Hermes CI Fix
ad68471b29
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
517 lines
21 KiB
Python
517 lines
21 KiB
Python
"""AI分析引擎 — 侧边栏智能分析"""
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from fastapi import APIRouter, Depends, HTTPException, Query, Request
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from fastapi.responses import StreamingResponse
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from sqlalchemy.orm import Session
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from sqlalchemy import func, text as sa_text, or_
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from app.database import get_db
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from app.deps import get_entity_id
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from app.auth_middleware import require_auth, require_role
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from app.models import KPIDefinition, KPIValue, KPIAlert, StrategicMap, User, ActionPlan, BudgetPlan, AISuggestion
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from app.utils.cache import get as cache_get, set as cache_set
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import json, hashlib, httpx, os
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from datetime import datetime, date
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router = APIRouter(prefix="/api/cma/ai", tags=["AI分析"],
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dependencies=[Depends(require_role("ceo", "finance", "business", "it"))],
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)
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# ============================================================
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# R1 决策建议生成（规则驱动，稳定可复现，落库 ai_suggestions）
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# ============================================================
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def _sug_dict(s: AISuggestion) -> dict:
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return {
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"id": s.id,
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"entity_id": s.entity_id,
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"source": s.source,
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"suggestion_type": s.suggestion_type,
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"target_type": s.target_type,
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"target_id": s.target_id,
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"title": s.title,
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"content": s.content,
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"suggestion_data": s.suggestion_data or {},
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"status": s.status,
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"applied_by": s.applied_by,
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"applied_at": s.applied_at.isoformat() if s.applied_at else None,
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"apply_detail": s.apply_detail or [],
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"created_at": s.created_at.isoformat() if s.created_at else None,
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}
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def _existing_unapplied(db: Session, entity_id: int, suggestion_type: str,
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target_id: int, title: str) -> bool:
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"""幂等：同entity+类型+目标+标题的未应用建议存在则跳过"""
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return db.query(AISuggestion).filter(
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AISuggestion.entity_id == entity_id,
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AISuggestion.suggestion_type == suggestion_type,
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AISuggestion.target_id == target_id,
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AISuggestion.title == title,
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AISuggestion.status == "unapplied",
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).first() is not None
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def generate_rule_suggestions(db: Session, entity_id: int,
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source: str = "dashboard", user_id: int = None,
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kpi_id: int = None) -> list:
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"""从数据规则生成决策建议并落库（R1，路线图2026-08-30）
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规则：
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1. KPI执行率<70% → 建议建行动方案（异常类）
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2. KPI执行率>110% → 建议上调KPI目标（机会类）
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3. 预算执行率>110% → 建议调预算（预算类）
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4. 有pending预警 → 建议建行动方案处理预警
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幂等：同 entity+type+target_id+title+status=unapplied 不重复建。
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"""
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now = datetime.now()
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period = now.strftime("%Y-%m")
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created = []
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def _add(suggestion_type: str, target_type: str, tid: int,
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title: str, content: str, suggestion_data: dict):
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nonlocal created
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if _existing_unapplied(db, entity_id, suggestion_type, tid, title):
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return
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sug = AISuggestion(
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entity_id=entity_id,
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user_id=user_id,
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source=source,
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suggestion_type=suggestion_type,
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target_type=target_type,
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target_id=tid,
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title=title,
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content=content,
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suggestion_data=suggestion_data,
