"""告警归因引擎 — 管理会计OS (P1-③ 2026-08-28) 告警从"差多少"到"差在哪+怎么办"： - 子KPI维度拆解: 查 kpi_hierarchy 下级KPI各自差异（量差方向） - 科目明细拆解: 查 kpi_subject_map → voucher_details 汇总科目发生额（价差方向） - 趋势归因: 复用 deviation_engine.check_trend_anomaly 连续3期检测 - 场景建议: 按 alert_type 联查 scenario_suggestions attribution JSON 结构: { "dimensions": [{"kpi_id":1,"kpi_name":"销售费用","deviation_value":-3.2,"deviation_rate":-18.6,"weight":0.5}], "subjects": [{"subject_code":"6601","subject_name":"销售费用","amount_diff":2.1,"share_pct":34.5}], "variance_type": "quantity_diff|price_diff|mixed", "trend": {"anomaly":true,"type":"continuous_decline","periods":["2026-06","2026-07","2026-08"],"message":"连续3期下滑"} } """ import logging from typing import Optional from sqlalchemy.orm import Session from sqlalchemy import func from app.models import KPIHierarchy, KPISubjectMap, VoucherDetail, ScenarioSuggestion, KPIDefinition logger = logging.getLogger("cma.alert_attribution") # 收入型KPI特征（量差方向: 子KPI量级偏离） REVENUE_TYPE_CODES = ( "SALES_TOTAL", "REVENUE", "F_REVENUE", "SALES_PROFIT_RATE", "CUSTOMER_COUNT", "NEW_CUSTOMER", "TURNOVER_RATE", ) def build_dimension_attribution(db: Session, kpi_id: int, period: str) -> list: """子KPI维度拆解 — 查 kpi_hierarchy 下级KPI各自差异（实际vs预算）""" from app.models import KPIValue, BudgetPlan children = db.query(KPIHierarchy).filter( KPIHierarchy.parent_kpi_id == kpi_id ).all() if not children: return [] result = [] for rel in children: child_id = rel.child_kpi_id actual = db.query(KPIValue).filter( KPIValue.kpi_id == child_id, KPIValue.period == period, KPIValue.actual_value.isnot(None), ).order_by(KPIValue.calculated_at.desc()).first() budget = db.query(BudgetPlan).filter( BudgetPlan.kpi_id == child_id, BudgetPlan.period == period, BudgetPlan.status == "active", ).order_by(BudgetPlan.updated_at.desc()).first() av = actual.actual_value if actual else None bv = budget.budget_value if budget else None dev_value = None dev_rate = None if av is not None and bv is not None and bv != 0: dev_value = round(av - bv, 2) dev_rate = round(dev_value / bv * 100, 2) child_kpi = db.query(KPIDefinition).filter(KPIDefinition.id == child_id).first() result.append({ "kpi_id": child_id, "kpi_name": child_kpi.kpi_name if child_kpi else f"KPI-{child_id}", "actual_value": av, "budget_value": bv, "deviation_value": dev_value, "deviation_rate": dev_rate, "weight": float(rel.weight or 0), }) # 按偏差绝对值降序，最异常的排前面 result.sort(key=lambda x: -(abs(x["deviation_value"]) if x["deviation_value"] is not None else 0)) return result def build_subject_attribution(db: Session, kpi_id: int, period: str) -> list: """科目明细拆解 — 查 kpi_subject_map → voucher_details 汇总科目发生额""" mappings = db.query(KPISubjectMap).filter(KPISubjectMap.kpi_id == kpi_id).all() if not mappings: return [] result = [] for m in mappings: q = db.query( func.coalesce(func.sum(VoucherDetail.debit_amount), 0), func.coalesce(func.sum(VoucherDetail.credit_amount), 0), ).filter( VoucherDetail.subject_code == m.subject_code, VoucherDetail.period == period, ) row = q.first() debit_sum = float(row[0] or 0) credit_sum = float(row[1] or 0) # 方向: credit贷方(收入/流入) / debit借方(费用/流出) if m.calc_type == "ratio": amount = credit_sum - debit_sum elif m.calc_type in ("avg", "other"): amount = (credit_sum - debit_sum) / 2 else: # sum amount = credit_sum - debit_sum amount = round(amount * float(m.weight or 1.0), 2) result.append({ "subject_code": m.subject_code, "subject_name": m.remark or m.subject_code, "amount_diff": amount, "calc_type": m.calc_type, "weight": float(m.weight or 1.0), }) total = sum(abs(r["amount_diff"]) for r in result) or 0 for r in result: r["share_pct"] = round(abs(r["amount_diff"]) / total * 100, 1) if total else 0 result.sort(key=lambda x: -abs(x["amount_diff"])) return result def detect_variance_type(kpi_code: str, dimensions: list, subjects: list) -> str: """量价差判定简化版： 成本型KPI科目发生额偏离 → price_diff（价差） 收入型KPI子KPI量级偏离 → quantity_diff（量差） 两者都有 → mixed """ has_dimension_dev = any(d.get("deviation_value") is not None and abs(d["deviation_value"]) > 0.01 for d in dimensions) has_subject_dev = any(abs(s.get("amount_diff", 0)) > 0.01 for s in subjects) is_revenue = any(code in (kpi_code or "").upper() for code in REVENUE_TYPE_CODES) if is_revenue: # 收入型: 子KPI(量)偏离为主 → quantity_diff if has_dimension_dev: return "quantity_diff" if has_subject_dev: return "price_diff" return "mixed" else: # 成本型: 科目发生额(价)偏离为主 → price_diff if has_subject_dev: return "price_diff" if has_dimension_dev: return "quantity_diff" return "mixed" def match_scenario(db: Session, alert_type: Optional[str]) -> Optional[dict]: """按 alert_type 取 scenario_suggestions 建议（四类模板）""" if not alert_type: return None s = db.query(ScenarioSuggestion).filter( ScenarioSuggestion.alert_type == alert_type ).order_by(ScenarioSuggestion.sort_order.asc(), ScenarioSuggestion.id.asc()).first() if not s: return None return { "scenario_id": s.id, "alert_type": s.alert_type, "title": s.title, "description": s.description, "action_template": s.action_template, "priority": s.priority, } def build_attribution(db: Session, kpi_id: int, period: str, alert_type: Optional[str] = None) -> dict: """组装完整归因JSON（供告警生成/详情接口共用）""" from app.utils.deviation_engine import check_trend_anomaly kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first() kpi_code = kpi.kpi_code if kpi else "" dimensions = build_dimension_attribution(db, kpi_id, period) subjects = build_subject_attribution(db, kpi_id, period) variance_type = detect_variance_type(kpi_code, dimensions, subjects) trend = check_trend_anomaly(db, kpi_id, period, consecutive=3) # 默认场景归类（未显式传入时按KPI名称特征推断） if not alert_type: alert_type = infer_alert_type(kpi_code, kpi.kpi_name if kpi else "") attribution = { "dimensions": dimensions, "subjects": subjects, "variance_type": variance_type, "trend": trend, } return attribution, alert_type def infer_alert_type(kpi_code: str = "", kpi_name: str = "") -> str: """按KPI特征推断告警场景类型（四类: cash_low/cash_critical/cost_high/revenue_drop）""" text = (kpi_code or "").upper() + (kpi_name or "") if any(k in text for k in ("CASH", "现金", "货币资金", "资金")): return "cash_low" if any(k in text for k in ("COST", "费用", "成本", "支出")): return "cost_high" if any(k in text for k in ("REVENUE", "收入", "销售", "营收")): return "revenue_drop" return "cost_high"