feat: 多租户实时切换 — 前端拦截器统一entity_id + 后端get_entity_id依赖 + dashboard 6端点支持租户过滤

- 前端: api/index.ts拦截器自动附加X-Entity-Id header + entity_id query参数
- 后端: 新增app/deps.py的get_entity_id公共依赖(query→header→默认1)
- dashboard.py: summary/kpis/finance-analysis/predict/my-kpis/my-dashboard全部支持entity_id
- 解决: 42个页面仅13个传entity_id导致切换企业后数据混乱

backend/app/api/dashboard.py

@@ -6,6 +6,7 @@ from datetime import datetime, timedelta
 from typing import Optional
 from app.database import get_db
 from app.auth_middleware import require_auth, require_role
+from app.deps import get_entity_id
 from app.models import KPIDefinition, KPIValue, KPIAlert, User
 from app.utils.cache import get as cache_get, set as cache_set
 import json
@@ -52,15 +53,18 @@ def period_prefix(period_type: str):
     return None
 
 @router.get("/summary")
-def get_dashboard_summary(role: str = Query("ceo"), period: str = Query("month"), db: Session = Depends(get_db)):
-    cache_key = f"summary:{role}:{period}"
+def get_dashboard_summary(role: str = Query("ceo"), period: str = Query("month"),
+                          db: Session = Depends(get_db), entity_id: int = Depends(get_entity_id)):
+    cache_key = f"summary:{role}:{period}:{entity_id}"
     cached = cache_get("dashboard", cache_key)
     if cached:
         return cached
-    kpi_total = db.query(func.count(KPIDefinition.id)).filter(KPIDefinition.status == "active").scalar()
+    kpi_total = db.query(func.count(KPIDefinition.id)).filter(
+        KPIDefinition.status == "active", KPIDefinition.entity_id == entity_id).scalar()
     alert_count = db.query(func.count(KPIAlert.id)).filter(KPIAlert.status == "pending").scalar()
     dims = db.query(KPIDefinition.dimension, func.count(KPIDefinition.id)).filter(
-        KPIDefinition.status == "active").group_by(KPIDefinition.dimension).all()
+        KPIDefinition.status == "active", KPIDefinition.entity_id == entity_id
+    ).group_by(KPIDefinition.dimension).all()
     
     # 读取最近一次同步状态（从日志文件最后一行）
     sync_status = {"last_sync": None, "status": "unknown", "detail": ""}
@@ -94,11 +98,14 @@ def get_dashboard_summary(role: str = Query("ceo"), period: str = Query("month")
 @router.get("/kpis")
 def get_dashboard_kpis(role: str = Query("ceo"), period: str = Query("month"),
                        start_date: str = Query(None), end_date: str = Query(None),
-                       db: Session = Depends(get_db)):
+                       db: Session = Depends(get_db), entity_id: int = Depends(get_entity_id)):
     start, end = parse_period(period, start_date, end_date)
     period_str = start.strftime("%Y-%m")
     
-    kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all()
+    kpis = db.query(KPIDefinition).filter(
+        KPIDefinition.status == "active",
+        KPIDefinition.entity_id == entity_id
+    ).all()
     result = []
     
     for k in kpis:
@@ -213,6 +220,7 @@ def get_finance_analysis(
     current_user: User = Depends(require_auth),
     period: str = Query("month"),
     db: Session = Depends(get_db),
+    entity_id: int = Depends(get_entity_id),
 ):
     """财务工作台分析数据"""
     period_str = datetime.now().strftime("%Y-%m")
@@ -220,6 +228,7 @@ def get_finance_analysis(
     finance_kpis = db.query(KPIDefinition).filter(
         KPIDefinition.status == "active",
         KPIDefinition.dimension == "finance",
+        KPIDefinition.entity_id == entity_id,
     ).all()
     
     kpi_data = []
@@ -269,7 +278,7 @@ def get_finance_analysis(
 
 
 @router.get("/predict")
-def predict_kpis(db: Session = Depends(get_db)):
+def predict_kpis(db: Session = Depends(get_db), entity_id: int = Depends(get_entity_id)):
     """基于历史趋势预测下月KPI值（简单线性回归）"""
     from datetime import datetime, timedelta
     
@@ -281,7 +290,10 @@ def predict_kpis(db: Session = Depends(get_db)):
         next_year += 1
     next_period = f"{next_year}-{next_month:02d}"
     
-    kpis = db.query(KPIDefinition).filter(KPIDefinition.status == "active").all()
+    kpis = db.query(KPIDefinition).filter(
+        KPIDefinition.status == "active",
+        KPIDefinition.entity_id == entity_id
+    ).all()
     predictions = []
     
     for k in kpis: