
+        source_content=SOURCE,
+        embed_model=_FakeEmbedModel(),
+    )
+
+    assert scores is not None
+    assert 0 < scores["question_answer"] <= 100
+    assert 0 < scores["answer_source"] <= 100
+    assert scores["overall"] == round((scores["question_answer"] + scores["answer_source"]) / 2, 2)
+
+
+def test_semantic_quality_scores_degrades_to_none_on_failure() -> None:
+    assert (
+        semantic_quality_scores(
+            RECORD,
+            source_content=SOURCE,
+            embed_model=_FailingEmbedModel(),
+        )
+        is None
+    )
+
+
+def test_composite_overall_weights_available_layers() -> None:
+    assert composite_overall(rule=80, semantic=90, judge=70) == round(80 * 0.35 + 90 * 0.20 + 70 * 0.45, 2)
+    assert composite_overall(rule=80, semantic=90) == round(80 * 0.6 + 90 * 0.4, 2)
+    assert composite_overall(rule=80) == 80.0
+    assert composite_overall(rule=None, judge=100) == 45.0
+
+
+def test_evaluate_result_record_combines_three_layers() -> None:
+    client = _FakeClient(
+        _judge_content({
+            "faithfulness": 5,
+            "correctness": 4,
+            "clarity": 5,
+            "completeness": 4,
+            "alignment": 5,
+        })
+    )
+    quality = evaluate_result_record(
+        RECORD,
+        source_content=SOURCE,
+        model={"api_url": "https://model.example", "online_model_name": "judge-model"},
+        config={"output_type": "standard", "generation_retries": 0},
+        client=client,
+        embed_model=_FakeEmbedModel(),
+    )
+
+    assert quality["evaluated"] is True
+    assert quality["judge"] is not None
+    assert quality["judge"]["model"] == "judge-model"
+    assert quality["semantic"] is not None
+    assert quality["layers"]["judge"] == quality["judge"]["overall"]
+    assert quality["overall"] == composite_overall(
+        rule=quality["layers"]["rule"],
+        semantic=quality["layers"]["semantic"],
+        judge=quality["layers"]["judge"],
+    )
+    # 评审提示词必须携带来源原文作为评分锚点（正文经 NFKC 归一化）。
+    user_message = client.calls[0]["payload"]["messages"][1]["content"]
+    assert "申请编号用于唯一标识一笔报销" in user_message
+
+
+def test_evaluate_result_record_degrades_when_model_fails() -> None:
+    quality = evaluate_result_record(
+        RECORD,
+        source_content=SOURCE,
+        model={"api_url": "https://model.example", "online_model_name": "judge-model"},
+        config={"output_type": "standard", "generation_retries": 0},
+        client=_RaisingClient(),
+        embed_model=_FakeEmbedModel(),
+    )
+
+    assert quality["judge"] is None
+    assert quality["layers"]["judge"] is None
+    assert quality["semantic"] is not None
+    assert quality["overall"] == composite_overall(
+        rule=quality["layers"]["rule"],
+        semantic=quality["layers"]["semantic"],
+    )
+
+
+def test_evaluate_result_record_without_model_runs_two_layers() -> None:
+    quality = evaluate_result_record(
+        RECORD,
+        source_content=SOURCE,
+        model=None,
+        embed_model=_FakeEmbedModel(),
+    )
+
+    assert quality["judge"] is None
+    assert quality["evaluated"] is True
+    assert quality["overall"] == composite_overall(
+        rule=quality["layers"]["rule"],
+        semantic=quality["layers"]["semantic"],
+    )
+
+
+def test_reevaluate_edited_record_drops_stale_judge() -> None:
+    previous = {
+        "evaluated": True,
+        "judge": {"overall": 90.0},
+    }
+    quality = reevaluate_edited_record(
+        {**RECORD, "output": "编辑后的新答案内容，用于验证重评逻辑。"},
+        source_content=SOURCE,
+        previous_quality=previous,
+        embed_model=_FakeEmbedModel(),
+    )
+
+    assert quality["evaluated"] is True
+    assert quality["judge"] is None
+    assert quality["layers"]["judge"] is None
+    assert quality["semantic"] is not None
+
+
+def test_reevaluate_edited_record_keeps_unevaluated_state() -> None:
+    quality = reevaluate_edited_record(
+        RECORD,
+        source_content=SOURCE,
+        previous_quality={},
+        embed_model=_FakeEmbedModel(),
+    )
+
+    assert quality["evaluated"] is False
+    assert quality["evaluated_at"] is None

backend/tests/test_document_chunking.py

@@ -9,6 +9,7 @@ from app.modules.data_process.document_chunking import (
     DocumentChunk,
     _compact_with_offsets,
     _document_converter,
+    _nodes_to_chunks,
     _project_layout_span,
     chunk_fixed_text,
     chunk_semantic_text,
@@ -103,6 +104,86 @@ def test_layout_projection_ignores_layout_whitespace_but_keeps_source_lines() ->
     assert cursor > 0
 
 
+def test_layout_projection_tolerates_list_numbers_inserted_by_serializer() -> None:
+    # Word 自动编号存放在 numbering.xml，python-docx 抽取的正文没有编号，
+    # 而 Docling 序列化切片时会补上 "1. " 前缀，投影不能因此失败。
+    source = "接入方式说明\n结构化数据接入需要先配置连接地址。\n非结构化接入需要上传文档。"
+    compact_source, offsets = _compact_with_offsets(source)
+    start, end, cursor = _project_layout_span(
+        source,
+        "1. 结构化数据接入需要先配置连接地址。\n2. 非结构化接入需要上传文档。",
+        compact_source=compact_source,
+        source_offsets=offsets,
+        compact_start=0,
+    )
+
+    assert start is not None and end is not None
+    assert source[start:end] == "结构化数据接入需要先配置连接地址。\n非结构化接入需要上传文档。"
+    assert cursor > 0
+
+
+def test_layout_projection_never_moves_cursor_backwards() -> None:
+    source = "重复段落内容。\n中间正文。\n重复段落内容。"
+    compact_source, offsets = _compact_with_offsets(source)
+    # 重复内容回退匹配命中已消费的更早位置时，游标必须保持不退。
+    _, _, cursor = _project_layout_span(
+        source,
+        "重复段落内容。",
+        compact_source=compact_source,
+        source_offsets=offsets,
+        compact_start=compact_source.index("中间正文"),
+    )
+    assert cursor >= compact_source.index("中间正文")
+
+
+def test_layout_projection_falls_back_to_line_anchors_for_inserted_content() -> None:
+    # 表格跨切片时 Docling 会在续片中重复表头，正文不再是连续子串；
+    # 按行锚点匹配仍应定位到表头所在行到末行数据之间的连续区间。
+    source = "表头甲\t表头乙\n第一行数据\t说明一\n第二行数据\t说明二"
+    compact_source, offsets = _compact_with_offsets(source)
+    start, end, _ = _project_layout_span(
+        source,
+        "表头甲 表头乙\n第二行数据 说明二",
+        compact_source=compact_source,
+        source_offsets=offsets,
+        compact_start=0,
+    )
+
+    assert start is not None and end is not None
+    assert source[start:end] == (
+        "表头甲\t表头乙\n第一行数据\t说明一\n第二行数据\t说明二"
+    )
+
+
+def test_layout_projection_refuses_low_coverage_anchor_match() -> None:
+    source = "完全无关的正文内容甲。\n完全无关的正文内容乙。"
+    compact_source, offsets = _compact_with_offsets(source)
+    start, end, cursor = _project_layout_span(
+        source,
+        "找不到的数据行内容\n另一条找不到的数据行内容",
+        compact_source=compact_source,
+        source_offsets=offsets,
+        compact_start=0,
+    )
+
+    assert start is None
+    assert end is None
+    assert cursor == 0
+
+
+def test_text_splitter_keeps_chunks_that_cannot_be_located() -> None:
+    class FakeNode:
+        def get_content(self) -> str:
+            return "这段文本在源文本中不存在。"
+
+    chunks = _nodes_to_chunks([FakeNode()], "完全不同的源文本。")
+
+    assert len(chunks) == 1
+    assert chunks[0].original_content == "这段文本在源文本中不存在。"
+    assert chunks[0].source_start is None
+    assert chunks[0].source_start_line is None
+
+
 def test_short_layout_chunk_merges_with_neighbor_and_keeps_page_provenance() -> None:
     source = "短标题\n这是一段足够长的正文内容，用于测试相邻切片合并。"
     chunks = [

compute/api/main.py

@@ -221,6 +221,35 @@ def create_app() -> FastAPI:
             return fallback_gpu_resources()
 
         items: list[dict[str, Any]] = []
+        processes_by_uuid: dict[str, list[dict[str, Any]]] = {}
+        try:
+            process_result = subprocess.run(
+                [
+                    "nvidia-smi",
+                    "--query-compute-apps=gpu_uuid,pid,process_name,used_memory",
+                    "--format=csv,noheader,nounits",
+                ],
+                check=True,
+                capture_output=True,
+                text=True,
+                timeout=5,
+            )
+            for process_line in process_result.stdout.splitlines():
+                process_parts = [part.strip() for part in process_line.split(",")]
+                if len(process_parts) < 4:
+                    continue
+                process_uuid, pid, process_name, used_memory = process_parts[:4]
+                processes_by_uuid.setdefault(process_uuid, []).append(
+                    {
+                        "pid": int(_safe_float(pid)),
+                        "name": process_name,
+                        "memory_used_gb": round(_safe_float(used_memory) / 1024, 2),
+                    }
+                )
+        except Exception:
+            # Some driver/runtime combinations do not expose compute-apps;
+            # utilization and memory metrics remain useful without processes.
+            pass
         for line in result.stdout.splitlines():
             parts = [part.strip() for part in line.split(",")]
             if len(parts) < 9:
@@ -244,7 +273,7 @@ def create_app() -> FastAPI:
                     "temperature": int(_safe_float(temp)),
                     "power_w": round(_safe_float(power), 1),
                     "power_limit_w": round(_safe_float(power_limit), 1),
-                    "processes": [],
+                    "processes": processes_by_uuid.get(uuid, []),
                 }
             )
         return items
@@ -747,6 +776,23 @@ def create_app() -> FastAPI:
             infer_backend: str (default: "huggingface")
             infer_dtype: str (default: "auto")
         """
+        requested_gpus = payload.get("gpu_indices")
+        if requested_gpus is None:
+            requested_gpus = payload.get("gpus") or []
+        try:
+            requested_gpus = sorted({int(item) for item in requested_gpus})
+        except (TypeError, ValueError) as exc:
+            raise HTTPException(status_code=400, detail=f"invalid GPU selection: {exc}") from exc
+        if any(item < 0 for item in requested_gpus):
+            raise HTTPException(status_code=400, detail="GPU index must be non-negative")
+        if requested_gpus:
+            known_gpus = {int(item.get("gpu_index", item.get("id", -1))) for item in gpu_resources()}
+            missing = sorted(set(requested_gpus) - known_gpus)
+            if missing:
+                raise HTTPException(status_code=409, detail=f"requested GPU not found: {missing}")
+            conflict = sorted(set(requested_gpus).intersection(process_manager.locked_gpus()))
+            if conflict:
+                raise HTTPException(status_code=409, detail=f"GPU already used by another compute job: {conflict}")
         session = get_inference_session()
         result = session.load(
             model_name_or_path=payload.get("model_name_or_path", ""),
@@ -754,6 +800,7 @@ def create_app() -> FastAPI:
             template=payload.get("template", "qwen"),
             infer_backend=payload.get("infer_backend", "huggingface"),
             infer_dtype=payload.get("infer_dtype", "auto"),
+            gpu_indices=requested_gpus,
         )
         return result
 

compute/engines/llama_factory/inference.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import os
 import threading
 import time
 import uuid
@@ -36,6 +37,7 @@ class InferenceSession:
         self._generating_args: dict[str, Any] = {}
         self._model_name: str = ""
         self._adapter_path: str = ""
+        self._gpu_indices: list[int] = []
         self._loaded_at: float = 0.0
 
     @property
@@ -53,6 +55,7 @@ class InferenceSession:
                 "loaded_at": self._loaded_at,
                 "request_id": self._request_id,
                 "error": self._error,
+                "gpu_indices": list(self._gpu_indices),
             }
 
     def wait_until_loaded(self, timeout: float | None = None) -> dict[str, Any]:
@@ -87,8 +90,12 @@ class InferenceSession:
         template="qwen",
         infer_backend="huggingface",
         infer_dtype="auto",
+        gpu_indices=None,
         **kwargs,
     ) -> dict[str, Any]:
+        requested_gpus = sorted({int(item) for item in (gpu_indices or [])})
+        if any(item < 0 for item in requested_gpus):
+            return {"loaded": False, "status": "error", "error": "GPU index must be non-negative"}
         with self._state_lock:
             if self._status == "loading":
                 # A model is already loading — dedupe, reuse the same request id.
@@ -97,6 +104,7 @@ class InferenceSession:
             self._status = "loading"
             self._error = ""
             self._request_id = uuid.uuid4().hex[:12]
+            self._gpu_indices = requested_gpus
             self._cancel_requested = False
             self._load_args = {
                 "model_name_or_path": model_name_or_path,
@@ -115,13 +123,21 @@ class InferenceSession:
 
     def _load_worker(self) -> None:
         """Build the ChatModel off the state lock so info() never blocks."""
+        with self._state_lock:
+            requested_gpus = list(self._gpu_indices)
         model = None
         tokenizer = None
         generating_args: dict[str, Any] = {}
         error = ""
+        previous_visible_devices = os.environ.get("CUDA_VISIBLE_DEVICES")
         try:
+            # Set visibility before LLaMA-Factory/PyTorch initializes CUDA.
+            if requested_gpus:
+                os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(item) for item in requested_gpus)
             if self._teardown_old:
                 self._release_model()
+                with self._state_lock:
+                    self._gpu_indices = requested_gpus
             from llamafactory.chat import ChatModel
             from llamafactory.hparams import get_infer_args
 
@@ -138,6 +154,12 @@ class InferenceSession:
                 generating_args = dict(generating_args)
         except Exception as exc:  # noqa: BLE001 - surface load failure via status
             error = str(exc)
+        finally:
+            if requested_gpus:
+                if previous_visible_devices is None:
+                    os.environ.pop("CUDA_VISIBLE_DEVICES", None)
+                else:
+                    os.environ["CUDA_VISIBLE_DEVICES"] = previous_visible_devices
         with self._state_lock:
             if error:
                 self._model = None
@@ -152,6 +174,7 @@ class InferenceSession:
                 self._model = None
                 self._tokenizer = None
                 self._status = "idle"
+                self._gpu_indices = []
                 return
             self._model = model
             self._tokenizer = tokenizer
@@ -189,6 +212,7 @@ class InferenceSession:
                 self._adapter_path = ""
                 self._loaded_at = 0.0
                 self._error = ""
+                self._gpu_indices = []
 
     def unload(self) -> dict[str, Any]:
         with self._state_lock:
@@ -208,6 +232,7 @@ class InferenceSession:
                 self._adapter_path = ""
                 self._loaded_at = 0.0
                 self._error = ""
+                self._gpu_indices = []
         return {"unloaded": True, "status": "idle"}
 
     def chat(self, messages, temperature=0.95, top_p=0.7, max_new_tokens=1024, do_sample=True, **kwargs) -> dict[str, Any]:

docker/app/.env

@@ -60,5 +60,6 @@ MINIO_ACCESS_KEY=minioadmin
 MINIO_SECRET_KEY=change_me_minio_secret
 MINIO_BUCKET=yg-ft-resources
 MINIO_SECURE=false
+MINIO_INLINE_MAX_BYTES=262144
 STORAGE_WAIT_SECONDS=300
 STORAGE_CHECK_INTERVAL_SECONDS=10

docker/app/docker-compose.yml

@@ -70,6 +70,7 @@ services:
       MINIO_SECRET_KEY: ${MINIO_SECRET_KEY:-minioadmin}
       MINIO_BUCKET: ${MINIO_BUCKET:-yg-ft-resources}
       MINIO_SECURE: ${MINIO_SECURE:-false}
+      MINIO_INLINE_MAX_BYTES: ${MINIO_INLINE_MAX_BYTES:-262144}
       STORAGE_WAIT_SECONDS: ${STORAGE_WAIT_SECONDS:-300}
       STORAGE_CHECK_INTERVAL_SECONDS: ${STORAGE_CHECK_INTERVAL_SECONDS:-10}
       DATA_PROCESS_STORAGE_DIR: ${DATA_PROCESS_STORAGE_DIR:-/data/yg-ft/data-process}

docs/governance-user-guide.md

@@ -1,9 +1,10 @@
 # 平台治理功能使用指南
 
-> 版本：v1.1
+
-> 日期：2026-08-13
+> 版本：v1.3
-> 适用版本：YG Zhilian v1.0+
+> 日期：2026-08-19
-> 更新说明：移除页面权限码设计，改为基于角色的简化权限模型
+> 适用版本：YG Fine-Tune Platform v1.0+
+> 更新说明：合并组织权限、审批和运行日志入口；取消项目空间菜单但保留旧接口兼容
 
 ---
 
@@ -40,28 +41,27 @@
 
 ### 1.3 入口在哪里？
 
-所有治理功能集中在左侧导航栏的 **「系统设置」** 和 **「平台治理」** 分组下：
+治理、组织和运维功能按职责分布在左侧导航栏的 **「平台治理」**、**「系统设置」** 和 **「算力资源」** 分组下：
 
 ```
-系统设置
-├── 用户设置        ← 用户 CRUD + 角色权限 + 密码管理（仅 admin）
-├── 平台性能        ← 系统监控
-└── 查看日志        ← 日志查看
-
 平台治理
-├── 租户管理        ← 组织/团队（仅 admin）
+├── 组织与权限      ← 用户与角色、租户与配额（仅 admin）
-├── 项目空间        ← 项目级资源隔离（仅 admin）
+├── 资源授权        ← 数据集、模型等资源授权（仅 admin）
-├── 审批模板        ← 定义哪些操作需要审批（仅 admin）
+└── 审批中心        ← 待审批请求与审批策略（仅 admin）
-├── 审批中心        ← 处理待审批请求（仅 admin）
+
-└── 审计日志        ← 查看所有操作记录（仅 admin）
+系统设置
+├── 平台性能        ← 系统资源监控
+└── 运行日志        ← 运行日志、审计记录、操作诊断
 
 算力资源
 └── 算力节点        ← GPU 分配与管理（仅 admin）
 ```
 