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status="unapplied",
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)
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db.add(sug)
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created.append(sug)
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# 查询KPI（可按kpi_id过滤）
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q = db.query(KPIDefinition).filter(KPIDefinition.entity_id == entity_id,
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KPIDefinition.status == "active")
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if kpi_id:
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q = q.filter(KPIDefinition.id == kpi_id)
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kpis = q.all()
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for k in kpis:
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latest = db.query(KPIValue).filter(
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KPIValue.kpi_id == k.id,
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or_(
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KPIValue.entity_id == entity_id,
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KPIValue.entity_id.is_(None),
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),
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).order_by(KPIValue.period.desc()).first()
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if not latest or latest.actual_value is None:
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continue
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actual = latest.actual_value
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target = k.target_value
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ratio = (actual / target) if target else None
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# 1. 异常：执行率<70% → 建行动方案
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if ratio is not None and ratio < 0.7:
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title = f"提升 {k.kpi_name}：达成率仅{ratio*100:.0f}%"
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content = (f"KPI[{k.kpi_name}] 最新期间{latest.period}实际值{actual:g}，"
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f"目标{target:g}，达成率{ratio*100:.1f}%，低于70%预警线。"
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f"建议制定专项改善行动方案。")
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_add("action_plan", "kpi", k.id, title, content, {
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"kpi_id": k.id, "priority": "high",
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"title": f"改善: {k.kpi_name}达成率提升",
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})
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# 2. 机会：执行率>110% → 上调KPI目标
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elif ratio is not None and ratio > 1.1:
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new_target = round(actual * 1.05, 2)
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title = f"上调 {k.kpi_name} 目标：达成率{ratio*100:.0f}%超预期"
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content = (f"KPI[{k.kpi_name}] 达成率{ratio*100:.1f}%超过110%，"
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f"建议将目标从{target:g}上调至{new_target:g}，保持牵引力。")
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_add("kpi_target", "kpi", k.id, title, content, {
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"kpi_id": k.id, "target_value": new_target,
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})
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# 3. 预算执行率>110% → 调预算
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budget_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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).all()
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for b in budget_rows:
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actual = db.query(func.max(KPIValue.actual_value)).filter(
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KPIValue.kpi_id == b.kpi_id,
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KPIValue.period == b.period,
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).scalar()
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if actual is None or b.budget_value is None or b.budget_value <= 0:
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continue
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exec_ratio = actual / b.budget_value
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if exec_ratio > 1.1:
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kpi_name = "KPI"
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k = db.query(KPIDefinition).filter(KPIDefinition.id == b.kpi_id).first()
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if k:
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kpi_name = k.kpi_name
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title = f"调整 {kpi_name} 预算：执行率{exec_ratio*100:.0f}%超预算"
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content = (f"预算[{kpi_name}] {period}预算值{b.budget_value:g}，"
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f"实际{actual:g}，执行率{exec_ratio*100:.1f}%超过110%。"
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f"建议同步调整预算/现金流/行动方案。")
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_add("budget_adjust", "budget", b.kpi_id, title, content, {
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"kpi_id": b.kpi_id, "period": period, "budget_value": round(actual, 2),
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})
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# 4. pending预警 → 建行动方案
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alerts = db.query(KPIAlert).filter(KPIAlert.status == "pending").all()
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for a in alerts:
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k = db.query(KPIDefinition).filter(KPIDefinition.id == a.kpi_id).first()
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kpi_name = k.kpi_name if k else f"KPI#{a.kpi_id}"
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title = f"处理预警：{kpi_name} {a.alert_message[:30]}"
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content = f"存在待处理预警（{a.alert_level}级）：{a.alert_message}。建议建立行动方案跟进。"
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_add("action_plan", "alert", a.id, title, content, {
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"kpi_id": a.kpi_id, "priority": "high" if a.alert_level == "red" else "medium",
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"alert_id": a.id,
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"title": f"处理预警: {kpi_name}",
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})
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if created:
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db.commit()
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for s in created:
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db.refresh(s)
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return created
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def _unapplied_suggestions(db: Session, entity_id: int, limit: int = 20) -> list:
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items = db.query(AISuggestion).filter(
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AISuggestion.entity_id == entity_id,
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AISuggestion.status == "unapplied",
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).order_by(AISuggestion.created_at.desc()).limit(limit).all()
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return [_sug_dict(s) for s in items]
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async def _call_deepseek(prompt: str) -> str:
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"""调用DeepSeek API"""
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api_key = os.getenv("DEEPSEEK_API_KEY", "sk-8e24e6eb87f2475e96ea0980002dc2e8")
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async with httpx.AsyncClient(timeout=30) as client:
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resp = await client.post(
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"https://api.deepseek.com/v1/chat/completions",
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headers={"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"},
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json={
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"model": "deepseek-chat",
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"messages": [
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{"role": "system", "content": "你是一名CMA管理会计师，擅长用数据驱动的方式分析企业经营状况，给出专业的财务分析和管理建议。回答要简洁、专业、有数据支撑。"},
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{"role": "user", "content": prompt}
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],
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"stream": False,
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"temperature": 0.3,
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}
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)
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data = resp.json()
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return data.get("choices", [{}])[0].get("message", {}).get("content", "")
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@router.get("/dashboard-analysis")
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async def dashboard_analysis(role: str = Query("ceo"), db: Session = Depends(get_db),
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entity_id: int = Depends(get_entity_id)):
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"""AI分析驾驶舱数据"""
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# 尝试缓存
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cache_key = f"dashboard_analysis:{role}:{entity_id}"
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cached = cache_get("ai", cache_key)
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if cached:
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# 缓存命中（LLM文本10分钟内不重复调用），但轻量规则建议仍执行（幂等）
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try:
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generate_rule_suggestions(db, entity_id, source="dashboard")
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except Exception:
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pass
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cached["suggestions"] = _unapplied_suggestions(db, entity_id)
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return cached
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# 获取当前KPI数据
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kpis = db.query(KPIDefinition).filter(KPIDefinition.entity_id == entity_id,
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KPIDefinition.status == "active").all()
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kpi_summary = []
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for k in kpis:
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latest = db.query(KPIValue).filter(KPIValue.kpi_id == k.id).order_by(KPIValue.period.desc()).first()
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kpi_summary.append({
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"name": k.kpi_name,
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"code": k.kpi_code,
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"dimension": k.dimension,
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"target": k.target_value,
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"actual": latest.actual_value if latest else None,
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"period": latest.period if latest else None,
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"unit": k.unit,
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})
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# 获取预警
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alerts = db.query(KPIAlert).filter(KPIAlert.status == "pending").count()
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# 构建分析prompt
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kpi_text = "\n".join([f"- {k['name']}({k['code']}): 目标={k['target']}, 实际={k['actual']}({k['period']}), 维度={k['dimension']}" for k in kpi_summary if k['actual'] is not None])
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prompt = f"""我是一家公司的管理层，以下是当前管理会计系统的KPI数据和系统状态，请给出专业的分析和管理建议：
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当前KPI数据：
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{kpi_text}
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待处理预警数：{alerts}
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请从以下三个方面分析：
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1. **核心发现**：当前数据反映的最关键问题是什么？
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2. **深入解读**：从CMA管理会计角度，这些数据意味着什么？
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3. **行动建议**：基于数据，财务和业务部门应该采取什么具体行动？
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注意：角色视角为{"CEO（总经理）" if role == "ceo" else "财务部" if role == "finance" else "业务部"}。"""
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try:
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analysis = await _call_deepseek(prompt)
