92444e7eae
- 新增 25+ 条风险规则（预算/报销/申请/通用类），完善风险规则模拟与反馈发布机制 - 引入费用审批动态路由、平台风险分级、预审与风险阶段管理 - 预算中心列表化改造，优化票据夹仪表盘与数字员工工作看板 - 新增 Hermes 风险线索收集器、Agent 链路追踪中心 - 扩展数字员工能力库（18 个领域 Skill）与交通费用自动预估 - 完善报销申请快速预览、权限控制与前端测试覆盖
148 lines
4.6 KiB
Python
148 lines
4.6 KiB
Python
from __future__ import annotations
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from datetime import datetime
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from typing import Any, Literal
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from pydantic import BaseModel, ConfigDict, Field, field_validator
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RiskObservationStatus = Literal[
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"pending_review",
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"confirmed",
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"false_positive",
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"ignored",
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"resolved",
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]
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RiskObservationFeedbackType = Literal[
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"confirm",
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"false_positive",
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"ignore",
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"resolve",
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"comment",
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]
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class RiskObservationFeedbackRead(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: str
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observation_id: str
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feedback_type: str
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action: str
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actor: str
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comment: str | None
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payload_json: dict[str, Any]
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decision: str = ""
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candidate_rule_source: str = ""
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confidence_score: float = 0.0
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escalation_target: str = ""
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supplement_required: bool = False
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created_at: datetime
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class RiskObservationRead(BaseModel):
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model_config = ConfigDict(from_attributes=True)
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id: str
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observation_key: str
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subject_type: str
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subject_key: str
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subject_label: str
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claim_id: str | None
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claim_no: str
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run_id: str | None
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execution_log_id: str | None
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risk_type: str
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risk_signal: str
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title: str
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description: str
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risk_score: int
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risk_level: str
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confidence_score: float
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control_stage: str
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control_mode: str
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automation_mode: str
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source: str
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algorithm_version: str
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status: str
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feedback_status: str
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contribution_scores_json: dict[str, Any]
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baseline_json: dict[str, Any]
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evidence_json: list[Any]
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graph_node_keys_json: list[Any]
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graph_edge_keys_json: list[Any]
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policy_refs_json: list[Any]
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similar_case_claim_ids_json: list[Any]
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ontology_json: dict[str, Any]
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decision_trace_json: dict[str, Any]
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sampling_strategy: dict[str, Any] = Field(default_factory=dict)
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evaluation_case_id: str = ""
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ontology_parse_id: str = ""
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ontology_version: str = ""
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domain: str = ""
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scenario: str = ""
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intent: str = ""
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ontology_entities_json: list[Any] = Field(default_factory=list)
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risk_signals_json: list[Any] = Field(default_factory=list)
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canonical_subject_key: str = ""
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created_at: datetime
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updated_at: datetime
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feedback_items: list[RiskObservationFeedbackRead] = Field(default_factory=list)
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class RiskObservationListRead(BaseModel):
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items: list[RiskObservationRead]
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total: int
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limit: int
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offset: int
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class RiskObservationFeedbackCreate(BaseModel):
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feedback_type: RiskObservationFeedbackType
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action: str | None = Field(default=None, max_length=50)
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actor: str | None = Field(default=None, max_length=100)
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comment: str | None = Field(default=None, max_length=1000)
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payload_json: dict[str, Any] = Field(default_factory=dict)
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@field_validator("action", "actor", "comment", mode="before")
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@classmethod
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def normalize_text(cls, value: Any) -> Any:
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if value is None:
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return None
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normalized = str(value).strip()
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return normalized or None
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class RiskObservationDashboardRead(BaseModel):
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window_days: int
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total_observations: int
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pending_count: int
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risk_clue_count: int = 0
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high_or_above_count: int
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confirmed_count: int
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false_positive_count: int
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feedback_sample_count: int = 0
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total_amount: float = 0.0
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average_score: float
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level_distribution: dict[str, int] = Field(default_factory=dict)
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status_distribution: dict[str, int] = Field(default_factory=dict)
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signal_distribution: dict[str, int] = Field(default_factory=dict)
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source_distribution: dict[str, int] = Field(default_factory=dict)
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automation_distribution: dict[str, int] = Field(default_factory=dict)
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department_distribution: dict[str, int] = Field(default_factory=dict)
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expense_type_distribution: dict[str, int] = Field(default_factory=dict)
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risk_type_distribution: dict[str, int] = Field(default_factory=dict)
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supplier_distribution: dict[str, int] = Field(default_factory=dict)
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employee_grade_distribution: dict[str, int] = Field(default_factory=dict)
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daily_trend: list[dict[str, Any]] = Field(default_factory=list)
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top_risk_signals: list[dict[str, Any]] = Field(default_factory=list)
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top_departments: list[dict[str, Any]] = Field(default_factory=list)
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top_employees: list[dict[str, Any]] = Field(default_factory=list)
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top_suppliers: list[dict[str, Any]] = Field(default_factory=list)
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top_expense_types: list[dict[str, Any]] = Field(default_factory=list)
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top_rules: list[dict[str, Any]] = Field(default_factory=list)
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candidate_rule_count: int = 0
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confirmation_rate: float
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false_positive_rate: float
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recent_high_observations: list[RiskObservationRead] = Field(default_factory=list)
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