-> ⚠️ 以上菜单**只有 admin 用户能看到**。普通用户登录后不会出现这些入口。
+> ⚠️ 平台治理、资源授权、审批中心和算力节点菜单仅 admin 用户能看到。运行日志入口继续沿用原权限，普通用户可查看系统/训练日志；审计记录和操作诊断页签仅 admin 可见。
 >
-> **重要变更（v1.1）**：非 admin 用户**默认可以访问所有业务功能菜单**（模型训练、评测、推理、数据集、数据处理等），无需管理员单独分配权限。
+> **重要变更（v1.2）**：非 admin 用户**默认可以访问所有业务功能菜单**（模型训练、评测、推理、数据集、数据处理等），无需管理员单独分配权限；治理和资源管理入口仍仅 admin 可见。
+
+> **当前菜单调整（v1.3）**：平台不再提供项目空间菜单和项目级操作入口。历史项目表、接口和旧地址仅作为兼容层保留，当前资源访问以用户所有权、租户边界（如启用）和资源 ACL 为准；新建业务资源不再要求项目字段。
 
 ---
 
@@ -73,24 +73,25 @@
 
 | 用户类型 | 可见菜单 | 说明 |
 |---------|---------|------|
-| **admin（管理员）** | **全部菜单** | 包括用户设置、平台治理、算力节点等管理功能 |
+| **admin（管理员）** | **全部菜单** | 包括组织权限、审批、运行日志、平台治理和算力节点等管理功能 |
-| **非 admin 用户** | **除管理功能外的所有业务菜单** | 模型训练/评测/推理、数据集、数据处理、日志等 |
+| **非 admin 用户** | **除管理功能外的所有业务菜单** | 模型训练/评测/推理、数据集、数据处理等 |
 
 > **核心原则**：
 > - 非 admin 用户**默认拥有所有业务功能的访问权限**，无需单独分配
 > - 仅以下功能**仅管理员可见**：
->   - `用户设置`（用户 CRUD、角色管理）
+>   - `平台治理 - 组织与权限`（用户、角色、租户与配额）
->   - `平台治理`（租户管理、项目空间、审批模板/中心、审计日志）
+>   - `平台治理`（资源授权、审批中心）
 >   - `算力节点`（GPU 分配）
+>   - `运行日志`中的审计记录和操作诊断
 >
 > 资源级别的访问控制通过 **ACL（访问控制列表）** 实现，详见第 4 章。
 
 ### 2.2 创建用户
 
-**路径**：`用户设置` → `创建用户`
+**路径**：`平台治理` → `组织与权限` → `用户与角色` → `创建用户`
 
 1. 以 admin 身份登录平台
-2. 进入「用户设置」页面
+2. 进入「组织与权限」页面的「用户与角色」页签
 3. 点击右上角「创建用户」按钮
 4. 填写信息：
    - **账号**：登录用户名（如 `zhangsan`）
@@ -107,12 +108,10 @@
 
 | 功能分组 | 包含菜单 | 路由前缀 |
 |---------|---------|----------|
-| 系统设置 - 用户设置 | 用户列表、创建用户、重置密码 | `/user-settings` |
+| 平台治理 - 组织与权限 | 用户、角色、租户与配额 | `/organization` |
-| 平台治理 - 租户管理 | 租户列表、配额设置 | `/tenants` |
+| 平台治理 - 资源授权 | 数据集、模型等资源 ACL | `/resource-acl` |
-| 平台治理 - 项目空间 | 项目列表、成员管理、ACL | `/projects` |
+| 平台治理 - 审批中心 | 待审批请求、审批历史与策略 | `/approval-instances` |
-| 平台治理 - 审批模板 | 审批流程定义 | `/approval-templates` |
+| 系统设置 - 运行日志 | 系统/训练日志；管理员可查看审计记录、操作诊断 | `/logs` |
-| 平台治理 - 审批中心 | 待审批请求处理 | `/approval-instances` |
-| 平台治理 - 审计日志 | 操作记录查询与导出 | `/audit-logs` |
 | 算力资源 - 算力节点 | GPU 分配与管理 | `/compute` |
 
 ### 2.4 重置用户密码
@@ -125,18 +124,18 @@
 3. 输入新密码，确认
 
 **方式二：用户自行修改**
-1. 用户登录后在「用户设置」页面点击「修改密码」按钮
+1. 用户登录后在「组织与权限」页面的「用户与角色」页签点击「修改密码」按钮
 2. 输入旧密码 + 新密码（至少 6 位）
 3. 确认修改
 
 ### 2.5 删除用户
 
-**路径**：`用户设置` → 用户列表 → 操作列「删除」
+**路径**：`组织与权限` → `用户与角色` → 用户列表 → 操作列「删除」
 
 > ⚠️ 删除用户时会**级联清理**其所有关联数据：
 > - 该用户创建的数据集、基座模型、微调产物、评测任务
 > - 该用户的 ACL 授权记录、GPU 分配记录
-> - 该用户的审批实例、审计日志、项目成员关系、登录会话
+> - 该用户的审批实例、审计日志、历史项目成员关系、登录会话
 > - **训练任务保留不删**（避免算力节点上的物理任务数据不一致）
 
 ---
@@ -148,7 +147,7 @@
 当服务器有多张 GPU 卡（如 8×A800）时，需要指定**哪个用户能用哪张卡**：
 
 - 避免两个人同时选同一张卡导致训练冲突
-- 按团队/项目隔离算力资源
+- 按用户和租户边界隔离算力资源
 - 控制每个用户的 GPU 配额
 
 ### 3.2 分配 GPU（仅 admin）
@@ -259,7 +258,6 @@ curl -X PUT /modelTF/resources/dataset/ds_alpaca_id/acl \
 | 删除他人的数据集 | 非 admin 删除别人创建的数据集 | 创建审批实例 或 admin 直接执行 |
 | 删除他人的模型 | 非 admin 删除别人创建的模型 | 同上 |
 | 停止他人的训练任务 | 非 admin 停止别人发起的任务 | 同上 |
-| 归档/删除项目空间 | 存在待审批变更时 | 拒绝执行 |
 
 **核心规则**：admin 做任何操作都直接执行（旁路）；普通用户操作他人资源时进入审批流程。
 
@@ -305,7 +303,7 @@ curl -X PUT /modelTF/resources/dataset/ds_alpaca_id/acl \
 3. 决策：「通过」或「拒绝」
 4. 决策结果自动执行对应操作并记录审计日志
 
-**审批模板**（`平台治理` → `审批模板`）：定义每种操作需要几步审批、每步谁来审。默认模板都是单步（admin 审批即可）。
+**审批策略**（`平台治理` → `审批中心` → `审批策略`）：定义每种操作需要几步审批、每步谁来审。默认模板都是单步（admin 审批即可）。
 
 ---
 
@@ -324,24 +322,29 @@ curl -X PUT /modelTF/resources/dataset/ds_alpaca_id/acl \
 
 ### 6.2 查询审计日志
 
-**路径**：`平台治理` → `审计日志`
+**路径**：`系统设置` → `运行日志` → `审计记录`
 
 支持筛选条件：
 
 | 筛选项 | 说明 |
 |---|---|
-| 操作人 | 按用户 ID 过滤 |
+| 租户 | 按租户名称选择 |
-| 动作类型 | 如 `user.create`, `dataset.delete`, `gpu.assign` 等 |
+| 操作人 | 按用户名称选择，不需要手工填写用户 ID |
-| 目标资源类型 | dataset / model / fine_tune_task 等 |
+| 动作类型 | 使用中文动作选择，例如创建数据集、删除模型、授予资源权限 |
+| 目标资源类型 | 使用中文资源类型选择，例如数据集、模型、训练任务 |
+| 关键词 | 模糊搜索目标 ID 或审计详情 |
+| 目标 ID | 对指定资源 ID 进行精确查询 |
 | 时间范围 | 开始时间 ~ 结束时间 |
 
+项目筛选已移除。底层接口仍兼容历史 `project_id` 参数，但当前平台不再提供项目菜单。
+
 ### 6.3 导出审计日志
 
-审计日志页面底部有「导出 CSV」按钮，导出的文件包含当前筛选条件下的全部记录，可用于合规审计或问题追溯。
+运行日志的「审计记录」页签提供「导出 CSV」按钮；「操作诊断」页签用于检索失败操作和接口耗时，可用于问题追溯。
 
 ### 6.4 日志保留策略
 
-审计日志受**留存策略**控制（`平台治理` → 租户管理 → 绑定留存策略）。默认保留 30 天，超期自动清理。
+审计日志受**留存策略**控制（`平台治理` → `组织与权限` → `租户与配额`）。默认保留 30 天，超期自动清理。
 
 ---
 
@@ -351,7 +354,7 @@ curl -X PUT /modelTF/resources/dataset/ds_alpaca_id/acl \
 
 根据 v1.1 权限模型：
 1. **业务菜单**（训练、评测、推理、数据集等）：普通用户**默认全部可见**，无需分配
-2. **管理菜单**（用户设置、租户管理、算力节点等）：**仅 admin 可见**，这是设计如此
+2. **管理菜单**（组织与权限、资源授权、审批中心、算力节点，以及运行日志中的审计/诊断页签）：**仅 admin 可见**，这是设计如此
 
 如果普通用户看不到业务菜单，请检查：
 - 用户是否正常登录（token 是否有效）
@@ -402,7 +405,7 @@ curl -H "Authorization: Bearer platform-token-admin" \
 ### Q6: 用户忘记密码怎么办？
 
 两种方案：
-1. **admin 重置**：在「用户设置」→ 用户列表 →「重置密码」
+1. **admin 重置**：在「组织与权限」→「用户与角色」→ 用户列表 →「重置密码」
 2. **用户自助修改**：用户登录后点击「修改密码」（需知道旧密码）
 
 如果是完全忘记且不是 admin，只能由 admin 重置。

docs/menu-functional-requirements.md

@@ -1,6 +1,8 @@
 # 菜单与功能需求总览
 
 > 本文根据当前前端侧边栏、路由、需求文档、接口文档、部署文档和 SQL 脚本整理。当前代码和 SQL 均按正式系统开发基线维护；Mock、Simulator 只能作为显式联调能力，不作为默认开发准则。
+>
+> 当前治理版本取消“项目空间”菜单。历史项目表和接口仅保留兼容，不再作为前端业务入口或新资源的必填隔离层。
 
 ## 1. 菜单分层
 
@@ -17,9 +19,11 @@
 | 数据治理 | 数据处理 | `/data-process` | `data-process` | 前端页面已有，后端待完整实现 | 文档上传、切片预览、LLM 生成、结果编辑、发布数据集 |
 | 其他工具 | 数据类型转换 | `/data-convert` | `data-convert` | 前端页面已有，后端待实现 | JSON/JSONL/Markdown 等格式转换任务 |
 | 算力资源 | 算力节点 | `/compute` | `compute` | 已接入节点管理接口 | 节点地址、权重、标签、启用状态、GPU、队列、资源副本 |
-| 系统设置 | 用户设置 | `/user-settings` | `user-settings` | 已接入基础用户接口 | 用户列表、创建用户、启停、页面权限 |
+| 平台治理 | 组织与权限 | `/organization` | `user-settings` | 新增合并入口 | 用户与角色、租户与配额、密码管理 |
+| 平台治理 | 资源授权 | `/resource-acl` | `user-settings` | 已接入 ACL 接口 | 数据集、模型等资源授权 |
+| 平台治理 | 审批中心 | `/approval-instances` | `user-settings` | 新增合并入口 | 待审批请求、审批历史、审批策略 |
 | 系统设置 | 平台性能 | `/hardware` | `hardware` | 已有接口，需接真实采集 | CPU、内存、磁盘、GPU、进程、网络监控 |
-| 系统设置 | 查看日志 | `/logs` | `logs` | 已有接口，需接真实日志文件 | 后端日志、error 日志、训练日志索引、日志内容查看 |
+| 系统设置 | 运行日志 | `/logs` | `logs` | 新增合并入口 | 运行日志、训练日志；管理员可查看审计记录、操作诊断 |
 
 ### 1.2 当前二级和隐藏路由
 
@@ -41,19 +45,17 @@
 | 数据集创建/编辑/预览 | `/dataset/create`、`/dataset/:id/edit`、`/dataset/:id/preview` | 数据集管理 | 数据集元数据、文件、版本与内容 |
 | 自定义工具 | `/tools`、`/tools/create`、`/tools/:id/edit` | 规划入口 | 路由存在，当前侧边栏未展示，后续可归入“其他工具” |
 | 算力子页 | `/compute/gpus`、`/compute/queue`、`/compute/nodes` | 算力节点 | 当前可作为页签或深链 |
-| 创建用户/权限设置 | `/user-settings/create`、`/user-settings/:id/permission` | 用户设置 | 用户创建和页面权限 |
+| 组织与权限内部页签 | `/user-settings`、`/tenants`、`/user-settings/create`、`/user-settings/:id/permission` | 平台治理 - 组织与权限 | 旧地址兼容，当前通过页签进入 |
+| 项目旧地址 | `/projects`、`/projects/:id` | 兼容跳转 | 跳转到组织与权限，不再展示项目管理 |
+| 审批策略旧地址 | `/approval-templates` | 兼容跳转 | 跳转到审批中心的策略页签 |
+| 日志旧地址 | `/audit-logs`、`/operation-logs` | 兼容跳转 | 跳转到运行日志对应页签 |
 | 无权限页 | `/permission-denied` | 系统页 | 路由守卫无权限跳转 |
 
-### 1.3 企业治理待补菜单
+### 1.3 后续治理扩展
 
 | 建议菜单分组 | 菜单 | 建议路由 | 优先级 | 必要性 |
 | --- | --- | --- | --- | --- |
-| 组织与项目 | 租户管理 | `/tenants`、`/tenants/:id` | P0 | 多租户隔离、配额、留存策略入口 |
+| 系统设置 | 运行日志扩展 | `/logs`、`/login-logs`、`/download-logs` | P1 | 增加登录审计、下载审计、导出审计维度 |
-| 组织与项目 | 项目空间 | `/projects`、`/projects/:id`、`/projects/:id/members` | P0 | 项目级模型/数据集/任务隔离 |
-| 组织与项目 | 资源授权 | `/projects/:id/permissions` 或资源详情弹窗 | P0 | 模型/数据集/任务级 ACL |
-| 治理中心 | 审批中心 | `/approvals`、`/approvals/:id` | P0 | 删除、发布、导出、停止他人任务等高风险动作 |
-| 治理中心 | 审批设置 | `/approval-settings` | P1 | 审批模板、审批人规则、超时策略 |
-| 治理中心 | 审计中心 | `/audit-logs`、`/login-logs`、`/download-logs` | P1 | 操作审计、登录审计、下载审计、导出 |
 | 运维中心 | 存储管理 | `/storage` | P1 | 本地磁盘占用、临时文件、checkpoint 清理、留存 |
 | 运维中心 | 训练引擎管理 | `/training-engines` | P2 | LLaMA-Factory 和后续引擎能力 schema、健康检查 |
 | 模型服务 | 模型服务治理 | `/model-services`、`/model-services/:id` | P1 | 测试/生产服务发布、调用统计、下线审批 |
@@ -62,7 +64,7 @@
 
 | 菜单/模块 | 主要接口 | 当前运行 SQL | 目标 SQL |
 | --- | --- | --- | --- |
-| 登录、用户设置 | `/modelTF/login`、`/modelTF/me`、`/modelTF/users` | `users` | `users`、`login_sessions`、`permissions`、`role_permissions`、`user_permission_overrides` |
+| 登录、组织与权限 | `/modelTF/login`、`/modelTF/me`、`/modelTF/users`、`/modelTF/tenants` | `users`、`tenants` | `users`、`login_sessions`、`permissions`、`role_permissions`、`user_permission_overrides`、`tenants` |
 | 服务看板 | `/modelTF/dashboard/overview`、`/modelTF/health` | 复用模型/数据集/任务/算力表 | `system_metric_snapshots`、`web_logs`、各业务表聚合 |
 | 模型管理 | `/modelTF/model-manage`、`/modelTF/model-manage/trained-models`、`/modelTF/model-manage/merge` | `models`、`trained_models` | `models`、`trained_models`、`storage_objects`、`local_import_jobs`、`resource_acl` |
 | 数据集管理 | `/modelTF/dataset-manage`、`/modelTF/dataset-manage/upload/{id}`、`/preview`、`/versions` | `datasets`、`dataset_files` | `datasets`、`dataset_files`、`dataset_file_versions`、`dataset_records`、`storage_objects` |
@@ -70,12 +72,12 @@
 | 训练日志 | `/modelTF/training-log-files`、`/modelTF/training-log-content` | 由任务表生成索引 | 日志文件元数据、`fine_tune_metrics`、`audit_logs` |
 | 算力节点 | `/modelTF/compute/nodes`、`/compute/gpus`、`/compute/queue`、`/compute/nodes/{id}/replicas` | `compute_nodes`、`gpus`、`resource_replicas`、`resource_sync_jobs` | `compute_nodes`、`gpu_devices`、`compute_node_engines`、`compute_jobs`、`resource_replicas`、`resource_sync_jobs` |
 | 平台性能 | `/modelTF/system-info`、`/modelTF/compute/gpus` | `gpus`、任务表 | `system_metric_snapshots`、`gpu_devices`、`compute_jobs` |
-| 查看日志 | `/modelTF/log-files`、`/modelTF/log-content`、`/modelTF/web-log` | 文件日志 | `web_logs`、`audit_logs`，大日志进入日志平台 |
+| 运行日志 | `/modelTF/log-files`、`/modelTF/log-content`、`/modelTF/web-log`、`/modelTF/audit-logs` | 文件日志 | `web_logs`、`audit_logs`，大日志进入日志平台 |
 | 模型评测 | `/modelTF/model-eval`、`/modelTF/dimension` | 当前运行 SQL 未覆盖 | `eval_tasks`、`eval_dimensions`、`eval_sample_results`、`eval_dimension_summaries` |
 | 模型推理/对比 | `/modelTF/model-compare`、`/modelTF/model-chat/*` | 当前运行 SQL 未覆盖 | `inference_tasks`、`inference_task_models`、`chat_sessions`、`chat_messages` |
 | 数据处理 | `/modelTF/data-process/*` | 当前运行 SQL 未覆盖 | `data_process_tasks`、`data_process_source_files`、`data_process_preview_items`、`data_process_results` |
 | 数据转换/自定义工具 | `/modelTF/data-convert/jobs`、`/modelTF/tools` | 当前运行 SQL 未覆盖 | `data_convert_jobs`、`custom_tools` |
-| 租户/项目/资源授权 | `/modelTF/tenants`、`/modelTF/projects`、`/modelTF/resources/{type}/{id}/acl` | 当前运行 SQL 未覆盖 | `tenants`、`tenant_users`、`projects`、`project_members`、`resource_acl` |
+| 租户/资源授权 | `/modelTF/tenants`、`/modelTF/resources/{type}/{id}/acl` | 当前运行 SQL 未覆盖 | `tenants`、`tenant_users`、`resource_acl`；`projects`、`project_members` 仅作兼容 |
 | 审批/审计/留存/配额 | `/modelTF/approvals`、`/modelTF/audit-logs`、`/modelTF/retention-policies`、`/modelTF/quotas/usage` | 当前运行 SQL 未覆盖 | `approval_templates`、`approval_instances`、`approval_steps`、`audit_logs`、`retention_policies`、`quotas`、`quota_usage` |
 