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except Exception as e:
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analysis = f"AI分析暂时不可用: {str(e)}"
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# R1: 规则驱动生成可落地决策建议（幂等落库）
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try:
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generate_rule_suggestions(db, entity_id, source="dashboard")
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except Exception as e:
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pass
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result = {"analysis": analysis, "kpi_count": len(kpi_summary), "alert_count": alerts,
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"suggestions": _unapplied_suggestions(db, entity_id)}
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# 缓存10分钟
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cache_set("ai", cache_key, result, ttl_seconds=600)
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return result
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@router.get("/kpi-analysis/{kpi_id}")
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async def kpi_analysis(kpi_id: int, db: Session = Depends(get_db),
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entity_id: int = Depends(get_entity_id)):
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"""AI分析单个KPI"""
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# 尝试缓存
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cache_key = f"kpi_analysis:{kpi_id}:{entity_id}"
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cached = cache_get("ai", cache_key)
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if cached:
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return cached
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kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first()
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if not kpi:
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raise HTTPException(404, "KPI不存在")
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values = db.query(KPIValue).filter(KPIValue.kpi_id == kpi_id).order_by(KPIValue.period.asc()).all()
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trend_data = []
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for v in values:
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trend_data.append({"period": v.period, "value": v.actual_value})
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prompt = f"""请分析以下KPI指标：
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KPI名称：{kpi.kpi_name}
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维度：{kpi.dimension}
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计算公式：{kpi.formula}
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目标值：{kpi.target_value}
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单位：{kpi.unit}
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负责部门：{kpi.responsible_dept}
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历史数据趋势：
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{json.dumps(trend_data, ensure_ascii=False, indent=2)}
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请分析：
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1. 当前表现如何，是否达到目标
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2. 趋势走势是否健康（上升/下降/波动）
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3. 存在什么风险
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4. 建议采取什么管理行动"""
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try:
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analysis = await _call_deepseek(prompt)
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except Exception as e:
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analysis = f"分析暂时不可用: {str(e)}"
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# R1: 生成该KPI的可落地建议
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try:
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generate_rule_suggestions(db, entity_id, source="kpi", kpi_id=kpi_id)
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except Exception as e:
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pass
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result = {"kpi_name": kpi.kpi_name, "analysis": analysis,
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"suggestions": _unapplied_suggestions(db, entity_id)}
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cache_set("ai", cache_key, result, ttl_seconds=600)
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return result
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async def _stream_analysis(prompt: str):
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"""流式调用DeepSeek并生成SSE事件"""
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async with httpx.AsyncClient(timeout=60) as client:
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async with client.stream(
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"POST",
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"https://api.deepseek.com/v1/chat/completions",
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headers={
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"Authorization": f"Bearer {os.getenv('DEEPSEEK_API_KEY', 'sk-8e24e6eb87f2475e96ea0980002dc2e8')}",
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"Content-Type": "application/json",
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},
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json={
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"model": "deepseek-chat",
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"messages": [
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{"role": "system", "content": "你是一名CMA管理会计师，擅长用数据驱动的方式分析企业经营状况，给出专业的财务分析和管理建议。"},
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{"role": "user", "content": prompt},
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],
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"stream": True,
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"temperature": 0.3,
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}
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) as response:
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async for line in response.aiter_lines():
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if not line or line.startswith(":"):
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continue
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if line.startswith("data: "):
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data_str = line[6:]
