在准备环境前提交次全部更改。

apps/etl/connectors/feiqiu/tasks/dws/index/__init__.py

@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+# AI_CHANGELOG [2026-02-13] 移除 RecallIndexTask/IntimacyIndexTask 导出，仅保留 WBI/NCI/ML/Relation
+"""
+指数算法任务模块
+
+包含：
+- WinbackIndexTask: 老客挽回指数 (WBI)
+- NewconvIndexTask: 新客转化指数 (NCI)
+- MlManualImportTask: ML 人工台账导入任务
+- RelationIndexTask: 关系指数计算任务（RS/OS/MS/ML）
+"""
+
+from .winback_index_task import WinbackIndexTask
+from .newconv_index_task import NewconvIndexTask
+from .ml_manual_import_task import MlManualImportTask
+from .relation_index_task import RelationIndexTask
+
+__all__ = [
+    'WinbackIndexTask',
+    'NewconvIndexTask',
+    'MlManualImportTask',
+    'RelationIndexTask',
+]

apps/etl/connectors/feiqiu/tasks/dws/index/base_index_task.py

@@ -0,0 +1,572 @@
+# -*- coding: utf-8 -*-
+# AI_CHANGELOG [2026-02-13] 更新 docstring：移除 RECALL/INTIMACY 引用，反映当前指数体系（WBI/NCI/RS/OS/MS/ML）
+"""
+指数算法任务基类
+
+功能说明：
+    - 提供半衰期时间衰减函数
+    - 提供分位数计算和分位截断
+    - 提供0-10映射方法
+    - 提供算法参数加载
+    - 提供分位点历史记录（用于EWMA平滑）
+
+算法原理：
+    1. 时间衰减函数（半衰期模型）：decay(d; h) = exp(-ln(2) * d / h)
+       当 d=h 时权重衰减到 0.5，越近权重越大
+    
+    2. 0-10映射流程：
+       Raw Score → Winsorize(P5, P95) → [可选Log/asinh压缩] → MinMax(0, 10)
+
+作者：ETL团队
+创建日期：2026-02-03
+"""
+
+from __future__ import annotations
+
+import math
+from abc import abstractmethod
+from dataclasses import dataclass
+from datetime import date, datetime
+from decimal import Decimal
+from typing import Any, Dict, List, Optional, Tuple
+
+from ..base_dws_task import BaseDwsTask, TaskContext
+
+
+# =============================================================================
+# 数据类定义
+# =============================================================================
+
+@dataclass
+class IndexParameters:
+    """指数算法参数数据类"""
+    params: Dict[str, float]
+    loaded_at: datetime
+
+
+@dataclass
+class PercentileHistory:
+    """分位点历史记录"""
+    percentile_5: float
+    percentile_95: float
+    percentile_5_smoothed: float
+    percentile_95_smoothed: float
+    record_count: int
+    calc_time: datetime
+
+
+# =============================================================================
+# 指数任务基类
+# =============================================================================
+
+class BaseIndexTask(BaseDwsTask):
+    """
+    指数算法任务基类
+    
+    提供指数计算通用功能：
+    1. 半衰期时间衰减函数
+    2. 分位数计算与截断
+    3. 0-10归一化映射
+    4. 算法参数加载
+    5. 分位点历史管理（EWMA平滑）
+    """
+    
+    # 子类需要定义的指数类型
+    INDEX_TYPE: str = ""
+    
+    # 参数缓存TTL（秒）
+    _index_params_ttl: int = 300
+
+    def __init__(self, config, db_connection, api_client, logger):
+        super().__init__(config, db_connection, api_client, logger)
+        # 参数缓存：按 index_type 隔离，避免单任务多指数串参
+        self._index_params_cache_by_type: Dict[str, IndexParameters] = {}
+    
+    # 默认参数
+    DEFAULT_LOOKBACK_DAYS = 60
+    DEFAULT_PERCENTILE_LOWER = 5
+    DEFAULT_PERCENTILE_UPPER = 95
+    DEFAULT_EWMA_ALPHA = 0.2
+    
+    # ==========================================================================
+    # 抽象方法（子类需实现）
+    # ==========================================================================
+    
+    @abstractmethod
+    def get_index_type(self) -> str:
+        """获取指数类型（如 WBI/NCI/RS/OS/MS/ML）"""
+        raise NotImplementedError
+    
+    # ==========================================================================
+    # 时间衰减函数
+    # ==========================================================================
+    
+    def decay(self, days: float, halflife: float) -> float:
+        """
+        半衰期衰减函数
+        
+        公式: decay(d; h) = exp(-ln(2) * d / h)
+        
+        解释：当 d=h 时权重衰减到 0.5；越近权重越大，符合"近期更重要"的直觉
+        
+        Args:
+            days: 事件距今天数 (d >= 0)
+            halflife: 半衰期 (h > 0)，单位：天
+            
+        Returns:
+            衰减后的权重，范围 (0, 1]
+            
+        Examples:
+            >>> decay(0, 7)   # 今天，权重=1.0
+            1.0
+            >>> decay(7, 7)   # 7天前，半衰期=7，权重=0.5
+            0.5
+            >>> decay(14, 7)  # 14天前，权重=0.25
+            0.25
+        """
+        if halflife <= 0:
+            raise ValueError("半衰期必须大于0")
+        if days < 0:
+            days = 0
+        return math.exp(-math.log(2) * days / halflife)
+    
+    # ==========================================================================
+    # 分位数计算
+    # ==========================================================================
+    
+    def calculate_percentiles(
+        self, 
+        scores: List[float], 
+        lower: int = 5, 
+        upper: int = 95
+    ) -> Tuple[float, float]:
+        """
+        计算分位点
+        
+        Args:
+            scores: 分数列表
+            lower: 下分位点百分比（默认5）
+            upper: 上分位点百分比（默认95）
+            
+        Returns:
+            (下分位值, 上分位值) 元组
+        """
+        if not scores:
+            return 0.0, 0.0
+        
+        sorted_scores = sorted(scores)
+        n = len(sorted_scores)
+        
+        # 计算分位点索引
+        lower_idx = max(0, int(n * lower / 100) - 1)
+        upper_idx = min(n - 1, int(n * upper / 100))
+        
+        return sorted_scores[lower_idx], sorted_scores[upper_idx]
+    
+    def winsorize(self, value: float, lower: float, upper: float) -> float:
+        """
+        分位截断（Winsorize）
+        
+        将值限制在 [lower, upper] 范围内
+        
+        Args:
+            value: 原始值
+            lower: 下限（P5分位）
+            upper: 上限（P95分位）
+            
+        Returns:
+            截断后的值
+        """
+        return min(max(value, lower), upper)
+    
+    # ==========================================================================
+    # 0-10映射
+    # ==========================================================================
+    
+    def normalize_to_display(
+        self, 
+        value: float, 
+        min_val: float, 
+        max_val: float,
+        use_log: bool = False,
+        compression: Optional[str] = None,
+        epsilon: float = 1e-6
+    ) -> float:
+        """
+        归一化到0-10分
+        
+        映射流程：
+        1. [可选] 压缩：y = ln(1 + x) / asinh(x)
+        2. MinMax映射：score = 10 * (y - min) / (max - min)
+        
+        Args:
+            value: 原始值（已Winsorize）
+            min_val: 最小值（通常为P5）
+            max_val: 最大值（通常为P95）
+            use_log: 是否使用log1p压缩（兼容历史参数）
+            compression: 压缩方式（none/log1p/asinh），优先级高于use_log
+            epsilon: 防除零小量
+            
+        Returns:
+            0-10范围的分数
+        """
+        compression_mode = self._resolve_compression(compression, use_log)
+        if compression_mode == "log1p":
+            value = math.log1p(value)
+            min_val = math.log1p(min_val)
+            max_val = math.log1p(max_val)
+        elif compression_mode == "asinh":
+            value = math.asinh(value)
+            min_val = math.asinh(min_val)
+            max_val = math.asinh(max_val)
+        
+        # 防止分母为0
+        range_val = max_val - min_val
+        if range_val < epsilon:
+            return 5.0  # 几乎全员相同时返回中间值
+        
+        score = 10.0 * (value - min_val) / range_val
+        
+        # 确保在0-10范围内
+        return max(0.0, min(10.0, score))
+    
+    def batch_normalize_to_display(
+        self,
+        raw_scores: List[Tuple[Any, float]],  # [(entity_id, raw_score), ...]
