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/schemas/agent_asset.py

@@ -124,6 +124,28 @@ class AgentAssetRiskRuleGenerateRequest(BaseModel):
     requires_attachment: bool = False
 
 
+class AgentAssetRiskRuleDraftUpdate(BaseModel):
+    rule_title: str | None = Field(default=None, min_length=2, max_length=80)
+    expense_category: str | None = Field(default=None, max_length=40)
+    natural_language: str | None = Field(default=None, min_length=8, max_length=2000)
+    requires_attachment: bool | None = None
+
+
+class AgentAssetRiskRuleRevisionCreate(BaseModel):
+    rule_title: str | None = Field(default=None, min_length=2, max_length=80)
+    expense_category: str | None = Field(default=None, max_length=40)
+    natural_language: str | None = Field(default=None, min_length=8, max_length=2000)
+    requires_attachment: bool | None = None
+    change_reason: str = Field(min_length=1, max_length=1000)
+
+
+class AgentAssetRiskRuleRegenerateRequest(BaseModel):
+    rule_title: str | None = Field(default=None, min_length=2, max_length=80)
+    expense_category: str | None = Field(default=None, max_length=40)
+    natural_language: str | None = Field(default=None, min_length=8, max_length=2000)
+    requires_attachment: bool | None = None
+
+
 class AgentAssetRiskRuleSampleCase(BaseModel):
     case_id: str | None = Field(default=None, max_length=60)
     name: str = Field(default="测试样例", min_length=1, max_length=80)
@@ -184,7 +206,9 @@ class AgentAssetRiskRuleSimulationRead(BaseModel):
     blocking_reason: str = ""
     message: str = ""
     field_values: dict[str, Any] = Field(default_factory=dict)
+    normalized_fields: dict[str, Any] = Field(default_factory=dict)
     evidence: dict[str, Any] = Field(default_factory=dict)
+    trace: dict[str, Any] = Field(default_factory=dict)
     attachments: list[dict[str, Any]] = Field(default_factory=list)
     recognized_fields: list[dict[str, Any]] = Field(default_factory=list)
     missing_fields: list[dict[str, Any]] = Field(default_factory=list)

server/src/app/schemas/agent_feedback.py

@@ -0,0 +1,75 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator
+
+
+def _blank_to_none(value: Any) -> Any:
+    if value is None:
+        return None
+    if isinstance(value, str):
+        normalized = value.strip()
+        return normalized or None
+    return value
+
+
+class AgentFeedbackCreate(BaseModel):
+    run_id: str | None = Field(default=None, max_length=50)
+    conversation_id: str | None = Field(default=None, max_length=50)
+    user_id: str | None = Field(default=None, max_length=100)
+    agent: str | None = Field(default=None, max_length=30)
+    source: str | None = Field(default=None, max_length=30)
+    session_type: str | None = Field(default=None, max_length=30)
+    operation_type: str | None = Field(default="assistant_round", max_length=50)
+    operation_status: str | None = Field(default=None, max_length=20)
+    rating: int = Field(ge=1, le=5)
+    reason: str | None = Field(default=None, max_length=1000)
+    context_json: dict[str, Any] = Field(default_factory=dict)
+
+    @field_validator(
+        "run_id",
+        "conversation_id",
+        "user_id",
+        "agent",
+        "source",
+        "session_type",
+        "operation_type",
+        "operation_status",
+        "reason",
+        mode="before",
+    )
+    @classmethod
+    def normalize_optional_text(cls, value: Any) -> Any:
+        return _blank_to_none(value)
+
+
+class AgentFeedbackRead(BaseModel):
+    model_config = ConfigDict(from_attributes=True)
+
+    id: str
+    feedback_id: str
+    run_id: str | None
+    conversation_id: str | None
+    user_id: str | None
+    agent: str
+    source: str
+    session_type: str
+    operation_type: str
+    operation_status: str
+    rating: int
+    reason: str | None
+    context_json: dict[str, Any]
+    created_at: datetime
+
+
+class AgentFeedbackSummaryRead(BaseModel):
+    window_limit: int
+    total_feedback: int
+    average_rating: float
+    low_rating_count: int
+    rating_distribution: dict[str, int] = Field(default_factory=dict)
+    agents: dict[str, int] = Field(default_factory=dict)
+    session_types: dict[str, int] = Field(default_factory=dict)
+    recent_low_feedback: list[dict[str, Any]] = Field(default_factory=list)

