185 lines
6.4 KiB
Python
185 lines
6.4 KiB
Python
"""置信度评分系统 — 财务Bot分析结论管理"""
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from fastapi import APIRouter, Depends, HTTPException, Query
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from sqlalchemy.orm import Session
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from sqlalchemy import func
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from app.database import get_db
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from app.auth_middleware import require_auth
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from app.models import AnalysisResult, KPIValue, KPIDefinition
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from typing import Optional, List
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from datetime import datetime
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router = APIRouter(prefix="/api/cma/analysis", tags=["置信度评分"],
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dependencies=[Depends(require_auth)],
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)
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def _calc_confidence(has_actual: bool, has_target: bool, has_trend: bool, has_review: bool) -> int:
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"""基于数据完整度自动计算置信度"""
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if has_review:
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return 95
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if has_actual and has_target and has_trend:
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return 85
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if has_actual and has_target:
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return 70
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if has_actual:
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return 50
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return 30 # 无实际值，基于推测
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@router.get("/result")
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async def get_analysis_results(
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period: Optional[str] = Query(None, description="期间 YYYY-MM"),
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kpi_code: Optional[str] = Query(None, description="KPI编码"),
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db: Session = Depends(get_db),
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):
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"""查询分析结论（按期间和/或KPI编码过滤）"""
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query = db.query(AnalysisResult).order_by(AnalysisResult.created_at.desc())
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if period:
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query = query.filter(AnalysisResult.period == period)
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if kpi_code:
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query = query.filter(AnalysisResult.kpi_code == kpi_code)
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results = query.all()
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return {
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"total": len(results),
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"period": period,
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"results": [
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{
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"id": r.id,
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"period": r.period,
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"结论": r.conclusion,
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"置信度": f"{r.confidence}%",
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"数据来源": r.data_source,
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"计算逻辑": r.calculation_logic,
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"可比基准": r.comparable_benchmark,
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"局限": r.limitations,
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"kpi_code": r.kpi_code,
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"kpi_name": r.kpi_name,
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"has_actual": bool(r.has_actual),
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"has_target": bool(r.has_target),
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"has_trend": bool(r.has_trend),
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"has_review": bool(r.has_review),
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"created_at": r.created_at.strftime("%Y-%m-%d %H:%M:%S") if r.created_at else None,
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}
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for r in results
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],
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}
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@router.post("/result")
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async def create_analysis_result(
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period: str = Query(..., description="期间 YYYY-MM"),
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conclusion: str = Query(..., description="分析结论"),
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data_source: Optional[str] = Query(None, description="数据来源"),
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calculation_logic: Optional[str] = Query(None, description="计算逻辑"),
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comparable_benchmark: Optional[str] = Query(None, description="可比基准"),
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limitations: Optional[str] = Query(None, description="局限说明"),
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kpi_code: Optional[str] = Query(None, description="关联KPI编码"),
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kpi_name: Optional[str] = Query(None, description="关联KPI名称"),
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has_actual: bool = Query(False, description="有实际值"),
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has_target: bool = Query(False, description="有目标值"),
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has_trend: bool = Query(False, description="有历史趋势"),
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has_review: bool = Query(False, description="有人工复核"),
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db: Session = Depends(get_db),
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):
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"""提交分析结果（自动计算置信度）"""
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confidence = _calc_confidence(has_actual, has_target, has_trend, has_review)
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result = AnalysisResult(
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period=period,
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conclusion=conclusion,
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confidence=confidence,
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data_source=data_source,
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calculation_logic=calculation_logic,
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comparable_benchmark=comparable_benchmark,
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limitations=limitations,
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kpi_code=kpi_code,
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kpi_name=kpi_name,
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has_actual=1 if has_actual else 0,
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has_target=1 if has_target else 0,
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has_trend=1 if has_trend else 0,
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has_review=1 if has_review else 0,
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)
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db.add(result)
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db.commit()
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db.refresh(result)
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return {
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"id": result.id,
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"period": result.period,
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"结论": result.conclusion,
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"置信度": f"{result.confidence}%",
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"数据来源": result.data_source,
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"计算逻辑": result.calculation_logic,
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"可比基准": result.comparable_benchmark,
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"局限": result.limitations,
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"confidence_score": result.confidence,
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"message": "分析结论已保存",
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}
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@router.delete("/result/{result_id}")
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async def delete_analysis_result(
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result_id: int,
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db: Session = Depends(get_db),
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):
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"""删除分析结论"""
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result = db.query(AnalysisResult).filter(AnalysisResult.id == result_id).first()
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if not result:
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raise HTTPException(404, "分析结论不存在")
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db.delete(result)
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db.commit()
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return {"message": "已删除"}
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@router.post("/auto-calculate")
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async def auto_calculate_confidence(
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period: str = Query(..., description="期间 YYYY-MM"),
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kpi_code: str = Query(..., description="KPI编码"),
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db: Session = Depends(get_db),
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):
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"""根据KPI数据完整性自动生成置信度评分"""
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# 查找KPI定义
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kpi = db.query(KPIDefinition).filter(KPIDefinition.kpi_code == kpi_code).first()
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if not kpi:
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raise HTTPException(404, f"KPI编码 {kpi_code} 不存在")
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# 查找该期间的实际值
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value = db.query(KPIValue).filter(
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KPIValue.kpi_id == kpi.id,
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KPIValue.period == period,
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).first()
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# 查找历史数据（趋势）
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trend_values = db.query(KPIValue).filter(
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KPIValue.kpi_id == kpi.id,
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).order_by(KPIValue.period.desc()).limit(6).all()
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has_actual = value is not None and value.actual_value is not None
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has_target = kpi.target_value is not None
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has_trend = len(trend_values) >= 2
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has_review = False
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confidence = _calc_confidence(has_actual, has_target, has_trend, has_review)
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return {
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"kpi_code": kpi_code,
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"kpi_name": kpi.kpi_name,
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"period": period,
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"has_actual": has_actual,
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"has_target": has_target,
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"has_trend": has_trend,
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"has_review": has_review,
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"confidence": confidence,
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"confidence_label": f"{confidence}%",
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"数据完备度": {
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"10%": "无数据",
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"50%": "有实际值",
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"70%": "有实际值+目标值",
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"85%": "有实际值+目标值+历史趋势",
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"95%": "有全部数据+人工复核",
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}.get(str(confidence), "基于推测"),
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}
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