+        use_log: bool = False,
+        compression: Optional[str] = None,
+        percentile_lower: int = 5,
+        percentile_upper: int = 95,
+        use_smoothing: bool = False,
+        site_id: Optional[int] = None,
+        index_type: Optional[str] = None,
+    ) -> List[Tuple[Any, float, float]]:
+        """
+        批量归一化Raw Score到Display Score
+        
+        流程：
+        1. 提取所有raw_score
+        2. 计算分位点（可选EWMA平滑）
+        3. Winsorize截断
+        4. MinMax映射到0-10
+        
+        Args:
+            raw_scores: (entity_id, raw_score) 元组列表
+            use_log: 是否使用log1p压缩（兼容历史参数）
+            compression: 压缩方式（none/log1p/asinh），优先级高于use_log
+            percentile_lower: 下分位百分比
+            percentile_upper: 上分位百分比
+            use_smoothing: 是否使用EWMA平滑分位点
+            site_id: 门店ID（平滑时需要）
+            index_type: 指数类型（平滑时用于分位历史隔离）
+            
+        Returns:
+            (entity_id, raw_score, display_score) 元组列表
+        """
+        if not raw_scores:
+            return []
+        
+        # 提取raw_score
+        scores = [s for _, s in raw_scores]
+        
+        # 计算分位点
+        q_l, q_u = self.calculate_percentiles(scores, percentile_lower, percentile_upper)
+        
+        # EWMA平滑
+        if use_smoothing and site_id is not None:
+            q_l, q_u = self._apply_ewma_smoothing(
+                site_id=site_id,
+                current_p5=q_l,
+                current_p95=q_u,
+                index_type=index_type,
+            )
+        
+        # 映射
+        results = []
+        compression_mode = self._resolve_compression(compression, use_log)
+        for entity_id, raw_score in raw_scores:
+            clipped = self.winsorize(raw_score, q_l, q_u)
+            display = self.normalize_to_display(
+                clipped,
+                q_l,
+                q_u,
+                compression=compression_mode,
+            )
+            results.append((entity_id, raw_score, round(display, 2)))
+        
+        return results
+    
+    # ==========================================================================
+    # 算法参数加载
+    # ==========================================================================
+    
+    def load_index_parameters(
+        self, 
+        index_type: Optional[str] = None,
+        force_reload: bool = False
+    ) -> Dict[str, float]:
+        """
+        加载指数算法参数
+        
+        Args:
+            index_type: 指数类型（默认使用子类定义的INDEX_TYPE）
+            force_reload: 是否强制重新加载
+            
+        Returns:
+            参数名到参数值的字典
+        """
+        if index_type is None:
+            index_type = self.get_index_type()
+        
+        now = datetime.now(self.tz)
+        cache_key = str(index_type).upper()
+        cache_item = self._index_params_cache_by_type.get(cache_key)
+
+        # 检查缓存
+        if (
+            not force_reload
+            and cache_item is not None
+            and (now - cache_item.loaded_at).total_seconds() < self._index_params_ttl
+        ):
+            return cache_item.params
+        
+        self.logger.debug("加载指数算法参数: %s", index_type)
+        
+        sql = """
+            SELECT param_name, param_value
+            FROM dws.cfg_index_parameters
+            WHERE index_type = %s
+              AND effective_from <= CURRENT_DATE
+              AND (effective_to IS NULL OR effective_to >= CURRENT_DATE)
+            ORDER BY effective_from DESC
+        """
+        
+        rows = self.db.query(sql, (index_type,))
+        
+        params = {}
+        seen = set()
+        for row in (rows or []):
+            row_dict = dict(row)
+            name = row_dict['param_name']
+            if name not in seen:
+                params[name] = float(row_dict['param_value'])
+                seen.add(name)
+        
+        self._index_params_cache_by_type[cache_key] = IndexParameters(
+            params=params,
+            loaded_at=now
+        )
+
+        return params
+
+    def get_param(
+        self,
+        name: str,
+        default: float = 0.0,
+        index_type: Optional[str] = None,
+    ) -> float:
+        """
+        获取单个参数值
+        
+        Args:
+            name: 参数名
+            default: 默认值
+            
+        Returns:
+            参数值
+        """
+        params = self.load_index_parameters(index_type=index_type)
+        return params.get(name, default)
+    
+    # ==========================================================================
+    # 分位点历史管理（EWMA平滑）
+    # ==========================================================================
+    
+    def get_last_percentile_history(
+        self,
+        site_id: int,
+        index_type: Optional[str] = None
+    ) -> Optional[PercentileHistory]:
+        """
+        获取最近一次分位点历史
+        
+        Args:
+            site_id: 门店ID
+            index_type: 指数类型
+            
+        Returns:
+            PercentileHistory 或 None
+        """
+        if index_type is None:
+            index_type = self.get_index_type()
+        
+        sql = """
+            SELECT 
+                percentile_5, percentile_95,
+                percentile_5_smoothed, percentile_95_smoothed,
+                record_count, calc_time
+            FROM dws.dws_index_percentile_history
+            WHERE site_id = %s AND index_type = %s
+            ORDER BY calc_time DESC
+            LIMIT 1
+        """
+        
+        rows = self.db.query(sql, (site_id, index_type))
+        
+        if not rows:
+            return None
+        
+        row = dict(rows[0])
+        return PercentileHistory(
+            percentile_5=float(row['percentile_5'] or 0),
+            percentile_95=float(row['percentile_95'] or 0),
+            percentile_5_smoothed=float(row['percentile_5_smoothed'] or 0),
+            percentile_95_smoothed=float(row['percentile_95_smoothed'] or 0),
+            record_count=int(row['record_count'] or 0),
+            calc_time=row['calc_time']
+        )
+    
+    def save_percentile_history(
+        self,
+        site_id: int,
+        percentile_5: float,
+        percentile_95: float,
+        percentile_5_smoothed: float,
+        percentile_95_smoothed: float,
+        record_count: int,
+        min_raw: float,
+        max_raw: float,
+        avg_raw: float,
+        index_type: Optional[str] = None
+    ) -> None:
+        """
+        保存分位点历史
+        
+        Args:
+            site_id: 门店ID
+            percentile_5: 原始5分位
+            percentile_95: 原始95分位
+            percentile_5_smoothed: 平滑后5分位
+            percentile_95_smoothed: 平滑后95分位
+            record_count: 记录数
+            min_raw: 最小Raw Score
+            max_raw: 最大Raw Score
+            avg_raw: 平均Raw Score
+            index_type: 指数类型
+        """
+        if index_type is None:
+            index_type = self.get_index_type()
+        
+        sql = """
+            INSERT INTO dws.dws_index_percentile_history (
+                site_id, index_type, calc_time,
+                percentile_5, percentile_95,
+                percentile_5_smoothed, percentile_95_smoothed,
+                record_count, min_raw_score, max_raw_score, avg_raw_score
+            ) VALUES (%s, %s, NOW(), %s, %s, %s, %s, %s, %s, %s, %s)
+        """
+        
+        with self.db.conn.cursor() as cur:
+            cur.execute(sql, (
+                site_id, index_type,
+                percentile_5, percentile_95,
+                percentile_5_smoothed, percentile_95_smoothed,
+                record_count, min_raw, max_raw, avg_raw
+            ))
+        self.db.conn.commit()
+    
+    def _apply_ewma_smoothing(
+        self,
+        site_id: int,
+        current_p5: float,
+        current_p95: float,
+        alpha: Optional[float] = None,
+        index_type: Optional[str] = None,
+    ) -> Tuple[float, float]:
+        """
+        应用EWMA平滑到分位点
+        
+        公式: Q_t = (1 - α) * Q_{t-1} + α * Q_now
+        
+        Args:
+            site_id: 门店ID
+            current_p5: 当前5分位
+            current_p95: 当前95分位
+            alpha: 平滑系数（默认0.2）
+            index_type: 指数类型（用于参数和历史隔离）
+            
+        Returns:
+            (平滑后的P5, 平滑后的P95)
+        """
+        if index_type is None:
+            index_type = self.get_index_type()
+
+        if alpha is None:
+            alpha = self.get_param(
+                'ewma_alpha',
+                self.DEFAULT_EWMA_ALPHA,
+                index_type=index_type,
+            )
+
+        history = self.get_last_percentile_history(site_id, index_type=index_type)
+        
+        if history is None:
+            # 首次计算，不平滑
+            return current_p5, current_p95
+        
+        smoothed_p5 = (1 - alpha) * history.percentile_5_smoothed + alpha * current_p5
+        smoothed_p95 = (1 - alpha) * history.percentile_95_smoothed + alpha * current_p95
+        
+        return smoothed_p5, smoothed_p95
+    
+    # ==========================================================================
+    # 统计工具方法
+    # ==========================================================================
+    
+    def calculate_median(self, values: List[float]) -> float:
+        """计算中位数"""
+        if not values:
+            return 0.0
+        sorted_vals = sorted(values)
+        n = len(sorted_vals)
+        mid = n // 2
+        if n % 2 == 0:
+            return (sorted_vals[mid - 1] + sorted_vals[mid]) / 2
+        return sorted_vals[mid]
+    
+    def calculate_mad(self, values: List[float]) -> float:
+        """
+        计算MAD（中位绝对偏差）
+        
+        MAD = median(|x - median(x)|)
+        
+        MAD是比标准差更稳健的离散度度量，不受极端值影响
+        """
+        if not values:
+            return 0.0
+        median_val = self.calculate_median(values)
+        deviations = [abs(v - median_val) for v in values]
+        return self.calculate_median(deviations)
+    
+    def safe_log(self, value: float, default: float = 0.0) -> float:
+        """安全的对数运算"""
+        if value <= 0:
+            return default
+        return math.log(value)
+    
+    def safe_ln1p(self, value: float) -> float:
+        """安全的ln(1+x)运算"""
+        if value < -1:
+            return 0.0
+        return math.log1p(value)
+
+    def _resolve_compression(self, compression: Optional[str], use_log: bool) -> str:
+        """规范化压缩方式"""
+        if compression is None:
+            return "log1p" if use_log else "none"
+        compression_key = str(compression).strip().lower()
+        if compression_key in ("none", "log1p", "asinh"):
+            return compression_key
+        if hasattr(self, "logger"):
+            self.logger.warning("未知压缩方式: %s，已降级为 none", compression)
+        return "none"

apps/etl/connectors/feiqiu/tasks/dws/index/member_index_base.py

@@ -0,0 +1,545 @@
+# -*- coding: utf-8 -*-
+"""
+会员层召回/转化指数共享逻辑
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from datetime import date, datetime, timedelta
+from decimal import Decimal
+from typing import Any, Dict, List, Optional, Tuple
+
+from .base_index_task import BaseIndexTask
+from ..base_dws_task import TaskContext
+
+
+@dataclass
+class MemberActivityData:
+    """Shared member activity features for WBI/NCI."""