server/src/app/schemas/agent_run.py

@@ -59,3 +59,21 @@ class AgentRunRead(BaseModel):
     finished_at: datetime | None
     tool_calls: list[AgentToolCallRead] = Field(default_factory=list)
     semantic_parse: SemanticParseRead | None = None
+
+
+class AgentRunStatsRead(BaseModel):
+    window_limit: int
+    total_runs: int
+    succeeded_runs: int
+    blocked_runs: int
+    failed_runs: int
+    tool_call_count: int
+    failed_tool_call_count: int
+    llm_call_count: int
+    failed_llm_call_count: int
+    model_fallback_count: int
+    model_guardrail_count: int
+    agents: dict[str, int] = Field(default_factory=dict)
+    statuses: dict[str, int] = Field(default_factory=dict)
+    tool_statuses: dict[str, int] = Field(default_factory=dict)
+    recent_errors: list[dict[str, Any]] = Field(default_factory=list)

server/src/app/schemas/auth.py

@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from datetime import datetime
 from typing import Any
 
 from pydantic import BaseModel, EmailStr, Field
@@ -34,3 +35,18 @@ class LoginResponse(BaseModel):
     ok: bool = True
     detail: str = "登录成功。"
     user: AuthUserRead
+    sessionId: str = ""
+
+
+class SessionFinishRequest(BaseModel):
+    reason: str = Field(default="manual", max_length=40)
+    lastActivityAt: datetime | None = None
+    activityEventCount: int = Field(default=0, ge=0)
+    pagePath: str = Field(default="", max_length=512)
+
+
+class SessionFinishResponse(BaseModel):
+    ok: bool = True
+    detail: str = "会话已结算。"
+    sessionId: str = ""
+    durationMs: int = 0

server/src/app/schemas/finance_dashboard.py

@@ -0,0 +1,21 @@
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class FinanceDashboardRead(BaseModel):
+    range_key: str
+    start_date: str
+    end_date: str
+    generated_at: str
+    has_real_data: bool
+    totals: dict[str, Any] = Field(default_factory=dict)
+    metric_meta: dict[str, Any] = Field(default_factory=dict)
+    trend: dict[str, Any] = Field(default_factory=dict)
+    spend_by_category: list[dict[str, Any]] = Field(default_factory=list)
+    exception_mix: list[dict[str, Any]] = Field(default_factory=list)
+    department_ranking: list[dict[str, Any]] = Field(default_factory=list)
+    bottlenecks: list[dict[str, Any]] = Field(default_factory=list)
+    budget_summary: dict[str, Any] = Field(default_factory=dict)

server/src/app/schemas/risk_observation.py

@@ -0,0 +1,145 @@
+from __future__ import annotations
+
+from datetime import datetime
+from typing import Any, Literal
+
+from pydantic import BaseModel, ConfigDict, Field, field_validator
+
+RiskObservationStatus = Literal[
+    "pending_review",
+    "confirmed",
+    "false_positive",
+    "ignored",
+    "resolved",
+]
+
+RiskObservationFeedbackType = Literal[
+    "confirm",
+    "false_positive",
+    "ignore",
+    "resolve",
+    "comment",
+]
+
+
+class RiskObservationFeedbackRead(BaseModel):
+    model_config = ConfigDict(from_attributes=True)
+
+    id: str
+    observation_id: str
+    feedback_type: str
+    action: str
+    actor: str
+    comment: str | None
+    payload_json: dict[str, Any]
+    decision: str = ""
+    candidate_rule_source: str = ""
+    confidence_score: float = 0.0
+    escalation_target: str = ""
+    supplement_required: bool = False
+    created_at: datetime
+
+
+class RiskObservationRead(BaseModel):
+    model_config = ConfigDict(from_attributes=True)
+
+    id: str
+    observation_key: str
+    subject_type: str
+    subject_key: str
+    subject_label: str
+    claim_id: str | None
+    claim_no: str
+    run_id: str | None
+    execution_log_id: str | None
+    risk_type: str
+    risk_signal: str
+    title: str
+    description: str
+    risk_score: int
+    risk_level: str
+    confidence_score: float
+    control_stage: str
+    control_mode: str
+    automation_mode: str
+    source: str
+    algorithm_version: str
+    status: str
+    feedback_status: str
+    contribution_scores_json: dict[str, Any]
+    baseline_json: dict[str, Any]
+    evidence_json: list[Any]
+    graph_node_keys_json: list[Any]
+    graph_edge_keys_json: list[Any]
+    policy_refs_json: list[Any]
+    similar_case_claim_ids_json: list[Any]
+    ontology_json: dict[str, Any]
+    decision_trace_json: dict[str, Any]
+    sampling_strategy: dict[str, Any] = Field(default_factory=dict)
+    evaluation_case_id: str = ""
+    ontology_parse_id: str = ""
+    ontology_version: str = ""
+    domain: str = ""
+    scenario: str = ""
+    intent: str = ""
+    ontology_entities_json: list[Any] = Field(default_factory=list)
+    risk_signals_json: list[Any] = Field(default_factory=list)
+    canonical_subject_key: str = ""
+    created_at: datetime
+    updated_at: datetime
+    feedback_items: list[RiskObservationFeedbackRead] = Field(default_factory=list)
+
+
+class RiskObservationListRead(BaseModel):
+    items: list[RiskObservationRead]
+    total: int
+    limit: int
+    offset: int
+
+
+class RiskObservationFeedbackCreate(BaseModel):
+    feedback_type: RiskObservationFeedbackType
+    action: str | None = Field(default=None, max_length=50)
+    actor: str | None = Field(default=None, max_length=100)
+    comment: str | None = Field(default=None, max_length=1000)
+    payload_json: dict[str, Any] = Field(default_factory=dict)
+
+    @field_validator("action", "actor", "comment", mode="before")
+    @classmethod
+    def normalize_text(cls, value: Any) -> Any:
+        if value is None:
+            return None
+        normalized = str(value).strip()
+        return normalized or None
+
+
+class RiskObservationDashboardRead(BaseModel):
+    window_days: int
+    total_observations: int
+    pending_count: int
+    high_or_above_count: int
+    confirmed_count: int
+    false_positive_count: int
+    total_amount: float = 0.0
+    average_score: float
+    level_distribution: dict[str, int] = Field(default_factory=dict)
+    status_distribution: dict[str, int] = Field(default_factory=dict)
+    signal_distribution: dict[str, int] = Field(default_factory=dict)
+    source_distribution: dict[str, int] = Field(default_factory=dict)
+    automation_distribution: dict[str, int] = Field(default_factory=dict)
+    department_distribution: dict[str, int] = Field(default_factory=dict)
+    expense_type_distribution: dict[str, int] = Field(default_factory=dict)
+    risk_type_distribution: dict[str, int] = Field(default_factory=dict)
+    supplier_distribution: dict[str, int] = Field(default_factory=dict)
+    employee_grade_distribution: dict[str, int] = Field(default_factory=dict)
+    daily_trend: list[dict[str, Any]] = Field(default_factory=list)
+    top_risk_signals: list[dict[str, Any]] = Field(default_factory=list)
+    top_departments: list[dict[str, Any]] = Field(default_factory=list)
+    top_employees: list[dict[str, Any]] = Field(default_factory=list)
+    top_suppliers: list[dict[str, Any]] = Field(default_factory=list)
+    top_expense_types: list[dict[str, Any]] = Field(default_factory=list)
+    top_rules: list[dict[str, Any]] = Field(default_factory=list)
+    candidate_rule_count: int = 0
+    confirmation_rate: float
+    false_positive_rate: float
+    recent_high_observations: list[RiskObservationRead] = Field(default_factory=list)

server/src/app/schemas/system_dashboard.py

@@ -0,0 +1,20 @@
+from __future__ import annotations
+
+from typing import Any
+
+from pydantic import BaseModel, Field
+
+
+class SystemDashboardRead(BaseModel):
+    window_days: int
+    generated_at: str
+    has_real_data: bool
+    totals: dict[str, Any] = Field(default_factory=dict)
+    agent_daily_ratio: dict[str, Any] = Field(default_factory=dict)
+    login_wave: dict[str, Any] = Field(default_factory=dict)
+    token_daily_wave: dict[str, Any] = Field(default_factory=dict)
+    user_token_usage: list[dict[str, Any]] = Field(default_factory=list)
+    accuracy_comparison: dict[str, Any] = Field(default_factory=dict)
+    usage_duration_summary: dict[str, Any] = Field(default_factory=dict)
+    feedback_summary: list[dict[str, Any]] = Field(default_factory=list)
+    tool_detail_rows: list[dict[str, Any]] = Field(default_factory=list)