 ## 3. 文档和脚本检查结论

docs/permissions-design.md

@@ -4,6 +4,8 @@
 > 日期：2026-08-02
 > 状态：设计基线，供后端实现和前端联调参照
 
+> **当前菜单基线（2026-08-19）**：平台治理已取消“项目空间”作为用户可见菜单和新资源的业务隔离层。当前前端入口为“组织与权限、资源授权、审批中心”，系统设置下的“运行日志”承载运行日志、审计记录和操作诊断。`projects`、`project_members` 表及相关后端接口仅作历史兼容，不删除、不要求新建资源填写 `project_id`。
+
 ---
 
 ## 目录
@@ -129,7 +131,7 @@
 | `compute` | `/compute` | 算力节点 |
 | `hardware` | `/hardware` | 平台性能 |
 | `logs` | `/logs`, `/training-log/:id` | 查看日志 |
-| `user-settings` | `/user-settings`, `/tenants`, `/projects`, `/approvals`, `/audit-logs` | 系统设置与平台治理 |
+| `user-settings` | `/organization`, `/resource-acl`, `/approval-instances`, `/logs` | 平台治理和运行日志（管理员） |
 
 ---
 

docs/platform-governance-menu-design.md

@@ -0,0 +1,120 @@
+# 平台治理菜单设计与开发计划
+
+> 版本：v1.0  
+> 日期：2026-08-19  
+> 状态：按本文档实施
+
+## 1. 设计结论
+
+当前项目已经有用户所有权、资源 ACL、租户接口和审计接口，但项目隔离尚未真正落地。核心资源的 `project_id` 当前没有有效业务数据，训练、评测、推理和数据集创建流程也没有统一的项目上下文。
+
+因此当前版本取消项目层级设计，资源权限统一采用：
+
+```text
+用户所有权 + 资源 ACL + 租户边界（可选）
+```
+
+项目相关数据库表和后端接口暂不物理删除，仅作为历史兼容能力保留，后续不再新增项目数据，也不在前端提供项目入口。
+
+## 2. 最终菜单
+
+```text
+平台治理
+├── 组织与权限
+├── 资源授权
+└── 审批中心
+
+系统设置
+├── 平台性能
+└── 运行日志
+    ├── 系统日志
+    ├── 训练日志
+    ├── 审计记录
+    └── 操作诊断
+
+算力资源
+└── 算力节点
+```
+
+### 2.1 组织与权限
+
+使用页签统一承载：
+
+- 用户与角色：用户 CRUD、启停、密码、角色。
+- 租户与配额：租户、GPU 配额、存储配额和资源数量配额。
+
+用户、租户和配额仍使用独立表和接口，不把组织配额字段混入用户表。单租户部署时可默认停留在“用户与角色”页签。
+
+### 2.2 资源授权
+
+保留当前资源 ACL 能力，支持数据集、训练模型等资源的 `read/write/execute/download/delete/admin` 权限。项目不再作为授权前置条件。
+
+### 2.3 审批中心
+
+统一使用页签承载：
+
+- 待审批/审批历史。
+- 我的申请。
+- 审批策略，仅管理员可见。
+
+“审批策略”不再作为独立一级菜单。
+
+### 2.4 运行日志
+
+在现有系统日志、训练日志基础上增加：
+
+- 审计记录：写操作、授权、审批、删除、导出等敏感操作。
+- 操作诊断：失败操作、错误类型和接口耗时。
+
+“审计中心”不再作为独立菜单。旧的 `/audit-logs` 和 `/operation-logs` 地址保留重定向。
+
+## 3. 兼容策略
+
+| 原入口 | 新入口/处理方式 |
+|---|---|
+| `/user-settings` | 重定向到 `/organization?tab=users` |
+| `/tenants` | 重定向到 `/organization?tab=tenants` |
+| `/approval-templates` | 重定向到 `/approval-instances?tab=strategies` |
+| `/audit-logs` | 重定向到 `/logs?tab=audit` |
+| `/operation-logs` | 重定向到 `/logs?tab=operations` |
+| `/projects` | 移除前端入口；旧地址重定向到组织与权限 |
+
+后端的租户、项目、审批、ACL、审计 API 暂不删除，保证已有脚本和历史客户端不立即失效。数据库不执行删表操作，也不新增项目字段迁移。
+
+## 4. 开发计划
+
+### 阶段一：导航和页面聚合
+
+1. 新增“组织与权限”聚合页面。
+2. 新增“审批中心”聚合页面。
+3. 扩展“运行日志”页面，加入审计和操作诊断页签。
+4. 调整侧边栏，只展示最终菜单。
+
+### 阶段二：兼容旧入口
+
+1. 旧用户、租户、审批策略、审计和操作日志路由改为重定向。
+2. 保留原页面组件、API 和后端路由，避免历史调用失效。
+3. 项目路由不再作为业务入口，不再新增项目数据。
+
+### 阶段三：权限和功能检查
+
+1. 管理员可以访问组织、租户、配额、审批、审计和 ACL。
+2. 普通用户不能访问平台治理菜单；运行日志基础页签继续保持原有访问权限。
+3. 审批策略页签仅管理员可见。
+4. 审计和操作诊断仍保留管理员可见能力。
+5. 数据集、模型、训练、评测、推理继续使用用户所有权和 ACL，不增加项目选择器。
+
+### 阶段四：验证
+
+- `npm run build`。
+- 检查旧路由重定向。
+- 检查管理员菜单显示。
+- 检查非管理员权限拦截。
+- 检查 Backend 健康接口和前端静态资源。
+
+## 5. 暂不处理事项
+
+- 不删除 `projects`、`project_members` 表。
+- 不删除后端项目模块，避免历史数据和接口调用中断。
+- 不把租户配额字段直接合并到 `users` 表。
+- 不改变现有数据集、模型、训练、评测、推理的业务接口格式。