+    member_id: int
+    site_id: int
+    tenant_id: int
+
+    member_create_time: Optional[datetime] = None
+    first_visit_time: Optional[datetime] = None
+    last_visit_time: Optional[datetime] = None
+    last_recharge_time: Optional[datetime] = None
+
+    t_v: float = 60.0
+    t_r: float = 60.0
+    t_a: float = 60.0
+
+    days_since_first_visit: Optional[int] = None
+    days_since_last_visit: Optional[int] = None
+    days_since_last_recharge: Optional[int] = None
+
+    visits_14d: int = 0
+    visits_60d: int = 0
+    visits_total: int = 0
+
+    spend_30d: float = 0.0
+    spend_180d: float = 0.0
+    sv_balance: float = 0.0
+    recharge_60d_amt: float = 0.0
+
+    interval_count: int = 0
+    intervals: List[float] = field(default_factory=list)
+    interval_ages_days: List[int] = field(default_factory=list)
+
+    recharge_unconsumed: int = 0
+
+
+class MemberIndexBaseTask(BaseIndexTask):
+    """会员指数共享基类，提供 execute() 模板方法。
+
+    子类只需实现：
+    - _calculate_scores(): 对会员活动数据计算原始评分
+    - _save_results(): 将归一化后的评分持久化到数据库
+
+    模板方法 execute() 按顺序执行：
+    获取站点信息 → 加载参数 → 构建会员活动数据 → _calculate_scores()
+    → 归一化 → _save_results()
+    """
+
+    DEFAULT_VISIT_LOOKBACK_DAYS = 180
+    DEFAULT_RECENCY_LOOKBACK_DAYS = 60
+    CASH_CARD_TYPE_ID = 2793249295533893
+
+    def execute(self, context: Optional[TaskContext] = None) -> Dict[str, Any]:
+        """模板方法：编排会员指数计算的完整流程。
+
+        流程：
+        1. 获取 site_id / tenant_id
+        2. 加载算法参数
+        3. 构建会员活动特征
+        4. 调用子类 _calculate_scores() 计算原始评分
+        5. 调用子类 _save_results() 完成归一化与持久化
+
+        Returns:
+            与原子类 execute() 相同结构的结果字典
+        """
+        self.logger.info("开始计算 %s", self.get_task_code())
+
+        site_id = self._get_site_id(context)
+        tenant_id = self._get_tenant_id()
+        params = self._load_params()
+
+        activities = self._build_member_activity(site_id, tenant_id, params)
+        if not activities:
+            self.logger.warning("No member activity data available; skip calculation")
+            return {'status': 'skipped', 'reason': 'no_data'}
+
+        raw_scores = self._calculate_scores(activities, params, site_id, tenant_id)
+
+        result = self._save_results(raw_scores, site_id, tenant_id, params, context)
+        return result
+
+    def _calculate_scores(
+        self,
+        activities: Dict[int, 'MemberActivityData'],
+        params: Dict[str, float],
+        site_id: int,
+        tenant_id: int,
+    ) -> Any:
+        """子类实现：对会员活动数据计算原始评分。
+
+        Args:
+            activities: member_id → MemberActivityData 映射
+            params: 算法参数字典
+            site_id: 门店ID
+            tenant_id: 租户ID
+
+        Returns:
+            原始评分数据（具体结构由子类定义）
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} 必须实现 _calculate_scores()"
+        )
+
+    def _save_results(
+        self,
+        raw_scores: Any,
+        site_id: int,
+        tenant_id: int,
+        params: Dict[str, float],
+        context: Optional[TaskContext],
+    ) -> Dict[str, Any]:
+        """子类实现：将评分数据归一化并持久化。
+
+        Args:
+            raw_scores: _calculate_scores() 的返回值
+            site_id: 门店ID
+            tenant_id: 租户ID
+            params: 算法参数字典
+            context: 任务上下文
+
+        Returns:
+            包含 status、member_count、records_inserted 等键的结果字典
+        """
+        raise NotImplementedError(
+            f"{self.__class__.__name__} 必须实现 _save_results()"
+        )
+
+    def _get_site_id(self, context: Optional[TaskContext]) -> int:
+        """获取门店ID"""
+        if context and hasattr(context, 'store_id') and context.store_id:
+            return context.store_id
+
+        site_id = self.config.get('app.default_site_id') or self.config.get('app.store_id')
+        if site_id is not None:
+            return int(site_id)
+
+        sql = "SELECT DISTINCT site_id FROM dwd.dwd_settlement_head WHERE site_id IS NOT NULL LIMIT 1"
+        rows = self.db.query(sql)
+        if rows:
+            value = dict(rows[0]).get('site_id')
+            if value is not None:
+                return int(value)
+
+        self.logger.warning("无法确定门店ID，使用 0 继续执行")
+        return 0
+
+    def _get_tenant_id(self) -> int:
+        """获取租户ID"""
+        tenant_id = self.config.get('app.tenant_id')
+        if tenant_id is not None:
+            return int(tenant_id)
+
+        sql = "SELECT DISTINCT tenant_id FROM dwd.dwd_settlement_head WHERE tenant_id IS NOT NULL LIMIT 1"
+        rows = self.db.query(sql)
+        if rows:
+            value = dict(rows[0]).get('tenant_id')
+            if value is not None:
+                return int(value)
+
+        self.logger.warning("无法确定租户ID，使用 0 继续执行")
+        return 0
+
+    def _load_params(self) -> Dict[str, float]:
+        """Load index parameters with defaults and runtime overrides."""
+        params = self.load_index_parameters()
+        result = dict(self.DEFAULT_PARAMS)
+        result.update(params)
+
+        # GUI/环境变量可通过 run.index_lookback_days 覆盖 recency 窗口
+        override_days = self.config.get('run.index_lookback_days')
+        if override_days is not None:
+            try:
+                override_days_int = int(override_days)
+                if override_days_int < 7 or override_days_int > 180:
+                    self.logger.warning(
+                        "%s: run.index_lookback_days=%s 超出建议范围[7,180]，已自动截断",
+                        self.get_task_code(),
+                        override_days,
+                    )
+                override_days_int = max(7, min(180, override_days_int))
+                result['lookback_days_recency'] = float(override_days_int)
+                self.logger.info(
+                    "%s: 使用回溯天数覆盖 lookback_days_recency=%d",
+                    self.get_task_code(),
+                    override_days_int,
+                )
+            except (TypeError, ValueError):
+                self.logger.warning(
+                    "%s: run.index_lookback_days=%s is invalid; ignore override and use parameter table value",
+                    self.get_task_code(),
+                    override_days,
+                )
+
+        return result
+
+    def _build_visit_condition_sql(self) -> str:
+        """Build visit-scope condition SQL."""
