diff --git a/backend/app/api/ai_analysis.py b/backend/app/api/ai_analysis.py index daa958e8..03c4c6d4 100644 --- a/backend/app/api/ai_analysis.py +++ b/backend/app/api/ai_analysis.py @@ -2,17 +2,185 @@ from fastapi import APIRouter, Depends, HTTPException, Query, Request from fastapi.responses import StreamingResponse from sqlalchemy.orm import Session -from sqlalchemy import func, text as sa_text +from sqlalchemy import func, text as sa_text, or_ from app.database import get_db +from app.deps import get_entity_id from app.auth_middleware import require_auth, require_role -from app.models import KPIDefinition, KPIValue, KPIAlert, StrategicMap, User, ActionPlan +from app.models import KPIDefinition, KPIValue, KPIAlert, StrategicMap, User, ActionPlan, BudgetPlan, AISuggestion from app.utils.cache import get as cache_get, set as cache_set import json, hashlib, httpx, os -from datetime import datetime +from datetime import datetime, date router = APIRouter(prefix="/api/cma/ai", tags=["AI分析"], dependencies=[Depends(require_role("ceo", "finance", "business", "it"))], ) + +# ============================================================ +# R1 决策建议生成(规则驱动,稳定可复现,落库 ai_suggestions) +# ============================================================ +def _sug_dict(s: AISuggestion) -> dict: + return { + "id": s.id, + "entity_id": s.entity_id, + "source": s.source, + "suggestion_type": s.suggestion_type, + "target_type": s.target_type, + "target_id": s.target_id, + "title": s.title, + "content": s.content, + "suggestion_data": s.suggestion_data or {}, + "status": s.status, + "applied_by": s.applied_by, + "applied_at": s.applied_at.isoformat() if s.applied_at else None, + "apply_detail": s.apply_detail or [], + "created_at": s.created_at.isoformat() if s.created_at else None, + } + + +def _existing_unapplied(db: Session, entity_id: int, suggestion_type: str, + target_id: int, title: str) -> bool: + """幂等:同entity+类型+目标+标题的未应用建议存在则跳过""" + return db.query(AISuggestion).filter( + AISuggestion.entity_id == entity_id, + AISuggestion.suggestion_type == suggestion_type, + AISuggestion.target_id == target_id, + AISuggestion.title == title, + AISuggestion.status == "unapplied", + ).first() is not None + + +def generate_rule_suggestions(db: Session, entity_id: int, + source: str = "dashboard", user_id: int = None, + kpi_id: int = None) -> list: + """从数据规则生成决策建议并落库(R1,路线图2026-08-30) + + 规则: + 1. KPI执行率<70% → 建议建行动方案(异常类) + 2. KPI执行率>110% → 建议上调KPI目标(机会类) + 3. 预算执行率>110% → 建议调预算(预算类) + 4. 有pending预警 → 建议建行动方案处理预警 + 幂等:同 entity+type+target_id+title+status=unapplied 不重复建。 + """ + now = datetime.now() + period = now.strftime("%Y-%m") + created = [] + + def _add(suggestion_type: str, target_type: str, tid: int, + title: str, content: str, suggestion_data: dict): + nonlocal created + if _existing_unapplied(db, entity_id, suggestion_type, tid, title): + return + sug = AISuggestion( + entity_id=entity_id, + user_id=user_id, + source=source, + suggestion_type=suggestion_type, + target_type=target_type, + target_id=tid, + title=title, + content=content, + suggestion_data=suggestion_data, + status="unapplied", + ) + db.add(sug) + created.append(sug) + + # 查询KPI(可按kpi_id过滤) + q = db.query(KPIDefinition).filter(KPIDefinition.entity_id == entity_id, + KPIDefinition.status == "active") + if kpi_id: + q = q.filter(KPIDefinition.id == kpi_id) + kpis = q.all() + + for k in kpis: + latest = db.query(KPIValue).filter( + KPIValue.kpi_id == k.id, + or_( + KPIValue.entity_id == entity_id, + KPIValue.entity_id.is_(None), + ), + ).order_by(KPIValue.period.desc()).first() + if not latest or latest.actual_value is None: + continue + actual = latest.actual_value + target = k.target_value + ratio = (actual / target) if target else None + + # 1. 异常:执行率<70% → 建行动方案 + if ratio is not None and ratio < 0.7: + title = f"提升 {k.kpi_name}:达成率仅{ratio*100:.0f}%" + content = (f"KPI[{k.kpi_name}] 最新期间{latest.period}实际值{actual:g}," + f"目标{target:g},达成率{ratio*100:.1f}%,低于70%预警线。" + f"建议制定专项改善行动方案。") + _add("action_plan", "kpi", k.id, title, content, { + "kpi_id": k.id, "priority": "high", + "title": f"改善: {k.kpi_name}达成率提升", + }) + # 2. 机会:执行率>110% → 上调KPI目标 + elif ratio is not None and ratio > 1.1: + new_target = round(actual * 1.05, 2) + title = f"上调 {k.kpi_name} 目标:达成率{ratio*100:.0f}%超预期" + content = (f"KPI[{k.kpi_name}] 达成率{ratio*100:.1f}%超过110%," + f"建议将目标从{target:g}上调至{new_target:g},保持牵引力。") + _add("kpi_target", "kpi", k.id, title, content, { + "kpi_id": k.id, "target_value": new_target, + }) + + # 3. 预算执行率>110% → 调预算 + budget_rows = db.query(BudgetPlan).filter( + BudgetPlan.entity_id == entity_id, + BudgetPlan.status == "active", + BudgetPlan.period == period, + ).all() + for b in budget_rows: + actual = db.query(func.max(KPIValue.actual_value)).filter( + KPIValue.kpi_id == b.kpi_id, + KPIValue.period == b.period, + ).scalar() + if actual is None or b.budget_value is None or b.budget_value <= 0: + continue + exec_ratio = actual / b.budget_value + if exec_ratio > 1.1: + kpi_name = "KPI" + k = db.query(KPIDefinition).filter(KPIDefinition.id == b.kpi_id).first() + if k: + kpi_name = k.kpi_name + title = f"调整 {kpi_name} 预算:执行率{exec_ratio*100:.0f}%超预算" + content = (f"预算[{kpi_name}] {period}预算值{b.budget_value:g}," + f"实际{actual:g},执行率{exec_ratio*100:.1f}%超过110%。" + f"建议同步调整预算/现金流/行动方案。") + _add("budget_adjust", "budget", b.kpi_id, title, content, { + "kpi_id": b.kpi_id, "period": period, "budget_value": round(actual, 2), + }) + + # 4. pending预警 → 建行动方案 + alerts = db.query(KPIAlert).filter(KPIAlert.status == "pending").all() + for a in alerts: + k = db.query(KPIDefinition).filter(KPIDefinition.id == a.kpi_id).first() + kpi_name = k.kpi_name if k else f"KPI#{a.kpi_id}" + title = f"处理预警:{kpi_name} {a.alert_message[:30]}" + 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。" + _add("action_plan", "alert", a.id, title, content, { + "kpi_id": a.kpi_id, "priority": "high" if a.alert_level == "red" else "medium", + "alert_id": a.id, + "title": f"处理预警: {kpi_name}", + }) + + if created: + db.commit() + for s in created: + db.refresh(s) + return created + + +def _unapplied_suggestions(db: Session, entity_id: int, limit: int = 20) -> list: + items = db.query(AISuggestion).filter( + AISuggestion.entity_id == entity_id, + AISuggestion.status == "unapplied", + ).order_by(AISuggestion.created_at.desc()).limit(limit).all() + return [_sug_dict(s) for s in items] + + async def _call_deepseek(prompt: str) -> str: """调用DeepSeek API""" api_key = os.getenv("DEEPSEEK_API_KEY", "sk-8e24e6eb87f2475e96ea0980002dc2e8") @@ -34,15 +202,23 @@ async def _call_deepseek(prompt: str) -> str: return data.get("choices", [{}])[0].get("message", {}).get("content", "") @router.get("/dashboard-analysis") -async def dashboard_analysis(role: str = Query("ceo"), db: Session = Depends(get_db)): +async def dashboard_analysis(role: str = Query("ceo"), db: Session = Depends(get_db), + entity_id: int = Depends(get_entity_id)): """AI分析驾驶舱数据""" # 尝试缓存 - cache_key = f"dashboard_analysis:{role}" + cache_key = f"dashboard_analysis:{role}:{entity_id}" cached = cache_get("ai", cache_key) if cached: + # 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) + try: + generate_rule_suggestions(db, entity_id, source="dashboard") + except Exception: + pass + cached["suggestions"] = _unapplied_suggestions(db, entity_id) return cached # 获取当前KPI数据 - kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all() + kpis = db.query(KPIDefinition).filter(KPIDefinition.entity_id == entity_id, + 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() @@ -81,17 +257,25 @@ async def dashboard_analysis(role: str = Query("ceo"), db: Session = Depends(get except Exception as e: analysis = f"AI分析暂时不可用: {str(e)}" - result = {"analysis": analysis, "kpi_count": len(kpi_summary), "alert_count": alerts} + # R1: 规则驱动生成可落地决策建议(幂等落库) + try: + generate_rule_suggestions(db, entity_id, source="dashboard") + except Exception as e: + pass + + result = {"analysis": analysis, "kpi_count": len(kpi_summary), "alert_count": alerts, + "suggestions": _unapplied_suggestions(db, entity_id)} # 缓存10分钟 cache_set("ai", cache_key, result, ttl_seconds=600) return result @router.get("/kpi-analysis/{kpi_id}") -async def kpi_analysis(kpi_id: int, db: Session = Depends(get_db)): +async def kpi_analysis(kpi_id: int, db: Session = Depends(get_db), + entity_id: int = Depends(get_entity_id)): """AI分析单个KPI""" # 尝试缓存 - cache_key = f"kpi_analysis:{kpi_id}" + cache_key = f"kpi_analysis:{kpi_id}:{entity_id}" cached = cache_get("ai", cache_key) if cached: return cached @@ -129,7 +313,14 @@ KPI名称:{kpi.kpi_name} except Exception as e: analysis = f"分析暂时不可用: {str(e)}" - result = {"kpi_name": kpi.kpi_name, "analysis": analysis} + # R1: 生成该KPI的可落地建议 + try: + generate_rule_suggestions(db, entity_id, source="kpi", kpi_id=kpi_id) + except Exception as e: + pass + + result = {"kpi_name": kpi.kpi_name, "analysis": analysis, + "suggestions": _unapplied_suggestions(db, entity_id)} cache_set("ai", cache_key, result, ttl_seconds=600) return result diff --git a/backend/app/api/ai_suggestions.py b/backend/app/api/ai_suggestions.py new file mode 100644 index 00000000..e5e018c4 --- /dev/null +++ b/backend/app/api/ai_suggestions.py @@ -0,0 +1,336 @@ +"""AI决策建议 — 一键应用到KPI/预算/行动方案 (路线图R1 2026-08-30) + +北极星④决策闭环:AI建议 → 点击应用 → 写库变更 → OperationLog留痕 → 前端可查已应用/未应用。 +应用动作复用现有 kpis/budget/action_plans 数据模型,不新建业务接口。 +""" +from fastapi import APIRouter, Depends, HTTPException, Query, Request +from sqlalchemy.orm import Session +from sqlalchemy import func +from typing import Optional +from datetime import datetime + +from app.database import get_db +from app.deps import get_entity_id, resolve_entity_for_request +from app.auth_middleware import require_auth, require_role +from app.models import AISuggestion, KPIDefinition, KPIValue, OperationLog, BudgetPlan, ActionPlan + +router = APIRouter(prefix="/api/cma/ai/suggestions", tags=["AI建议"], + dependencies=[Depends(require_role("ceo", "finance", "business", "it"))], +) + + +def _sug_dict(s: AISuggestion) -> dict: + return { + "id": s.id, + "entity_id": s.entity_id, + "user_id": s.user_id, + "source": s.source, + "suggestion_type": s.suggestion_type, + "target_type": s.target_type, + "target_id": s.target_id, + "title": s.title, + "content": s.content, + "suggestion_data": s.suggestion_data or {}, + "status": s.status, + "applied_by": s.applied_by, + "applied_at": s.applied_at.isoformat() if s.applied_at else None, + "apply_detail": s.apply_detail or [], + "created_at": s.created_at.isoformat() if s.created_at else None, + } + + +@router.post("") +def create_suggestion( + request: Request, + data: dict, + db: Session = Depends(get_db), + current_user=Depends(require_auth), +): + """创建AI建议(前端AI分析/手动保存建议)""" + suggestion_type = data.get("suggestion_type") or data.get("type") + title = (data.get("title") or "").strip() + if not suggestion_type: + raise HTTPException(400, "缺少 suggestion_type (kpi_target/budget_adjust/action_plan)") + if not title: + raise HTTPException(400, "缺少 title") + + entity_id = resolve_entity_for_request(request, data.get("entity_id") or 1) + + sug = AISuggestion( + entity_id=entity_id, + user_id=getattr(current_user, "id", None), + source=data.get("source", "manual"), + suggestion_type=suggestion_type, + target_type=data.get("target_type", "kpi"), + target_id=data.get("target_id"), + title=title, + content=data.get("content"), + suggestion_data=data.get("suggestion_data") or {}, + status="unapplied", + ) + db.add(sug) + db.commit() + db.refresh(sug) + return {"success": True, "message": "建议已保存", "data": _sug_dict(sug)} + + +@router.get("") +def list_suggestions( + status: Optional[str] = Query(None, description="unapplied/applied/dismissed"), + suggestion_type: Optional[str] = Query(None), + entity_id: int = Depends(get_entity_id), + db: Session = Depends(get_db), +): + """建议列表(前端建议中心/详情页查看已应用/未应用状态)""" + query = db.query(AISuggestion).filter(AISuggestion.entity_id == entity_id) + if status: + query = query.filter(AISuggestion.status == status) + if suggestion_type: + query = query.filter(AISuggestion.suggestion_type == suggestion_type) + items = query.order_by(AISuggestion.created_at.desc()).limit(200).all() + return {"data": [_sug_dict(s) for s in items], "total": len(items)} + + +@router.get("/{suggestion_id}") +def get_suggestion(suggestion_id: int, db: Session = Depends(get_db)): + """建议详情""" + s = db.query(AISuggestion).filter(AISuggestion.id == suggestion_id).first() + if not s: + raise HTTPException(404, "建议不存在") + return {"data": _sug_dict(s)} + + +def _apply_kpi_target(db: Session, sug: AISuggestion, params: dict, current_user) -> dict: + """改KPI目标""" + target_value = params.get("target_value") + if target_value is None: + raise HTTPException(400, "应用kpi_target需要 target_value") + kpi_id = params.get("kpi_id") or sug.target_id + if not kpi_id: + raise HTTPException(400, "缺少 kpi_id") + kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first() + if not kpi: + raise HTTPException(404, f"KPI {kpi_id} 不存在") + before = kpi.target_value + kpi.target_value = float(target_value) + db.flush() + detail_item = { + "target_type": "kpi", + "target_id": kpi.id, + "target_name": kpi.kpi_name, + "action": "update_target_value", + "before": before, + "after": float(target_value), + } + db.add(OperationLog( + user_id=getattr(current_user, "id", None), + action="ai_suggestion_apply", + target_type="kpi", + target_id=kpi.id, + detail={ + "suggestion_id": sug.id, + "suggestion_title": sug.title, + "apply_action": "kpi_target", + "before": before, + "after": float(target_value), + }, + )) + return detail_item + + +def _apply_budget_adjust(db: Session, sug: AISuggestion, params: dict, current_user) -> dict: + """调预算(BudgetPlan upsert,按 kpi_id+period)""" + period = params.get("period") + budget_value = params.get("budget_value") + if not period or budget_value is None: + raise HTTPException(400, "应用budget_adjust需要 period + budget_value") + kpi_id = params.get("kpi_id") or sug.target_id + if not kpi_id: + raise HTTPException(400, "缺少 kpi_id") + kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first() + if not kpi: + raise HTTPException(404, f"KPI {kpi_id} 不存在") + + # 解析期间 2026-09 → year=2026 month=9 + try: + parts = period.split("-") + year = int(parts[0]) + month = int(parts[1]) + except Exception: + raise HTTPException(400, f"period格式错误: {period} (需要 YYYY-MM)") + + plan = db.query(BudgetPlan).filter( + BudgetPlan.entity_id == sug.entity_id, + BudgetPlan.kpi_id == kpi_id, + BudgetPlan.period == period, + BudgetPlan.budget_year == year, + BudgetPlan.budget_month == month, + BudgetPlan.status == "active", + ).first() + + before = None + if plan: + before = plan.budget_value + plan.budget_value = float(budget_value) + else: + plan = BudgetPlan( + entity_id=sug.entity_id, + kpi_id=kpi_id, + period=period, + budget_value=float(budget_value), + budget_year=year, + budget_month=month, + version="v1.0", + status="active", + source_type="ai_suggestion", + calc_logic=f"AI建议应用 #{sug.id}: {sug.title}", + created_by=getattr(current_user, "name", "") or "", + ) + db.add(plan) + db.flush() + detail_item = { + "target_type": "budget", + "target_id": plan.id, + "target_name": f"{kpi.kpi_name}[{period}]", + "action": "update_budget" if before is not None else "create_budget", + "before": before, + "after": float(budget_value), + } + db.add(OperationLog( + user_id=getattr(current_user, "id", None), + action="ai_suggestion_apply", + target_type="budget", + target_id=plan.id, + detail={ + "suggestion_id": sug.id, + "suggestion_title": sug.title, + "apply_action": "budget_adjust", + "kpi_id": kpi_id, + "period": period, + "before": before, + "after": float(budget_value), + }, + )) + return detail_item + + +def _apply_action_plan(db: Session, sug: AISuggestion, params: dict, current_user) -> dict: + """建行动方案""" + title = (params.get("title") or "").strip() or sug.title + kpi_id = params.get("kpi_id") or sug.target_id + if not kpi_id: + raise HTTPException(400, "缺少 kpi_id") + kpi = db.query(KPIDefinition).filter(KPIDefinition.id == kpi_id).first() + if not kpi: + raise HTTPException(404, f"KPI {kpi_id} 不存在") + + due_date = None + if params.get("due_date"): + try: + due_date = datetime.strptime(str(params["due_date"])[:10], "%Y-%m-%d") + except Exception: + due_date = None + + plan = ActionPlan( + kpi_id=kpi_id, + title=title, + description=params.get("description") or sug.content or f"由AI建议 #{sug.id} 生成: {sug.title}", + assignee=params.get("assignee") or "", + priority=params.get("priority") or "medium", + due_date=due_date, + status="pending", + progress=0, + created_by=getattr(current_user, "name", "") or "ai_suggestion", + ) + db.add(plan) + db.flush() + detail_item = { + "target_type": "action_plan", + "target_id": plan.id, + "target_name": title, + "action": "create_action_plan", + "before": None, + "after": plan.id, + } + db.add(OperationLog( + user_id=getattr(current_user, "id", None), + action="ai_suggestion_apply", + target_type="action_plan", + target_id=plan.id, + detail={ + "suggestion_id": sug.id, + "suggestion_title": sug.title, + "apply_action": "action_plan", + "kpi_id": kpi_id, + "plan_title": title, + }, + )) + return detail_item + + +_APPLYERS = { + "kpi_target": _apply_kpi_target, + "budget_adjust": _apply_budget_adjust, + "action_plan": _apply_action_plan, +} + + +@router.post("/{suggestion_id}/apply") +def apply_suggestion( + suggestion_id: int, + data: dict, + db: Session = Depends(get_db), + current_user=Depends(require_auth), +): + """应用建议:改KPI目标 / 调预算 / 建行动方案(写库+操作日志留痕) + + Body 示例: + {"action": "kpi_target", "target_value": 2000000} + {"action": "budget_adjust", "period": "2026-09", "budget_value": 100000} + {"action": "action_plan", "title": "...", "assignee": "...", "priority": "high", "due_date": "2026-09-30"} + """ + sug = db.query(AISuggestion).filter(AISuggestion.id == suggestion_id).first() + if not sug: + raise HTTPException(404, "建议不存在") + if sug.status == "applied": + raise HTTPException(400, "该建议已应用,不能重复应用") + if sug.status == "dismissed": + raise HTTPException(400, "该建议已忽略,如需应用请重新创建") + + action = data.get("action") or sug.suggestion_type + applier = _APPLYERS.get(action) + if not applier: + raise HTTPException(400, f"不支持的应用动作: {action} (支持 kpi_target/budget_adjust/action_plan)") + + # 应用参数 = 请求体参数 覆盖 建议默认参数 + params = dict(sug.suggestion_data or {}) + params.update({k: v for k, v in data.items() if k != "action" and v is not None}) + + detail_item = applier(db, sug, params, current_user) + sug.status = "applied" + sug.applied_by = getattr(current_user, "name", "") or "" + sug.applied_user_id = getattr(current_user, "id", None) + sug.applied_at = datetime.now() + sug.apply_detail = [detail_item] + db.commit() + db.refresh(sug) + return { + "success": True, + "message": "建议已应用并留痕", + "data": _sug_dict(sug), + } + + +@router.post("/{suggestion_id}/dismiss") +def dismiss_suggestion( + suggestion_id: int, + db: Session = Depends(get_db), + current_user=Depends(require_auth), +): + """忽略建议""" + sug = db.query(AISuggestion).filter(AISuggestion.id == suggestion_id).first() + if not sug: + raise HTTPException(404, "建议不存在") + sug.status = "dismissed" + db.commit() + return {"success": True, "message": "建议已忽略"} diff --git a/backend/app/main.py b/backend/app/main.py index c4d79abd..4da84608 100644 --- a/backend/app/main.py +++ b/backend/app/main.py @@ -5,7 +5,7 @@ from fastapi.middleware.cors import CORSMiddleware from fastapi.responses import JSONResponse from dotenv import load_dotenv from app.database import init_db -from app.api import auth, kpis, kpi_governance, templates, maps, dashboard, data, alerts, ai_analysis, alert_rules, users, thresholds, notifications, permissions, action_plans, alignment, org, objectives, versions, budget, cost, predict, reports, security, knowledge, bot_bridge, bot_bridge_v2, lead, tenant, customer_dashboard, deviation_push, budget_generate, knowledge_articles, kpi_causality, data_quality, bi_reports, entities, bsc_layers, okr, okr_templates, subjects, driver_budget, bot_kpis, ontology, bot_iron_law, analysis_results, expenses, cash, tax_compliance, verify, growth_quality, products, data_classification, value_sources, zero_based, derivation_rules, cash_classify +from app.api import auth, kpis, kpi_governance, templates, maps, dashboard, data, alerts, ai_analysis, ai_suggestions, alert_rules, users, thresholds, notifications, permissions, action_plans, alignment, org, objectives, versions, budget, cost, predict, reports, security, knowledge, bot_bridge, bot_bridge_v2, lead, tenant, customer_dashboard, deviation_push, budget_generate, knowledge_articles, kpi_causality, data_quality, bi_reports, entities, bsc_layers, okr, okr_templates, subjects, driver_budget, bot_kpis, ontology, bot_iron_law, analysis_results, expenses, cash, tax_compliance, verify, growth_quality, products, data_classification, value_sources, zero_based, derivation_rules, cash_classify from app.utils.cache import clear_all as clear_cache, delete as delete_cache from scripts.erp_sync import run_sync as run_erp_sync from app.auth_middleware import require_auth @@ -43,6 +43,7 @@ app.include_router(dashboard.router) app.include_router(data.router) app.include_router(alerts.router) app.include_router(ai_analysis.router) +app.include_router(ai_suggestions.router) app.include_router(alert_rules.router) app.include_router(users.router) app.include_router(thresholds.router) diff --git a/backend/app/models/__init__.py b/backend/app/models/__init__.py index de94e663..d13351ed 100644 --- a/backend/app/models/__init__.py +++ b/backend/app/models/__init__.py @@ -911,3 +911,25 @@ class CashPlanUnclassified(Base): status = Column(String(20), default="pending", comment="pending/classified/ignored") created_at = Column(DateTime, server_default=func.now()) resolved_at = Column(DateTime, nullable=True) + + +class AISuggestion(Base): + """AI决策建议 — 一键应用到KPI/预算/行动方案 (路线图R1 2026-08-30)""" + __tablename__ = "ai_suggestions" + id = Column(Integer, primary_key=True, index=True) + entity_id = Column(Integer, default=1, comment="企业ID") + user_id = Column(Integer, nullable=True, comment="建议创建人ID") + source = Column(String(30), default="dashboard", comment="来源: dashboard/kpi/budget/manual/rule") + suggestion_type = Column(String(30), nullable=False, comment="kpi_target/budget_adjust/action_plan") + target_type = Column(String(30), nullable=False, comment="kpi/budget/action_plan") + target_id = Column(Integer, nullable=True, comment="目标ID (KPI ID/预算KPI ID等)") + title = Column(String(300), nullable=False, comment="建议标题") + content = Column(Text, nullable=True, comment="建议内容/理由") + suggestion_data = Column(JSON, nullable=True, comment="应用参数: {target_value, period, budget_value, plan_title, ...}") + status = Column(String(20), default="unapplied", comment="unapplied/applied/dismissed") + applied_by = Column(String(100), nullable=True, comment="应用人姓名") + applied_user_id = Column(Integer, nullable=True, comment="应用人ID") + applied_at = Column(DateTime, nullable=True, comment="应用时间") + apply_detail = Column(JSON, nullable=True, comment="应用结果明细: [{target_type,target_id,action,before,after}]") + created_at = Column(DateTime, server_default=func.now()) + updated_at = Column(DateTime, server_default=func.now(), onupdate=func.now()) diff --git a/backend/reports/closed_loop_check_20260830.md b/backend/reports/closed_loop_check_20260830.md new file mode 100644 index 00000000..053c72dd --- /dev/null +++ b/backend/reports/closed_loop_check_20260830.md @@ -0,0 +1,27 @@ +# 预算↔现金流↔行动 闭环自检报告 +**检查时间**: 2026-08-30 11:43:16 + +## 账套 #1 · 期间 2026-08 +- 🔴 营业收入(2026-08) + 预算 75 / 实际 150000 = 执行率 200000.0%(超预算) + 现金流计划: 0 条 | 行动方案: 11 条 + ⚠️ 缺失: 现金流(本期间有其他计划但未关联本KPI) + 💡 预算执行率200000%异常,请同步现金流情况核对(营业收入 2026-08) +- 🟡 净利润(2026-08) + 预算 16.67 / 实际 0 = 执行率 0.0%(低执行) + 现金流计划: 0 条 | 行动方案: 3 条 + ⚠️ 缺失: 现金流(本期间有其他计划但未关联本KPI) + 💡 预算执行率0%异常,请同步现金流情况核对(净利润 2026-08) +- 🔴 渠补率(2026-08) + 预算 12.78 / 实际 75 = 执行率 586.9%(超预算) + 现金流计划: 0 条 | 行动方案: 4 条 + ⚠️ 缺失: 现金流(本期间有其他计划但未关联本KPI) + 💡 预算执行率587%异常,请同步现金流情况核对(渠补率 2026-08) +- 🟡 经营性现金流(2026-08) + 预算 16.67 / 实际 -93000 = 执行率 -557888.4%(低执行) + 现金流计划: 0 条 | 行动方案: 0 条 + ⚠️ 缺失: 现金流(本期间有其他计划但未关联本KPI)、行动方案 + 💡 预算执行率-557888%异常,请同步现金流情况核对、行动方案(经营性现金流 2026-08) + +--- +共发现异常 4 项 \ No newline at end of file diff --git a/backend/scripts/closed_loop_check.py b/backend/scripts/closed_loop_check.py new file mode 100644 index 00000000..09fcbfcc --- /dev/null +++ b/backend/scripts/closed_loop_check.py @@ -0,0 +1,188 @@ +"""预算↔现金流↔行动 三闭环异常自检 — 路线图R5 (2026-08-30) + +预算闭环加固:预算执行率异常(<70% 或 >110%)的KPI, +检查是否同步了 现金流计划(CashPlan) 和 行动方案(ActionPlan), +缺失则输出提示(防止"预算改了,现金流/行动没跟上")。 + +输出:控制台 + reports/closed_loop_check_YYYYMMDD.md +用法: /root/cma-management/backend/venv/bin/python3 scripts/closed_loop_check.py [--period 2026-08] [--push] +""" +import sys +import os +import json +import argparse +from datetime import datetime + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from app.database import get_session_local +from app.models import KPIDefinition, KPIValue, BudgetPlan, CashPlan, ActionPlan + +LOW_RATIO = 0.7 +HIGH_RATIO = 1.1 +REPORTS_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "reports") + + +def check_entity(db, entity_id: int, period: str) -> dict: + """检测一个账套的闭环状态""" + issues = [] + rows = db.query(BudgetPlan).filter( + BudgetPlan.entity_id == entity_id, + BudgetPlan.status == "active", + BudgetPlan.period == period, + BudgetPlan.budget_value > 0, + ).all() + + seen = set() + for b in rows: + key = (b.kpi_id, b.period) + if key in seen: + continue + seen.add(key) + + k = db.query(KPIDefinition).filter(KPIDefinition.id == b.kpi_id).first() + kpi_name = k.kpi_name if k else f"KPI#{b.kpi_id}" + + actual = db.query(KPIValue).filter( + KPIValue.kpi_id == b.kpi_id, + KPIValue.period == b.period, + KPIValue.actual_value.isnot(None), + ).order_by(KPIValue.calculated_at.desc()).first() + + actual_val = actual.actual_value if actual else None + if actual_val is None: + continue + ratio = actual_val / b.budget_value + abnormal = ratio < LOW_RATIO or ratio > HIGH_RATIO + if not abnormal: + continue + + # 现金流检查:该KPI该期间是否有收付款计划(related_kpi_id 或 budget_plan_id 关联) + period_start = datetime.strptime(period + "-01", "%Y-%m-%d") + if period.endswith("-12"): + period_end = datetime(period_start.year + 1, 1, 1) + else: + period_end = datetime(period_start.year, period_start.month + 1, 1) + cash_plans = db.query(CashPlan).filter( + CashPlan.entity_id == entity_id, + CashPlan.status.in_(["pending", "completed"]), + CashPlan.plan_date >= period_start, + CashPlan.plan_date < period_end, + ).filter( + (CashPlan.related_kpi_id == b.kpi_id) | (CashPlan.budget_plan_id == b.id) + ).count() + # 兜底:无关联但期间内有任意现金流计划也算基本闭环 + any_cash = db.query(CashPlan).filter( + CashPlan.entity_id == entity_id, + CashPlan.status.in_(["pending", "completed"]), + CashPlan.plan_date >= period_start, + CashPlan.plan_date < period_end, + ).count() + + # 行动检查:该KPI是否有非完成的行动方案 + actions = db.query(ActionPlan).filter( + ActionPlan.kpi_id == b.kpi_id, + ActionPlan.status.in_(["pending", "in_progress"]), + ).count() + + missing = [] + if cash_plans == 0: + if any_cash > 0: + missing.append("现金流(本期间有其他计划但未关联本KPI)") + else: + missing.append("现金流") + if actions == 0: + missing.append("行动方案") + + level = "critical" if ratio > HIGH_RATIO else "warning" + issues.append({ + "kpi_id": b.kpi_id, + "kpi_name": kpi_name, + "period": period, + "budget_value": b.budget_value, + "actual_value": actual_val, + "exec_ratio": round(ratio * 100, 1), + "abnormal_type": "超预算" if ratio > HIGH_RATIO else "低执行", + "level": level, + "cash_plan_count": cash_plans, + "action_plan_count": actions, + "missing": missing, + "suggestion": ( + f"预算执行率{ratio*100:.0f}%异常,请同步" + + ("现金流计划" if "现金流" in missing else "现金流情况核对") + + ("、行动方案" if "行动方案" in missing else "") + + f"({kpi_name} {period})" + ), + }) + + return {"entity_id": entity_id, "period": period, "issues": issues} + + +def build_report(results: list, checked_at: str) -> str: + lines = [f"# 预算↔现金流↔行动 闭环自检报告", f"**检查时间**: {checked_at}", ""] + total_issues = 0 + for r in results: + lines.append(f"## 账套 #{r['entity_id']} · 期间 {r['period']}") + if not r["issues"]: + lines.append("- ✅ 无预算执行率异常") + for it in r["issues"]: + total_issues += 1 + icon = "🔴" if it["level"] == "critical" else "🟡" + lines.append(f"- {icon} {it['kpi_name']}({it['period']})") + lines.append(f" 预算 {it['budget_value']:g} / 实际 {it['actual_value']:g} = 执行率 {it['exec_ratio']}%({it['abnormal_type']})") + lines.append(f" 现金流计划: {it['cash_plan_count']} 条 | 行动方案: {it['action_plan_count']} 条") + if it["missing"]: + lines.append(f" ⚠️ 缺失: {'、'.join(it['missing'])}") + lines.append(f" 💡 {it['suggestion']}") + else: + lines.append(f" ✅ 三闭环已同步") + lines.append("") + lines.append(f"---") + lines.append(f"共发现异常 {total_issues} 项") + return "\n".join(lines) + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--period", default=datetime.now().strftime("%Y-%m")) + parser.add_argument("--entity-id", type=int, default=1) + parser.add_argument("--push", action="store_true", help="异常时推送企微(8800/send)") + args = parser.parse_args() + + os.makedirs(REPORTS_DIR, exist_ok=True) + checked_at = datetime.now().strftime("%Y-%m-%d %H:%M:%S") + + db = get_session_local()() + try: + result = check_entity(db, args.entity_id, args.period) + report = build_report([result], checked_at) + print(report) + + # 写报告文件 + fname = f"closed_loop_check_{datetime.now().strftime('%Y%m%d')}.md" + fpath = os.path.join(REPORTS_DIR, fname) + with open(fpath, "w", encoding="utf-8") as f: + f.write(report) + print(f"\n📄 报告已写入: {fpath}") + + # 异常推送 + if args.push and result["issues"]: + try: + import urllib.request + import urllib.parse + content = f"## 🔄 预算闭环自检({args.period})\n" + for it in result["issues"][:10]: + content += f"- {it['kpi_name']} 执行率{it['exec_ratio']}% 缺{'/'.join(it['missing']) or '无'}\n" + content += f"\n共{len(result['issues'])}项异常,详见系统报告" + data = urllib.parse.urlencode({"msg": content, "source": "管理会计OS"}).encode("utf-8") + req = urllib.request.Request("http://127.0.0.1:8800/send", data=data) + with urllib.request.urlopen(req, timeout=15) as resp: + print("推送:", resp.read().decode()[:200]) + except Exception as e: + print(f"推送失败: {e}") + finally: + db.close() + + +if __name__ == "__main__": + main() diff --git a/backend/scripts/daily_push.py b/backend/scripts/daily_push.py new file mode 100644 index 00000000..e2b35961 --- /dev/null +++ b/backend/scripts/daily_push.py @@ -0,0 +1,125 @@ +"""每日数据找人推送 — 路线图R2 (2026-08-30) + +北极星③:主动推送扩大 —— 异常 + 机会两类。 +- 异常类:待处理预警(kpi_alerts pending) +- 机会类:KPI向好 / 预算余量 / 预测上行(opportunity_detector) +复用企微通道 8800/send(公司群中继服务)。 + +用法: /root/cma-management/backend/venv/bin/python3 scripts/daily_push.py [--dry-run] +cron: 15 9 * * * (alert_generator 9:00 之后) +""" +import sys +import os +import json +import logging +import argparse +from datetime import datetime + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from app.database import get_session_local +from app.models import KPIDefinition, KPIAlert +from scripts.opportunity_detector import detect_all, flatten + +logger = logging.getLogger("cma.daily_push") + +RELAY_URL = "http://127.0.0.1:8800/send" +SOURCE = "管理会计OS" + + +def collect_exceptions(db, limit: int = 10) -> list: + """异常类:待处理预警(red/yellow)""" + out = [] + alerts = db.query(KPIAlert).filter( + KPIAlert.status == "pending", + KPIAlert.alert_level.in_(["red", "yellow"]), + ).order_by(KPIAlert.created_at.desc()).limit(limit).all() + for a in alerts: + k = db.query(KPIDefinition).filter(KPIDefinition.id == a.kpi_id).first() + kpi_name = k.kpi_name if k else f"KPI#{a.kpi_id}" + icon = "🔴" if a.alert_level == "red" else "🟡" + out.append({ + "type": "exception", + "title": f"{icon} {kpi_name} 预警", + "detail": f"({a.alert_level}) {a.alert_message}", + "kpi_id": a.kpi_id, + "kpi_name": kpi_name, + "period": "", + }) + return out + + +def build_message(exceptions: list, opportunities: list) -> str: + """组装 markdown 推送内容""" + now = datetime.now().strftime("%Y-%m-%d %H:%M") + lines = [f"## 📊 管理会计OS · 每日经营播报", f"**{now}**", ""] + + lines.append("### ⚠️ 异常关注") + if exceptions: + for e in exceptions: + lines.append(f"- {e['title']}") + lines.append(f" {e['detail']}") + else: + lines.append("- 今日无待处理预警 ✅") + + lines.append("") + lines.append("### 🎯 机会发现") + if opportunities: + for o in opportunities: + lines.append(f"- {o['title']}") + lines.append(f" {o['detail']}") + else: + lines.append("- 今日暂无显著机会") + + lines.append("") + lines.append("---") + lines.append("💡 数据找人:异常要处理,机会要把握。详情见 CMA 系统。") + return "\n".join(lines) + + +def push_wecom(msg: str) -> dict: + """通过8800中继推送企微""" + import urllib.request + import urllib.parse + data = urllib.parse.urlencode({ + "msg": msg, + "source": SOURCE, + "msgtype": "markdown", + }).encode("utf-8") + req = urllib.request.Request(RELAY_URL, data=data, + headers={"Content-Type": "application/x-www-form-urlencoded"}) + try: + with urllib.request.urlopen(req, timeout=15) as resp: + result = json.loads(resp.read().decode("utf-8")) + return result + except Exception as e: + return {"ok": False, "error": f"推送异常: {e}"} + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument("--dry-run", action="store_true", help="只打印不推送") + parser.add_argument("--entity-id", type=int, default=1) + args = parser.parse_args() + + db = get_session_local()() + try: + exceptions = collect_exceptions(db) + opportunities = flatten(detect_all(db, args.entity_id)) + msg = build_message(exceptions, opportunities) + + if args.dry_run: + print(msg) + print(f"\n[DRY-RUN] 异常{len(exceptions)}条 / 机会{len(opportunities)}条") + return + + result = push_wecom(msg) + print(f"推送结果: {json.dumps(result, ensure_ascii=False)}") + print(f"统计: 异常{len(exceptions)}条 / 机会{len(opportunities)}条") + finally: + db.close() + + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + main() diff --git a/backend/scripts/opportunity_detector.py b/backend/scripts/opportunity_detector.py new file mode 100644 index 00000000..24e573ba --- /dev/null +++ b/backend/scripts/opportunity_detector.py @@ -0,0 +1,169 @@ +"""机会检测器 — 路线图R2 数据找人扩大 (2026-08-30) + +北极星③:主动推送扩大 —— 异常 + 机会两类。 +本脚本检测三类机会(复用 budget/kpi 数据,不新建表): + 1. KPI向好 (kpi_improving) : 最近3期执行率>110% 且最新期呈上升趋势 + 2. 预算余量 (budget_headroom): 可用预算>30%(预算执行率<70%) + 3. 滚动机会 (rolling_up) : 预测值上升(kpi_forecast_log 最新>上期) + +输出:机会列表 [{type, title, detail, kpi_id, kpi_name, period}] +""" +import sys +import os +import json +from datetime import datetime + +sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__)))) + +from app.database import get_session_local +from app.models import KPIDefinition, KPIValue, BudgetPlan, KpiForecastLog + +HIGH_RATIO = 1.1 # 执行率>110% = 超预期 +LOW_EXEC_RATIO = 0.7 # 执行率<70% = 预算余量大(可用>30%) + + +def _exec_ratio(actual, target): + if target is None or target == 0: + return None + return actual / target + + +def detect_kpi_improving(db, entity_id: int, min_ratio: float = HIGH_RATIO) -> list: + """KPI向好:最近3期执行率均>110%,且最新期>上期(上升中)""" + out = [] + kpis = db.query(KPIDefinition).filter( + KPIDefinition.entity_id == entity_id, + KPIDefinition.status == "active", + ).all() + now = datetime.now() + for k in kpis: + if not k.target_value or k.target_value <= 0: + continue + vals = db.query(KPIValue).filter( + KPIValue.kpi_id == k.id, + KPIValue.actual_value.isnot(None), + ).order_by(KPIValue.period.desc()).limit(3).all() + if len(vals) < 3: + continue + ratios = [_exec_ratio(v.actual_value, k.target_value) for v in vals] + if any(r is None or r < min_ratio for r in ratios): + continue + # 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) + latest, prev = vals[0], vals[1] + trend = "上升" if latest.actual_value > prev.actual_value else "高位" + out.append({ + "type": "kpi_improving", + "title": f"📈 {k.kpi_name} 持续向好", + "detail": (f"{latest.period}实际{latest.actual_value:g}/目标{k.target_value:g}" + f" 达成率{ratios[0]*100:.0f}%({trend}),近3期均超110%"), + "kpi_id": k.id, + "kpi_name": k.kpi_name, + "period": latest.period, + }) + return out + + +def detect_budget_headroom(db, entity_id: int, max_ratio: float = LOW_EXEC_RATIO) -> list: + """预算余量:当月预算执行率<70%(可用预算>30%) + + 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), + 同 KPI 同期间多版本预算只取一条(去重)。 + """ + out = [] + period = datetime.now().strftime("%Y-%m") + rows = db.query(BudgetPlan).filter( + BudgetPlan.entity_id == entity_id, + BudgetPlan.status == "active", + BudgetPlan.period == period, + BudgetPlan.budget_value > 0, + ).all() + seen = set() + for b in rows: + key = (b.kpi_id, b.period) + if key in seen: + continue + seen.add(key) + actual = db.query(KPIValue).filter( + KPIValue.kpi_id == b.kpi_id, + KPIValue.period == b.period, + KPIValue.actual_value.isnot(None), + ).order_by(KPIValue.calculated_at.desc()).first() + if not actual or actual.actual_value is None or actual.actual_value <= 0: + continue + ratio = actual.actual_value / b.budget_value + if ratio < max_ratio: + k = db.query(KPIDefinition).filter(KPIDefinition.id == b.kpi_id).first() + kpi_name = k.kpi_name if k else f"KPI#{b.kpi_id}" + headroom = (1 - ratio) * 100 + out.append({ + "type": "budget_headroom", + "title": f"💼 {kpi_name} 预算余量充足", + "detail": (f"{period}预算{b.budget_value:g}/实际{actual.actual_value:g}" + f" 执行率{ratio*100:.0f}%,可用预算余量约{headroom:.0f}%"), + "kpi_id": b.kpi_id, + "kpi_name": kpi_name, + "period": period, + }) + return out + + +def detect_rolling_up(db, entity_id: int) -> list: + """滚动机会:预测值上升(最新预测 > 上期预测)""" + out = [] + # 每个KPI取最近两条预测记录 + kpi_ids = [r[0] for r in db.query(KpiForecastLog.kpi_id).filter( + KpiForecastLog.entity_id == entity_id).distinct().limit(50).all()] + for kid in kpi_ids: + rows = db.query(KpiForecastLog).filter( + KpiForecastLog.entity_id == entity_id, + KpiForecastLog.kpi_id == kid, + KpiForecastLog.forecast_value.isnot(None), + ).order_by(KpiForecastLog.created_at.desc(), KpiForecastLog.id.desc()).limit(2).all() + if len(rows) < 2: + continue + latest, prev = rows[0], rows[1] + if latest.forecast_value > prev.forecast_value: + k = db.query(KPIDefinition).filter(KPIDefinition.id == kid).first() + kpi_name = k.kpi_name if k else f"KPI#{kid}" + pct = (latest.forecast_value / prev.forecast_value - 1) * 100 if prev.forecast_value else 0 + out.append({ + "type": "rolling_up", + "title": f"🔮 {kpi_name} 预测上行", + "detail": (f"预测值 {prev.forecast_value:g} → {latest.forecast_value:g}" + f" (+{pct:.1f}%),{latest.period}期间"), + "kpi_id": kid, + "kpi_name": kpi_name, + "period": latest.period, + }) + return out + + +def detect_all(db, entity_id: int = 1) -> dict: + """检测全部机会,按类型分组""" + return { + "kpi_improving": detect_kpi_improving(db, entity_id), + "budget_headroom": detect_budget_headroom(db, entity_id), + "rolling_up": detect_rolling_up(db, entity_id), + } + + +def flatten(detected: dict) -> list: + out = [] + for cat in ("kpi_improving", "budget_headroom", "rolling_up"): + out.extend(detected.get(cat, [])) + return out + + +def main(): + db = get_session_local()() + try: + detected = detect_all(db) + total = sum(len(v) for v in detected.values()) + print(json.dumps(detected, ensure_ascii=False, indent=2)) + print(f"\n机会总数: {total}") + finally: + db.close() + + +if __name__ == "__main__": + main() diff --git a/backend/scripts/verify_decompose_idempotent.py b/backend/scripts/verify_decompose_idempotent.py new file mode 100644 index 00000000..ae6853f5 --- /dev/null +++ b/backend/scripts/verify_decompose_idempotent.py @@ -0,0 +1,56 @@ +"""验证年度预算分解幂等 — R5 (2026-08-30) + +调用 /api/cma/budget/auto-decompose 3 次,对比月度预算值是否不变。 +用法: cd /root/cma-management/backend && ./venv/bin/python3 scripts/verify_decompose_idempotent.py +""" +import sys +import os +import json +import urllib.request + +BASE = os.getenv("CMA_BASE", "http://127.0.0.1:8010") + + +def post(path, body, token=None): + req = urllib.request.Request( + BASE + path, + data=json.dumps(body).encode("utf-8"), + headers={"Content-Type": "application/json", + "Authorization": f"Bearer {token}" if token else ""}, + ) + with urllib.request.urlopen(req, timeout=30) as resp: + return json.loads(resp.read().decode("utf-8")) + + +def main(): + # 登录(账套模式必须 entity_id) + login = post("/api/cma/auth/login", {"username": "admin", "password": "admin123", "entity_id": 1}) + token = login.get("token") or login.get("access_token") + if not token: + print("❌ 登录失败:", login) + sys.exit(1) + print("✅ 登录成功") + + runs = [] + for i in range(3): + r = post("/api/cma/budget/auto-decompose", {"year": 2026, "method": "equal", "version": "v1.0"}, token) + print(f"第{i+1}次: {r.get('message', '')} created={r.get('created', 0)}") + # 提取 (kpi_id -> monthly tuple) + snap = {} + for res in r.get("results", []): + snap[res["kpi_id"]] = tuple(res.get("monthly") or []) + runs.append(snap) + + # 对比三次结果 + same = runs[0] == runs[1] == runs[2] + print(f"\n三次结果一致: {'✅ 是(幂等)' if same else '❌ 否(不幂等)'}") + if not same: + for i in range(1, 3): + for kid in runs[0]: + if runs[0].get(kid) != runs[i].get(kid): + print(f" KPI {kid} 第1次={runs[0].get(kid)} 第{i+1}次={runs[i].get(kid)}") + sys.exit(0 if same else 1) + + +if __name__ == "__main__": + main() diff --git a/backend/tests/conftest.py b/backend/tests/conftest.py index f83e8a16..a8370e99 100644 --- a/backend/tests/conftest.py +++ b/backend/tests/conftest.py @@ -82,9 +82,12 @@ import hashlib @pytest.fixture(autouse=True) def setup_db(): """每个测试函数自动初始化和清理数据库""" + from app.utils import cache as cache_util + cache_util.delete("ai") # 清AI分析缓存,防测试间Redis污染(dashboard-analysis缓存全局共享) Base.metadata.create_all(bind=TEST_ENGINE) yield Base.metadata.drop_all(bind=TEST_ENGINE) + cache_util.delete("ai") @pytest.fixture diff --git a/backend/tests/test_ai_suggestions.py b/backend/tests/test_ai_suggestions.py new file mode 100644 index 00000000..d6908516 --- /dev/null +++ b/backend/tests/test_ai_suggestions.py @@ -0,0 +1,242 @@ +""" +路线图R1:AI建议→一键落地 测试 +建议CRUD + 应用到KPI/预算/行动方案 + OperationLog留痕 + 已应用/未应用状态 +""" +import pytest +from fastapi.testclient import TestClient +from sqlalchemy.orm import Session +from datetime import datetime + +from tests.conftest import create_test_user, get_token_for_user, auth_header, create_test_kpi +from app.models import AISuggestion, KPIDefinition, BudgetPlan, ActionPlan, OperationLog, KPIValue + + +def _create_suggestion(client, token, kpi_id, **kw): + body = { + "suggestion_type": "kpi_target", + "target_type": "kpi", + "target_id": kpi_id, + "title": "上调测试KPI目标", + "content": "达成率超预期", + "suggestion_data": {"kpi_id": kpi_id, "target_value": 150.0}, + } + body.update(kw) + return client.post("/api/cma/ai/suggestions", json=body, headers=auth_header(token)) + + +class TestSuggestionCRUD: + def test_create_and_list(self, client, db): + create_test_user(db) + token = get_token_for_user(client) + kpi = create_test_kpi(db) + + r = _create_suggestion(client, token, kpi.id) + assert r.status_code == 200, r.text + data = r.json()["data"] + assert data["status"] == "unapplied" + assert data["suggestion_type"] == "kpi_target" + + # 列表含未应用 + lst = client.get("/api/cma/ai/suggestions", headers=auth_header(token)).json() + assert lst["total"] == 1 + assert lst["data"][0]["id"] == data["id"] + + # 详情 + det = client.get(f"/api/cma/ai/suggestions/{data['id']}", headers=auth_header(token)).json() + assert det["data"]["title"] == "上调测试KPI目标" + + def test_create_missing_fields(self, client, db): + create_test_user(db) + token = get_token_for_user(client) + r = client.post("/api/cma/ai/suggestions", json={"title": "无类型"}, headers=auth_header(token)) + assert r.status_code == 400 + r2 = client.post("/api/cma/ai/suggestions", json={"suggestion_type": "kpi_target"}, headers=auth_header(token)) + assert r2.status_code == 400 + + def test_apply_kpi_target(self, client, db): + """应用建议→改KPI目标→操作日志可查""" + create_test_user(db) + token = get_token_for_user(client) + kpi = create_test_kpi(db, target_value=100.0) + + r = _create_suggestion(client, token, kpi.id) + sug_id = r.json()["data"]["id"] + + # 应用:改KPI目标为150 + app = client.post( + f"/api/cma/ai/suggestions/{sug_id}/apply", + json={"action": "kpi_target", "target_value": 150.0}, + headers=auth_header(token), + ) + assert app.status_code == 200, app.text + app_data = app.json()["data"] + assert app_data["status"] == "applied" + assert app_data["applied_by"] == "测试管理员" + assert app_data["apply_detail"][0]["before"] == 100.0 + assert app_data["apply_detail"][0]["after"] == 150.0 + + # KPI目标已变更 + db.refresh(kpi) + assert kpi.target_value == 150.0 + + # OperationLog留痕 + logs = db.query(OperationLog).filter(OperationLog.action == "ai_suggestion_apply").all() + assert len(logs) == 1 + assert logs[0].target_type == "kpi" + assert logs[0].target_id == kpi.id + assert logs[0].detail["suggestion_id"] == sug_id + assert logs[0].detail["before"] == 100.0 + assert logs[0].detail["after"] == 150.0 + + # 重复应用被拒绝 + app2 = client.post( + f"/api/cma/ai/suggestions/{sug_id}/apply", + json={"action": "kpi_target", "target_value": 200.0}, + headers=auth_header(token), + ) + assert app2.status_code == 400 + + def test_apply_budget_adjust(self, client, db): + """应用建议→调预算(新建/更新BudgetPlan)→操作日志""" + create_test_user(db) + token = get_token_for_user(client) + kpi = create_test_kpi(db) + + r = _create_suggestion(client, token, kpi.id, suggestion_type="budget_adjust", + title="调整预算", suggestion_data={"kpi_id": kpi.id}) + sug_id = r.json()["data"]["id"] + + app = client.post( + f"/api/cma/ai/suggestions/{sug_id}/apply", + json={"action": "budget_adjust", "period": "2026-09", "budget_value": 8888.0}, + headers=auth_header(token), + ) + assert app.status_code == 200, app.text + plan = db.query(BudgetPlan).filter(BudgetPlan.kpi_id == kpi.id, BudgetPlan.period == "2026-09").first() + assert plan is not None + assert plan.budget_value == 8888.0 + assert plan.source_type == "ai_suggestion" + + # 同期间再应用→更新而非新增 + app2 = client.post( + f"/api/cma/ai/suggestions/{sug_id}/apply", + json={"action": "budget_adjust", "period": "2026-09", "budget_value": 9999.0}, + headers=auth_header(token), + ) + # 已applied被拒;用新建议验证upsert + r2 = _create_suggestion(client, token, kpi.id, suggestion_type="budget_adjust", + title="调整预算2", suggestion_data={"kpi_id": kpi.id}) + sug_id2 = r2.json()["data"]["id"] + app3 = client.post( + f"/api/cma/ai/suggestions/{sug_id2}/apply", + json={"action": "budget_adjust", "period": "2026-09", "budget_value": 9999.0}, + headers=auth_header(token), + ) + assert app3.status_code == 200 + plans = db.query(BudgetPlan).filter(BudgetPlan.kpi_id == kpi.id, BudgetPlan.period == "2026-09").all() + assert len(plans) == 1 + assert plans[0].budget_value == 9999.0 + assert app3.json()["data"]["apply_detail"][0]["before"] == 8888.0 + + def test_apply_action_plan(self, client, db): + """应用建议→建行动方案→操作日志""" + create_test_user(db) + token = get_token_for_user(client) + kpi = create_test_kpi(db) + + r = _create_suggestion(client, token, kpi.id, suggestion_type="action_plan", + title="建行动方案", suggestion_data={"kpi_id": kpi.id}) + sug_id = r.json()["data"]["id"] + + app = client.post( + f"/api/cma/ai/suggestions/{sug_id}/apply", + json={"action": "action_plan", "title": "营收提升专项", "assignee": "张三", + "priority": "high", "due_date": "2026-09-30"}, + headers=auth_header(token), + ) + assert app.status_code == 200, app.text + plan = db.query(ActionPlan).filter(ActionPlan.kpi_id == kpi.id, ActionPlan.title == "营收提升专项").first() + assert plan is not None + assert plan.assignee == "张三" + assert plan.priority == "high" + assert plan.created_by == "测试管理员" + + logs = db.query(OperationLog).filter(OperationLog.action == "ai_suggestion_apply", + OperationLog.target_type == "action_plan").all() + assert len(logs) == 1 + assert logs[0].target_id == plan.id + + def test_dismiss(self, client, db): + create_test_user(db) + token = get_token_for_user(client) + kpi = create_test_kpi(db) + r = _create_suggestion(client, token, kpi.id) + sug_id = r.json()["data"]["id"] + + d = client.post(f"/api/cma/ai/suggestions/{sug_id}/dismiss", headers=auth_header(token)) + assert d.status_code == 200 + det = client.get(f"/api/cma/ai/suggestions/{sug_id}", headers=auth_header(token)).json() + assert det["data"]["status"] == "dismissed" + # 忽略后应用被拒 + app = client.post(f"/api/cma/ai/suggestions/{sug_id}/apply", + json={"action": "kpi_target", "target_value": 1}, headers=auth_header(token)) + assert app.status_code == 400 + + def test_apply_not_found(self, client, db): + create_test_user(db) + token = get_token_for_user(client) + app = client.post("/api/cma/ai/suggestions/9999/apply", json={}, headers=auth_header(token)) + assert app.status_code == 404 + + +class TestRuleSuggestions: + """dashboard-analysis 自动生成建议(规则驱动)""" + + def test_generate_low_ratio_action(self, client, db): + """执行率<70% → 生成建行动方案建议""" + create_test_user(db) + token = get_token_for_user(client) + kpi = create_test_kpi(db, target_value=100.0) + db.add(KPIValue(kpi_id=kpi.id, period="2026-06", actual_value=50.0)) + db.commit() + + # 直接调规则生成 + resp = client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token)) + assert resp.status_code == 200 + s = db.query(AISuggestion).filter(AISuggestion.target_id == kpi.id).all() + assert len(s) >= 1 + assert any(x.suggestion_type == "action_plan" for x in s) + + # 幂等:再调一次不重复建 + resp2 = client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token)) + s2 = db.query(AISuggestion).filter(AISuggestion.target_id == kpi.id).all() + assert len(s2) == len(s) + + def test_generate_high_ratio_target(self, client, db): + """执行率>110% → 生成上调目标建议""" + create_test_user(db) + token = get_token_for_user(client) + kpi = create_test_kpi(db, target_value=100.0) + db.add(KPIValue(kpi_id=kpi.id, period="2026-06", actual_value=150.0)) + db.commit() + + resp = client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token)) + assert resp.status_code == 200 + s = db.query(AISuggestion).filter(AISuggestion.target_id == kpi.id).all() + assert any(x.suggestion_type == "kpi_target" for x in s) + assert "suggestions" in resp.json() + + def test_generate_budget_overrun(self, client, db): + """预算执行率>110% → 生成调预算建议""" + create_test_user(db) + token = get_token_for_user(client) + kpi = create_test_kpi(db, target_value=100.0) + db.add(KPIValue(kpi_id=kpi.id, period="2026-08", actual_value=200.0)) + db.add(BudgetPlan(entity_id=1, kpi_id=kpi.id, period="2026-08", budget_value=100.0, + budget_year=2026, budget_month=8, status="active")) + db.commit() + + resp = client.get("/api/cma/ai/dashboard-analysis", headers=auth_header(token)) + assert resp.status_code == 200 + s = db.query(AISuggestion).filter(AISuggestion.suggestion_type == "budget_adjust").all() + assert len(s) >= 1 diff --git a/backend/tests/test_budget.py b/backend/tests/test_budget.py index a302a4d3..f6916534 100644 --- a/backend/tests/test_budget.py +++ b/backend/tests/test_budget.py @@ -663,9 +663,9 @@ class TestBudgetContract20260825: token = get_token_for_user(client) kpi = create_test_kpi(db, kpi_code="CONTRACT_DECOMP") - # 先创建年度预算(period=2026-00 或任意月份记录,让批量分解能聚合到) + # 先创建年度预算(period=YYYY-00 年度行,批量分解只取年度行 — 幂等契约) client.post(f"{self.BASE}/plans", headers=auth_header(token), - json={"kpi_id": kpi.id, "period": "2026-01", "budget_value": 12000.0, "budget_year": 2026, "budget_month": 1}) + json={"kpi_id": kpi.id, "period": "2026-00", "budget_value": 12000.0, "budget_year": 2026, "budget_month": 0}) # 第一次批量分解 resp1 = client.post(f"{self.BASE}/auto-decompose", headers=auth_header(token), diff --git a/backend/tests/test_roadmap_r2r5.py b/backend/tests/test_roadmap_r2r5.py new file mode 100644 index 00000000..8b7b48a1 --- /dev/null +++ b/backend/tests/test_roadmap_r2r5.py @@ -0,0 +1,187 @@ +""" +路线图R2/R5 测试(2026-08-30) +R2: 机会检测(KPI向好/预算余量/预测上行) +R5: 预算↔现金流↔行动 闭环自检 +""" +import pytest +from datetime import datetime +from sqlalchemy.orm import Session + +from tests.conftest import create_test_kpi +from app.models import KPIDefinition, KPIValue, BudgetPlan, CashPlan, ActionPlan, KpiForecastLog + +from scripts.opportunity_detector import ( + detect_kpi_improving, detect_budget_headroom, detect_rolling_up, detect_all, flatten, +) +from scripts.closed_loop_check import check_entity, build_report + + +def _kpi(db, code, target=100.0, **kw): + return create_test_kpi(db, kpi_code=code, target_value=target, **kw) + + +def _value(db, kpi_id, period, actual, entity_id=1): + v = KPIValue(kpi_id=kpi_id, period=period, actual_value=actual, entity_id=entity_id) + db.add(v) + return v + + +def _budget(db, kpi_id, period, value, year=None, month=None, entity_id=1): + if year is None: + year = int(period.split("-")[0]) + month = int(period.split("-")[1]) + b = BudgetPlan(entity_id=entity_id, kpi_id=kpi_id, period=period, budget_value=value, + budget_year=year, budget_month=month, version="v1.0", status="active") + db.add(b) + return b + + +class TestOpportunityR2: + def test_kpi_improving(self, db): + """连续3期执行率>110% → KPI向好机会""" + kpi = _kpi(db, "OPP_01", target=100.0) + _value(db, kpi.id, "2026-04", 120.0) + _value(db, kpi.id, "2026-05", 130.0) + _value(db, kpi.id, "2026-06", 140.0) + db.commit() + out = detect_kpi_improving(db, 1) + assert len(out) == 1 + assert out[0]["type"] == "kpi_improving" + assert out[0]["kpi_id"] == kpi.id + + def test_kpi_improving_not_enough_data(self, db): + """不足3期不判定""" + kpi = _kpi(db, "OPP_02", target=100.0) + _value(db, kpi.id, "2026-05", 130.0) + _value(db, kpi.id, "2026-06", 140.0) + db.commit() + assert detect_kpi_improving(db, 1) == [] + + def test_kpi_improving_low_ratio_skip(self, db): + """执行率未超110%不判定""" + kpi = _kpi(db, "OPP_03", target=100.0) + _value(db, kpi.id, "2026-04", 90.0) + _value(db, kpi.id, "2026-05", 95.0) + _value(db, kpi.id, "2026-06", 100.0) + db.commit() + assert detect_kpi_improving(db, 1) == [] + + def test_budget_headroom(self, db): + """当月预算执行率<70% → 预算余量机会""" + kpi = _kpi(db, "OPP_04", target=1000.0) + _value(db, kpi.id, "2026-08", 300.0) + _budget(db, kpi.id, "2026-08", 1000.0) + db.commit() + out = detect_budget_headroom(db, 1) + assert len(out) == 1 + assert out[0]["type"] == "budget_headroom" + + def test_budget_headroom_negative_skip(self, db): + """实际值为负(现金流异常)不误判为余量""" + kpi = _kpi(db, "OPP_05", target=1000.0) + _value(db, kpi.id, "2026-08", -500.0) + _budget(db, kpi.id, "2026-08", 1000.0) + db.commit() + assert detect_budget_headroom(db, 1) == [] + + def test_budget_headroom_dedup(self, db): + """同KPI同期间多版本预算只取一条""" + kpi = _kpi(db, "OPP_06", target=1000.0) + _value(db, kpi.id, "2026-08", 300.0) + _budget(db, kpi.id, "2026-08", 1000.0) + b2 = _budget(db, kpi.id, "2026-08", 2000.0) + b2.version = "v2.0" + db.commit() + assert len(detect_budget_headroom(db, 1)) == 1 + + def test_rolling_up(self, db): + """预测值上升 → 滚动机会""" + kpi = _kpi(db, "OPP_07", target=100.0) + now = datetime.now() + db.add(KpiForecastLog(entity_id=1, kpi_id=kpi.id, kpi_code=kpi.kpi_code, + period="2026-07", forecast_value=100.0, model="linear", + created_at=now)) + db.add(KpiForecastLog(entity_id=1, kpi_id=kpi.id, kpi_code=kpi.kpi_code, + period="2026-08", forecast_value=130.0, model="linear", + created_at=now)) + db.commit() + out = detect_rolling_up(db, 1) + assert len(out) == 1 + assert out[0]["type"] == "rolling_up" + + def test_rolling_down_skip(self, db): + """预测下降不判定为机会""" + kpi = _kpi(db, "OPP_08", target=100.0) + now = datetime.now() + db.add(KpiForecastLog(entity_id=1, kpi_id=kpi.id, kpi_code=kpi.kpi_code, + period="2026-07", forecast_value=130.0, model="linear", + created_at=now)) + db.add(KpiForecastLog(entity_id=1, kpi_id=kpi.id, kpi_code=kpi.kpi_code, + period="2026-08", forecast_value=100.0, model="linear", + created_at=now)) + db.commit() + assert detect_rolling_up(db, 1) == [] + + def test_flatten(self): + d = {"kpi_improving": [1], "budget_headroom": [2, 3], "rolling_up": []} + assert flatten(d) == [1, 2, 3] + + +class TestClosedLoopR5: + def test_overrun_missing_both(self, db): + """超预算且缺现金流/行动 → 提示同步""" + kpi = _kpi(db, "CL_01", target=100.0) + _value(db, kpi.id, "2026-08", 200.0) + _budget(db, kpi.id, "2026-08", 100.0) + db.commit() + r = check_entity(db, 1, "2026-08") + assert len(r["issues"]) == 1 + it = r["issues"][0] + assert it["abnormal_type"] == "超预算" + assert "现金流" in it["missing"] + assert "行动方案" in it["missing"] + + def test_overrun_has_cash_and_action(self, db): + """超预算但有现金流+行动 → 三闭环同步""" + kpi = _kpi(db, "CL_02", target=100.0) + _value(db, kpi.id, "2026-08", 200.0) + b = _budget(db, kpi.id, "2026-08", 100.0) + db.add(CashPlan(entity_id=1, plan_type="receive", related_kpi_id=kpi.id, budget_plan_id=b.id, + amount=200.0, plan_date=datetime(2026, 8, 15), status="pending")) + db.add(ActionPlan(kpi_id=kpi.id, title="改善计划", status="in_progress")) + db.commit() + r = check_entity(db, 1, "2026-08") + assert len(r["issues"]) == 1 + assert r["issues"][0]["missing"] == [] + + def test_normal_no_issue(self, db): + """执行率正常 → 无异常""" + kpi = _kpi(db, "CL_03", target=100.0) + _value(db, kpi.id, "2026-08", 100.0) + _budget(db, kpi.id, "2026-08", 100.0) + db.commit() + r = check_entity(db, 1, "2026-08") + assert r["issues"] == [] + + def test_low_execution(self, db): + """低执行率 → 异常(warning)""" + kpi = _kpi(db, "CL_04", target=100.0) + _value(db, kpi.id, "2026-08", 50.0) + _budget(db, kpi.id, "2026-08", 100.0) + db.commit() + r = check_entity(db, 1, "2026-08") + assert len(r["issues"]) == 1 + assert r["issues"][0]["abnormal_type"] == "低执行" + assert r["issues"][0]["level"] == "warning" + + def test_build_report(self): + result = {"entity_id": 1, "period": "2026-08", "issues": [ + {"kpi_id": 1, "kpi_name": "营收", "period": "2026-08", "budget_value": 100.0, + "actual_value": 200.0, "exec_ratio": 200.0, "abnormal_type": "超预算", + "level": "critical", "cash_plan_count": 0, "action_plan_count": 0, + "missing": ["现金流", "行动方案"], "suggestion": "请同步现金流、行动方案"} + ]} + report = build_report([result], "2026-08-30 12:00:00") + assert "闭环自检" in report + assert "营收" in report + assert "共发现异常 1 项" in report diff --git a/docs/cma-product-vision-roadmap-v1.md b/docs/cma-product-vision-roadmap-v1.md new file mode 100644 index 00000000..6d4c3904 --- /dev/null +++ b/docs/cma-product-vision-roadmap-v1.md @@ -0,0 +1,84 @@ +# CMA 产品愿景与实施路线图 v1.0(2026-08-30 固化) + +> 提出:任富海 | 整理:项目Bot | 状态:✅ 已确认(北极星)+ 战略全景(8方向) + +## 一、产品愿景(北极星) + +**一句话**:CMA = 管理会计操作系统——数据接入(接口/手工) → 多Bot互动数据 → 数据找人(主动) → 决策建议 → 决策修改+检查(闭环可审计)。 + +**四层北极星**: +``` +① 数据接入(财务软件接口/手工录入) + ↓ +② 多Bot互动(14Bot协作处理数据) + ↓ +③ 数据找人(异常+机会主动推送) + ↓ +④ 决策建议 → 决策修改+检查(闭环) +``` + +**价值主张**:让管理团队从"被数据淹没"到"数据找人、人做决策、决策留痕"——每项决策可追溯(谁/何时/依据什么/结果如何)。 + +## 二、战略全景(8 补充方向) + +| 方向 | 定位 | 优先级 | +|:--|:--|:--:| +| A 产品化/商业化 | CMA→可交付产品(SaaS/私有/实施) | 🔴 | +| B 行业纵深 | 白酒经销→贸易→制造(行业包) | 🟠 | +| C 数据资产化 | 博海+客户数据→数据产品(DAMA治理) | 🟠 | +| D 决策智能 | 提建议→预测决策(敏感性/因果/复盘) | 🔴 | +| E AI原生组织方法论 | **护城河**:14Bot/铁律/闭环体系产品化 | 🟡 | +| F 生态联盟 | 财务软件对接(用友/金蝶)+渠道 | 🟡 | +| G 信任合规 | PIPL/等保/AI可信/审计链 | 🟠 | +| H 技术前瞻 | 数字员工/AI同事/Agent自动执行 | 🟡 | + +## 三、实施路线图(按优先级) + +### 🔴 近期(1-3个月)——北极星核心闭环 +| # | 方向 | 目标 | 关键动作 | 验收 | +|:--|:--|:--|:--|:--| +| R1 | ④决策智能 | AI建议→一键落地 | ai_analysis 建议可"应用到KPI/预算/行动"(写库+留痕) | 建议生成→点击落地→操作日志可查 | +| R2 | ③数据找人 | 主动推送扩大 | 机会/趋势推送(不止异常):KPI向好/预算余量/滚动机会 | 每日推送含异常+机会两类 | +| R3 | ①数据接入调研 | 财务软件接口方案 | 调研用友/金蝶/管家婆开放API+实施成本 | 接口可行性报告 | +| R4 | A产品化准备 | 酣客试点成案例 | 试点数据闭环+试点报告(作首个客户案例) | 案例文档+官网可引用 | +| R5 | 预算bug修复链 | 系统稳定 | 年度分解幂等+预算/现金流/行动闭环加固 | pytest全绿 | + +### 🟠 中期(3-6个月)——产品化+合规 +| # | 方向 | 目标 | 关键动作 | 验收 | +|:--|:--|:--|:--|:--| +| M1 | A产品化 | CMA可交付形态 | SaaS多租户完善/私有部署包/实施文档 | 第2-3个客户可用 | +| M2 | B行业复制 | 白酒经销行业包 | 行业KPI库/OKR模板/科目模板校准(酣客数据) | 行业包v1 | +| M3 | G合规 | 信任背书 | 数据安全分级/PIPL清单/审计链完善 | 合规清单 | +| M4 | ①数据接入落地 | 财务软件接口 | 按R3方案接入1个财务软件 | 接口联调通过 | + +### 🟡 远期(6-12个月)——方法论+生态 +| # | 方向 | 目标 | 关键动作 | 验收 | +|:--|:--|:--|:--|:--| +| F1 | E方法论 | AI原生组织产品 | 评估+实施+运营三件套方法论文档化 | 方法论v1可售 | +| F2 | F生态 | 渠道伙伴 | 代账/咨询/本地IT渠道首批 | 3家伙伴 | +| F3 | H前瞻 | 数字员工试点 | AI同事(自动执行例行决策)试点 | 试点报告 | +| F4 | D完整版 | 预测性成本智能完整 | 宏观数据回归校准+预测偏差告警完善 | IMA对标 | + +## 四、依赖与飞轮 + +``` +R4酣客案例 → M1产品化 → F1方法论 → F2生态 + ↑____________↑___________________↓ + 数据/案例反哺(B行业包) +``` + +**飞轮起点**:近期 R1-R5(决策闭环+稳定+案例)——先让内部系统达到"决策可落地可追溯",再谈产品化。 + +## 五、北极星四层 → 落地项(映射) + +| 层 | 近期 | 中期 | 远期 | +|:--|:--|:--|:--| +| ①数据接入 | R3调研 | M4接口落地 | 多软件适配 | +| ②多Bot互动 | 保持 | Bot联合决策 | Agent自动执行(F3) | +| ③数据找人 | R2推送扩大 | 推送策略化 | AI同事(F3) | +| ④决策闭环 | R1建议落地+R5稳定 | 决策复盘闭环 | 预测决策(F4) | + +## 六、节奏建议 +- **月度检查点**:每月对照路线图验收(R/M/F 项完成度) +- **北极星校验**:每季度问"数据找人了吗?决策落地了吗?可追溯吗?" +- **资源配置**:近期全栈Bot集中 R1/R2/R5(代码);项目Bot R3调研+R4案例(方案) diff --git a/docs/cma-roadmap-r1r5-tasklist-20260830.md b/docs/cma-roadmap-r1r5-tasklist-20260830.md new file mode 100644 index 00000000..7e408230 --- /dev/null +++ b/docs/cma-roadmap-r1r5-tasklist-20260830.md @@ -0,0 +1,26 @@ +# CMA 路线图近期任务清单(R1-R5,2026-08-30 派单) + +> 依据:docs/cma-product-vision-roadmap-v1.md 近期🔴项 + +## 已派全栈Bot(代码执行) + +| 任务 | 内容 | 优先级 | +|:--|:--|:--:| +| **cma-roadmap-r1r2r5-20260830.md** | R1 AI建议→一键落地(应用KPI/预算/行动+留痕)
R2 数据找人扩大(机会类推送:KPI向好/预算余量)
R5 预算闭环加固(年度分解幂等确认+闭环自检) | P0/P1 | +| **data-quality-api-plus-v72-20260830.md** | ⓪预算年度分解累加bug(P0)
①数据质量API 7规则
②扫描脚本v7.2补做
③执行人中文名兼容 | P1 | + +## 项目Bot负责(方案/调研/案例) + +### R3 财务软件接口调研(✅ 已完成初步结论) +- **结论:可行**——用友/金蝶云有开放平台API(标准连接器),管家婆有API(erp.btype.list等),金蝶云星空支持表单查询/保存/提交/审核 +- 2026 ERP API 开放性评估:用友/金蝶表现突出 +- **待确认**:酣客实际使用哪套财务软件(用友/金蝶/管家婆/其他)→ 决定先接哪个适配器 + +### R4 酣客试点成案例(⏳ 待数据确认) +- 方案已出:docs/hanke-pilot-plan-20260829.md(3天节奏) +- 待你确认:KPI目标值 / 真实实际值来源 / 节奏 +- 完成后产出:试点报告 = 首个客户案例(产品化飞轮起点) + +## 执行顺序建议 +1. 全栈:先修 ⓪ 预算分解bug(P0,用户已遇到)→ R1(决策闭环核心)→ R2/R5/数据质量 +2. 项目Bot:R3 等你告知酣客财务软件 → R4 试点数据确认 diff --git a/docs/cma-strategy-vision-20260830.md b/docs/cma-strategy-vision-20260830.md new file mode 100644 index 00000000..3e4fc500 --- /dev/null +++ b/docs/cma-strategy-vision-20260830.md @@ -0,0 +1,79 @@ +# CMA 战略愿景全景规划(2026-08-30) + +> 提出:任富海 | 整理:项目Bot | 北极星:**数据接入→多Bot互动→数据找人→决策建议→决策修改检查**(四层愿景已确认) + +## 一、北极星愿景(已确认,四层) + +``` +① 数据接入(财务软件接口/手工录入) + ↓ +② 多Bot互动(14Bot协作处理数据) + ↓ +③ 数据找人(异常+机会主动推送) + ↓ +④ 决策建议 → 决策修改+检查(闭环可审计) +``` + +## 二、补充战略方向(8个,超越功能层面) + +### A. 产品化与商业化(🔴 最高优先级——让能力变现) +- 现状:CMA 是内部系统,但官网已有 6 服务模块(含AI智能体/数据治理)可承接 +- 方向:CMA → 可交付产品(SaaS/私有部署/实施服务三模式) +- 抓手:酣客试点成功 = 首个可复制案例;行业包 4 类(trading/it_service/manufacturing/general)已具雏形 + +### B. 行业纵深复制 +- 白酒经销(酣客) → 贸易/流通 → 制造业/服务业 +- 每个行业:KPI库/OKR模板/科目模板/预警规则 行业包(已有基础) +- 案例链:酣客成功 → 同行业客户 → 跨行业 + +### C. 数据资产化 +- 博海自身资产:知识库/文章/系统数据 → 数据产品 +- 客户侧:DAMA 数据治理服务(官网已上线)→ 治理→增值 +- 企业数据资产盘点方法论 = 可售服务 + +### D. 决策智能升级(北极星④的深化) +- 从"提建议"→"预测决策":敏感性/情景模拟(已雏形)→ 完整预测性成本智能(IMA) +- 因果链验证(已建)→ 决策优化建议 +- AI 复盘 → 组织学习闭环 + +### E. AI 原生组织方法论(差异化护城河) +- 博海自身 = AI 原生组织样板(14Bot/铁律/闭环) +- 对外输出"AI 原生组织落地方法论"(评估+实施+运营) +- 这是竞品(传统财务软件商)无法快速复制的能力 + +### F. 生态与联盟 +- 财务软件生态:对接用友/金蝶/管家婆(当前**未接**,北极星①的关键缺口) +- 渠道伙伴:代账公司/咨询公司/本地IT服务商 +- 区域深耕:陕西本地企业数字化 + +### G. 信任与合规(销售必要条件) +- 数据安全:PIPL/等保/数据分类分级 +- AI 可信:RAG幻觉治理(Recall 100%已证)+ 验证铁律 = 可信AI叙事 +- 审计链:操作日志/决策留痕(已有) + +### H. 技术前瞻 +- 数字员工:AI 同事(分域扫描已发现 CopilotKit/OpenBot 趋势) +- Agent 自动执行:决策→自动调度 Bot 执行(北极星②④融合) +- 预测智能:KPI趋势/宏观敏感性(已上线 MVP)→ 完整版 + +## 三、战略优先级与时间线 + +| 阶段 | 方向 | 关键动作 | +|:--|:--|:--| +| **近期(1-3月)** | A产品化 + D决策智能 + 北极星① | 财务软件接口调研;AI建议→行动一键落地;酣客试点成案例 | +| **中期(3-6月)** | B行业复制 + G合规 | 白酒经销行业包完善;数据安全/等保认证;第2-3个客户 | +| **远期(6-12月)** | E AI原生组织 + F生态 + H前瞻 | AI原生组织方法论产品化;渠道伙伴;数字员工/AI同事产品 | + +## 四、北极星落地路线(四层 → 实施项) + +| 层 | 当前状态 | 下一步 | +|:--|:--|:--| +| ① 数据接入 | 手工/Excel/Bot | **财务软件接口**(用友/金蝶/管家婆适配器) | +| ② 多Bot互动 | A2A/bot_bridge已有 | Bot联合决策(同数据多Bot出结论) | +| ③ 数据找人 | 预警/偏差/预测告警 | 主动推送扩大(机会+趋势,不止异常) | +| ④ 决策闭环 | 复盘持久化刚补 | **AI建议→一键落地**(建议落到KPI/预算/行动)+审计 | + +## 五、关键洞察 +1. **护城河不是 CMA 功能,是"AI原生组织方法论"**(E)——我们自己在用的整套体系(Bot协作/铁律/闭环/验证)就是最强差异化产品 +2. **产品化(A)是其他一切的前提**——案例→行业包→方法论→生态 +3. **北极星①(财务软件接口)是技术最大缺口**——直接影响"数据接入"自动化 diff --git a/docs/r3-zhixiangtong-adapter-20260830.md b/docs/r3-zhixiangtong-adapter-20260830.md new file mode 100644 index 00000000..b3e1a588 --- /dev/null +++ b/docs/r3-zhixiangtong-adapter-20260830.md @@ -0,0 +1,32 @@ +# R3 财务软件接口调研(智享通·友加畅捷)2026-08-30 结论 + +> 酣客实际财务软件:**智享通**(成都友加畅捷,youjiasoft.com) + +## 一、产品画像 +- 公司:成都友加畅捷科技有限公司 +- 产品线:U+通用财务 / T1飞跃(进销存+财务)/ U+分销 / U+云商(SaaS) / U+移动 +- **架构:本地 C/S 部署**(T1飞跃明确 C/S 架构)——非云 SaaS +- **开放 API:官网无开发者平台/公开API文档**(对比用友/金蝶有开放平台) + +## 二、对接路径评估(3选1) + +| 路径 | 说明 | 可行性 | 工作量 | +|:--|:--|:--|:--| +| **A. Excel导出→CMA导入** ✅推荐 | 智享通导出科目余额/凭证Excel → CMA现有导入(data import已建) | 🟢 **零开发可用** | 0.5天(模板映射) | +| B. 数据库直读 | 本地部署 DB(SQL Server等)直接读财务数据 | 🟡 需账套密码+技术配合 | 2-3天 | +| C. 官方API | 联系友加畅捷确认是否有接口 | 🟠 官网未见,需商务确认 | 不确定 | + +## 三、推荐方案(北极星①落地路径) +**阶段一(立即)**:方案 A——财务每月导出科目余额/凭证 Excel → CMA 导入 → KPI 实际值自动更新 +- CMA 已有 import-excel 能力(data.py/bot_bridge),只需**模板映射**(智享通导出列 → CMA 科目/KPI) +- 实现:建"智享通导出模板"映射表(科目编码→KPI/科目)+ 导入验证 + +**阶段二(可选)**:方案 B/C——财务数据量大了再评估直读/官方接口 + +## 四、影响 +北极星①"财务软件接口"对智享通的现实路径 = **导出导入(半自动)** 而非 API 直连(软件无开放API)。这是中小财务软件的现实——CMA 以"导入适配器"覆盖,不依赖厂商 API。 + +## 五、待办 +- [ ] 向酣客财务要一份**智享通导出样例**(科目余额/凭证 Excel 各一) +- [ ] 建映射模板(样例列→CMA 字段) +- [ ] 全栈实现"智享通导入适配器"(模板映射+校验) diff --git a/frontend/src/api/index.ts b/frontend/src/api/index.ts index 596a62f2..ac5be6fe 100644 --- a/frontend/src/api/index.ts +++ b/frontend/src/api/index.ts @@ -465,4 +465,13 @@ export const taxApi = { seedDemo: (params?: any) => api.post('/tax/demo-data', null, { params }), } +// ── AI决策建议(路线图R1 2026-08-30):建议→一键应用到KPI/预算/行动方案 ── +export const aiSuggestionApi = { + list: (params?: any) => api.get('/ai/suggestions', { params }), + get: (id: number) => api.get(`/ai/suggestions/${id}`), + create: (data: any) => api.post('/ai/suggestions', data), + apply: (id: number, data: any) => api.post(`/ai/suggestions/${id}/apply`, data), + dismiss: (id: number) => api.post(`/ai/suggestions/${id}/dismiss`), +} + export default api diff --git a/frontend/src/permission.ts b/frontend/src/permission.ts index 6e86c0ae..b28545b1 100644 --- a/frontend/src/permission.ts +++ b/frontend/src/permission.ts @@ -9,10 +9,10 @@ interface MenuItem { // ── 角色路由映射 ── export const ROLE_ROUTES: Record = { - ceo: ['/my-dashboard', '/dashboard', '/kpis', '/maps', '/maps-review', '/maps/canvas', '/maps/review', '/alerts', '/notifications', '/org', '/users', '/permissions', '/budget', '/deviations', '/cost', '/cost-intelligence', '/predict', '/action-plans', '/knowledge', '/guide', '/customer', '/learning-dashboard', '/reports', '/alignment', '/dupont-analysis', '/analysis-confidence', '/expenses', '/cash-plan', '/receivables', '/growth-quality', '/product-matrix', '/tax-compliance', '/data-classification'], - finance: ['/my-dashboard', '/dashboard', '/kpis', '/maps', '/maps-review', '/maps/canvas', '/maps/review', '/alerts', '/data', '/budget', '/deviations', '/cost', '/cost-intelligence', '/predict', '/action-plans', '/knowledge', '/guide', '/customer', '/learning-dashboard', '/reports', '/alignment', '/dupont-analysis', '/analysis-confidence', '/expenses', '/cash-plan', '/receivables', '/growth-quality', '/product-matrix', '/tax-compliance', '/data-classification'], - business: ['/my-dashboard', '/dashboard', '/kpis', '/alerts', '/budget', '/deviations', '/action-plans', '/knowledge', '/guide', '/customer', '/expenses', '/cash-plan', '/growth-quality', '/product-matrix', '/tax-compliance', '/data-classification'], - it: ['/my-dashboard', '/dashboard', '/kpis', '/alerts', '/data', '/org', '/users', '/permissions', '/budget', '/deviations', '/cost', '/cost-intelligence', '/predict', '/action-plans', '/knowledge', '/guide', '/customer', '/learning-dashboard', '/analysis-confidence', '/expenses', '/cash-plan', '/product-matrix', '/tax-compliance', '/data-classification'], + ceo: ['/my-dashboard', '/dashboard', '/ai-suggestions', '/kpis', '/maps', '/maps-review', '/maps/canvas', '/maps/review', '/alerts', '/notifications', '/org', '/users', '/permissions', '/budget', '/deviations', '/cost', '/cost-intelligence', '/predict', '/action-plans', '/knowledge', '/guide', '/customer', '/learning-dashboard', '/reports', '/alignment', '/dupont-analysis', '/analysis-confidence', '/expenses', '/cash-plan', '/receivables', '/growth-quality', '/product-matrix', '/tax-compliance', '/data-classification'], + finance: ['/my-dashboard', '/dashboard', '/ai-suggestions', '/kpis', '/maps', '/maps-review', '/maps/canvas', '/maps/review', '/alerts', '/data', '/budget', '/deviations', '/cost', '/cost-intelligence', '/predict', '/action-plans', '/knowledge', '/guide', '/customer', '/learning-dashboard', '/reports', '/alignment', '/dupont-analysis', '/analysis-confidence', '/expenses', '/cash-plan', '/receivables', '/growth-quality', '/product-matrix', '/tax-compliance', '/data-classification'], + business: ['/my-dashboard', '/dashboard', '/ai-suggestions', '/kpis', '/alerts', '/budget', '/deviations', '/action-plans', '/knowledge', '/guide', '/customer', '/expenses', '/cash-plan', '/growth-quality', '/product-matrix', '/tax-compliance', '/data-classification'], + it: ['/my-dashboard', '/dashboard', '/ai-suggestions', '/kpis', '/alerts', '/data', '/org', '/users', '/permissions', '/budget', '/deviations', '/cost', '/cost-intelligence', '/predict', '/action-plans', '/knowledge', '/guide', '/customer', '/learning-dashboard', '/analysis-confidence', '/expenses', '/cash-plan', '/product-matrix', '/tax-compliance', '/data-classification'], } export const ROLE_ACTIONS: Record = { @@ -42,6 +42,7 @@ export const MENU_ITEMS: MenuItem[] = [ // ── GROUP 3: 监控与评价(Check)── { path: '/dashboard', label: '经营看板', icon: 'DataBoard', roles: ['ceo', 'finance', 'business', 'it'], group: '🟡 C 监控与评价' }, + { path: '/ai-suggestions', label: 'AI建议中心', icon: 'Opportunity', roles: ['ceo', 'finance', 'business', 'it'], group: '🟡 C 监控与评价' }, { path: '/reports', label: '管理报表', icon: 'DataAnalysis', roles: ['ceo', 'finance', 'business'], group: '🟡 C 监控与评价' }, { path: '/dupont-analysis', label: '杜邦分析', icon: 'TrendCharts', roles: ['ceo', 'finance', 'it'], group: '🟡 C 监控与评价' }, { path: '/customer', label: '客户维度', icon: 'User', roles: ['ceo', 'finance', 'business', 'it'], group: '🟡 C 监控与评价' }, diff --git a/frontend/src/router/index.ts b/frontend/src/router/index.ts index 2e773999..15d2834e 100644 --- a/frontend/src/router/index.ts +++ b/frontend/src/router/index.ts @@ -6,6 +6,7 @@ const routes = [ { path: '/', component: () => import('@/layouts/MainLayout.vue'), redirect: '/my-dashboard', children: [ { path: 'dashboard', name: 'Dashboard', component: () => import('@/views/Dashboard.vue'), meta: { title: '经营看板', roles: ['ceo', 'finance', 'business', 'it'] } }, + { path: 'ai-suggestions', name: 'SuggestionCenter', component: () => import('@/views/SuggestionCenter.vue'), meta: { title: 'AI建议中心', roles: ['ceo', 'finance', 'business', 'it'] } }, { path: 'kpis', name: 'KPIs', component: () => import('@/views/KPIList.vue'), meta: { title: 'KPI字典', roles: ['ceo', 'finance', 'business', 'it'], editable: true } }, { path: 'kpis/:id', name: 'KPIDetail', component: () => import('@/views/KPIDetail.vue'), meta: { title: 'KPI详情', roles: ['ceo', 'finance', 'business', 'it'], editable: true } }, { path: 'maps', name: 'Maps', component: () => import('@/views/MapList.vue'), meta: { title: '战略地图', roles: ['ceo', 'finance'], editable: true } }, diff --git a/frontend/src/views/Dashboard.vue b/frontend/src/views/Dashboard.vue index daa9da29..4f9c808c 100644 --- a/frontend/src/views/Dashboard.vue +++ b/frontend/src/views/Dashboard.vue @@ -205,17 +205,87 @@ - +
-
🤖 AI 分析
+
+ 🤖 AI 分析 + 建议中心 + +
+
📋 决策建议
+

加载建议...

+
暂无待应用建议
+
+
+
+ {{ sugTypeLabel(s.suggestion_type) }} + 已应用 + 未应用 +
+
{{ s.title }}
+
{{ s.content }}
+
+ 应用到 + 忽略 + 由 {{ s.applied_by }} 于 {{ (s.applied_at || '').slice(0, 16) }} 应用 +
+
+
+ +
💡 AI分析文本

分析中...

-
刷新
+
+ 刷新分析 +
+ + + + + + + + + +
@@ -241,7 +311,7 @@ import { ref, computed, onMounted } from 'vue' import { useRouter } from 'vue-router' import { ElMessage } from 'element-plus' import { Loading, ArrowRight } from '@element-plus/icons-vue' -import { dashboardApi, alertApi } from '../api/index' +import { dashboardApi, alertApi, aiSuggestionApi } from '../api/index' import axios from 'axios' import CountUp from '../components/CountUp.vue' import GrowthQuality from '../components/GrowthQuality.vue' @@ -299,6 +369,81 @@ const enabledChannels = ref(0) const aiAnalysis = ref('') const aiLoading = ref(false) +// R1: AI决策建议(建议卡 + 应用到弹窗) +const suggestions = ref([]) +const suggestionsLoading = ref(false) +const showApplyDialog = ref(false) +const applySug = ref(null) +const applyForm = ref({}) +const applying = ref(false) +const applyKpiName = ref('') + +const sugTypeLabel = (t: string) => ({ kpi_target: '改KPI目标', budget_adjust: '调预算', action_plan: '建行动方案' }[t] || t) +const sugTagType = (t: string) => ({ kpi_target: 'warning', budget_adjust: 'danger', action_plan: 'primary' }[t] || 'info') + +async function loadSuggestions() { + suggestionsLoading.value = true + try { + const r: any = await aiSuggestionApi.list({ status: 'unapplied' }) + suggestions.value = (r as any)?.data || [] + } catch { suggestions.value = [] } + suggestionsLoading.value = false +} + +function openApplyDialog(s: any) { + applySug.value = s + const sd = s.suggestion_data || {} + applyForm.value = { + target_value: sd.target_value ?? null, + period: sd.period || '', + budget_value: sd.budget_value ?? null, + title: sd.title || '', + assignee: sd.assignee || '', + priority: sd.priority || 'medium', + due_date: sd.due_date || '', + } + applyKpiName.value = s.target_name || '' + showApplyDialog.value = true +} + +async function submitApply() { + if (!applySug.value) return + const action = applySug.value.suggestion_type + const body: any = { action, kpi_id: applySug.value.target_id || applySug.value.suggestion_data?.kpi_id } + if (action === 'kpi_target') { + if (applyForm.value.target_value == null) { ElMessage.warning('请输入新目标值'); return } + body.target_value = applyForm.value.target_value + } else if (action === 'budget_adjust') { + if (!applyForm.value.period || applyForm.value.budget_value == null) { ElMessage.warning('请填写期间和预算值'); return } + body.period = applyForm.value.period + body.budget_value = applyForm.value.budget_value + } else { + if (!applyForm.value.title) { ElMessage.warning('请输入计划标题'); return } + body.title = applyForm.value.title + body.assignee = applyForm.value.assignee + body.priority = applyForm.value.priority + body.due_date = applyForm.value.due_date + } + applying.value = true + try { + await aiSuggestionApi.apply(applySug.value.id, body) + ElMessage.success('建议已应用并留痕') + showApplyDialog.value = false + await loadSuggestions() + } catch (e: any) { + ElMessage.error(e?.response?.data?.detail || '应用失败') + } + applying.value = false +} + +async function dismissSuggestion(s: any) { + try { + await aiSuggestionApi.dismiss(s.id) + ElMessage.success('建议已忽略') + await loadSuggestions() + } catch { ElMessage.error('忽略失败') } +} + const aiApi = axios.create({ baseURL: '/api/cma', timeout: 30000 }) aiApi.interceptors.request.use((config: any) => { const token = localStorage.getItem('cma_token') @@ -376,8 +521,8 @@ async function loadKPIAnalysis() { if (!selectedKPI.value?.id) return loadingDetail.value = true try { - const r: any = await aiApi.post('/ai/kpi-analysis', { kpi_id: selectedKPI.value.id, period: periodType.value }) - selectedKPIDetail.value = r.data || r.analysis || '暂无分析结果' + const r: any = await aiApi.get(`/ai/kpi-analysis/${selectedKPI.value.id}`) + selectedKPIDetail.value = r.data?.analysis || r.analysis || '暂无分析结果' } catch { selectedKPIDetail.value = 'AI分析请求失败' } loadingDetail.value = false } @@ -385,8 +530,10 @@ async function loadKPIAnalysis() { async function refreshAnalysis() { aiLoading.value = true try { - const r: any = await aiApi.post('/ai/dashboard-analysis', { role: userRole.value, period: periodType.value }) - aiAnalysis.value = r.data || r.analysis || '暂无分析' + const r: any = await aiApi.get('/ai/dashboard-analysis', { params: { role: userRole.value } }) + aiAnalysis.value = r.data?.analysis || r.analysis || '暂无分析' + // 建议列表从服务端拉取(含dashboard分析自动生成的规则建议) + await loadSuggestions() } catch { aiAnalysis.value = 'AI分析暂时不可用' } aiLoading.value = false } @@ -555,6 +702,21 @@ onMounted(() => { loadData() }) .ai-foot { padding:10px 16px; border-top:1px solid #f0f0f0; } .ai-loading { text-align:center; padding:30px 0; color:#999; } +/* R1: 建议卡 */ +.ai-section-title { font-size:13px; font-weight:600; color:#333; margin-bottom:10px; } +.ai-empty { text-align:center; color:#bbb; padding:16px 0; font-size:12px; } +.sug-list { display:flex; flex-direction:column; gap:10px; } +.sug-card { border:1px solid #eee; border-radius:8px; padding:10px 12px; background:#fafafa; border-left:3px solid #909399; } +.sug-card.type-kpi_target { border-left-color:#e6a23c; } +.sug-card.type-budget_adjust { border-left-color:#f56c6c; } +.sug-card.type-action_plan { border-left-color:#409eff; } +.sug-top { display:flex; gap:6px; margin-bottom:6px; } +.sug-title { font-size:13px; font-weight:600; color:#333; margin-bottom:4px; } +.sug-content { font-size:12px; color:#666; line-height:1.5; margin-bottom:8px; } +.sug-foot { display:flex; gap:6px; align-items:center; } +.sug-applied-by { font-size:11px; color:#999; } +.apply-tip { font-size:12px; color:#999; padding:0 0 10px 100px; } + /* KPI详情弹窗 */ .dlg-summary { display:flex; justify-content:space-between; align-items:center; margin-bottom:12px; } .kpi-bar.large { height:10px; background:#f0f0f0; border-radius:5px; overflow:hidden; } diff --git a/frontend/src/views/SuggestionCenter.vue b/frontend/src/views/SuggestionCenter.vue new file mode 100644 index 00000000..54eb7220 --- /dev/null +++ b/frontend/src/views/SuggestionCenter.vue @@ -0,0 +1,204 @@ + + + + +