docs/当前项目开发进度.md

@@ -0,0 +1,347 @@
+# 当前项目开发进度
+
+> 评估基线：2026-08-19 当前工作区代码、数据库初始化脚本、Docker 部署文件、前端页面和现有设计文档。
+>
+> 本文以代码实际情况为准。设计文档中已经提出但代码没有形成完整闭环的内容，统一标记为“部分完成”或“未完成”。
+
+## 一、项目定位与总体结论
+
+当前项目是一个面向多用户、多算力节点的模型训练与推理平台，主要链路为：
+
+```text
+Vue 前端
+    |
+FastAPI Backend API
+    |-- PostgreSQL：业务元数据、权限、任务状态、小型内容和预览数据
+    |-- Redis：会话、限流、短期缓存和任务辅助状态
+    |-- MinIO：模型、数据集、报告和大文件的统一对象存储
+    |-- Compute API / Agent：训练、推理、评测、模型合并和 GPU 执行
+    |
+多台算力节点
+```
+
+整体判断：
+
+| 范围 | 当前状态 | 结论 |
+|---|---|---|
+| 平台基础架构 | 基本完成 | 前后端、数据库、Redis、MinIO、Compute Agent 和 Docker 部署均已具备 |
+| 核心业务闭环 | 基本可用 | 数据集、数据处理、数据转换、训练、模型、推理、评测均有页面和接口 |
+| 多算力节点 | 部分完成 | 节点选择、GPU 分配和缓存准备已经接入，跨节点一致性和失败恢复仍需加强 |
+| 权限治理 | 部分完成 | 登录、角色、权限码、ACL、审批、审计已实现，但完整的租户/项目隔离尚未闭环 |
+| MinIO 统一存储 | 部分完成 | 大文件和模型已接入，仍存在兼容性的本地路径和部分数据双写/回退路径 |
+| 生产可靠性 | 未完成 | 缓存容量治理、对象清理、流式上传、归档重试、备份和高可用尚未完成 |
+| 前端体验 | 基本可用，需优化 | 构建问题已持续修复，但页面响应等待、首屏体积和部分错误提示仍需优化 |
+
+## 二、已完成的功能
+
+### 2.1 平台基础与部署
+
+- 已建立 Vue 3 + TypeScript + Vite 前端工程。
+- 已建立 FastAPI 后端服务，提供登录、平台管理和模型业务接口。
+- 已建立 Compute API / Agent，用于连接算力节点并执行训练、推理、评测和模型处理任务。
+- 已使用 PostgreSQL 保存核心业务数据，Redis 提供会话、限流和缓存能力。
+- 已增加 MinIO 服务及 Backend 的 MinIO 配置，支持和 Compute Agent 分离部署。
+- 已提供 `docker/app`、`docker/compute`、`docker/minio` 和 `docker/offline` 部署目录。
+- 已考虑后端、算力服务、MinIO 分布在不同服务器时使用独立网络；Compute 节点访问 MinIO 需要配置所有节点都能访问的固定 IP 或 DNS。
+- 离线部署目录已经同步后端、算力相关源码和初始化 SQL 的主要改造内容。
+
+### 2.2 登录、用户和权限基础
+
+- 用户登录、退出、当前用户信息和密码修改接口已经存在。
+- 已有 Token 会话、Redis 会话记录和登录限流逻辑。
+- 已建立用户、角色、权限码和角色权限关系。
+- 已实现管理员、普通用户等基础角色分层。
+- 已实现页面路由守卫、菜单过滤和前端按钮级权限的基础能力。
+- 已建立资源 ACL 管理页面和相关接口，可对用户或角色授予资源级权限。
+- 已建立审批模板、审批实例和审批步骤的基本数据模型与页面。
+- 已建立运行日志、审计日志查询页面及审计记录写入机制。
+- 已加入软删除相关字段和部分删除逻辑，避免直接物理删除业务资源。
+
+### 2.3 算力节点与 GPU 资源
+
+- 已实现算力节点的新增、编辑、启用、禁用、维护/删除、连通性测试和健康检查。
+- 已实现节点列表、节点详情、节点副本/同步状态和 Compute Agent 连接。
+- 已实现 GPU 信息发现、GPU 状态查询和队列查询。
+- 已建立 `gpu_allocations`、`gpu_assignments`、调度锁等资源分配表。
+- 训练任务已经支持选择调度节点和一张或多张 GPU，并在预检阶段校验资源可用性。
+- Compute Agent 已支持训练、评测、推理和缓存准备等任务接口。
+- 已存在资源副本和同步任务模型，用于记录节点侧资源同步状态。
+
+### 2.4 数据集管理
+
+- 已实现数据集创建、列表、详情、编辑、删除和文件上传。
+- 已实现数据集文件下载、预览、记录列表和数据记录编辑入口。
+- 已处理 JSON 与 JSONL 的记录数差异：JSON 数组按元素计数，JSONL 按有效行计数，避免把整个 JSON 文件误按行数统计。
+- 已提供数据集版本列表、版本详情、创建版本、切换当前激活版本和删除版本接口。
+- 已增加数据集文件、版本、数据记录等初始化表结构。
+- 已支持数据集文件在数据库小内容和 MinIO 大文件之间按策略存储。
+- 已在训练预检中检查数据集文件是否存在、是否可从 MinIO 获取以及是否能准备到目标算力节点。
+
+### 2.5 数据处理与数据转换
+
+- 已提供结构化数据、非结构化数据、外部数据源的处理创建流程。
+- 已实现源文件上传、预览、分片/切分、生成、质量检查、去重和结果管理等数据处理流程。
+- 已支持处理结果生成数据集或导入数据集版本。
+- 已建立数据处理任务、源文件、预览项、结果等数据表。
+- 已提供 JSON、JSONL 等数据格式转换页面和后端任务接口。
+- 已将数据转换输出接入 MinIO/数据库分层存储：小型文本结果可存数据库，大文件存 MinIO。
+- 已处理输出文件下载和转换结果元数据保存问题。
+
+### 2.6 模型训练
+
+- 已实现训练任务创建、配置预检、命令预览、启动、停止、重试和删除。
+- 已接入 LLaMA-Factory 等训练适配逻辑。
+- 已支持选择训练数据、基座模型、算力节点和 GPU。
+- 已实现训练日志获取、训练任务概览、诊断信息、检查点和训练指标查询。
+- 前端训练详情已经具备训练曲线解析和展示逻辑，日志轮询间隔已调整为 3 秒。
+- 已增加 GPU 详情展示入口，包括显存和利用率等 Compute Agent 上报信息。
+- 已支持训练任务的 MinIO 数据准备和目标算力节点缓存准备。
+
+### 2.7 模型管理与权重合并
+
+- 已实现在线模型/基座模型和训练模型的列表、创建、详情、用途修改和删除。
+- 已建立模型、训练模型、模型血缘、模型产物和导出任务相关表。
+- 已提供权重合并入口，能够根据训练任务准备基座模型和 Adapter，并提交 Compute Agent 执行合并。
+- 已增加模型产物和 MinIO 对象关联字段。
+- 合并结果能够在任务完成后归档到 MinIO 的设计和主要代码路径已经建立。
+
+### 2.8 模型推理与模型对比
+
+- 已实现推理模型列表、创建、详情和删除入口。
+- 已实现模型加载、卸载、服务启动、服务状态查询和对话调用。
+- 已实现模型对比任务及多模型聊天相关接口。
+- 已增加推理失败重试、停止和资源释放的处理路径。
+- 已支持根据页面选择的算力节点准备模型缓存，兼容训练时所选节点优先的业务要求。
+- Compute Agent 已提供本地推理会话和模型缓存状态接口。
+
+### 2.9 模型评测
+
+- 已实现评测任务列表、创建、详情和删除。
+- 已实现评测维度管理和评测规则配置页面。
+- 已建立评测任务、评测维度、对比任务等数据库表。
+- 已支持选择模型、数据集、评测维度、算力节点和 GPU 的基础流程。
+- 已接入 Compute Agent 执行评测任务，并保存评测结果和报告相关元数据。
+
+### 2.10 数据存储策略
+
+- 已建立 `storage_objects`、`storage_cache_jobs` 等 MinIO 元数据和缓存任务表。
+- 已建立 MinIO 对象上传、下载、预签名 URL 和节点缓存准备的主要接口。
+- 已采用分层策略：
+  - 小型 JSON、JSONL、CSV、任务参数快照、预览数据保留在数据库，降低频繁预览的 MinIO 延迟。
+  - 模型权重、训练产物、评测报告和大文件使用 MinIO。
+  - 小型 PDF、DOCX、XLSX 仍优先存 MinIO，以保留原始二进制文件内容。
+- 已增加 `data_convert_tasks.output_content`，用于保存小型转换结果，避免所有小结果都依赖 MinIO。
+- Backend 和离线包中的 `000_full_init.sql` 已同步，当前两份初始化脚本内容一致。
+
+## 三、部分完成、仍需完善的功能
+
+### 3.1 MinIO 统一数据源尚未完全闭环
+
+当前 MinIO 已成为模型、大文件和跨节点资源的主存储方向，但仍保留以下兼容路径：
+
+- Compute Agent 仍有本地文件上传、导入本地模型和扫描本地模型目录的旧接口。
+- 部分历史数据仍使用数据库中的 `content` 或 `output_content` 字段，这是当前已确认的小文件性能策略，不是错误，但必须统一记录来源、大小、校验值和版本。
+- 大文件在部分代码路径中仍通过 `read()` 或 `put_bytes()` 一次性读入内存，未完成流式或分片上传。
+- MinIO 对象和业务资源之间采用多态 `resource_type/resource_id` 关联，数据库没有直接外键，删除和数据一致性需要应用层保证。
+- 删除业务资源后，对应 MinIO 对象的延迟清理、失败重试和孤儿对象扫描尚未形成完整闭环。
+
+### 3.2 MinIO 预签名接口的权限边界需要加强
+
+当前预签名接口已经存在，但 PUT 上传场景仍需要重点补强：
+
+- 需要根据资源类型和资源 ID 校验当前用户的写权限，而不应只校验读取权限。
+- 需要服务端生成并校验对象 Key，避免客户端任意写入其他用户或其他资源的对象路径。
+- 需要增加上传完成确认接口，校验对象实际存在、大小和校验值后再写入业务表。
+- 需要限制允许的 Bucket、Content-Type、大小和有效期。
+- 需要记录预签名创建、上传完成、失败和过期事件，便于审计。
+
+### 3.3 激活版本和跨节点资源版本仍需加强
+
+数据集已经有 `active_version_id` 和版本表，但以下场景仍需补充：
+
+- 训练、推理和评测必须只使用资源当前激活版本，并在任务创建时固化版本 ID。
+- 同一文件名的不同版本不能只依靠文件名同步，应使用资源 ID、版本 ID 和对象 Key 组成唯一定位。
+- 已创建任务在后续切换激活版本后，不能被意外切换到新版本。
+- 需要为每个准备到算力节点的资源保存版本、对象 ETag/校验值和本地路径清单。
+- 历史版本的数据库内容回退和 MinIO 对象回退逻辑还需要补全并增加测试。
+
+### 3.4 权限 2.0 尚未完全落地
+
+已有用户、角色、权限码、ACL、审批和审计基础，但仍存在以下差距：
+
+- 租户、用户、资源、算力节点、模型、数据集之间的隔离规则没有全部在 SQL 查询层统一执行。
+- 项目空间设计已经讨论过取消，但数据库中仍保留 `projects`、`project_members` 等历史结构，需要明确兼容策略和最终迁移方式。
+- 训练创建的模型、数据集和训练任务之间的联合权限约束还没有完全统一。
+- 评测、推理、模型合并、导出、缓存准备等动作需要逐一校验资源读权限和操作权限。
+- 前端按钮权限已经有基础实现，但不能替代后端鉴权；仍需要对所有关键动作进行后端默认拒绝校验。
+- 审批拦截范围、管理员豁免规则和跨租户资源访问规则需要形成可执行矩阵。
+
+### 3.5 模型合并、导出和评测报告闭环不足
+
+- 权重合并前自动准备 Base Model 和 Adapter 的主要路径已建立，但失败时的清理、重试和幂等性仍需加强。
+- 合并结果归档到 MinIO 的逻辑主要依赖任务完成轮询，服务重启或轮询中断时可能需要补偿扫描。
+- 模型导出任务目前有查询模型和表结构，但完整的创建、执行、进度、失败重试和下载闭环尚未完成。
+- 评测结果和报告字段已经存在，但报告对象归档、报告下载、报告版本和报告与任务的稳定关联仍需验证。
+- 评测指标配置和执行器返回指标之间仍需要强类型映射，避免前端显示为通用的 `custom`。
+
+### 3.6 GPU 资源分配需要统一到所有任务类型
+
+- 训练已经有较完整的节点/GPU 选择和预检流程。
+- 推理和评测已经出现节点选择、缓存准备和 GPU 选择的接入代码，但还需要确认从页面选择到 Compute Agent 启动参数、进程环境变量和释放逻辑的全链路生效。
+- 需要防止同一张 GPU 被多个任务绕过调度锁重复占用。
+- 需要处理服务异常退出、Backend 重启、Compute Agent 重启后的分配回收和状态对账。
+- 训练详情中的显存使用量、GPU 使用率等指标依赖 Compute Agent 上报，仍需要校验采样时间、单位、空值和任务对应关系。
+
+## 四、尚未完成的功能
+
+以下功能在当前代码中没有形成可验收的完整闭环，或仍处于设计/基础代码阶段：
+
+1. **完整的租户隔离和资源继承模型**：所有列表、详情、下载、缓存、训练、推理、评测和导出接口都需要统一的租户范围过滤。
+2. **项目取消后的正式数据迁移方案**：需要决定历史项目数据如何归属到用户或租户，并提供一次性迁移脚本和回滚方案。
+3. **预签名上传完成确认和对象校验**：包括 Key 白名单、ACL、大小限制、哈希/ETag 和状态回写。
+4. **MinIO 对象生命周期管理**：软删除后的延迟删除、失败重试、孤儿对象扫描、对象引用检查和管理员清理入口。
+5. **Compute Agent 缓存治理**：容量上限、LRU/TTL、运行任务保护、磁盘占用监控、缓存清单和版本校验。
+6. **统一的资源归档编排器**：训练、合并、评测和推理相关产物需要支持断点恢复、幂等重试和服务重启补偿。
+7. **模型导出完整流程**：导出任务创建、格式/量化参数、进度、失败重试、MinIO 归档和下载权限。
+8. **流式和分片文件传输**：避免大文件上传、下载和对象复制时将完整内容读入 Backend 或 Compute Agent 内存。
+9. **生产级 MinIO 安全和高可用**：默认密钥替换、TLS、网络访问控制、管理员 Console 隔离、容量监控、备份和恢复。
+10. **完整的端到端测试和持续集成**：至少覆盖单节点、多节点、多 GPU、跨用户、跨租户、版本切换、MinIO 不可用和服务重启恢复。
+11. **统一数据库迁移体系**：当前初始化 SQL 适合新库初始化，但尚未替代正式的版本化迁移工具；已有数据库更新仍需要明确迁移脚本和执行记录。
+
+## 五、需要优化的功能
+
+### 5.1 后端响应性能
+
+- 页面列表接口需要避免每条记录重复查询用户、资源、MinIO 元数据和 Compute 节点状态。
+- MinIO 的 Bucket 检查、对象 Head 和预签名生成应使用连接复用、短期缓存和批量查询。
+- 训练、推理、评测页面不应通过过短间隔轮询大量详情接口，应按任务状态动态退避，并在完成后停止轮询。
+- 对 dashboard、节点健康、GPU 状态等高频数据应区分实时数据和缓存数据。
+- 后端日志轮询和健康检查日志需要继续降噪，仅在状态变化、失败或达到较长周期时输出。
+
+### 5.2 前端加载和交互
+
+- 列表页面应区分首屏 loading、刷新 loading、操作 loading，避免整页长时间无反馈。
+- 推理、评测、训练详情应使用统一的任务状态刷新策略和超时提示。
+- 前端仍有 FontAwesome 在线资源解析警告，应清理对外部网络文件的依赖，保证离线环境打开速度。
+- 应继续拆分首屏大体积 chunk，并减少一次性加载不相关页面组件。
+- GPU 选择组件需要明确显示空闲、占用、不可达、预留和已分配状态。
+- 错误提示应携带资源名称、节点名称、版本和下一步处理建议，减少只显示 500/404 的情况。
+
+### 5.3 训练、推理和评测可靠性
+
+- 所有任务创建前应执行同一套资源权限、版本存在性、MinIO 可用性和 GPU 原子分配校验。
+- 任务创建接口应支持幂等键，避免前端重复点击造成重复任务。
+- 节点不可达时应快速失败或进入可见的等待状态，不能让页面长时间无反馈。
+- 失败重试应区分网络瞬时失败、资源不足、模型文件缺失、参数错误和执行器失败。
+- 任务停止后必须释放 GPU 分配、推理端口、缓存锁和临时目录。
+
+### 5.4 数据和模型一致性
+
+- 每个对象都应保存大小、校验值、版本 ID、来源、创建者、租户和引用状态。
+- 数据库中的小文件内容和 MinIO 对象不能同时被当作可独立修改的主副本；需要明确唯一写入入口。
+- 数据集激活版本变更需要留下审计记录，并影响后续任务创建但不改变已创建任务。
+- 模型权重、Adapter、合并结果和导出结果需要形成完整血缘关系。
+
+## 六、数据库和初始化脚本状态
+
+当前 `backend/app/db/sql/000_full_init.sql` 已包含以下主要类别：
+
+- 用户、模型、训练模型、模型血缘、模型产物、模型导出任务。
+- 数据集、数据集文件、数据集版本、数据集记录。
+- 算力节点、GPU、GPU 分配、调度锁、Compute Job。
+- 资源副本、资源同步任务、MinIO 对象、缓存任务。
+- 评测任务、评测维度、模型对比任务。
+- 租户、项目兼容表、项目成员、角色、会话、ACL。
+- 审批模板、审批实例、审批步骤、审计日志、留存策略。
+- 数据处理任务、源文件、预览项、处理结果、数据转换任务。
+
+已确认的近期字段包括：
+
+- `model_artifacts.storage_object_id`
+- `model_artifacts.storage_backend`
+- `dataset_files.storage_object_id`
+- `data_convert_tasks.output_content`
+- `data_convert_tasks.output_storage_object_id`
+- `data_convert_tasks.storage_backend`
+- `eval_tasks.report_storage_object_id`
+
+离线包中的 `docker/offline/src/backend/app/db/sql/000_full_init.sql` 应与主工程初始化脚本保持同步。需要注意：
+
+- 初始化 SQL 主要用于新数据库或新数据卷；已有数据库不能仅靠重启容器自动获得全部新字段。
+- 生产/测试数据库需要执行可追踪的迁移脚本，并在迁移前备份或生成结构快照。
+- `ensure_schema` 类运行时补字段逻辑只能作为兼容兜底，不能替代正式迁移。
+- 后续如果正式移除项目设计，需要先完成数据归属迁移，再决定是否删除历史表，不能直接从初始化 SQL 中删除表。
+
+## 七、当前验证结果
+
+已完成的静态和局部验证：
+
+- Backend 和离线 Backend 源码 `compileall` 检查通过。
+- MinIO 分层策略冒烟验证通过：小型 JSON/JSONL 可落数据库，小型二进制和超过阈值的内容进入 MinIO。
+- 主工程和离线包初始化 SQL 已做同步检查，内容一致。
+- 前端此前已完成 `npm run build` 类型错误修复，构建剩余问题主要是非阻断的资源/分包警告。
+- 已对训练日志、数据集 JSON/JSONL 统计、MinIO 资源准备等重点链路进行过问题修复。
+
+当前不能据此宣称“全量功能测试通过”：
+
+- 现有部分自动化测试仍保留旧的本地文件或旧 MinIO 行为假设，需要按当前分层存储策略更新。
+- WSL Docker 运行时验证受当前环境的 `E_ACCESSDENIED` 影响，不能在本次文档生成时完成全部容器健康、数据库字段和跨节点测试。
+- 多节点、多 GPU、MinIO 临时不可用、服务重启恢复和跨用户权限测试仍需要在可用运行环境中执行。
+
+## 八、下一阶段开发计划
+
+### P0：安全与数据正确性
+
+1. 完善 MinIO 预签名 PUT 的资源写权限、对象 Key 白名单、大小/类型限制和上传完成确认。
+2. 统一任务创建时的资源版本固化，训练、推理、评测只使用已授权的激活版本快照。
+3. 逐一补齐评测、推理、模型合并、模型导出、缓存准备的后端权限校验和审计记录。
+4. 完成租户隔离查询范围，清理或兼容历史项目字段，补充数据迁移脚本。
+
+### P1：跨节点可靠性
+
+1. 建立资源清单/manifest，记录 MinIO 对象版本、校验值、目标节点路径和缓存状态。
+2. 完善训练、合并、评测和推理的准备、执行、归档、失败重试和服务重启补偿。
+3. 完善 GPU 原子分配、异常回收、节点重连对账和任务释放。
+4. 增加缓存容量、TTL/LRU、运行任务保护和磁盘占用监控。
+5. 增加 MinIO 对象引用清理、孤儿对象扫描和软删除回收任务。
+
+### P2：性能与用户体验
+
+1. 优化页面列表接口和高频轮询，采用批量查询、短期缓存和动态退避。
+2. 将大文件上传/下载/复制改为流式或分片传输。
+3. 统一前端任务状态组件、loading、超时、重试和错误诊断信息。
+4. 处理前端离线资源警告，继续拆分首屏 chunk。
+5. 统一 GPU 状态展示及训练指标采样时间、单位和空值处理。
+
+### P3：工程化和上线准备
+
+1. 建立正式数据库版本迁移机制和离线升级脚本。
+2. 增加 CI：前端类型检查/构建、Backend 单元测试、Compute Agent 测试、SQL 新库初始化测试。
+3. 增加多节点端到端测试和 MinIO 故障注入测试。
+4. 完善 MinIO TLS、密钥管理、网络隔离、监控、备份和恢复方案。
+5. 建立生产运行手册，包括首次部署、升级、回滚、数据库迁移、对象清理和故障处理。
+
+## 九、阶段验收标准
+
+完成下一阶段后，至少应满足：
+
+- 用户只能看到和操作其所属租户授权的模型、数据集、训练任务、推理服务和评测任务。
+- 任何任务创建都能明确记录用户、租户、资源版本、算力节点、GPU 列表和 MinIO 对象版本。
+- 同一个节点的同一张 GPU 不能被两个活动任务同时分配。
+- MinIO 临时不可用时，任务进入可解释的等待/失败状态，并能按策略重试，页面不会无限等待。
+- Backend 或 Compute Agent 重启后，任务、缓存、GPU 分配和归档状态可以对账恢复。
+- 训练、合并、评测和推理产物都能在 MinIO 中找到，并且可以通过权限校验后的接口下载或使用。
+- 删除资源后不会继续出现在普通列表中，关联对象能够按引用状态延迟清理并留下审计记录。
+- 新数据库初始化和已有数据库迁移后，所有业务接口不再因为缺表或缺字段启动失败。
+- 离线部署不依赖外部字体、图标或 CDN，前端首屏和核心业务操作在无网络环境下可用。
+
+## 十、相关文件索引
+
+- 平台架构：[platform-architecture-requirements.md](./platform-architecture-requirements.md)
+- 权限设计：[permissions-design.md](./permissions-design.md)
+- MinIO 与 Compute 缓存方案：[minio-compute-cache-plan.md](./minio-compute-cache-plan.md)
+- 平台治理菜单设计：[platform-governance-menu-design.md](./platform-governance-menu-design.md)
+- 数据处理设计：[data-process-design.md](./data-process-design.md)
+- 数据库初始化脚本：[../backend/app/db/sql/000_full_init.sql](../backend/app/db/sql/000_full_init.sql)
+- 离线部署目录：[../docker/offline](../docker/offline)
+

frontend/src/api/modules/audit.ts

@@ -20,6 +20,8 @@ export interface AuditQuery {
   actor_id?: string
   action?: string
   target_type?: string
+  target_id?: string
+  keyword?: string
   start_time?: string
   end_time?: string
   limit?: number

frontend/src/api/modules/dataProcess.ts

@@ -20,6 +20,8 @@ import type {
   DataProcessPublishResult,
   DataProcessQualityScore,
   DataProcessResult,
+  DataProcessResultBatchEvaluatePayload,
+  DataProcessResultBatchEvaluateResult,
   DataProcessResultBatchRegeneratePayload,
   DataProcessResultBatchRegenerateResult,
   DataProcessResultRegeneratePayload,
@@ -320,5 +322,14 @@ export const regenerateDataProcessResults = (
   { timeout: 240_000 },
 )
 
+export const evaluateDataProcessResults = (
+  taskId: string | number,
+  payload: DataProcessResultBatchEvaluatePayload,
+) => post<DataProcessResultBatchEvaluateResult>(
+  `/data-process/${encodeURIComponent(taskId)}/results/evaluate-batch`,
+  payload,
+  { timeout: 240_000 },
+)
+
 export const publishDataProcess = (taskId: string | number, payload: DataProcessPublishPayload) =>
   post<DataProcessPublishResult>(`/data-process/${encodeURIComponent(taskId)}/publish`, payload)

frontend/src/api/modules/fineTune.ts

@@ -42,12 +42,23 @@ export interface FineTunePreflightResult {
   sync_results?: Array<Record<string, unknown>>
 }
 
+export interface FineTuneGpuStatus {
+  source: string
+  items: Array<Record<string, unknown>>
+  selected_gpus: number[]
+  error?: string
+}
+
 /** 训练任务列表 */
 export const getFineTuneList = () => get<FineTuneTask[]>('/fine-tune')
 
 /** 训练任务详情 */
 export const getFineTune = (id: string | number) => get<FineTuneTask>(`/fine-tune/${id}`)
 
+/** 获取任务所在 Compute 节点的实时 GPU 指标 */
+export const getFineTuneGpuStatus = (id: string | number) =>
+  get<FineTuneGpuStatus>(`/fine-tune/${id}/gpu-status`)
+
 /** 任务名查重 */
 export const checkFineTuneName = (name: string) =>
   get<{ exists: boolean }>('/fine-tune/check-name', { name })

frontend/src/components/AppSidebar.vue

@@ -15,6 +15,10 @@ const activeMenu = computed(() => {
   if (seg === 'training-log') return 'fine-tune'
   // 维度管理归到模型评测
   if (route.path.includes('model-eval/dimension')) return 'model-eval'
+  // 组织与权限承接用户、租户和历史治理入口
+  if (route.path.startsWith('/organization') || route.path.startsWith('/user-settings') || route.path.startsWith('/tenants')) return 'organization'
+  // 运行日志承接审计和操作诊断两个历史入口
+  if (route.path.startsWith('/logs') || route.path.startsWith('/audit-logs') || route.path.startsWith('/operation-logs')) return 'logs'
   // 对比对话归到模型推理
   if (route.path.startsWith('/model-compare/chat')) return 'model-inference'
   // 合并权重归到模型管理
@@ -77,21 +81,16 @@ const menuGroups: MenuGroup[] = [
   {
     title: '平台治理',
     items: [
-      { key: 'tenants', label: '租户管理', icon: 'fa-building', to: '/tenants', permission: 'user-settings' },
+      { key: 'organization', label: '组织与权限', icon: 'fa-users', to: '/organization', permission: 'user-settings' },
-      { key: 'projects', label: '项目空间', icon: 'fa-folder', to: '/projects', permission: 'user-settings' },
       { key: 'resource-acl', label: '资源授权', icon: 'fa-key', to: '/resource-acl', permission: 'user-settings' },
-      { key: 'audit-logs', label: '审计日志', icon: 'fa-history', to: '/audit-logs', permission: 'user-settings' },
-      { key: 'operation-logs', label: '操作日志', icon: 'fa-list', to: '/operation-logs', permission: 'user-settings' },
-      { key: 'approval-templates', label: '审批模板', icon: 'fa-list-alt', to: '/approval-templates', permission: 'user-settings' },
       { key: 'approval-instances', label: '审批中心', icon: 'fa-check-square', to: '/approval-instances', permission: 'user-settings' },
     ],
   },
   {
     title: '系统设置',
     items: [
-      { key: 'user-settings', label: '用户设置', icon: 'fa-users', to: '/user-settings', permission: 'user-settings' },
       { key: 'hardware', label: '平台性能', icon: 'fa-bar-chart', to: '/hardware', permission: 'hardware' },
-      { key: 'logs', label: '查看日志', icon: 'fa-file-text', to: '/logs', permission: 'logs' },
+      { key: 'logs', label: '运行日志', icon: 'fa-file-text', to: '/logs', permission: 'logs' },
     ],
   },
 ]
@@ -101,9 +100,9 @@ const menuGroups: MenuGroup[] = [
  *
  * 1. admin 用户：可以看到所有菜单
  * 2. 非 admin 用户：
- *    - 默认可见所有业务菜单（模型训练、评测、推理、数据集、数据处理、转换、性能、日志等）
+ *    - 默认可见所有业务菜单（模型训练、评测、推理、数据集、数据处理、转换、性能、运行日志等）
  *    - 仅以下菜单对非 admin 不可见：
- *      - user-settings（用户设置、租户管理、项目空间、审批模板/中心、审计日志）
+ *      - user-settings（组织与权限、资源授权、审批；运行日志中的审计/诊断页签）
  *      - compute（算力节点/GPU 分配）
  *
  * 注意：移除了旧的权限码（permission code）过滤逻辑，