+        return """
+            (
+                s.settle_type = 1
+                OR (
+                    s.settle_type = 3
+                    AND EXISTS (
+                        SELECT 1
+                        FROM dwd.dwd_assistant_service_log asl
+                        JOIN dws.cfg_skill_type st
+                            ON asl.skill_id = st.skill_id
+                            AND st.course_type_code = 'BONUS'
+                            AND st.is_active = TRUE
+                        WHERE asl.order_settle_id = s.order_settle_id
+                          AND asl.site_id = s.site_id
+                          AND asl.tenant_member_id = s.member_id
+                          AND asl.is_delete = 0
+                    )
+                )
+            )
+        """
+
+    def _extract_visit_day_rows(
+        self,
+        site_id: int,
+        start_date: date,
+        end_date: date,
+    ) -> List[Dict[str, Any]]:
+        """提取到店记录（按天去重）"""
+        condition_sql = self._build_visit_condition_sql()
+        sql = f"""
+            WITH visit_source AS (
+                SELECT
+                    COALESCE(NULLIF(s.member_id, 0), mca.tenant_member_id) AS canonical_member_id,
+                    s.pay_time,
+                    s.pay_amount
+                FROM dwd.dwd_settlement_head s
+                LEFT JOIN dwd.dim_member_card_account mca
+                    ON s.member_card_account_id = mca.member_card_id
+                   AND mca.scd2_is_current = 1
+                   AND mca.register_site_id = s.site_id
+                   AND COALESCE(mca.is_delete, 0) = 0
+                WHERE s.site_id = %s
+                  AND s.pay_time >= %s
+                  AND s.pay_time < %s + INTERVAL '1 day'
+                  AND {condition_sql}
+            )
+            SELECT
+                canonical_member_id AS member_id,
+                DATE(pay_time) AS visit_date,
+                MAX(pay_time) AS last_visit_time,
+                SUM(COALESCE(pay_amount, 0)) AS day_pay_amount
+            FROM visit_source
+            WHERE canonical_member_id > 0
+            GROUP BY canonical_member_id, DATE(pay_time)
+            ORDER BY canonical_member_id, visit_date
+        """
+        rows = self.db.query(sql, (site_id, start_date, end_date))
+        return [dict(row) for row in (rows or [])]
+
+    def _extract_recharge_rows(
+        self,
+        site_id: int,
+        start_date: date,
+        end_date: date,
+    ) -> Dict[int, Dict[str, Any]]:
+        """提取充值记录（近60天）"""
+        sql = """
+            WITH recharge_source AS (
+                SELECT
+                    COALESCE(NULLIF(r.member_id, 0), mca.tenant_member_id) AS canonical_member_id,
+                    r.pay_time,
+                    r.pay_amount
+                FROM dwd.dwd_recharge_order r
+                LEFT JOIN dwd.dim_member_card_account mca
+                    ON r.tenant_member_card_id = mca.member_card_id
+                   AND mca.scd2_is_current = 1
+                   AND mca.register_site_id = r.site_id
+                   AND COALESCE(mca.is_delete, 0) = 0
+                WHERE r.site_id = %s
+                  AND r.settle_type = 5
+                  AND r.pay_time >= %s
+                  AND r.pay_time < %s + INTERVAL '1 day'
+            )
+            SELECT
+                canonical_member_id AS member_id,
+                MAX(pay_time) AS last_recharge_time,
+                SUM(COALESCE(pay_amount, 0)) AS recharge_60d_amt
+            FROM recharge_source
+            WHERE canonical_member_id > 0
+            GROUP BY canonical_member_id
+        """
+        rows = self.db.query(sql, (site_id, start_date, end_date))
+        result: Dict[int, Dict[str, Any]] = {}
+        for row in (rows or []):
+            row_dict = dict(row)
+            result[int(row_dict['member_id'])] = row_dict
+        return result
+
+    def _extract_member_create_times(self, member_ids: List[int]) -> Dict[int, datetime]:
+        """提取会员建档时间"""
+        if not member_ids:
+            return {}
+        member_ids_str = ','.join(str(m) for m in member_ids)
+        sql = f"""
+            SELECT 
+                member_id,
+                create_time
+            FROM dwd.dim_member
+            WHERE member_id IN ({member_ids_str})
+              AND scd2_is_current = 1
+        """
+        rows = self.db.query(sql)
+        result = {}
+        for row in (rows or []):
+            row_dict = dict(row)
+            member_id = int(row_dict['member_id'])
+            create_time = row_dict.get('create_time')
+            if create_time:
+                result[member_id] = create_time
+        return result
+
+    def _extract_first_visit_times(self, site_id: int, member_ids: List[int]) -> Dict[int, datetime]:
+        """提取首次到店时间（全量）"""
+        if not member_ids:
+            return {}
+        member_ids_str = ','.join(str(m) for m in member_ids)
+        condition_sql = self._build_visit_condition_sql()
+        sql = f"""
+            WITH visit_source AS (
+                SELECT
+                    COALESCE(NULLIF(s.member_id, 0), mca.tenant_member_id) AS canonical_member_id,
+                    s.pay_time
+                FROM dwd.dwd_settlement_head s
+                LEFT JOIN dwd.dim_member_card_account mca
+                    ON s.member_card_account_id = mca.member_card_id
+                   AND mca.scd2_is_current = 1
+                   AND mca.register_site_id = s.site_id
+                   AND COALESCE(mca.is_delete, 0) = 0
+                WHERE s.site_id = %s
+                  AND {condition_sql}
+            )
+            SELECT
+                canonical_member_id AS member_id,
+                MIN(pay_time) AS first_visit_time
+            FROM visit_source
+            WHERE canonical_member_id IN ({member_ids_str})
+            GROUP BY canonical_member_id
+        """
+        rows = self.db.query(sql, (site_id,))
+        result = {}
+        for row in (rows or []):
+            row_dict = dict(row)
+            member_id = int(row_dict['member_id'])
+            first_visit_time = row_dict.get('first_visit_time')
+            if first_visit_time:
+                result[member_id] = first_visit_time
+        return result
+
+    def _extract_sv_balances(self, site_id: int, tenant_id: int, member_ids: List[int]) -> Dict[int, Decimal]:
+        """Fetch member stored-value card balances."""