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if data_str.strip() == "[DONE]":
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break
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try:
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chunk = json.loads(data_str)
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delta = chunk.get("choices", [{}])[0].get("delta", {}).get("content", "")
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if delta:
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yield f"data: {json.dumps({'text': delta})}\n\n"
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except json.JSONDecodeError:
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continue
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yield "data: {\"text\": \"[DONE]\"}\n\n"
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@router.get("/dashboard-analysis-stream")
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async def dashboard_analysis_stream(role: str = Query("ceo"), db: Session = Depends(get_db)):
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"""AI分析驾驶舱数据 — SSE流式输出"""
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cache_key = f"dashboard_analysis:{role}"
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||
cached = cache_get("ai", cache_key)
|
||
if cached:
|
||
# 缓存存在，直接以流的形式一次性返回
|
||
full_text = cached.get("analysis", "")
|
||
async def cached_stream():
|
||
yield f"data: {json.dumps({'text': full_text})}\n\n"
|
||
yield "data: {\"text\": \"[DONE]\"}\n\n"
|
||
return StreamingResponse(cached_stream(), media_type="text/event-stream")
|
||
|
||
kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all()
|
||
kpi_summary = []
|
||
for k in kpis:
|
||
latest = db.query(KPIValue).filter(KPIValue.kpi_id == k.id).order_by(KPIValue.period.desc()).first()
|
||
kpi_summary.append({
|
||
"name": k.kpi_name, "code": k.kpi_code, "dimension": k.dimension,
|
||
"target": k.target_value, "actual": latest.actual_value if latest else None,
|
||
"period": latest.period if latest else None, "unit": k.unit,
|
||
})
|
||
alerts = db.query(KPIAlert).filter(KPIAlert.status == "pending").count()
|
||
kpi_text = "\n".join([f"- {k['name']}({k['code']}): 目标={k['target']}, 实际={k['actual']}({k['period']}), 维度={k['dimension']}" for k in kpi_summary if k['actual'] is not None])
|
||
role_label = {"ceo": "CEO（总经理）", "finance": "财务部", "business": "业务部"}.get(role, "管理层")
|
||
prompt = f"""我是一家公司的管理层，以下是当前管理会计系统的KPI数据和系统状态，请给出专业的分析和管理建议：
|
||
|
||
当前KPI数据：
|
||
{kpi_text}
|
||
|
||
待处理预警数：{alerts}
|
||
|
||
请从以下三个方面分析：
|
||
1. **核心发现**：当前数据反映的最关键问题是什么？
|
||
2. **深入解读**：从CMA管理会计角度，这些数据意味着什么？
|
||
3. **行动建议**：基于数据，财务和业务部门应该采取什么具体行动？
|
||
|
||
注意：角色视角为{role_label}。"""
|
||
|
||
return StreamingResponse(_stream_analysis(prompt), media_type="text/event-stream")
|
||
|
||
|
||
@router.post("/ask")
|
||
async def ask_question(
|
||
request: Request,
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(require_auth),
|
||
):
|
||
"""自然语言查询 — CEO问企业经营问题"""
|
||
body = await request.json()
|
||
question = body.get("question", "").strip()
|
||
|
||
if not question:
|
||
raise HTTPException(400, "请输入问题")
|
||
|
||
# 收集系统数据作为上下文
|
||
kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all()
|
||
kpi_context = []
|
||
for k in kpis:
|
||
latest = db.query(KPIValue).filter(KPIValue.kpi_id == k.id).order_by(KPIValue.period.desc()).first()
|
||
alert = db.query(KPIAlert).filter(KPIAlert.kpi_id == k.id, KPIAlert.status == "pending").first()
|
||
kpi_context.append(
|
||
f"{k.kpi_name}({k.kpi_code}): 当前值={latest.actual_value if latest else '无'}"
|
||
f"{' ⚠️' + alert.alert_level if alert else ''}"
|
||
)
|
||
|
||
# 获取改善计划
|
||
plans = db.query(ActionPlan).order_by(ActionPlan.created_at.desc()).limit(10).all()
|
||
plan_context = [f"- {p.title}({p.assignee}, {p.status}, {p.progress}%)" for p in plans]
|
||
|
||
# 获取预警
|
||
red_alerts = db.query(KPIAlert).filter(KPIAlert.status == "pending", KPIAlert.alert_level == "red").count()
|
||
yellow_alerts = db.query(KPIAlert).filter(KPIAlert.status == "pending", KPIAlert.alert_level == "yellow").count()
|
||
|
||
system_context = f"""你是管理会计OS的AI助手，基于以下企业数据回答管理层问题。
|
||
|
||
时间：{datetime.now().strftime('%Y-%m-%d %H:%M')}
|
||
当前用户：{current_user.name} ({current_user.role})
|
||
|
||
## KPI数据
|
||
{chr(10).join(kpi_context)}
|
||
|
||
## 预警概况
|
||
红色(紧急): {red_alerts}条 | 黄色(预警): {yellow_alerts}条
|
||
|
||
## 改善计划
|
||
{chr(10).join(plan_context) if plan_context else '暂无'}
|
||
|
||
请基于以上数据回答问题。如果问题需要具体数据但上下文中没有，可以根据KPI编码名称推断。回答要简洁、有数据支撑。"""
|
||
|
||
prompt = f"{system_context}\n\n用户问题：{question}"
|
||
|
||
try:
|
||
analysis = await _call_deepseek(prompt)
|
||
except Exception as e:
|
||
analysis = f"查询失败: {str(e)}"
|
||
|
||
return {"question": question, "answer": analysis, "timestamp": datetime.now().isoformat()}
|
||
|
||
|
||
@router.post("/review-plans")
|
||
async def review_plans(
|
||
db: Session = Depends(get_db),
|
||
current_user: User = Depends(require_auth),
|
||
):
|
||
"""AI复盘改善行动计划执行效果"""
|
||
plans = db.query(ActionPlan).order_by(ActionPlan.created_at.asc()).all()
|
||
|
||
if not plans:
|
||
return {"analysis": "暂无改善行动计划，无法复盘"}
|
||
|
||
plan_text = []
|
||
for p in plans:
|
||
kpi = db.query(KPIDefinition).filter(KPIDefinition.id == p.kpi_id).first()
|
||
kpi_name = kpi.kpi_name if kpi else "未知"
|
||
latest = db.query(KPIValue).filter(KPIValue.kpi_id == p.kpi_id).order_by(KPIValue.period.desc()).first()
|
||
plan_text.append(
|
||
f"- {p.title}\n"
|
||
f" 关联KPI: {kpi_name}(当前值: {latest.actual_value if latest else '无'})\n"
|
||
f" 负责人: {p.assignee} | 状态: {p.status} | 进度: {p.progress}%\n"
|
||
f" 描述: {p.description}\n"
|
||
f" 截止日: {p.due_date.strftime('%Y-%m-%d') if p.due_date else '无'}"
|
||
)
|
||
|
||
completed = sum(1 for p in plans if p.status == "completed")
|
||
in_progress = sum(1 for p in plans if p.status == "in_progress")
|
||
pending = sum(1 for p in plans if p.status == "pending")
|
||
|
||
prompt = f"""请复盘以下改善行动计划的执行情况：
|
||
|
||
## 改善计划概览
|
||
总数: {len(plans)} | 已完成: {completed} | 进行中: {in_progress} | 待开始: {pending}
|
||
|
||
## 各计划详情
|
||
{chr(10).join(plan_text)}
|
||
|
||
请分析：
|
||
1. **执行概况**：整体执行到位吗？哪些计划需要重点关注？
|
||
2. **效果评估**：已完成的计划是否真正改善了关联KPI？
|
||
3. **风险提示**：哪些计划存在延期或执行不力的风险？
|
||
4. **改进建议**：接下来应该调整或优先推进哪些计划？"""
|
||
|
||
try:
|
||
analysis = await _call_deepseek(prompt)
|
||
except Exception as e:
|
||
analysis = f"复盘失败: {str(e)}"
|
||
|
||
return {
|
||
"analysis": analysis,
|
||
"stats": {"total": len(plans), "completed": completed, "in_progress": in_progress, "pending": pending},
|
||
"timestamp": datetime.now().isoformat(),
|
||
}
|