frontend/src/plugins/echarts.ts

@@ -3,18 +3,21 @@
  */
 import { use } from 'echarts/core'
 import { CanvasRenderer } from 'echarts/renderers'
-import { BarChart, PieChart } from 'echarts/charts'
+import { BarChart, PieChart, RadarChart } from 'echarts/charts'
 import {
   GridComponent,
   TooltipComponent,
   LegendComponent,
+  RadarComponent,
 } from 'echarts/components'
 
 use([
   CanvasRenderer,
   BarChart,
   PieChart,
+  RadarChart,
   GridComponent,
   TooltipComponent,
   LegendComponent,
+  RadarComponent,
 ])

frontend/src/router/index.ts

@@ -32,11 +32,17 @@ const routes: RouteRecordRaw[] = [
         meta: { title: '服务看板' },
       },
       // 平台治理
+      {
+        path: 'organization',
+        name: 'organization',
+        component: () => import('@/views/governance/OrganizationPermissionView.vue'),
+        meta: { title: '组织与权限', pageSurface: 'self', permission: 'user-settings' },
+      },
       {
         path: 'tenants',
         name: 'tenants',
-        component: () => import('@/views/tenants/TenantListView.vue'),
+        redirect: '/organization?tab=tenants',
-        meta: { title: '租户管理', permission: 'user-settings' },
+        meta: { title: '租户与配额', permission: 'user-settings' },
       },
       {
         path: 'tenants/:id',
@@ -47,37 +53,37 @@ const routes: RouteRecordRaw[] = [
       {
         path: 'projects',
         name: 'projects',
-        component: () => import('@/views/projects/ProjectListView.vue'),
+        redirect: '/organization?tab=users',
-        meta: { title: '项目空间', permission: 'user-settings' },
+        meta: { title: '组织与权限', permission: 'user-settings' },
       },
       {
         path: 'projects/:id',
         name: 'project-detail',
-        component: () => import('@/views/projects/ProjectDetailView.vue'),
+        redirect: '/organization?tab=users',
-        meta: { title: '项目详情', permission: 'user-settings' },
+        meta: { title: '组织与权限', permission: 'user-settings' },
       },
       {
         path: 'audit-logs',
         name: 'audit-logs',
-        component: () => import('@/views/audit/AuditLogView.vue'),
+        redirect: '/logs?tab=audit',
-        meta: { title: '审计日志', permission: 'user-settings' },
+        meta: { title: '运行日志', permission: 'user-settings' },
       },
       {
         path: 'operation-logs',
         name: 'operation-logs',
-        component: () => import('@/views/audit/OperationLogView.vue'),
+        redirect: '/logs?tab=operations',
-        meta: { title: '操作日志', permission: 'user-settings' },
+        meta: { title: '运行日志', permission: 'user-settings' },
       },
       {
         path: 'approval-templates',
         name: 'approval-templates',
-        component: () => import('@/views/approvals/ApprovalTemplateView.vue'),
+        redirect: '/approval-instances?tab=strategies',
-        meta: { title: '审批模板', permission: 'user-settings' },
+        meta: { title: '审批中心', permission: 'user-settings' },
       },
       {
         path: 'approval-instances',
         name: 'approval-instances',
-        component: () => import('@/views/approvals/ApprovalInstanceView.vue'),
+        component: () => import('@/views/approvals/ApprovalCenterView.vue'),
         meta: { title: '审批中心', permission: 'user-settings' },
       },
       {
@@ -302,14 +308,14 @@ const routes: RouteRecordRaw[] = [
       {
         path: 'logs',
         name: 'logs',
-        component: () => import('@/views/system/LogsView.vue'),
+        component: () => import('@/views/system/RuntimeLogsView.vue'),
-        meta: { title: '查看日志' },
+        meta: { title: '运行日志', pageSurface: 'self', permission: 'logs' },
       },
       {
         path: 'user-settings',
         name: 'user-settings',
-        component: () => import('@/views/system/UserSettingsView.vue'),
+        redirect: '/organization?tab=users',
-        meta: { title: '用户设置', pageSurface: 'self', permission: 'user-settings' },
+        meta: { title: '组织与权限', pageSurface: 'self', permission: 'user-settings' },
       },
       {
         path: 'user-settings/create',
@@ -357,6 +363,7 @@ const permissionBySegment: Record<string, PermissionCode> = {
   tools: 'data-convert',
   hardware: 'hardware',
   logs: 'logs',
+  organization: 'user-settings',
   'user-settings': 'user-settings',
   tenants: 'user-settings',
   projects: 'user-settings',
@@ -379,7 +386,7 @@ function requiredPermission(path: string, explicit?: unknown) {
 // 权限控制规则（基于 governance-user-guide.md 设计）：
 // - admin 用户：可以访问所有页面
 // - 非 admin 用户：默认可访问所有业务页面（训练、评测、推理、数据等）
-//   仅以下页面限制 admin 访问：user-settings、compute（算力节点）
+//   仅治理与资源管理页面限制 admin 访问：organization、user-settings、compute
 router.beforeEach((to, _from, next) => {
   if (!to.meta.public) routeLoading.value = true
   const auth = useAuthStore()
@@ -404,7 +411,7 @@ router.beforeEach((to, _from, next) => {
   if (!to.meta.skipPermission) {
     const permission = requiredPermission(to.path, to.meta.permission)
     // 仅限制管理员专属页面的访问权限
-    // user-settings（用户设置、租户管理、项目空间、审批、审计日志）仅 admin 可访问
+    // user-settings（组织与权限、资源授权、审批中心、运行日志）仅 admin 可访问
     if (permission === 'user-settings' && !auth.isAdmin) {
       next({ name: 'permission-denied', replace: true })
       return

frontend/src/types/dataProcess.ts

@@ -398,6 +398,52 @@ export interface DataProcessResultBatchRegenerateResult {
   failures: DataProcessResultBatchRegenerateFailure[]
 }
 
+export interface DataProcessResultBatchEvaluateItem {
+  result_id: string
+  expected_updated_at: string
+}
+
+export interface DataProcessResultBatchEvaluatePayload {
+  items: DataProcessResultBatchEvaluateItem[]
+}
+
+export interface DataProcessResultBatchEvaluateFailure {
+  result_id: string
+  code: 'conflict' | 'skipped' | 'evaluation_failed' | 'internal_error'
+  message: string
+}
+
+export interface DataProcessResultBatchEvaluateResult {
+  batch_id: string
+  total: number
+  succeeded: number
+  failed: number
+  duration_ms: number
+  items: DataProcessResult[]
+  failures: DataProcessResultBatchEvaluateFailure[]
+}
+
+export interface DataProcessQualitySemantic {
+  question_answer?: number
+  answer_source?: number
+  overall?: number
+}
+
+export interface DataProcessQualityJudge {
+  scores?: Record<string, number>
+  overall?: number
+  reason?: string
+  issues?: string[]
+  model?: string
+  output_type?: string
+}
+
+export interface DataProcessQualityLayers {
+  rule?: number | null
+  semantic?: number | null
+  judge?: number | null
+}
+
 export interface DataProcessQualityScore {
   overall?: number
   completeness?: number
@@ -408,6 +454,11 @@ export interface DataProcessQualityScore {
   is_valid?: boolean
   flags?: string[]
   fingerprint?: string
+  semantic?: DataProcessQualitySemantic | null
+  judge?: DataProcessQualityJudge | null
+  layers?: DataProcessQualityLayers | null
+  evaluated?: boolean
+  evaluated_at?: string | null
   source_pages?: number[]
   heading_path?: string[]
   source_locator?: DataProcessSourceLocator

frontend/src/types/index.ts

@@ -218,6 +218,8 @@ export interface LoadedModel {
   port?: number
   node_id?: string
   node_name?: string
+  gpu_indices?: number[]
+  gpus?: number[]
   error?: string
 }
 
@@ -239,6 +241,8 @@ export interface CompareModelRef {
   gpu_id: number
   node_id?: string
   node_name?: string
+  gpu_indices?: number[]
+  gpus?: number[]
   source?: string
   port?: number
 }
@@ -282,7 +286,9 @@ export interface StartEvalPayload {
   eval_task_name: string
   eval_type: EvalType
   model_id: string | number
-  gpu_id: string | number
+  gpu_id: string | number | string[]
+  gpu_indices?: number[]
+  gpus?: number[]
   compute_node_id?: string
   dataset_id: string | number
   dimension_id: string | number

frontend/src/views/approvals/ApprovalCenterView.vue

@@ -0,0 +1,66 @@
+<script setup lang="ts">
+import { computed } from 'vue'
+import { useRoute, useRouter } from 'vue-router'
+import ApprovalInstanceView from './ApprovalInstanceView.vue'
+import ApprovalTemplateView from './ApprovalTemplateView.vue'
+
+const route = useRoute()
+const router = useRouter()
+
+const activeTab = computed<'instances' | 'mine' | 'strategies'>({
+  get: () => route.query.tab === 'strategies' ? 'strategies' : route.query.tab === 'mine' ? 'mine' : 'instances',
+  set: (value: string) => {
+    void router.replace({ query: value === 'instances' ? {} : { tab: value } })
+  },
+})
+</script>
+
+<template>
+  <div class="approval-center">
+    <header class="page-header">
+      <div>
+        <h2>审批中心</h2>
+        <p>集中处理审批申请、审批历史和审批策略。</p>
+      </div>
+    </header>
+
+    <el-tabs v-model="activeTab">
+      <el-tab-pane label="审批申请" name="instances">
+        <ApprovalInstanceView v-if="activeTab === 'instances'" />
+      </el-tab-pane>
+      <el-tab-pane label="我的申请" name="mine">
+        <ApprovalInstanceView v-if="activeTab === 'mine'" mine />
+      </el-tab-pane>
+      <el-tab-pane label="审批策略" name="strategies">
+        <ApprovalTemplateView v-if="activeTab === 'strategies'" />
+      </el-tab-pane>
+    </el-tabs>
+  </div>
+</template>
+
+<style scoped lang="scss">
+.approval-center {
+  min-height: 100%;
+  padding: 20px;
+}
+
+.page-header {
+  margin-bottom: 4px;
+
+  h2 {
+    margin: 0;
+    color: #1f2937;
+    font-size: 22px;
+  }
+
+  p {
+    margin: 6px 0 0;
+    color: #64748b;
+    font-size: 13px;
+  }
+}
+
+:deep(.page) {
+  padding: 16px 0 0;
+}
+</style>

frontend/src/views/approvals/ApprovalInstanceView.vue

@@ -1,11 +1,15 @@
 <script setup lang="ts">
-import { onMounted, reactive, ref } from 'vue'
+import { computed, onMounted, ref } from 'vue'
 import { ElMessage } from 'element-plus'
 import DataTablePage from '@/components/DataTablePage.vue'
 import { getApprovalInstances, decideApproval, type ApprovalInstance } from '@/api/modules/approval'
 import { getUsers } from '@/api/modules/system'
+import { useAuthStore } from '@/stores/auth'
 import type { SystemUser } from '@/types'
 
+const props = defineProps<{ mine?: boolean }>()
+const auth = useAuthStore()
+
 const loading = ref(false)
 const instances = ref<ApprovalInstance[]>([])
 const users = ref<SystemUser[]>([])
@@ -14,6 +18,14 @@ const showDecide = ref(false)
 const current = ref<ApprovalInstance | null>(null)
 const decision = ref({ step_index: 0, approver_id: '', approved: true, comment: '' })
 
+const visibleInstances = computed(() => {
+  if (!props.mine) return instances.value
+  const currentUserId = auth.currentUser?.id
+  return currentUserId
+    ? instances.value.filter((item) => item.applicant_id === currentUserId)
+    : []
+})
+
 const statusOptions = [
   { label: '待审批', value: 'pending' },
   { label: '已通过', value: 'approved' },
@@ -77,7 +89,7 @@ onMounted(() => {
 
 <template>
   <div class="page">
-    <DataTablePage title="审批实例" :data="instances" :loading="loading" searchable :search-fields="['resource_type', 'resource_id']">
+    <DataTablePage :title="props.mine ? '我的申请' : '审批申请'" :data="visibleInstances" :loading="loading" searchable :search-fields="['resource_type', 'resource_id']">
       <template #toolbar-extra>
         <el-select v-model="statusFilter" placeholder="状态" clearable style="width: 140px" @change="load">
           <el-option v-for="s in statusOptions" :key="s.value" :label="s.label" :value="s.value" />

frontend/src/views/audit/AuditLogView.vue

@@ -2,16 +2,21 @@
 import { onMounted, reactive, ref } from 'vue'
 import { ElMessage } from 'element-plus'
 import { getAuditLogs, exportAuditLogs, type AuditLog, type AuditQuery } from '@/api/modules/audit'
+import { getUsers, type SystemUser } from '@/api/modules/system'
+import { getTenants, type Tenant } from '@/api/modules/tenant'
 
 const loading = ref(false)
 const logs = ref<AuditLog[]>([])
 const total = ref(0)
+const users = ref<SystemUser[]>([])
+const tenants = ref<Tenant[]>([])
 const query = reactive<AuditQuery>({
   tenant_id: '',
-  project_id: '',
   actor_id: '',
   action: '',
   target_type: '',
+  target_id: '',
+  keyword: '',
   start_time: '',
   end_time: '',
   limit: 50,
@@ -21,6 +26,87 @@ const query = reactive<AuditQuery>({
 // 时间范围（el-date-picker 双向绑定数组 [start, end]）
 const timeRange = ref<[string, string] | null>(null)
 
+const actionOptions = [
+  { value: 'create_dataset', label: '创建数据集' },
+  { value: 'update_dataset', label: '修改数据集' },
+  { value: 'delete_dataset', label: '删除数据集' },
+  { value: 'create_model', label: '创建模型' },
+  { value: 'update_model', label: '修改模型' },
+  { value: 'delete_model', label: '删除模型' },
+  { value: 'create_fine_tune', label: '创建训练任务' },
+  { value: 'update_fine_tune', label: '修改训练任务' },
+  { value: 'delete_fine_tune', label: '删除训练任务' },
+  { value: 'create_inference', label: '创建推理任务' },
+  { value: 'update_inference', label: '修改推理任务' },
+  { value: 'delete_inference', label: '删除推理任务' },
+  { value: 'create_user', label: '创建用户' },
+  { value: 'update_user', label: '修改用户' },
+  { value: 'delete_user', label: '删除用户' },
+  { value: 'tenant.create', label: '创建租户' },
+  { value: 'tenant.update', label: '修改租户' },
+  { value: 'tenant.delete', label: '删除租户' },
+  { value: 'tenant.quota.set', label: '设置租户配额' },
+  { value: 'tenant.retention.set', label: '设置留存策略' },
+  { value: 'grant_acl', label: '授予资源权限' },
+  { value: 'revoke_acl', label: '撤销资源权限' },
+  { value: 'gpu.assign', label: '分配算力卡' },
+  { value: 'gpu.release', label: '释放算力卡' },
+  { value: 'create', label: '创建' },
+  { value: 'update', label: '修改' },
+  { value: 'delete', label: '删除' },
+  { value: 'start', label: '启动' },
+  { value: 'stop', label: '停止' },
+  { value: 'upload', label: '上传' },
+  { value: 'download', label: '下载' },
+  { value: 'convert', label: '转换' },
+  { value: 'merge', label: '合并权重' },
+  { value: 'publish', label: '发布' },
+  { value: 'retry', label: '重试' },
+  { value: 'login', label: '登录' },
+  { value: 'logout', label: '退出登录' },
+]
+
+const targetTypeOptions = [
+  { value: 'dataset', label: '数据集' },
+  { value: 'model', label: '模型' },
+  { value: 'fine_tune_task', label: '训练任务' },
+  { value: 'fine_tune', label: '训练任务' },
+  { value: 'inference_task', label: '推理任务' },
+  { value: 'inference', label: '推理任务' },
+  { value: 'eval_task', label: '评测任务' },
+  { value: 'trained_model', label: '训练模型' },
+  { value: 'convert_task', label: '数据转换任务' },
+  { value: 'tenant', label: '租户' },
+  { value: 'user', label: '用户' },
+  { value: 'resource_acl', label: '资源权限' },
+  { value: 'gpu', label: '算力卡' },
+  { value: 'retention_policy', label: '留存策略' },
+  { value: 'module', label: '业务模块' },
+  { value: 'api', label: '接口' },
+]
+
+const actionLabels = Object.fromEntries(actionOptions.map((item) => [item.value, item.label]))
+const targetTypeLabels = Object.fromEntries(targetTypeOptions.map((item) => [item.value, item.label]))
+
+function userName(id?: string) {
+  if (!id) return '系统'
+  const user = users.value.find((item) => item.id === id)
+  return user ? `${user.display_name || user.username}（${user.username}）` : id
+}
+
+function tenantName(id?: string) {
+  if (!id) return '未关联租户'
+  return tenants.value.find((item) => item.id === id)?.name || id
+}
+
+function actionName(action?: string) {
+  return action ? actionLabels[action] || action : '未记录'
+}
+
+function targetTypeName(type?: string) {
+  return type ? targetTypeLabels[type] || type : '未指定'
+}
+
 function applyTimeRange() {
   if (timeRange.value && timeRange.value.length === 2) {
     query.start_time = timeRange.value[0]
@@ -42,6 +128,25 @@ async function load() {
   }
 }
 
+async function loadFilterOptions() {
+  const [userResult, tenantResult] = await Promise.allSettled([getUsers(), getTenants()])
+  if (userResult.status === 'fulfilled') users.value = userResult.value
+  if (tenantResult.status === 'fulfilled') tenants.value = tenantResult.value
+}
+
+function resetFilters() {
+  query.tenant_id = ''
+  query.actor_id = ''
+  query.action = ''
+  query.target_type = ''
+  query.target_id = ''
+  query.keyword = ''
+  query.start_time = ''
+  query.end_time = ''
+  timeRange.value = null
+  void load()
+}
+
 async function handleExport() {
   try {
     const blob = await exportAuditLogs({ ...query, limit: 10000, offset: 0 })
@@ -56,7 +161,9 @@ async function handleExport() {
   }
 }
 
