feat: 新增风险图谱算法与系统仪表盘及操作反馈体系

后端新增风险图谱算法模块、风险观察与反馈服务、规则 DSL
校验器和可解释性引擎，完善系统仪表盘和财务仪表盘统计，
优化 agent 运行和编排执行链路，清理旧开发文档，前端新增
系统趋势、负载热力图等多种仪表盘图表组件，完善操作反馈
对话框和工作台日期选择器，优化报销创建和审批详情交互，
补充单元测试覆盖。

server/src/app/services/agent_feedback.py

@@ -0,0 +1,112 @@
+from __future__ import annotations
+
+from typing import Any
+
+from sqlalchemy import select
+from sqlalchemy.orm import Session
+
+from app.db.base import Base
+from app.models.agent_feedback import AgentOperationFeedback
+from app.schemas.agent_feedback import (
+    AgentFeedbackCreate,
+    AgentFeedbackRead,
+    AgentFeedbackSummaryRead,
+)
+
+LOW_RATING_MAX = 3
+
+
+class AgentFeedbackService:
+    def __init__(self, db: Session) -> None:
+        self.db = db
+
+    def ensure_storage_ready(self) -> None:
+        Base.metadata.create_all(bind=self.db.get_bind(), tables=[AgentOperationFeedback.__table__])
+
+    def create_feedback(self, payload: AgentFeedbackCreate) -> AgentFeedbackRead:
+        self.ensure_storage_ready()
+        feedback = AgentOperationFeedback(
+            run_id=payload.run_id,
+            conversation_id=payload.conversation_id,
+            user_id=payload.user_id,
+            agent=payload.agent or "",
+            source=payload.source or "",
+            session_type=payload.session_type or "",
+            operation_type=payload.operation_type or "assistant_round",
+            operation_status=payload.operation_status or "",
+            rating=int(payload.rating),
+            reason=self._normalize_reason(payload.reason),
+            context_json=self._normalize_context(payload.context_json),
+        )
+        self.db.add(feedback)
+        self.db.commit()
+        self.db.refresh(feedback)
+        return AgentFeedbackRead.model_validate(feedback)
+
+    def summarize_feedback(
+        self,
+        *,
+        agent: str | None = None,
+        session_type: str | None = None,
+        limit: int = 200,
+    ) -> AgentFeedbackSummaryRead:
+        self.ensure_storage_ready()
+        stmt = select(AgentOperationFeedback).order_by(AgentOperationFeedback.created_at.desc()).limit(limit)
+        if agent:
+            stmt = stmt.where(AgentOperationFeedback.agent == agent)
+        if session_type:
+            stmt = stmt.where(AgentOperationFeedback.session_type == session_type)
+
+        feedback_items = list(self.db.scalars(stmt).all())
+        rating_distribution = {str(score): 0 for score in range(1, 6)}
+        agents: dict[str, int] = {}
+        session_types: dict[str, int] = {}
+        low_feedback: list[dict[str, Any]] = []
+        total_rating = 0
+
+        for item in feedback_items:
+            rating = max(1, min(int(item.rating or 0), 5))
+            total_rating += rating
+            rating_distribution[str(rating)] = rating_distribution.get(str(rating), 0) + 1
+            if item.agent:
+                agents[item.agent] = agents.get(item.agent, 0) + 1
+            if item.session_type:
+                session_types[item.session_type] = session_types.get(item.session_type, 0) + 1
+            if rating <= LOW_RATING_MAX:
+                low_feedback.append(
+                    {
+                        "feedback_id": item.feedback_id,
+                        "run_id": item.run_id,
+                        "conversation_id": item.conversation_id,
+                        "user_id": item.user_id,
+                        "agent": item.agent,
+                        "session_type": item.session_type,
+                        "rating": rating,
+                        "reason": item.reason,
+                        "created_at": item.created_at,
+                    }
+                )
+
+        total_feedback = len(feedback_items)
+        average_rating = round(total_rating / total_feedback, 2) if total_feedback else 0.0
+        return AgentFeedbackSummaryRead(
+            window_limit=limit,
+            total_feedback=total_feedback,
+            average_rating=average_rating,
+            low_rating_count=len(low_feedback),
+            rating_distribution=rating_distribution,
+            agents=agents,
+            session_types=session_types,
+            recent_low_feedback=low_feedback[:10],
+        )
+
+    @staticmethod
+    def _normalize_reason(value: str | None) -> str | None:
+        normalized = str(value or "").strip()
+        return normalized[:1000] if normalized else None
+
+    @staticmethod
+    def _normalize_context(value: dict[str, Any] | None) -> dict[str, Any]:
+        if not isinstance(value, dict):
+            return {}
+        return value