+        if not member_ids:
+            return {}
+        member_ids_str = ','.join(str(m) for m in member_ids)
+        sql = f"""
+            SELECT 
+                tenant_member_id AS member_id,
+                SUM(CASE WHEN card_type_id = %s THEN balance ELSE 0 END) AS sv_balance
+            FROM dwd.dim_member_card_account
+            WHERE tenant_id = %s
+              AND register_site_id = %s
+              AND scd2_is_current = 1
+              AND COALESCE(is_delete, 0) = 0
+              AND tenant_member_id IN ({member_ids_str})
+            GROUP BY tenant_member_id
+        """
+        rows = self.db.query(sql, (self.CASH_CARD_TYPE_ID, tenant_id, site_id))
+        result: Dict[int, Decimal] = {}
+        for row in (rows or []):
+            row_dict = dict(row)
+            member_id = int(row_dict['member_id'])
+            result[member_id] = row_dict.get('sv_balance') or Decimal('0')
+        return result
+
+    def _build_member_activity(
+        self,
+        site_id: int,
+        tenant_id: int,
+        params: Dict[str, float],
+    ) -> Dict[int, MemberActivityData]:
+        """构建会员活动特征"""
+        now = datetime.now(self.tz)
+        base_date = now.date()
+
+        visit_lookback_days = int(params.get('visit_lookback_days', self.DEFAULT_VISIT_LOOKBACK_DAYS))
+        recency_days = int(params.get('lookback_days_recency', self.DEFAULT_RECENCY_LOOKBACK_DAYS))
+
+        visit_start_date = base_date - timedelta(days=visit_lookback_days)
+        visit_rows = self._extract_visit_day_rows(site_id, visit_start_date, base_date)
+
+        member_day_rows: Dict[int, List[Dict[str, Any]]] = {}
+        for row in (visit_rows or []):
+            member_id = int(row['member_id'])
+            member_day_rows.setdefault(member_id, []).append(row)
+
+        recharge_start_date = base_date - timedelta(days=recency_days)
+        recharge_rows = self._extract_recharge_rows(site_id, recharge_start_date, base_date)
+
+        member_ids = set(member_day_rows.keys()) | set(recharge_rows.keys())
+        if not member_ids:
+            return {}
+
+        member_id_list = list(member_ids)
+        member_create_times = self._extract_member_create_times(member_id_list)
+        first_visit_times = self._extract_first_visit_times(site_id, member_id_list)
+        sv_balances = self._extract_sv_balances(site_id, tenant_id, member_id_list)
+
+        results: Dict[int, MemberActivityData] = {}
+        for member_id in member_ids:
+            data = MemberActivityData(
+                member_id=member_id,
+                site_id=site_id,
+                tenant_id=tenant_id,
+            )
+
+            day_rows = member_day_rows.get(member_id, [])
+            if day_rows:
+                day_rows_sorted = sorted(day_rows, key=lambda x: x['visit_date'])
+                data.visits_total = len(day_rows_sorted)
+
+                last_visit_time = max(r.get('last_visit_time') for r in day_rows_sorted)
+                data.last_visit_time = last_visit_time
+
+                # 近14/60天到店次数
+                days_14_ago = base_date - timedelta(days=14)
+                days_60_ago = base_date - timedelta(days=60)
+                for r in day_rows_sorted:
+                    visit_date = r.get('visit_date')
+                    if visit_date is None:
+                        continue
+                    if visit_date >= days_14_ago:
+                        data.visits_14d += 1
+                    if visit_date >= days_60_ago:
+                        data.visits_60d += 1
+
+                # 消费金额
+                days_30_ago = base_date - timedelta(days=30)
+                for r in day_rows_sorted:
+                    visit_date = r.get('visit_date')
+                    day_pay = float(r.get('day_pay_amount') or 0)
+                    data.spend_180d += day_pay
+                    if visit_date and visit_date >= days_30_ago:
+                        data.spend_30d += day_pay
+
+                # 计算到店间隔（按天）
+                visit_dates = [r.get('visit_date') for r in day_rows_sorted if r.get('visit_date')]
+                intervals: List[float] = []
+                interval_ages_days: List[int] = []
+                for i in range(1, len(visit_dates)):
+                    interval = (visit_dates[i] - visit_dates[i - 1]).days
+                    intervals.append(float(min(recency_days, interval)))
+                    interval_ages_days.append(max(0, (base_date - visit_dates[i]).days))
+                data.intervals = intervals
+                data.interval_ages_days = interval_ages_days
+                data.interval_count = len(intervals)
+
+            recharge_info = recharge_rows.get(member_id)
+            if recharge_info:
+                data.last_recharge_time = recharge_info.get('last_recharge_time')
+                data.recharge_60d_amt = float(recharge_info.get('recharge_60d_amt') or 0)
+
+            data.member_create_time = member_create_times.get(member_id)
+            data.first_visit_time = first_visit_times.get(member_id)
+            sv_balance = sv_balances.get(member_id)
+            if sv_balance is not None:
+                data.sv_balance = float(sv_balance)
+
+            # 时间差计算
+            if data.first_visit_time:
+                data.days_since_first_visit = (base_date - data.first_visit_time.date()).days
+            if data.last_visit_time:
+                data.days_since_last_visit = (base_date - data.last_visit_time.date()).days
+            if data.last_recharge_time:
+                data.days_since_last_recharge = (base_date - data.last_recharge_time.date()).days
+
+            # tV/tR/tA
+            data.t_v = float(min(recency_days, data.days_since_last_visit)) if data.days_since_last_visit is not None else float(recency_days)
+            data.t_r = float(min(recency_days, data.days_since_last_recharge)) if data.days_since_last_recharge is not None else float(recency_days)
+            data.t_a = float(min(data.t_v, data.t_r))
+
+            # 充值是否未回访
+            if data.last_recharge_time and (data.last_visit_time is None or data.last_recharge_time > data.last_visit_time):
+                data.recharge_unconsumed = 1
+
+            results[member_id] = data
+
+        return results
+
+    def classify_segment(
+        self,
+        data: MemberActivityData,
+        params: Dict[str, float],
+    ) -> Tuple[str, str, bool]:
+        """Classify member into NEW/OLD/STOP buckets."""
+        recency_days = int(params.get('lookback_days_recency', self.DEFAULT_RECENCY_LOOKBACK_DAYS))
+        enable_stop_exception = int(params.get('enable_stop_high_balance_exception', 0)) == 1
+        high_balance_threshold = float(params.get('high_balance_threshold', 1000))
+
+        if data.t_a >= recency_days:
+            if enable_stop_exception and data.sv_balance >= high_balance_threshold:
+                return "STOP", "STOP_HIGH_BALANCE", True
+            return "STOP", "STOP", False
+
+        new_visit_threshold = int(params.get('new_visit_threshold', 2))
+        new_days_threshold = int(params.get('new_days_threshold', 30))
+        recharge_recent_days = int(params.get('recharge_recent_days', 14))
+        new_recharge_max_visits = int(params.get('new_recharge_max_visits', 10))
+
+        is_new_by_visits = data.visits_total <= new_visit_threshold
+        is_new_by_first_visit = data.days_since_first_visit is not None and data.days_since_first_visit <= new_days_threshold
+        is_new_by_recharge = (
+            data.recharge_unconsumed == 1
+            and data.days_since_last_recharge is not None
+            and data.days_since_last_recharge <= recharge_recent_days
+            and data.visits_total <= new_recharge_max_visits
+        )
+
+        if is_new_by_visits or is_new_by_first_visit or is_new_by_recharge:
+            return "NEW", "NEW", True
+
+        return "OLD", "OLD", True
+
+
+

apps/etl/connectors/feiqiu/tasks/dws/index/ml_manual_import_task.py

@@ -0,0 +1,623 @@
+# -*- coding: utf-8 -*-
+"""
+ML 人工台账导入任务。
+
+设计目标：
+1. 人工台账作为 ML 唯一真源；
+2. 同一订单支持多助教归因，默认均分；
+3. 覆盖策略：
+   - 近 30 天：按 site_id + biz_date 日覆盖；
+   - 超过 30 天：按固定纪元（2026-01-01）切 30 天批次覆盖。
+"""
+
+from __future__ import annotations
+
+import os
+import uuid
+from dataclasses import dataclass
+from datetime import date, datetime, timedelta
+from decimal import Decimal
+from pathlib import Path
+from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
+
+from .base_index_task import BaseIndexTask
+from ..base_dws_task import TaskContext
+
+
+@dataclass(frozen=True)
+class ImportScope:
+    """导入覆盖范围定义。"""
+
+    site_id: int
+    scope_type: str  # DAY / P30
+    start_date: date
+    end_date: date
+
+    @property
+    def scope_key(self) -> str:
+        if self.scope_type == "DAY":
+            return f"DAY:{self.site_id}:{self.start_date.isoformat()}"
+        return (
+            f"P30:{self.site_id}:{self.start_date.isoformat()}:{self.end_date.isoformat()}"
+        )
+
+
+class MlManualImportTask(BaseIndexTask):
+    """导入并拆分 ML 人工台账（订单宽表 + 助教分摊窄表）。"""
+
+    INDEX_TYPE = "ML"
+    EPOCH_ANCHOR = date(2026, 1, 1)
+    HISTORICAL_BUCKET_DAYS = 30
+    ASSISTANT_SLOT_COUNT = 5
+
+    # Excel 模板字段（按列顺序）