-onMounted(load)
+onMounted(() => {
+  void Promise.all([loadFilterOptions(), load()])
+})
 </script>
 
 <template>
@@ -66,21 +173,32 @@ onMounted(load)
       <el-button @click="handleExport">导出 CSV</el-button>
     </div>
     <el-card class="filter-card">
-      <el-form :inline="true">
+      <el-form :inline="true" class="filter-form">
         <el-form-item label="租户">
-          <el-input v-model="query.tenant_id" placeholder="tenant_id" clearable />
+          <el-select v-model="query.tenant_id" placeholder="全部租户" clearable filterable style="width: 190px">
-        </el-form-item>
+            <el-option v-for="tenant in tenants" :key="tenant.id" :label="tenant.name" :value="tenant.id" />
-        <el-form-item label="项目">
+          </el-select>
-          <el-input v-model="query.project_id" placeholder="project_id" clearable />
         </el-form-item>
         <el-form-item label="操作人">
-          <el-input v-model="query.actor_id" placeholder="actor_id" clearable />
+          <el-select v-model="query.actor_id" placeholder="全部用户" clearable filterable style="width: 210px">
+            <el-option v-for="user in users" :key="user.id" :label="`${user.display_name || user.username}（${user.username}）`" :value="user.id" />
+          </el-select>
         </el-form-item>
         <el-form-item label="动作">
-          <el-input v-model="query.action" placeholder="action" clearable />
+          <el-select v-model="query.action" placeholder="全部动作" clearable filterable style="width: 180px">
+            <el-option v-for="item in actionOptions" :key="item.value" :label="item.label" :value="item.value" />
+          </el-select>
         </el-form-item>
         <el-form-item label="目标类型">
-          <el-input v-model="query.target_type" placeholder="target_type" clearable />
+          <el-select v-model="query.target_type" placeholder="全部资源" clearable filterable style="width: 160px">
+            <el-option v-for="item in targetTypeOptions" :key="item.value" :label="item.label" :value="item.value" />
+          </el-select>
+        </el-form-item>
+        <el-form-item label="关键词">
+          <el-input v-model="query.keyword" placeholder="资源 ID 或详情" clearable style="width: 220px" />
+        </el-form-item>
+        <el-form-item label="目标 ID">
+          <el-input v-model="query.target_id" placeholder="精确查询，可选" clearable style="width: 180px" />
         </el-form-item>
         <el-form-item label="时间范围">
           <el-date-picker
@@ -97,16 +215,24 @@ onMounted(load)
         </el-form-item>
         <el-form-item>
           <el-button type="primary" @click="load">查询</el-button>
+          <el-button @click="resetFilters">重置</el-button>
         </el-form-item>
       </el-form>
     </el-card>
     <el-table :data="logs" v-loading="loading" border stripe class="log-table">
       <el-table-column prop="time" label="时间" min-width="180" />
-      <el-table-column prop="tenant_id" label="租户" min-width="120" />
+      <el-table-column label="租户" min-width="140">
-      <el-table-column prop="project_id" label="项目" min-width="120" />
+        <template #default="{ row }">{{ tenantName(row.tenant_id) }}</template>
-      <el-table-column prop="actor_id" label="操作人" min-width="120" />
+      </el-table-column>
-      <el-table-column prop="action" label="动作" min-width="140" />
+      <el-table-column label="操作人" min-width="180">
-      <el-table-column prop="target_type" label="目标类型" min-width="120" />
+        <template #default="{ row }">{{ userName(row.actor_id) }}</template>
+      </el-table-column>
+      <el-table-column label="动作" min-width="140">
+        <template #default="{ row }">{{ actionName(row.action) }}</template>
+      </el-table-column>
+      <el-table-column label="目标类型" min-width="120">
+        <template #default="{ row }">{{ targetTypeName(row.target_type) }}</template>
+      </el-table-column>
       <el-table-column prop="target_id" label="目标 ID" min-width="140" show-overflow-tooltip />
       <el-table-column prop="detail" label="详情" min-width="200" show-overflow-tooltip />
       <el-table-column prop="client_ip" label="IP" min-width="120" />
@@ -120,6 +246,7 @@ onMounted(load)
 .page-header { display: flex; align-items: center; justify-content: space-between; margin-bottom: 16px; }
 .page-title { margin: 0; font-size: 18px; }
 .filter-card { margin-bottom: 16px; }
+.filter-form { display: flex; flex-wrap: wrap; }
 .log-table { margin-top: 8px; }
 .pager { margin-top: 12px; text-align: right; color: #909399; }
 </style>

frontend/src/views/audit/OperationLogView.vue

@@ -313,7 +313,7 @@ onMounted(() => {
       </el-table-column>
       <el-table-column label="操作" width="80" align="center" fixed="right">
         <template #default="{ row }">
-          <el-button link type="primary" size="small" @click="showDetail(row)">详情</el-button>
+          <el-button link type="primary" size="small" @click="showDetail(row as OperationLog)">详情</el-button>
         </template>
       </el-table-column>
     </el-table>

frontend/src/views/data-convert/DataConvertView.vue

@@ -2,7 +2,7 @@
 import { onMounted, ref } from 'vue'
 import { ElMessage, ElMessageBox } from 'element-plus'
 import { Plus, Delete, Refresh } from '@element-plus/icons-vue'
-import type { TagProps, UploadRequestOptions, UploadFile } from 'element-plus'
+import type { TagProps, UploadRequestOptions, UploadFile, UploadRawFile } from 'element-plus'
 import PageCard from '@/components/PageCard.vue'
 import {
   getDataConvertTasks,
@@ -30,9 +30,9 @@ async function load() {
 }
 
 // 文件上传前的校验（仅校验文件格式）
-function beforeUpload(file: UploadFile) {
+function beforeUpload(file: UploadRawFile) {
   // 检查文件类型
-  const isJson = file.name.endsWith('.json') || file.raw?.type === 'application/json'
+  const isJson = file.name.endsWith('.json') || file.type === 'application/json'
   if (!isJson) {
     ElMessage.error('只能上传 .json 格式的文件')
     return false

frontend/src/views/data-process/DataProcessCreateView.vue

@@ -18,6 +18,7 @@ import {
   previewAffectingOptionsFor,
 } from './create/dataProcessCreateState'
 import { useDataProcessGeneration } from './create/useDataProcessGeneration'
+import { useDataProcessEvaluation } from './create/useDataProcessEvaluation'
 import { useDataProcessPreviewBuild } from './create/useDataProcessPreviewBuild'
 import { useDataProcessRegeneration } from './create/useDataProcessRegeneration'
 import { createDefaultExternalSource, externalSourcePayload, restoreExternalSourceConfig, sourceConfigForBackend } from './create/externalSourceConfig'
@@ -126,6 +127,19 @@ const {
   outputType: activeOutputType,
   beforeGenerate: beforeStartGeneration,
 })
+const {
+  evaluation,
+  evaluateAllResults,
+  resetEvaluation,
+} = useDataProcessEvaluation({
+  taskId,
+  results,
+  selectedResultId,
+})
+// 生成结果被重置（重新切分/上传/重新生成配置）时同步清空评测进度。
+watch(results, (items) => {
+  if (!items.length) resetEvaluation()
+})
 const { enqueueSourceUpload, sourceUploading } = useDataProcessSourceUpload({
   taskId,
   uploadedFiles,
@@ -1156,10 +1170,12 @@ onMounted(() => {
             :preview-items="previewItems"
             :regenerating-result-id="regeneratingResultId"
             :bulk-regeneration="bulkRegeneration"
+            :evaluation="evaluation"
             :output-type="activeOutputType"
             @update:field="updateResultField"
             @regenerate:all="regenerateAllResults"
             @regenerate:item="regenerateResult"
+            @evaluate:all="evaluateAllResults"
           />
         </div>
       </div>

frontend/src/views/data-process/DataProcessDetailView.vue

@@ -5,6 +5,7 @@ import { ElMessage, ElMessageBox } from 'element-plus'
 import PageCard from '@/components/PageCard.vue'
 import { usePolling } from '@/composables/usePolling'
 import {
+  evaluateDataProcessResults,
   getDataProcessProgress,
   getDataProcessResults,
   getDataProcessTask,
@@ -13,6 +14,7 @@ import {
   restoreDataProcessResult,
   updateDataProcessResult,
 } from '@/api/modules/dataProcess'
+import QualityRadarPopover from './create/QualityRadarPopover.vue'
 import type {
   DataProcessDatasetSplit,
   DataProcessPublishPayload,
@@ -456,13 +458,101 @@ function resultStatusType(status: DataProcessResultStatus) {
 }
 
 function qualityScoreLabel(value: DataProcessResult['quality_score']) {
-  if (value == null) return '-'
+  if (value == null || !value.evaluated) return '-'
   const score = value.overall
   return Number.isFinite(score) ? Number(score).toFixed(1) : '-'
 }
 
-function qualityFlagsLabel(value: DataProcessResult['quality_score']) {
+function qualityScoreTone(value: DataProcessResult['quality_score']) {
-  return value?.flags?.length ? value.flags.join('、') : '未命中质量规则'
+  const score = Number(value?.overall)
+  if (!value?.evaluated || !Number.isFinite(score)) return ''
+  return score >= 80 ? 'is-success' : score >= 60 ? 'is-warning' : 'is-danger'
+}
+
+function qualityScoreEvaluated(value: DataProcessResult['quality_score']) {
+  return Boolean(value?.evaluated && Number.isFinite(Number(value?.overall)))
+}
+
+const evaluationRunning = ref(false)
+const evaluationProgress = reactive({
+  visible: false,
+  total: 0,
+  completed: 0,
+  succeeded: 0,
+  failed: 0,
+})
+// 与批量重生成一致的分块大小，单批在接口 240 秒超时预算内。
+const EVALUATION_CHUNK_SIZE = 12
+const canEvaluate = computed(() => (
+  detail.value?.status === 'completed' && !hasCurrentPublishedDataset.value
+))
+const evaluationPercentage = computed(() => (
+  evaluationProgress.total
+    ? Math.round((evaluationProgress.completed / evaluationProgress.total) * 100)
+    : 0
+))
+
+async function loadAllResultIds() {
+  const first = await getDataProcessResults(taskId.value, { page: 1, page_size: 500 })
+  const items = [...first.items]
+  const pages = Math.ceil(first.total / first.page_size)
+  for (let page = 2; page <= pages; page += 1) {
+    const next = await getDataProcessResults(taskId.value, { page, page_size: 500 })
+    items.push(...next.items)
+  }
+  return items
+}
+
+async function runResultEvaluation() {
+  if (evaluationRunning.value || !canEvaluate.value) return
+  evaluationRunning.value = true
+  Object.assign(evaluationProgress, {
+    visible: true,
+    total: 0,
+    completed: 0,
+    succeeded: 0,
+    failed: 0,
+  })
+  try {
+    const candidates = (await loadAllResultIds()).filter((item) => item.updated_at)
+    if (!candidates.length) {
+      ElMessage.info('当前没有可评测的结果')
+      return
+    }
+    evaluationProgress.total = candidates.length
+    for (let offset = 0; offset < candidates.length; offset += EVALUATION_CHUNK_SIZE) {
+      const chunk = candidates.slice(offset, offset + EVALUATION_CHUNK_SIZE)
+      try {
+        const evaluated = await evaluateDataProcessResults(taskId.value, {
+          items: chunk.map((item) => ({
+            result_id: String(item.id),
+            expected_updated_at: item.updated_at as string,
+          })),
+        })
+        evaluationProgress.completed += evaluated.total
+        evaluationProgress.succeeded += evaluated.succeeded
+        evaluationProgress.failed += evaluated.failed
+      } catch {
+        evaluationProgress.completed = evaluationProgress.total
+        evaluationProgress.failed += candidates.length - offset
+        break
+      }
+    }
+    await loadResults()
+    if (evaluationProgress.failed === 0) {
+      ElMessage.success(`数据评测完成：成功 ${evaluationProgress.succeeded} 条`)
+    } else if (evaluationProgress.succeeded > 0) {
+      ElMessage.warning(
+        `数据评测完成：成功 ${evaluationProgress.succeeded} 条，失败 ${evaluationProgress.failed} 条`,
+      )
+    } else {
+      ElMessage.error(`数据评测失败：${evaluationProgress.failed} 条结果未完成评测`)
+    }
+  } catch {
+    ElMessage.error('数据评测中断，已完成的评分保持不变')
+  } finally {
+    evaluationRunning.value = false
+  }
 }
 
 function replaceResult(updated: DataProcessResult) {
@@ -824,10 +914,33 @@ onBeforeUnmount(() => {
               <el-option label="已修改" value="modified" />
               <el-option label="无效" value="invalid" />
             </el-select>
+            <el-button
+              v-if="canEvaluate"
+              type="primary"
+              plain
+              :loading="evaluationRunning"
+              :disabled="resultLoading"
+              @click="runResultEvaluation"
+            >
+              <i v-if="!evaluationRunning" class="fa fa-check-square-o" aria-hidden="true" /> 数据评测
+            </el-button>
             <el-button :loading="resultLoading" @click="loadResults"><i class="fa fa-refresh" /></el-button>
           </div>
         </div>
 
+        <div v-if="evaluationProgress.visible" class="evaluation-progress">
+          <span>
+            数据评测 {{ evaluationProgress.completed }} / {{ evaluationProgress.total }}
+            · 成功 {{ evaluationProgress.succeeded }} · 失败 {{ evaluationProgress.failed }}
+          </span>
+          <el-progress
+            :percentage="evaluationPercentage"
+            :show-text="false"
+            :stroke-width="5"
+            :color="evaluationProgress.failed > 0 ? '#d97706' : '#5b50f2'"
+          />
+        </div>
+
         <el-table
           v-if="results.length"
           :data="results"
@@ -845,9 +958,26 @@ onBeforeUnmount(() => {
           </template>
           <el-table-column label="质量分" width="88" align="center">
             <template #default="{ row }">
-              <el-tooltip :content="qualityFlagsLabel((row as DataProcessResult).quality_score)">
+              <el-popover
-                <span>{{ qualityScoreLabel((row as DataProcessResult).quality_score) }}</span>
+                v-if="qualityScoreEvaluated((row as DataProcessResult).quality_score)"
-              </el-tooltip>
+                placement="top"
+                :width="296"
+                trigger="hover"
+                :show-after="150"
+                popper-class="quality-radar-popper"
+              >
+                <template #reference>
+                  <span
+                    class="detail-quality-score"
+                    :class="qualityScoreTone((row as DataProcessResult).quality_score)"
+                  >{{ qualityScoreLabel((row as DataProcessResult).quality_score) }}</span>
+                </template>
+                <QualityRadarPopover
+                  :quality="(row as DataProcessResult).quality_score!"
+                  :score="Number((row as DataProcessResult).quality_score?.overall)"
+                />
+              </el-popover>
+              <span v-else class="detail-quality-empty">{{ qualityScoreLabel((row as DataProcessResult).quality_score) }}</span>
             </template>
           </el-table-column>
           <el-table-column label="状态" width="90" align="center">
@@ -1099,6 +1229,35 @@ onBeforeUnmount(() => {
 .result-filters :deep(.el-select) { width: 120px; }
 .result-section :deep(.el-table) { border-radius: 0; }
 
