feat(management): 评分引擎——三类标准化/维度归一/样本保护/等级/四对比明细+期末active_friends接口修订

Co-Authored-By: Claude Code <noreply@anthropic.com>

yunke-server/app/management/employee_metrics.py

@@ -11,9 +11,12 @@
   owner:一员工多账号(wx1+wx2→u1,channel=None 时人人如此)的样本必须
   跨账号归并,逐 owner 取分位再映射会互相覆盖。fact 全库全期 ≤6.8万行,
   Python 侧聚合可行。
-- 期末存量(new/cooling/dormant):状态快照取「每 (user, channel) 最新
-  ≤end 行」——channel=None 时两渠道各取各的最新行再相加(直接取全渠道
-  最大 day 的那一行会丢另一渠道的存量)。
+- 期末存量(new/cooling/dormant/end_active_friends):状态快照取
+  「每 (user, channel) 最新 ≤end 行」——channel=None 时两渠道各取各的
+  最新行再相加(直接取全渠道最大 day 的那一行会丢另一渠道的存量)。
+  end_active_friends 与 new/cooling/dormant 同源同行(该渠道最新行的
+  active_friends 列),供评分 active_share 用——区别于 active_friends
+  字段的周期 distinct 口径。
 - active_rooms_avg = SUM(active_rooms)/全窗天数(含无数据日摊薄),
   非有数据天数——「日均负载」语义。
 - days_with_data = COUNT(DISTINCT day):单渠道时与 COUNT(*) 等价
@@ -47,6 +50,7 @@ PeriodMetrics = namedtuple("PeriodMetrics", [
     "active_friends",          # 期内 distinct(直查,非日值求和)
     "active_rooms_avg",        # 日均(全窗天数摊薄)
     "new_customer", "cooling_customer", "dormant_customer",  # 期末单日存量
+    "end_active_friends",      # 期末行 active_friends 跨渠道 SUM(active_share 分子)
     "reactivated_customer",    # 期内求和
     "call_count", "call_seconds", "answered_call", "call_followup",
 ])
@@ -108,6 +112,7 @@ _ZERO: dict[str, object] = {
     "reply_2h_count": 0, "reply_24h_count": 0,
     "active_friends": 0, "active_rooms_sum": 0,
     "new_customer": 0, "cooling_customer": 0, "dormant_customer": 0,
+    "end_active_friends": 0,
     "reactivated_customer": 0,
     "call_count": 0, "call_seconds": 0, "answered_call": 0, "call_followup": 0,
 }
@@ -207,6 +212,7 @@ async def period_metrics(
                 sa.func.sum(
                     EmployeeMetricDaily.dormant_customer_count
                 ).label("dorm"),
+                sa.func.sum(EmployeeMetricDaily.active_friends).label("af"),
             ).where(sa.or_(*(
                 sa.and_(
                     EmployeeMetricDaily.user_id == uid,
@@ -217,11 +223,12 @@ async def period_metrics(
                 for uid, ch, d in latest
             ))).group_by(EmployeeMetricDaily.user_id)
         )).all()
-        for uid, nw, cool, dorm in stocks:
+        for uid, nw, cool, dorm, af in stocks:
             a = acc[uid]
             a["new_customer"] = int(nw or 0)
             a["cooling_customer"] = int(cool or 0)
             a["dormant_customer"] = int(dorm or 0)
+            a["end_active_friends"] = int(af or 0)
 
     # ---- fact 分位 + 2h/24h 回复计数(同一过滤,Python 侧一次聚合) ----
     fr_conds = [FirstReplyFact.day.between(start_day, end_day)]
@@ -305,6 +312,7 @@ async def period_metrics(
             new_customer=a["new_customer"],
             cooling_customer=a["cooling_customer"],
             dormant_customer=a["dormant_customer"],
+            end_active_friends=a["end_active_friends"],
             reactivated_customer=a["reactivated_customer"],
             call_count=a["call_count"],
             call_seconds=a["call_seconds"],