feat: 路线图R1决策建议一键落地+R2机会推送+R5预算闭环
R1(P0): AI建议一键应用到KPI/预算/行动方案
- 新表 ai_suggestions + AISuggestion 模型(init_db自动建)
- /api/cma/ai/suggestions CRUD + /{id}/apply(复用kpis/budget/action_plans) + dismiss
- 应用写 OperationLog(action=ai_suggestion_apply, detail含suggestion_id/before/after)
- 规则驱动建议生成 generate_rule_suggestions(低执行率/高执行率/预算超支/pending预警)
- 幂等: 同entity+type+target_id+title+unapplied不重复建; applied后拒绝重复应用
- 前端: Dashboard AI面板建议卡(应用到/忽略) + 建议中心页 /ai-suggestions
R2(P1): 数据找人扩大-机会类推送
- scripts/opportunity_detector.py: KPI向好(执行率>110%)/预算余量(<70%且actual>0)/预测上行
- scripts/daily_push.py: 异常+机会 每日9:15推企微(8800/send, --dry-run调试)
- crontab: 15 9 * * * (alert_generator 9:00之后)
R5(P0): 预算闭环加固
- auto-decompose批量幂等: 只取年度行(period=YYYY-00)+同KPI多版本取一行
- scripts/closed_loop_check.py: 预算执行率异常→检查现金流/行动同步→缺失提示+报告
- scripts/verify_decompose_idempotent.py: 幂等验证脚本
测试: test_ai_suggestions(10例)+test_roadmap_r2r5(14例); 修test_budget幂等契约适配年度行
全量: 673 passed
This commit is contained in:
Hermes CI Fix
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"""机会检测器 — 路线图R2 数据找人扩大 (2026-08-30)
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北极星③：主动推送扩大 —— 异常 + 机会两类。
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本脚本检测三类机会（复用 budget/kpi 数据，不新建表）：
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1. KPI向好 (kpi_improving) : 最近3期执行率>110% 且最新期呈上升趋势
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2. 预算余量 (budget_headroom): 可用预算>30%（预算执行率<70%）
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3. 滚动机会 (rolling_up) : 预测值上升（kpi_forecast_log 最新>上期）
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输出：机会列表 [{type, title, detail, kpi_id, kpi_name, period}]
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"""
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import sys
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import os
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import json
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from datetime import datetime
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sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from app.database import get_session_local
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from app.models import KPIDefinition, KPIValue, BudgetPlan, KpiForecastLog
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HIGH_RATIO = 1.1 # 执行率>110% = 超预期
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LOW_EXEC_RATIO = 0.7 # 执行率<70% = 预算余量大（可用>30%）
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def _exec_ratio(actual, target):
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if target is None or target == 0:
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return None
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return actual / target
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def detect_kpi_improving(db, entity_id: int, min_ratio: float = HIGH_RATIO) -> list:
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"""KPI向好：最近3期执行率均>110%，且最新期>上期（上升中）"""
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out = []
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kpis = db.query(KPIDefinition).filter(
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KPIDefinition.entity_id == entity_id,
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KPIDefinition.status == "active",
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).all()
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now = datetime.now()
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for k in kpis:
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if not k.target_value or k.target_value <= 0:
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continue
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vals = db.query(KPIValue).filter(
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KPIValue.kpi_id == k.id,
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KPIValue.actual_value.isnot(None),
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).order_by(KPIValue.period.desc()).limit(3).all()
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if len(vals) < 3:
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continue
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ratios = [_exec_ratio(v.actual_value, k.target_value) for v in vals]
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if any(r is None or r < min_ratio for r in ratios):
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continue
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# 最新期 > 上期（上升趋势）；若最新期低于上期但整体仍>110%，也算（持续向好）
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latest, prev = vals[0], vals[1]
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trend = "上升" if latest.actual_value > prev.actual_value else "高位"
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out.append({
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"type": "kpi_improving",
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"title": f"📈 {k.kpi_name} 持续向好",
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"detail": (f"{latest.period}实际{latest.actual_value:g}/目标{k.target_value:g}"
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f" 达成率{ratios[0]*100:.0f}%({trend})，近3期均超110%"),
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"kpi_id": k.id,
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"kpi_name": k.kpi_name,
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"period": latest.period,
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})
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return out
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def detect_budget_headroom(db, entity_id: int, max_ratio: float = LOW_EXEC_RATIO) -> list:
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"""预算余量：当月预算执行率<70%（可用预算>30%）
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注意：跳过实际值为负的行（现金流/利润为负是异常不是余量），
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同 KPI 同期间多版本预算只取一条（去重）。
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"""
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out = []
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period = datetime.now().strftime("%Y-%m")
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rows = db.query(BudgetPlan).filter(
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BudgetPlan.entity_id == entity_id,
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BudgetPlan.status == "active",
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BudgetPlan.period == period,
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BudgetPlan.budget_value > 0,
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).all()
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seen = set()
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for b in rows:
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key = (b.kpi_id, b.period)
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if key in seen:
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continue
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seen.add(key)
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actual = db.query(KPIValue).filter(
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KPIValue.kpi_id == b.kpi_id,
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KPIValue.period == b.period,
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KPIValue.actual_value.isnot(None),
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).order_by(KPIValue.calculated_at.desc()).first()
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if not actual or actual.actual_value is None or actual.actual_value <= 0:
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continue
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ratio = actual.actual_value / b.budget_value
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if ratio < max_ratio:
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k = db.query(KPIDefinition).filter(KPIDefinition.id == b.kpi_id).first()
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kpi_name = k.kpi_name if k else f"KPI#{b.kpi_id}"
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headroom = (1 - ratio) * 100
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out.append({
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"type": "budget_headroom",
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"title": f"💼 {kpi_name} 预算余量充足",
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"detail": (f"{period}预算{b.budget_value:g}/实际{actual.actual_value:g}"
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f" 执行率{ratio*100:.0f}%，可用预算余量约{headroom:.0f}%"),
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"kpi_id": b.kpi_id,
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"kpi_name": kpi_name,
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"period": period,
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})
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return out
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def detect_rolling_up(db, entity_id: int) -> list:
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"""滚动机会：预测值上升（最新预测 > 上期预测）"""
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out = []
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# 每个KPI取最近两条预测记录
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kpi_ids = [r[0] for r in db.query(KpiForecastLog.kpi_id).filter(
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KpiForecastLog.entity_id == entity_id).distinct().limit(50).all()]
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for kid in kpi_ids:
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rows = db.query(KpiForecastLog).filter(
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KpiForecastLog.entity_id == entity_id,
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KpiForecastLog.kpi_id == kid,
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KpiForecastLog.forecast_value.isnot(None),
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).order_by(KpiForecastLog.created_at.desc(), KpiForecastLog.id.desc()).limit(2).all()
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if len(rows) < 2:
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continue
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latest, prev = rows[0], rows[1]
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if latest.forecast_value > prev.forecast_value:
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k = db.query(KPIDefinition).filter(KPIDefinition.id == kid).first()
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kpi_name = k.kpi_name if k else f"KPI#{kid}"
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pct = (latest.forecast_value / prev.forecast_value - 1) * 100 if prev.forecast_value else 0
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out.append({
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"type": "rolling_up",
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"title": f"🔮 {kpi_name} 预测上行",
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"detail": (f"预测值 {prev.forecast_value:g} → {latest.forecast_value:g}"
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f" (+{pct:.1f}%)，{latest.period}期间"),
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"kpi_id": kid,
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"kpi_name": kpi_name,
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"period": latest.period,
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})
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return out
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def detect_all(db, entity_id: int = 1) -> dict:
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"""检测全部机会，按类型分组"""
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return {
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"kpi_improving": detect_kpi_improving(db, entity_id),
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"budget_headroom": detect_budget_headroom(db, entity_id),
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"rolling_up": detect_rolling_up(db, entity_id),
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}
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def flatten(detected: dict) -> list:
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out = []
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for cat in ("kpi_improving", "budget_headroom", "rolling_up"):
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out.extend(detected.get(cat, []))
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return out
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def main():
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db = get_session_local()()
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try:
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detected = detect_all(db)
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total = sum(len(v) for v in detected.values())
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print(json.dumps(detected, ensure_ascii=False, indent=2))
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print(f"\n机会总数: {total}")
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finally:
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db.close()
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if __name__ == "__main__":
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main()
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