+.evaluation-progress {
+  display: grid;
+  grid-template-columns: 1fr 220px;
+  align-items: center;
+  gap: 14px;
+  padding: 10px 18px;
+  color: #667085;
+  background: #f8f9fc;
+  font-size: 12px;
+}
+
+.detail-quality-score {
+  display: inline-block;
+  min-width: 44px;
+  padding: 2px 8px;
+  border-radius: 10px;
+  color: #475467;
+  background: #f2f4f7;
+  font-weight: 700;
+  font-variant-numeric: tabular-nums;
+  cursor: default;
+
+  &.is-success { color: #067647; background: #e6f4ee; }
+  &.is-warning { color: #b54708; background: #fef0c7; }
+  &.is-danger { color: #b42318; background: #fee4e2; }
+}
+
+.detail-quality-empty { color: #98a2b3; }
+
 :global(.data-process-result-tooltip) {
   box-sizing: border-box;
   max-width: min(520px, calc(100vw - 32px));

frontend/src/views/data-process/create/QualityRadarPopover.vue

@@ -0,0 +1,252 @@
+<script setup lang="ts">
+import { computed } from 'vue'
+import VChart from 'vue-echarts'
+import '@/plugins/echarts'
+import type { EChartsOption } from 'echarts'
+import type { ResultQualityDetails } from './types'
+
+const props = defineProps<{
+  quality: ResultQualityDetails
+  score?: number
+}>()
+
+// 轴标签按词意预置断行，避免长中文标签把雷达网格挤偏或被机械切词。
+const JUDGE_DIMENSION_LABELS: Record<string, string> = {
+  faithfulness: '忠实度',
+  correctness: '正确性',
+  clarity: '问题\n清晰度',
+  completeness: '回答\n完整性',
+  alignment: '指令\n对齐',
+  reasoning_validity: '推理\n有效性',
+  chosen_quality: 'chosen\n质量',
+  rejected_quality: 'rejected\n质量',
+  preference_reasonableness: '偏好\n区分',
+}
+
+const SEMANTIC_DIMENSION_LABELS: Record<string, string> = {
+  question_answer: '问答\n相关',
+  answer_source: '来源\n覆盖',
+}
+
+interface RadarDimension {
+  name: string
+  value: number
+}
+
+const radarDimensions = computed<RadarDimension[]>(() => {
+  const dimensions: RadarDimension[] = []
+  for (const [key, value] of Object.entries(props.quality?.judge?.scores ?? {})) {
+    dimensions.push({
+      name: JUDGE_DIMENSION_LABELS[key] ?? key,
+      value: Math.round(value * 20),
+    })
+  }
+  for (const [key, value] of Object.entries(props.quality?.semantic ?? {})) {
+    if (key === 'overall' || typeof value !== 'number') continue
+    dimensions.push({
+      name: SEMANTIC_DIMENSION_LABELS[key] ?? key,
+      value: Math.round(value),
+    })
+  }
+  return dimensions
+})
+
+// 可用维度太少时雷达图失去意义，降级为分层分数展示。
+const showRadar = computed(() => radarDimensions.value.length >= 3)
+
+const radarOption = computed<EChartsOption>(() => ({
+  radar: {
+    indicator: radarDimensions.value.map((dimension) => ({
+      name: dimension.name,
+      max: 100,
+    })),
+    radius: '56%',
+    center: ['50%', '50%'],
+    splitNumber: 4,
+    axisName: {
+      color: '#667085',
+      fontSize: 10,
+      lineHeight: 13,
+    },
+    splitArea: { areaStyle: { color: ['#fbfbfd', '#f2f4f8'] } },
+    splitLine: { lineStyle: { color: '#e4e7ec' } },
+    axisLine: { lineStyle: { color: '#e4e7ec' } },
+  },
+  series: [{
+    type: 'radar',
+    symbol: 'circle',
+    symbolSize: 3,
+    data: [{
+      value: radarDimensions.value.map((dimension) => dimension.value),
+      name: '质量维度',
+      areaStyle: { color: 'rgba(91, 80, 242, 0.18)' },
+      lineStyle: { color: '#5b50f2', width: 1.5 },
+      itemStyle: { color: '#5b50f2' },
+    }],
+  }],
+}))
+
+const layerScores = computed(() => {
+  const layers = props.quality?.layers ?? {}
+  return [
+    { label: '规则层', value: layers.rule },
+    { label: '语义层', value: layers.semantic },
+    { label: '评审层', value: layers.judge },
+  ].filter((layer): layer is { label: string; value: number } => (
+    typeof layer.value === 'number'
+  ))
+})
+
+const displayScore = computed(() => {
+  if (typeof props.score === 'number' && !isNaN(props.score)) {
+    return props.score.toFixed(1)
+  }
+  return null
+})
+
+const scoreTone = computed(() => {
+  const numScore = props.score ?? 0
+  return numScore >= 80 ? 'is-success' : numScore >= 60 ? 'is-warning' : 'is-danger'
+})
+</script>
+
+<template>
+  <div class="quality-popover">
+    <div v-if="displayScore !== null" class="popover-header">
+      <strong>质量评测</strong>
+      <span class="popover-score" :class="scoreTone">{{ displayScore }}</span>
+    </div>
+
+    <VChart
+      v-if="showRadar"
+      class="quality-radar"
+      :option="radarOption"
+      autoresize
+    />
+    <div v-else class="radar-fallback">
+      维度数据不足，已评测维度少于 3 个时以分层分数为准。
+    </div>
+
+    <div class="layer-scores">
+      <div v-for="layer in layerScores" :key="layer.label" class="layer-item">
+        <span>{{ layer.label }}</span>
+        <el-progress
+          :percentage="Math.round(layer.value)"
+          :stroke-width="6"
+          :show-text="false"
+          :color="layer.value >= 80 ? '#12b76a' : layer.value >= 60 ? '#f0b429' : '#d92d20'"
+        />
+        <em>{{ layer.value.toFixed(0) }}</em>
+      </div>
+    </div>
+
+    <p v-if="quality?.judge?.reason" class="judge-reason">{{ quality.judge.reason }}</p>
+    <div v-if="quality?.judge?.issues?.length" class="judge-issues">
+      <span v-for="issue in quality.judge.issues" :key="issue" class="issue-tag">{{ issue }}</span>
+    </div>
+    <div v-if="quality?.judge?.model" class="judge-model">评审模型：{{ quality.judge.model }}</div>
+  </div>
+</template>
+
+<style scoped lang="scss">
+.quality-popover {
+  display: flex;
+  flex-direction: column;
+  gap: 10px;
+  width: 100%;
+  box-sizing: border-box;
+}
+
+.popover-header {
+  display: flex;
+  align-items: center;
+  justify-content: space-between;
+  padding-bottom: 6px;
+  border-bottom: 1px solid #f2f4f7;
+
+  strong {
+    color: #344054;
+    font-size: 13px;
+  }
+}
+
+.popover-score {
+  color: #344054;
+  font-size: 18px;
+  font-weight: 700;
+  font-variant-numeric: tabular-nums;
+
+  &.is-success { color: #12b76a; }
+  &.is-warning { color: #d99b0b; }
+  &.is-danger { color: #d92d20; }
+}
+
+.quality-radar {
+  width: 100%;
+  height: 220px;
+  box-sizing: border-box;
+}
+
+.radar-fallback {
+  padding: 18px 10px;
+  color: #98a2b3;
+  font-size: 12px;
+  text-align: center;
+}
+
+.layer-scores {
+  display: flex;
+  flex-direction: column;
+  gap: 6px;
+}
+
+.layer-item {
+  display: grid;
+  grid-template-columns: 44px 1fr 28px;
+  align-items: center;
+  gap: 8px;
+  color: #667085;
+  font-size: 11px;
+
+  em {
+    color: #344054;
+    font-style: normal;
+    font-weight: 600;
+    text-align: right;
+    font-variant-numeric: tabular-nums;
+  }
+}
+
+.judge-reason {
+  margin: 0;
+  color: #475467;
+  font-size: 12px;
+  line-height: 1.6;
+}
+
+.judge-issues {
+  display: flex;
+  flex-wrap: wrap;
+  gap: 4px;
+}
+
+.issue-tag {
+  padding: 2px 8px;
+  color: #b54708;
+  background: #fef0c7;
+  border-radius: 3px;
+  font-size: 11px;
+}
+
+.judge-model {
+  color: #98a2b3;
+  font-size: 11px;
+}
+</style>
+
+<style lang="scss">
+.quality-radar-popper {
+  padding: 14px 16px !important;
+}
+</style>
+

frontend/src/views/data-process/create/ResultEditorStep.vue

@@ -1,6 +1,7 @@
 <script setup lang="ts">
 import { computed, ref } from 'vue'
-import type { BulkResultRegenerationState, PreviewItem, ResultItem } from './types'
+import QualityRadarPopover from './QualityRadarPopover.vue'
+import type { BulkResultRegenerationState, PreviewItem, ResultEvaluationState, ResultItem } from './types'
 import type { DataProcessOutputType } from '@/types/dataProcess'
 
 const props = defineProps<{
@@ -9,6 +10,7 @@ const props = defineProps<{
   selectedId: string | null
   regeneratingResultId: string | null
   bulkRegeneration: BulkResultRegenerationState
+  evaluation: ResultEvaluationState
   outputType: DataProcessOutputType
 }>()
 
@@ -17,6 +19,7 @@ const emit = defineEmits<{
   'update:field': [id: string, field: 'instruction' | 'input' | 'output' | 'chosen' | 'rejected', value: string]
   'regenerate:item': [id: string]
   'regenerate:all': []
+  'evaluate:all': []
 }>()
 
 const search = ref('')
@@ -30,6 +33,15 @@ const bulkRegenerationActive = computed(() => props.bulkRegeneration.status ===
 const bulkRegenerationVisible = computed(() => (
   props.bulkRegeneration.status !== 'idle' && props.bulkRegeneration.total > 0
 ))
+const evaluationActive = computed(() => props.evaluation.status === 'running')
+const evaluationVisible = computed(() => (
+  props.evaluation.status !== 'idle' && props.evaluation.total > 0
+))
+const evaluationPercentage = computed(() => {
+  if (!props.evaluation.total) return 0
+  return Math.round((props.evaluation.completed / props.evaluation.total) * 100)
+})
+const evaluatedCount = computed(() => props.items.filter((item) => item.qualityDetails?.evaluated).length)
 const bulkRegenerationPercentage = computed(() => (
   props.bulkRegeneration.total > 0
     ? Math.round((props.bulkRegeneration.completed / props.bulkRegeneration.total) * 100)
@@ -98,22 +110,48 @@ function selectRelative(offset: number) {
 <template>
   <section class="result-step">
     <div class="result-workspace">
-      <aside class="result-list-pane" :class="{ 'has-bulk-progress': bulkRegenerationVisible }">
+      <aside class="result-list-pane" :class="{ 'has-bulk-progress': bulkRegenerationVisible || evaluationVisible }">
         <div class="pane-header result-list-header">
-          <div class="result-list-title"><strong>生成结果</strong><span>共 {{ items.length }} 条</span></div>
+          <div class="result-list-title">
+            <strong>生成结果</strong><span>共 {{ items.length }} 条<template v-if="evaluatedCount"> · 已评测 {{ evaluatedCount }}</template></span>
+          </div>
+          <div class="result-list-actions">
+            <el-button
+              size="small"
+              plain
+              :loading="evaluationActive"
+              :disabled="!items.length || bulkRegenerationActive || Boolean(regeneratingResultId)"
+              @click="emit('evaluate:all')"
+            >
+              <i v-if="!evaluationActive" class="fa fa-check-square-o" style="margin-right: 4px;" />
+              数据评测
+            </el-button>
             <el-button
               v-if="invalidCount > 0"
               size="small"
               plain
               type="primary"
               :loading="bulkRegenerationActive"
-            :disabled="Boolean(regeneratingResultId) || bulkRegenerationActive"
+              :disabled="Boolean(regeneratingResultId) || bulkRegenerationActive || evaluationActive"
               @click="emit('regenerate:all')"
             >
               <i v-if="!bulkRegenerationActive" class="fa fa-refresh" style="margin-right: 4px;" />
               {{ bulkRegenerationActive ? '重新生成中' : `全部重新生成（${invalidCount}）` }}
             </el-button>
           </div>
+        </div>
+        <div v-if="evaluationVisible" class="bulk-regeneration-progress">
+          <div>
+            <span>数据评测 {{ evaluation.completed }} / {{ evaluation.total }}</span>
+            <span>成功 {{ evaluation.succeeded }} · 失败 {{ evaluation.failed }}</span>
+          </div>
+          <el-progress
+            :percentage="evaluationPercentage"
+            :show-text="false"
+            :stroke-width="5"
+            :color="evaluation.failed > 0 ? '#d97706' : '#5b50f2'"
+          />
+        </div>
         <div v-if="bulkRegenerationVisible" class="bulk-regeneration-progress">
           <div>
             <span>已处理 {{ bulkRegeneration.completed }} / {{ bulkRegeneration.total }}</span>
@@ -146,6 +184,23 @@ function selectRelative(offset: number) {
               <strong>{{ item.instruction || '未填写指令' }}</strong>
               <small>{{ outputType === 'dpo' ? (item.chosen || '未填写 Chosen') : (item.output || '未填写输出') }}</small>
             </span>
+            <el-popover
+              v-if="item.qualityScore != null && item.qualityDetails"
+              placement="right"
+              :width="296"
+              trigger="hover"
+              :show-after="150"
+              popper-class="quality-radar-popper"
+            >
+              <template #reference>
+                <span
+                  class="result-score"
+                  :class="item.qualityScore >= 80 ? 'is-success' : item.qualityScore >= 60 ? 'is-warning' : 'is-danger'"
+                  @click.stop
+                >{{ item.qualityScore.toFixed(0) }}</span>
+              </template>
+              <QualityRadarPopover :quality="item.qualityDetails" :score="item.qualityScore" />
+            </el-popover>
             <i v-if="itemRegenerating(item.id)" class="css-spinner" />
             <i
               v-else
@@ -303,6 +358,42 @@ function selectRelative(offset: number) {
   }
 }
 
+.result-list-actions {
+  display: flex;
+  flex: none;
+  align-items: center;
+  gap: 8px;
+}
+
+.result-score {
+  flex: none;
+  min-width: 34px;
+  padding: 2px 8px;
+  border-radius: 10px;
+  color: #475467;
+  background: #f2f4f7;
+  font-size: 12px;
+  font-weight: 700;
+  font-variant-numeric: tabular-nums;
+  text-align: center;
+  cursor: default;
+
+  &.is-success {
+    color: #067647;
+    background: #e6f4ee;
+  }
+
+  &.is-warning {
+    color: #b54708;
+    background: #fef0c7;
+  }
+
+  &.is-danger {
+    color: #b42318;
+    background: #fee4e2;
+  }
+}
+
 .pane-header {
   display: flex;
   align-items: center;

frontend/src/views/data-process/create/types.ts

@@ -1,6 +1,9 @@
 import type {
   DataProcessOutputType,
   DataProcessPreviewFileStatus,
+  DataProcessQualityJudge,
+  DataProcessQualityLayers,
+  DataProcessQualitySemantic,
   DataProcessReasoningDetail,
 } from '@/types/dataProcess'
 
@@ -165,6 +168,14 @@ export interface GenerationState {
   message: string
 }
 
+export interface ResultQualityDetails {
+  semantic?: DataProcessQualitySemantic | null
+  judge?: DataProcessQualityJudge | null
+  layers?: DataProcessQualityLayers | null
+  evaluated?: boolean
+  flags?: string[]
+}
+
 export interface ResultItem {
   id: string
   previewItemId: string | null
@@ -188,7 +199,7 @@ export interface ResultItem {
   error?: string
   split?: 'train' | 'validation' | 'test'
   qualityScore?: number
-  qualityDetails?: Record<string, number>
+  qualityDetails?: ResultQualityDetails
   updatedAt?: string
 }
 
@@ -201,3 +212,11 @@ export interface BulkResultRegenerationState {
   targetIds: string[]
   failedIds: string[]
 }
+
+export interface ResultEvaluationState {
+  status: 'idle' | 'running' | 'completed' | 'partial' | 'failed'
+  total: number
+  completed: number
+  succeeded: number
+  failed: number
+}

frontend/src/views/data-process/create/useDataProcessEvaluation.ts

@@ -0,0 +1,126 @@
+import { computed, reactive, type Ref } from 'vue'
+import { ElMessage } from 'element-plus'
+import { evaluateDataProcessResults } from '@/api/modules/dataProcess'
+import { mapResult } from './useDataProcessGeneration'
+import type { ResultEvaluationState, ResultItem } from './types'
+
+interface EvaluationBindings {
+  taskId: Ref<string | null>
+  results: Ref<ResultItem[]>
+  selectedResultId: Ref<string | null>
+}
+
+// 与批量重新生成一致的分块大小：4 个后端 worker 消费三轮，
+// 单条评测最长 60 秒，12 条在批量接口 240 秒超时预算内。
+const EVALUATION_CHUNK_SIZE = 12
+
+function hasUnsavedChanges(item: ResultItem) {
+  return item.instruction !== item.savedInstruction
+    || item.input !== item.savedInput
+    || item.output !== item.savedOutput
+    || item.chosen !== item.savedChosen
+    || item.rejected !== item.savedRejected
+}
+
+export function useDataProcessEvaluation(bindings: EvaluationBindings) {
+  const evaluation = reactive<ResultEvaluationState>({
+    status: 'idle',
+    total: 0,
+    completed: 0,
+    succeeded: 0,
+    failed: 0,
+  })
+  const evaluationBusy = computed(() => evaluation.status === 'running')
+
+  function resetEvaluation() {
+    Object.assign(evaluation, {
+      status: 'idle',
+      total: 0,
+      completed: 0,
+      succeeded: 0,
+      failed: 0,
+    })
+  }
+
+  async function evaluateAllResults() {
+    const taskId = bindings.taskId.value
+    if (!taskId) return false
+    if (evaluationBusy.value) {
+      ElMessage.warning('请等待当前数据评测完成')
+      return false
+    }
+
+    const candidates = bindings.results.value.filter((item) => item.updatedAt)
+    if (!candidates.length) {
+      ElMessage.info('当前没有可评测的结果')
+      return false
+    }
+    const unsaved = candidates.find(hasUnsavedChanges)
+    if (unsaved) {
+      bindings.selectedResultId.value = unsaved.id
+      ElMessage.warning('存在未保存的修改，请先保存后再进行数据评测')
+      return false
+    }
+
+    Object.assign(evaluation, {
+      status: 'running',
+      total: candidates.length,
+      completed: 0,
+      succeeded: 0,
+      failed: 0,
+    })
+
+    let interrupted = false
+    try {
+      for (let offset = 0; offset < candidates.length; offset += EVALUATION_CHUNK_SIZE) {
+        const chunk = candidates.slice(offset, offset + EVALUATION_CHUNK_SIZE)
+        try {
+          const evaluated = await evaluateDataProcessResults(taskId, {
+            items: chunk.map((item) => ({
+              result_id: item.id,
+              expected_updated_at: item.updatedAt as string,
+            })),
+          })
+          for (const item of evaluated.items) {
+            const index = bindings.results.value.findIndex((entry) => entry.id === String(item.id))
+            if (index >= 0) bindings.results.value[index] = mapResult(item)
+          }
+          evaluation.completed += evaluated.total
+          evaluation.succeeded += evaluated.succeeded
+          evaluation.failed += evaluated.failed
+        } catch {
+          const remaining = candidates.slice(offset)
+          evaluation.completed = evaluation.total
+          evaluation.failed += remaining.length
+          interrupted = true
+          break
+        }
+      }
+
+      if (!interrupted && evaluation.failed === 0) {
+        evaluation.status = 'completed'
+        ElMessage.success(`数据评测完成：成功 ${evaluation.succeeded} 条`)
+      } else if (evaluation.succeeded > 0) {
+        evaluation.status = 'partial'
+        ElMessage.warning(
+          `数据评测完成：成功 ${evaluation.succeeded} 条，失败 ${evaluation.failed} 条`,
+        )
+      } else {
+        evaluation.status = 'failed'
+        ElMessage.error(`数据评测失败：${evaluation.failed} 条结果未完成评测`)
+      }
+      return evaluation.failed === 0
+    } catch {
+      evaluation.status = 'failed'
+      ElMessage.error('批量数据评测意外中断，已完成的评分保持不变')
+      return false
+    }
+  }
+
+  return {
+    evaluation,
+    evaluationBusy,
+    evaluateAllResults,
+    resetEvaluation,
+  }
+}

frontend/src/views/data-process/create/useDataProcessGeneration.ts

@@ -28,6 +28,7 @@ const POLL_INTERVAL_MS = 1500
 const BULK_REGENERATION_CHUNK_SIZE = 12
 
 function mapResult(item: DataProcessResult): ResultItem {
+  const quality = item.quality_score
   return {
     id: String(item.id),
     previewItemId: item.preview_item_id == null ? null : String(item.preview_item_id),
@@ -50,11 +51,21 @@ function mapResult(item: DataProcessResult): ResultItem {
     status: item.status,
     error: item.error || undefined,
     split: item.split || undefined,
-    qualityScore: item.quality_score?.overall,
+    // 生成阶段只有内部规则分，界面不展示；数据评测完成后才显示组合分。
+    qualityScore: quality?.evaluated ? quality.overall : undefined,
+    qualityDetails: {
+      semantic: quality?.semantic ?? null,
+      judge: quality?.judge ?? null,
+      layers: quality?.layers ?? null,
+      evaluated: Boolean(quality?.evaluated),
+      flags: quality?.flags ?? [],
+    },
     updatedAt: item.updated_at,
   }
 }
 