+    TEMPLATE_COLUMNS = [
+        "site_id",
+        "biz_date",
+        "external_id",
+        "member_id",
+        "pay_time",
+        "order_amount",
+        "currency",
+        "assistant_id_1",
+        "assistant_name_1",
+        "assistant_id_2",
+        "assistant_name_2",
+        "assistant_id_3",
+        "assistant_name_3",
+        "assistant_id_4",
+        "assistant_name_4",
+        "assistant_id_5",
+        "assistant_name_5",
+        "remark",
+    ]
+
+    def get_task_code(self) -> str:
+        return "DWS_ML_MANUAL_IMPORT"
+
+    def get_target_table(self) -> str:
+        return "dws_ml_manual_order_source"
+
+    def get_primary_keys(self) -> List[str]:
+        return ["site_id", "external_id", "import_scope_key", "row_no"]
+
+    def get_index_type(self) -> str:
+        return self.INDEX_TYPE
+
+    def execute(self, context: Optional[TaskContext]) -> Dict[str, Any]:
+        """
+        执行导入。
+
+        说明：该任务按“文件”运行，不依赖时间窗口。调度器会以工具任务方式直接触发。
+        """
+        file_path = self._resolve_file_path()
+        if not file_path:
+            raise ValueError(
+                "未找到 ML 台账文件，请通过环境变量 ML_MANUAL_LEDGER_FILE 或配置 run.ml_manual_ledger_file 指定"
+            )
+
+        rows = self._read_excel_rows(file_path)
+        if not rows:
+            self.logger.warning("台账文件为空：%s", file_path)
+            return {
+                "status": "SUCCESS",
+                "counts": {
+                    "source_rows": 0,
+                    "alloc_rows": 0,
+                    "deleted_source_rows": 0,
+                    "deleted_alloc_rows": 0,
+                    "scopes": 0,
+                },
+            }
+
+        now = datetime.now(self.tz)
+        today = now.date()
+        import_batch_no = self._build_import_batch_no(now)
+        import_file_name = Path(file_path).name
+        import_user = self._resolve_import_user()
+
+        source_rows: List[Dict[str, Any]] = []
+        alloc_rows: List[Dict[str, Any]] = []
+        scope_set: Dict[Tuple[int, str, date, date], ImportScope] = {}
+
+        for idx, raw in enumerate(rows, start=2):
+            normalized = self._normalize_row(raw, row_no=idx, file_path=file_path)
+            row_scope = self.resolve_scope(
+                site_id=normalized["site_id"],
+                biz_date=normalized["biz_date"],
+                today=today,
+            )
+            scope_set[(row_scope.site_id, row_scope.scope_type, row_scope.start_date, row_scope.end_date)] = row_scope
+
+            source_row = self._build_source_row(
+                normalized=normalized,
+                scope=row_scope,
+                import_batch_no=import_batch_no,
+                import_file_name=import_file_name,
+                import_user=import_user,
+                import_time=now,
+            )
+            source_rows.append(source_row)
+
+            alloc_rows.extend(
+                self._build_alloc_rows(
+                    normalized=normalized,
+                    scope=row_scope,
+                    import_batch_no=import_batch_no,
+                    import_file_name=import_file_name,
+                    import_user=import_user,
+                    import_time=now,
+                )
+            )
+
+        scopes = list(scope_set.values())
+        deleted_source_rows, deleted_alloc_rows = self._delete_by_scopes(scopes)
+        inserted_source = self._insert_source_rows(source_rows)
+        upserted_alloc = self._upsert_alloc_rows(alloc_rows)
+
+        self.db.conn.commit()
+        self.logger.info(
+            "ML 人工台账导入完成: file=%s source=%d alloc=%d scopes=%d",
+            file_path,
+            inserted_source,
+            upserted_alloc,
+            len(scopes),
+        )
+        return {
+            "status": "SUCCESS",
+            "counts": {
+                "source_rows": inserted_source,
+                "alloc_rows": upserted_alloc,
+                "deleted_source_rows": deleted_source_rows,
+                "deleted_alloc_rows": deleted_alloc_rows,
+                "scopes": len(scopes),
+            },
+        }
+
+    def _resolve_file_path(self) -> Optional[str]:
+        """解析台账文件路径。"""
+        raw_path = (
+            self.config.get("run.ml_manual_ledger_file")
+            or self.config.get("run.ml_manual_file")
+            or os.getenv("ML_MANUAL_LEDGER_FILE")
+        )
+        if not raw_path:
+            return None
+        candidate = Path(str(raw_path)).expanduser()
+        if not candidate.is_absolute():
+            candidate = Path.cwd() / candidate
+        if not candidate.exists():
+            raise FileNotFoundError(f"台账文件不存在: {candidate}")
+        return str(candidate)
+
+    def _read_excel_rows(self, file_path: str) -> List[Dict[str, Any]]:
+        """读取 Excel 为行字典列表。"""
+        try:
+            from openpyxl import load_workbook
+        except Exception as exc:  # noqa: BLE001
+            raise RuntimeError(
+                "缺少 openpyxl 依赖，无法读取 Excel，请先安装 openpyxl"
+            ) from exc
+
+        wb = load_workbook(file_path, data_only=True)
+        ws = wb.active
+        header_row = next(ws.iter_rows(min_row=1, max_row=1, values_only=True), None)
+        if not header_row:
+            return []
+
+        headers = [str(col).strip() if col is not None else "" for col in header_row]
+        if not headers:
+            return []
+
+        rows: List[Dict[str, Any]] = []
+        for values in ws.iter_rows(min_row=2, values_only=True):
+            if values is None:
+                continue
+            row_dict = {headers[i]: values[i] for i in range(min(len(headers), len(values)))}
+            if self._is_empty_row(row_dict):
+                continue
+            rows.append(row_dict)
+        return rows
+
+    @staticmethod
+    def _is_empty_row(row: Dict[str, Any]) -> bool:
+        for value in row.values():
+            if value is None:
+                continue
+            if isinstance(value, str) and not value.strip():
+                continue
+            return False
+        return True
+
+    def _normalize_row(
+        self,
+        raw: Dict[str, Any],
+        row_no: int,
+        file_path: str,
+    ) -> Dict[str, Any]:
+        """规范化单行字段。"""
+        site_id = self._to_int(raw.get("site_id"), fallback=self.config.get("app.store_id"))
+        biz_date = self._to_date(raw.get("biz_date"))
+        pay_time = self._to_datetime(raw.get("pay_time"), fallback_date=biz_date)
+        external_id = str(raw.get("external_id") or "").strip()
+        if not external_id:
+            raise ValueError(f"台账行 {row_no} 缺少 external_id（订单ID）: {file_path}")
+
+        member_id = self._to_int(raw.get("member_id"), fallback=0)
+        order_amount = self._to_decimal(raw.get("order_amount"))
+        currency = str(raw.get("currency") or "CNY").strip().upper() or "CNY"
+        remark = str(raw.get("remark") or "").strip()
+
+        assistants: List[Tuple[int, str]] = []
+        for idx in range(1, self.ASSISTANT_SLOT_COUNT + 1):
+            aid = self._to_int(raw.get(f"assistant_id_{idx}"), fallback=None)
+            name = str(raw.get(f"assistant_name_{idx}") or "").strip()
+            if aid is None:
+                continue
+            assistants.append((aid, name))
+
+        return {
+            "site_id": site_id,
+            "biz_date": biz_date,
+            "external_id": external_id,
+            "member_id": member_id,
+            "pay_time": pay_time,
+            "order_amount": order_amount,
+            "currency": currency,
+            "assistants": assistants,
+            "remark": remark,
+            "row_no": row_no,
+        }
+
+    def _build_source_row(
+        self,
+        *,
+        normalized: Dict[str, Any],
+        scope: ImportScope,
+        import_batch_no: str,
+        import_file_name: str,
+        import_user: str,
+        import_time: datetime,
+    ) -> Dict[str, Any]:
+        """构造宽表入库行。"""
+        assistants: Sequence[Tuple[int, str]] = normalized["assistants"]
+        row = {
+            "site_id": normalized["site_id"],
+            "biz_date": normalized["biz_date"],
+            "external_id": normalized["external_id"],
+            "member_id": normalized["member_id"],
+            "pay_time": normalized["pay_time"],
+            "order_amount": normalized["order_amount"],
+            "currency": normalized["currency"],
+            "import_batch_no": import_batch_no,
+            "import_file_name": import_file_name,
+            "import_scope_key": scope.scope_key,
+            "import_time": import_time,
+            "import_user": import_user,
+            "row_no": normalized["row_no"],
+            "remark": normalized["remark"],
+        }
+        for idx in range(1, self.ASSISTANT_SLOT_COUNT + 1):
+            aid, aname = (assistants[idx - 1] if idx - 1 < len(assistants) else (None, None))
+            row[f"assistant_id_{idx}"] = aid
+            row[f"assistant_name_{idx}"] = aname
+        return row
+
+    def _build_alloc_rows(
+        self,
+        *,
+        normalized: Dict[str, Any],
+        scope: ImportScope,
+        import_batch_no: str,
+        import_file_name: str,
+        import_user: str,
+        import_time: datetime,
+    ) -> List[Dict[str, Any]]:
+        """构造窄表分摊行。"""
+        assistants: Sequence[Tuple[int, str]] = normalized["assistants"]
+        if not assistants:
+            return []
+
+        n = Decimal(str(len(assistants)))
+        share_ratio = Decimal("1") / n
+        rows: List[Dict[str, Any]] = []
+        for assistant_id, assistant_name in assistants:
+            allocated_amount = normalized["order_amount"] * share_ratio
+            rows.append(
+                {
+                    "site_id": normalized["site_id"],
+                    "biz_date": normalized["biz_date"],
+                    "external_id": normalized["external_id"],
+                    "member_id": normalized["member_id"],
+                    "pay_time": normalized["pay_time"],
+                    "order_amount": normalized["order_amount"],
+                    "assistant_id": assistant_id,
+                    "assistant_name": assistant_name,
+                    "share_ratio": share_ratio,
+                    "allocated_amount": allocated_amount,
+                    "currency": normalized["currency"],
+                    "import_scope_key": scope.scope_key,
+                    "import_batch_no": import_batch_no,
+                    "import_file_name": import_file_name,
+                    "import_time": import_time,
+                    "import_user": import_user,
+                }
+            )
+        return rows
+
+    @classmethod
+    def resolve_scope(cls, site_id: int, biz_date: date, today: date) -> ImportScope:
+        """按规则解析覆盖范围。"""
+        day_diff = (today - biz_date).days
+        if day_diff <= cls.HISTORICAL_BUCKET_DAYS:
+            return ImportScope(
+                site_id=site_id,
+                scope_type="DAY",
+                start_date=biz_date,
+                end_date=biz_date,
+            )
+
+        bucket_start, bucket_end = cls.resolve_p30_bucket(biz_date)
+        return ImportScope(
+            site_id=site_id,
+            scope_type="P30",
+            start_date=bucket_start,
+            end_date=bucket_end,
+        )
+
+    @classmethod
+    def resolve_p30_bucket(cls, biz_date: date) -> Tuple[date, date]:
+        """固定纪元 30 天分桶。"""
+        delta_days = (biz_date - cls.EPOCH_ANCHOR).days
+        bucket_index = delta_days // cls.HISTORICAL_BUCKET_DAYS
+        bucket_start = cls.EPOCH_ANCHOR + timedelta(days=bucket_index * cls.HISTORICAL_BUCKET_DAYS)
+        bucket_end = bucket_start + timedelta(days=cls.HISTORICAL_BUCKET_DAYS - 1)
+        return bucket_start, bucket_end
+
+    def _delete_by_scopes(self, scopes: Iterable[ImportScope]) -> Tuple[int, int]:
+        """按 scope 先删后写，保证整批覆盖。"""
+        deleted_source = 0
+        deleted_alloc = 0
+        with self.db.conn.cursor() as cur:
+            for scope in scopes:
+                if scope.scope_type == "DAY":
+                    cur.execute(
+                        """
+                        DELETE FROM dws.dws_ml_manual_order_source
+                        WHERE site_id = %s AND biz_date = %s
+                        """,
+                        (scope.site_id, scope.start_date),
+                    )
+                    deleted_source += max(cur.rowcount, 0)
+                    cur.execute(
+                        """
+                        DELETE FROM dws.dws_ml_manual_order_alloc
+                        WHERE site_id = %s AND biz_date = %s
+                        """,
+                        (scope.site_id, scope.start_date),
+                    )
+                    deleted_alloc += max(cur.rowcount, 0)
+                else:
+                    cur.execute(
+                        """
+                        DELETE FROM dws.dws_ml_manual_order_source
+                        WHERE site_id = %s AND biz_date >= %s AND biz_date <= %s
+                        """,
+                        (scope.site_id, scope.start_date, scope.end_date),
+                    )
+                    deleted_source += max(cur.rowcount, 0)
+                    cur.execute(
+                        """
+                        DELETE FROM dws.dws_ml_manual_order_alloc
+                        WHERE site_id = %s AND biz_date >= %s AND biz_date <= %s
+                        """,
+                        (scope.site_id, scope.start_date, scope.end_date),
+                    )
+                    deleted_alloc += max(cur.rowcount, 0)
+        return deleted_source, deleted_alloc
+
+    def _insert_source_rows(self, rows: List[Dict[str, Any]]) -> int:
+        if not rows:
+            return 0
+        columns = [
+            "site_id",
+            "biz_date",
+            "external_id",
+            "member_id",
+            "pay_time",
+            "order_amount",
+            "currency",
+            "assistant_id_1",
+            "assistant_name_1",
+            "assistant_id_2",
+            "assistant_name_2",
+            "assistant_id_3",
+            "assistant_name_3",
+            "assistant_id_4",
+            "assistant_name_4",
+            "assistant_id_5",
+            "assistant_name_5",
+            "import_batch_no",
+            "import_file_name",
+            "import_scope_key",
+            "import_time",
+            "import_user",
+            "row_no",
+            "remark",
+            "created_at",
+            "updated_at",
+        ]
+        sql = f"""
+            INSERT INTO dws.dws_ml_manual_order_source ({", ".join(columns)})
+            VALUES ({", ".join(["%s"] * len(columns))})
+        """
+        inserted = 0
+        with self.db.conn.cursor() as cur:
+            for row in rows:
+                values = [
+                    row.get("site_id"),
+                    row.get("biz_date"),
+                    row.get("external_id"),
+                    row.get("member_id"),
+                    row.get("pay_time"),
+                    row.get("order_amount"),
+                    row.get("currency"),
+                    row.get("assistant_id_1"),
+                    row.get("assistant_name_1"),
+                    row.get("assistant_id_2"),
+                    row.get("assistant_name_2"),
+                    row.get("assistant_id_3"),
+                    row.get("assistant_name_3"),
+                    row.get("assistant_id_4"),
+                    row.get("assistant_name_4"),
+                    row.get("assistant_id_5"),
+                    row.get("assistant_name_5"),
+                    row.get("import_batch_no"),
+                    row.get("import_file_name"),
+                    row.get("import_scope_key"),
+                    row.get("import_time"),
+                    row.get("import_user"),
+                    row.get("row_no"),
+                    row.get("remark"),
+                    row.get("import_time"),
+                    row.get("import_time"),
+                ]
+                cur.execute(sql, values)
+                inserted += max(cur.rowcount, 0)
+        return inserted
+
+    def _upsert_alloc_rows(self, rows: List[Dict[str, Any]]) -> int:
+        if not rows:
+            return 0
+        columns = [
+            "site_id",
+            "biz_date",
+            "external_id",
+            "member_id",
+            "pay_time",
+            "order_amount",
+            "assistant_id",
+            "assistant_name",
+            "share_ratio",
+            "allocated_amount",
+            "currency",
+            "import_scope_key",
+            "import_batch_no",
+            "import_file_name",
+            "import_time",
+            "import_user",
+            "created_at",
+            "updated_at",
+        ]
+        sql = f"""
+            INSERT INTO dws.dws_ml_manual_order_alloc ({", ".join(columns)})
+            VALUES ({", ".join(["%s"] * len(columns))})
+            ON CONFLICT (site_id, external_id, assistant_id)
+            DO UPDATE SET
+                biz_date = EXCLUDED.biz_date,
+                member_id = EXCLUDED.member_id,
+                pay_time = EXCLUDED.pay_time,
+                order_amount = EXCLUDED.order_amount,
+                assistant_name = EXCLUDED.assistant_name,
+                share_ratio = EXCLUDED.share_ratio,
+                allocated_amount = EXCLUDED.allocated_amount,
+                currency = EXCLUDED.currency,
+                import_scope_key = EXCLUDED.import_scope_key,
+                import_batch_no = EXCLUDED.import_batch_no,
+                import_file_name = EXCLUDED.import_file_name,
+                import_time = EXCLUDED.import_time,
+                import_user = EXCLUDED.import_user,
+                updated_at = NOW()
+        """
+        affected = 0
+        with self.db.conn.cursor() as cur:
+            for row in rows:
+                values = [
+                    row.get("site_id"),
+                    row.get("biz_date"),
+                    row.get("external_id"),
+                    row.get("member_id"),
+                    row.get("pay_time"),
+                    row.get("order_amount"),
+                    row.get("assistant_id"),
+                    row.get("assistant_name"),
+                    row.get("share_ratio"),