+export { mapResult }
+
 export function useDataProcessGeneration(bindings: GenerationBindings) {
   const results = ref<ResultItem[]>([])
   const selectedResultId = ref<string | null>(null)

frontend/src/views/eval/EvalCreateView.vue

@@ -39,7 +39,7 @@ const createdDimensionId = ref<string | number>('')
 const taskForm = ref<EvalTaskSetupDraft>({
   eval_task_name: '',
   model_id: '',
-  gpu_id: '',
+  gpu_id: [],
   data_source: 'dataset',
   dataset_id: '',
   leaderboard: false,
@@ -145,12 +145,24 @@ async function handleSubmit() {
     const dimensionId = await resolveDimensionId()
     // GPU 选择为「节点:GPU序号」复合值，解析出节点与 GPU 序号，
     // 多算力节点时必须把节点信息传给后端，否则会派发到错误的算力节点
-    const [gpuNodeId, gpuIndex] = String(taskForm.value.gpu_id).split(':')
+    const selectedGpuKeys = Array.isArray(taskForm.value.gpu_id)
+      ? taskForm.value.gpu_id
+      : [String(taskForm.value.gpu_id)]
+    const gpuSelections = selectedGpuKeys
+      .map((key) => {
+        const [nodeId, gpuIndex] = String(key).split(':')
+        return { nodeId, gpuIndex: Number(gpuIndex) }
+      })
+      .filter((item) => item.nodeId && Number.isInteger(item.gpuIndex) && item.gpuIndex >= 0)
+    const gpuNodeId = gpuSelections[0]?.nodeId || ''
+    const gpuIndices = gpuSelections.map((item) => item.gpuIndex)
     const evalResult: any = await startEval({
       eval_task_name: taskForm.value.eval_task_name,
       eval_type: 'custom',
       model_id: taskForm.value.model_id,
-      gpu_id: Number(gpuIndex) || 0,
+      gpu_id: gpuIndices[0] ?? 0,
+      gpu_indices: gpuIndices,
+      gpus: gpuIndices,
       compute_node_id: gpuNodeId || '',
       dataset_id: taskForm.value.data_source === 'dataset' ? taskForm.value.dataset_id : '',
       dimension_id: dimensionId,

frontend/src/views/eval/create/EvalTaskSetupStep.vue

@@ -6,7 +6,7 @@ import type { DatasetItem, GpuInfo, TrainedModel } from '@/types'
 export interface EvalTaskSetupDraft {
   eval_task_name: string
   model_id: string | number
-  gpu_id: string | number
+  gpu_id: string | number | string[]
   data_source: 'dataset' | 'inference'
   dataset_id: string | number
   leaderboard: boolean
@@ -23,6 +23,13 @@ defineProps<{
 const form = defineModel<EvalTaskSetupDraft>({ required: true })
 const formRef = ref<FormInstance>()
 
+function handleGpuChange(value: string | number | string[]) {
+  const keys = Array.isArray(value) ? value.map(String) : [String(value || '')]
+  const nodeId = keys[0]?.split(':', 1)[0]
+  if (!nodeId || !Array.isArray(form.value.gpu_id)) return
+  form.value.gpu_id = keys.filter((key) => key.split(':', 1)[0] === nodeId)
+}
+
 const rules: FormRules<EvalTaskSetupDraft> = {
   eval_task_name: [
     { required: true, message: '请输入任务名称', trigger: 'blur' },
@@ -101,7 +108,16 @@ defineExpose({ validate })
     </el-form-item>
 
     <el-form-item label="选择 GPU" prop="gpu_id">
-      <el-select v-model="form.gpu_id" placeholder="请选择 GPU" style="width: 100%" :loading="loading">
+      <el-select
+        v-model="form.gpu_id"
+        multiple
+        collapse-tags
+        collapse-tags-tooltip
+        placeholder="请选择同一算力节点内的一张或多张 GPU"
+        style="width: 100%"
+        :loading="loading"
+        @change="handleGpuChange"
+      >
         <el-option
           v-for="gpu in gpus"
           :key="`${gpu.node_id || ''}:${gpu.id ?? 0}`"

frontend/src/views/eval/create/StartEvalStep.vue

@@ -21,7 +21,7 @@ function nameOf<T extends { id: string | number; name?: string }>(items: T[], id
 
 /** GPU 选择为「节点:GPU序号」复合值，解析并展示为可读标签 */
 const gpuLabel = computed(() => {
-  const key = String(props.task.gpu_id || '')
+  const key = Array.isArray(props.task.gpu_id) ? String(props.task.gpu_id[0] || '') : String(props.task.gpu_id || '')
   const gpu = props.gpus.find((g) => `${g.node_id || ''}:${g.id ?? 0}` === key)
   if (gpu) return `${gpu.node_name || gpu.node_code || '算力节点'} / GPU ${gpu.id ?? 0}`
   const [nodeId, idx] = key.split(':')

frontend/src/views/governance/OrganizationPermissionView.vue

@@ -0,0 +1,68 @@
+<script setup lang="ts">
+import { computed } from 'vue'
+import { useRoute, useRouter } from 'vue-router'
+import UserSettingsView from '@/views/system/UserSettingsView.vue'
+import TenantListView from '@/views/tenants/TenantListView.vue'
+
+const route = useRoute()
+const router = useRouter()
+
+const activeTab = computed({
+  get: () => route.query.tab === 'tenants' ? 'tenants' : 'users',
+  set: (value: string) => {
+    void router.replace({ query: { tab: value === 'tenants' ? 'tenants' : 'users' } })
+  },
+})
+</script>
+
+<template>
+  <div class="organization-page">
+    <header class="page-header">
+      <div>
+        <h2>组织与权限</h2>
+        <p>统一管理平台用户、角色、租户和资源配额。</p>
+      </div>
+    </header>
+
+    <el-tabs v-model="activeTab">
+      <el-tab-pane label="用户与角色" name="users">
+        <UserSettingsView v-if="activeTab === 'users'" />
+      </el-tab-pane>
+      <el-tab-pane label="租户与配额" name="tenants">
+        <TenantListView v-if="activeTab === 'tenants'" />
+      </el-tab-pane>
+    </el-tabs>
+  </div>
+</template>
+
+<style scoped lang="scss">
+.organization-page {
+  min-height: 100%;
+  padding: 20px;
+}
+
+.page-header {
+  margin-bottom: 4px;
+
+  h2 {
+    margin: 0;
+    color: #1f2937;
+    font-size: 22px;
+  }
+
+  p {
+    margin: 6px 0 0;
+    color: #64748b;
+    font-size: 13px;
+  }
+}
+
+:deep(.user-settings),
+:deep(.page) {
+  padding: 16px 0 0;
+}
+
+:deep(.user-settings .page-header > div:first-child) {
+  display: none;
+}
+</style>

frontend/src/views/inference/InferenceCreateView.vue

@@ -4,8 +4,7 @@ import { useRouter } from 'vue-router'
 import { ElMessage, type FormInstance, type FormRules } from 'element-plus'
 import PageCard from '@/components/PageCard.vue'
 import { getModelList, getTrainedModels } from '@/api/modules/model'
-import { getSystemInfo } from '@/api/modules/system'
+import { getComputeGpus, getComputeNodes, type ComputeNode } from '@/api/modules/compute'
-import { getComputeNodes, type ComputeNode } from '@/api/modules/compute'
 import { createCompare, loadCompare } from '@/api/modules/compare'
 import type { ModelItem, TrainedModel, GpuInfo } from '@/types'
 
@@ -83,8 +82,8 @@ const form = reactive({
   description: '',
   /** 选中的模型 key（单选） */
   model_key: '',
-  /** 使用的 GPU */
+  /** 使用的 GPU（同一节点内可多选） */
-  gpu_key: '',
+  gpu_keys: [] as string[],
 })
 
 const rules: FormRules = {
@@ -94,12 +93,26 @@ const rules: FormRules = {
 
 /** 当前选中的模型对象 */
 const selectedModel = computed(() => modelMap.value[form.model_key])
-const selectedGpu = computed(() => idleGpus.value.find((g) => `${g.node_id || ''}:${g.id ?? 0}` === form.gpu_key))
+const selectedGpus = computed(() => idleGpus.value.filter((gpu) => form.gpu_keys.includes(gpuKey(gpu))))
+
+function gpuKey(gpu: GpuInfo) {
+  return `${gpu.node_id || ''}:${gpu.id ?? 0}`
+}
+
+function handleGpuChange(keys: string[]) {
+  const nodeId = keys[0]?.split(':', 1)[0]
+  if (!nodeId) return
+  const filtered = keys.filter((key) => key.split(':', 1)[0] === nodeId)
+  if (filtered.length !== keys.length) {
+    ElMessage.info('一次推理只能使用同一算力节点内的 GPU，已忽略其它节点的选择')
+  }
+  form.gpu_keys = filtered
+}
 
 watch(selectedModel, (model) => {
   if (!model?.compute_node_id) return
   const gpu = idleGpus.value.find((item) => item.node_id === model.compute_node_id)
-  if (gpu) form.gpu_key = `${gpu.node_id || ''}:${gpu.id ?? 0}`
+  if (gpu) form.gpu_keys = [gpuKey(gpu)]
 })
 
 async function handleSubmit() {
@@ -111,6 +124,10 @@ async function handleSubmit() {
       ElMessage.warning('请选择模型')
       return
     }
+    if (!selectedGpus.value.length) {
+      ElMessage.warning('请至少选择一张空闲 GPU')
+      return
+    }
     submitting.value = true
     startupStatus.value = '正在创建推理任务...'
     try {
@@ -130,9 +147,11 @@ async function handleSubmit() {
             model_name: m.name,
             model_path: m.model_path,
             source: m.source,
-            gpu_id: selectedGpu.value?.id ?? 0,
+            gpu_id: selectedGpus.value[0]?.id ?? 0,
-            node_id: selectedGpu.value?.node_id || m.compute_node_id,
+            gpu_indices: selectedGpus.value.map((gpu) => Number(gpu.id ?? 0)),
-            node_name: selectedGpu.value?.node_name || m.compute_node_name,
+            gpus: selectedGpus.value.map((gpu) => Number(gpu.id ?? 0)),
+            node_id: selectedGpus.value[0]?.node_id || m.compute_node_id,
+            node_name: selectedGpus.value[0]?.node_name || m.compute_node_name,
           },
         ],
       })
@@ -169,17 +188,17 @@ async function loadData() {
     const [db, trained, sys, nodes] = await Promise.all([
       getModelList(),
       getTrainedModels(),
-      getSystemInfo(),
+      getComputeGpus(),
       getComputeNodes(),
     ])
     dbModels.value = db || []
     trainedModels.value = trained?.models || []
-    gpus.value = sys?.gpu || []
+    gpus.value = (sys || []) as unknown as GpuInfo[]
     computeNodes.value = nodes || []
     // 默认选中第一个空闲 GPU
     if (idleGpus.value.length > 0) {
       const firstGpu = idleGpus.value[0]
-      form.gpu_key = `${firstGpu.node_id || ''}:${firstGpu.id ?? 0}`
+      form.gpu_keys = [gpuKey(firstGpu)]
     }
   } catch {
     // ignore
@@ -228,12 +247,20 @@ onMounted(loadData)
       </el-form-item>
 
       <el-form-item label="GPU">
-        <el-select v-model="form.gpu_key" style="width: 400px">
+        <el-select
+          v-model="form.gpu_keys"
+          multiple
+          collapse-tags
+          collapse-tags-tooltip
+          style="width: 400px"
+          placeholder="请选择同一算力节点内的一张或多张 GPU"
+          @change="handleGpuChange"
+        >
           <el-option
             v-for="g in idleGpus"
-            :key="`${g.node_id || ''}:${g.id ?? 0}`"
+            :key="gpuKey(g)"
             :label="`${g.node_name || g.node_code || '算力节点'} / ${g.name} (GPU${g.id ?? 0}) [空闲]`"
-            :value="`${g.node_id || ''}:${g.id ?? 0}`"
+            :value="gpuKey(g)"
           />
         </el-select>
       </el-form-item>

frontend/src/views/system/RuntimeLogsView.vue

@@ -0,0 +1,78 @@
+<script setup lang="ts">
+import { computed } from 'vue'
+import { useRoute, useRouter } from 'vue-router'
+import { useAuthStore } from '@/stores/auth'
+import LogsView from './LogsView.vue'
+import AuditLogView from '@/views/audit/AuditLogView.vue'
+import OperationLogView from '@/views/audit/OperationLogView.vue'
+
+const route = useRoute()
+const router = useRouter()
+const auth = useAuthStore()
+
+const activeTab = computed({
+  get: () => {
+    if (!auth.isAdmin) return 'runtime'
+    if (route.query.tab === 'audit') return 'audit'
+    if (route.query.tab === 'operations') return 'operations'
+    return 'runtime'
+  },
+  set: (value: string) => {
+    void router.replace({ query: value === 'runtime' ? {} : { tab: value } })
+  },
+})
+</script>
+
+<template>
+  <div class="runtime-logs">
+    <header class="page-header">
+      <div>
+        <h2>运行日志</h2>
+        <p>查看系统运行、训练任务、审计记录和操作诊断信息。</p>
+      </div>
+    </header>
+
+    <el-tabs v-model="activeTab">
+      <el-tab-pane label="运行日志" name="runtime">
+        <LogsView v-if="activeTab === 'runtime'" />
+      </el-tab-pane>
+      <el-tab-pane v-if="auth.isAdmin" label="审计记录" name="audit">
+        <AuditLogView v-if="activeTab === 'audit'" />
+      </el-tab-pane>
+      <el-tab-pane v-if="auth.isAdmin" label="操作诊断" name="operations">
+        <OperationLogView v-if="activeTab === 'operations'" />
+      </el-tab-pane>
+    </el-tabs>
+  </div>
+</template>
+
+<style scoped lang="scss">
+.runtime-logs {
+  min-height: 100%;
+  padding: 20px;
+}
+
+.page-header {
+  margin-bottom: 4px;
+
+  h2 {
+    margin: 0;
+    color: #1f2937;
+    font-size: 22px;
+  }
+
+  p {
+    margin: 6px 0 0;
+    color: #64748b;
+    font-size: 13px;
+  }
+}
+
+:deep(.page) {
+  padding: 16px 0 0;
+}
+
+:deep(.page-header .page-title) {
+  display: none;
+}
+</style>

frontend/src/views/system/TrainingLogView.vue

@@ -8,10 +8,9 @@ import TrainingTaskOverview from './training-log/TrainingTaskOverview.vue'
 import { usePolling } from '@/composables/usePolling'
 import '@/plugins/echarts-training-log'
 import { useModelsStore } from '@/stores/models'
-import { getFineTune, getFineTuneDiagnostics, getFineTuneLogs, getFineTuneMetrics, type TrainingDiagnostic } from '@/api/modules/fineTune'
+import { getFineTune, getFineTuneDiagnostics, getFineTuneGpuStatus, getFineTuneLogs, getFineTuneMetrics, type TrainingDiagnostic } from '@/api/modules/fineTune'
 import { getTrainingLogFiles, getTrainingLogContent } from '@/api/modules/log'
 import { getDataset } from '@/api/modules/dataset'
-import { getSystemInfo } from '@/api/modules/system'
 import { TRAIN_TYPE_MAP, TRAIN_METHOD_MAP } from '@/constants'
 import {
   buildMetricChartOption,
@@ -205,15 +204,20 @@ async function loadDataset(datasetId: string | number) {
   }
 }
 
-async function loadGpuStatus() {
+async function loadGpuStatus(currentTask: FineTuneTask) {
   try {
-    const systemInfo = await getSystemInfo()
+    const live = await getFineTuneGpuStatus(currentTask.id)
-    gpuPool.value = systemInfo.gpu ?? []
+    if (live.source === 'compute' && live.items.length) {
+      gpuPool.value = live.items as unknown as GpuInfo[]
       gpuUpdatedAt.value = new Date()
       gpuLoadError.value = ''
+      return
+    }
+    gpuPool.value = []
+    gpuLoadError.value = live.error || 'Compute 节点暂未返回实时 GPU 指标'
   } catch {
     gpuLoadError.value = 'GPU 监控数据暂时不可用'
-    if (!gpuUpdatedAt.value) gpuPool.value = []
+    gpuPool.value = []
   }
 }
 
@@ -306,7 +310,7 @@ async function refreshAll() {
     const datasetPromise = currentTask.train_dataset_id
       ? loadDataset(currentTask.train_dataset_id)
       : Promise.resolve()
-    await Promise.all([datasetPromise, loadLog(currentTask), loadGpuStatus(), loadDiagnostics(currentTask)])
+    await Promise.all([datasetPromise, loadLog(currentTask), loadGpuStatus(currentTask), loadDiagnostics(currentTask)])
     await loadMetrics(currentTask)
   } finally {
     loading.value = false

frontend/src/views/system/UserCreateView.vue

@@ -12,7 +12,7 @@ const form = reactive<CreateUserPayload>({
   username: '',
   display_name: '',
   password: 'platform123',
-  role: 'user',
+  role: 'operator',
   status: 'active',
   permissions: [],
 })
@@ -45,7 +45,7 @@ async function submit() {
       <el-form-item label="角色">
         <el-select v-model="form.role" style="width: 100%">
           <el-option label="管理员" value="admin" />
-          <el-option label="普通用户" value="user" />
+          <el-option label="普通用户" value="operator" />
         </el-select>
       </el-form-item>
       <el-form-item label="状态">