+                    row.get("allocated_amount"),
+                    row.get("currency"),
+                    row.get("import_scope_key"),
+                    row.get("import_batch_no"),
+                    row.get("import_file_name"),
+                    row.get("import_time"),
+                    row.get("import_user"),
+                    row.get("import_time"),
+                    row.get("import_time"),
+                ]
+                cur.execute(sql, values)
+                affected += max(cur.rowcount, 0)
+        return affected
+
+    @staticmethod
+    def _to_int(value: Any, fallback: Optional[int] = None) -> Optional[int]:
+        if value is None:
+            return fallback
+        if isinstance(value, str) and not value.strip():
+            return fallback
+        try:
+            return int(value)
+        except Exception:  # noqa: BLE001
+            return fallback
+
+    @staticmethod
+    def _to_decimal(value: Any) -> Decimal:
+        if value is None or value == "":
+            return Decimal("0")
+        return Decimal(str(value))
+
+    @staticmethod
+    def _to_date(value: Any) -> date:
+        if isinstance(value, datetime):
+            return value.date()
+        if isinstance(value, date):
+            return value
+        if isinstance(value, str):
+            text = value.strip()
+            if not text:
+                raise ValueError("biz_date 不能为空")
+            if len(text) >= 10:
+                return datetime.fromisoformat(text[:10]).date()
+            return datetime.fromisoformat(text).date()
+        raise ValueError(f"无法解析 biz_date: {value}")
+
+    @staticmethod
+    def _to_datetime(value: Any, fallback_date: date) -> datetime:
+        if isinstance(value, datetime):
+            return value
+        if isinstance(value, date):
+            return datetime.combine(value, datetime.min.time())
+        if isinstance(value, str):
+            text = value.strip()
+            if text:
+                text = text.replace("/", "-")
+                try:
+                    return datetime.fromisoformat(text)
+                except Exception:  # noqa: BLE001
+                    if len(text) >= 19:
+                        return datetime.strptime(text[:19], "%Y-%m-%d %H:%M:%S")
+                    return datetime.fromisoformat(text[:10])
+        return datetime.combine(fallback_date, datetime.min.time())
+
+    @staticmethod
+    def _build_import_batch_no(now: datetime) -> str:
+        return f"MLM_{now.strftime('%Y%m%d%H%M%S')}_{str(uuid.uuid4())[:8]}"
+
+    @staticmethod
+    def _resolve_import_user() -> str:
+        return (
+            os.getenv("ETL_OPERATOR")
+            or os.getenv("USERNAME")
+            or os.getenv("USER")
+            or "system"
+        )
+
+
+__all__ = ["MlManualImportTask", "ImportScope"]

apps/etl/connectors/feiqiu/tasks/dws/index/newconv_index_task.py

@@ -0,0 +1,397 @@
+# -*- coding: utf-8 -*-
+"""
+新客转化指数（NCI）计算任务。"""
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional
+
+from .member_index_base import MemberActivityData, MemberIndexBaseTask
+from ..base_dws_task import TaskContext
+
+
+@dataclass
+class MemberNewconvData:
+    activity: MemberActivityData
+    status: str
+    segment: str
+
+    need_new: float = 0.0
+    salvage_new: float = 0.0
+    recharge_new: float = 0.0
+    value_new: float = 0.0
+    welcome_new: float = 0.0
+
+    raw_score_welcome: Optional[float] = None
+    raw_score_convert: Optional[float] = None
+    raw_score: Optional[float] = None
+    display_score_welcome: Optional[float] = None
+    display_score_convert: Optional[float] = None
+    display_score: Optional[float] = None
+
+
+class NewconvIndexTask(MemberIndexBaseTask):
+    """新客转化指数（NCI）计算任务。"""
+
+    INDEX_TYPE = "NCI"
+
+    DEFAULT_PARAMS = {
+        # 通用参数
+        'lookback_days_recency': 60,
+        'visit_lookback_days': 180,
+        'percentile_lower': 5,
+        'percentile_upper': 95,
+        'compression_mode': 0,
+        'use_smoothing': 1,
+        'ewma_alpha': 0.2,
+        # 分流参数
+        'new_visit_threshold': 2,
+        'new_days_threshold': 30,
+        'recharge_recent_days': 14,
+        'new_recharge_max_visits': 10,
+        # NCI参数
+        'no_touch_days_new': 3,
+        't2_target_days': 7,
+        'salvage_start': 30,
+        'salvage_end': 60,
+        'welcome_window_days': 3,
+        'active_new_visit_threshold_14d': 2,
+        'active_new_recency_days': 7,
+        'active_new_penalty': 0.2,
+        'h_recharge': 7,
+        'amount_base_M0': 300,
+        'balance_base_B0': 500,
+        'value_w_spend': 1.0,
+        'value_w_bal': 0.8,
+        'w_welcome': 1.0,
+        'w_need': 1.6,
+        'w_re': 0.8,
+        'w_value': 1.0,
+        # STOP高余额例外（默认关闭）
+        'enable_stop_high_balance_exception': 0,
+        'high_balance_threshold': 1000,
+    }
+
+    def get_task_code(self) -> str:
+        return "DWS_NEWCONV_INDEX"
+
+    def get_target_table(self) -> str:
+        return "dws_member_newconv_index"
+
+    def get_primary_keys(self) -> List[str]:
+        return ['site_id', 'member_id']
+
+    def get_index_type(self) -> str:
+        return self.INDEX_TYPE
+
+    # CHANGE 2026-07-25 | intent: 迁移至 MemberIndexBaseTask 模板方法
+    # 原 execute() 拆分为 _calculate_scores() + _save_results()，
+    # 由基类 execute() 统一编排。
+
+    def _calculate_scores(
+        self,
+        activities: Dict[int, MemberActivityData],
+        params: Dict[str, float],
+        site_id: int,
+        tenant_id: int,
+    ) -> List[MemberNewconvData]:
+        """对会员活动数据计算 NCI 原始评分。
+
+        Returns:
+            评分后的 MemberNewconvData 列表
+        """
+        newconv_list: List[MemberNewconvData] = []
+        for activity in activities.values():
+            segment, status, in_scope = self.classify_segment(activity, params)
+            if not in_scope:
+                continue
+
+            if segment != "NEW":
+                continue
+
+            data = MemberNewconvData(activity=activity, status=status, segment=segment)
+            self._calculate_nci_scores(data, params)
+            newconv_list.append(data)
+
+        return newconv_list
+
+    def _save_results(
+        self,
+        raw_scores: List[MemberNewconvData],
+        site_id: int,
+        tenant_id: int,
+        params: Dict[str, float],
+        context: Optional[TaskContext],
+    ) -> Dict[str, Any]:
+        """归一化 NCI 评分并持久化到数据库。"""
+        newconv_list = raw_scores
+
+        if not newconv_list:
+            self.logger.warning("No new-member rows to calculate")
+            return {'status': 'skipped', 'reason': 'no_new_members'}
+
+        # 归一化 Display Score
+        score_pairs = [
+            (d.activity.member_id, d.raw_score)
+            for d in newconv_list
+            if d.raw_score is not None
+        ]
+        if score_pairs:
+            use_smoothing = int(params.get('use_smoothing', 1)) == 1
+            total_score_map = self._normalize_score_pairs(
+                score_pairs,
+                params=params,
+                site_id=site_id,
+                use_smoothing=use_smoothing,
+            )
+            for data in newconv_list:
+                if data.activity.member_id in total_score_map:
+                    data.display_score = total_score_map[data.activity.member_id]
+
+            raw_scores_welcome = [
+                (d.activity.member_id, d.raw_score_welcome)
+                for d in newconv_list
+                if d.raw_score_welcome is not None
+            ]
+            welcome_score_map = self._normalize_score_pairs(
+                raw_scores_welcome,
+                params=params,
+                site_id=site_id,
+                use_smoothing=False,
+            )
+            for data in newconv_list:
+                if data.activity.member_id in welcome_score_map:
+                    data.display_score_welcome = welcome_score_map[data.activity.member_id]
+
+            raw_scores_convert = [
+                (d.activity.member_id, d.raw_score_convert)
+                for d in newconv_list
+                if d.raw_score_convert is not None
+            ]
+            convert_score_map = self._normalize_score_pairs(
+                raw_scores_convert,
+                params=params,
+                site_id=site_id,
+                use_smoothing=False,
+            )
+            for data in newconv_list:
+                if data.activity.member_id in convert_score_map:
+                    data.display_score_convert = convert_score_map[data.activity.member_id]
+
+            # 保存分位点历史
+            all_raw = [float(score) for _, score in score_pairs]
+            q_l, q_u = self.calculate_percentiles(
+                all_raw,
+                int(params['percentile_lower']),
+                int(params['percentile_upper'])
+            )
+            if use_smoothing:
+                smoothed_l, smoothed_u = self._apply_ewma_smoothing(site_id, q_l, q_u)
+            else:
+                smoothed_l, smoothed_u = q_l, q_u
+            self.save_percentile_history(
+                site_id=site_id,
+                percentile_5=q_l,
+                percentile_95=q_u,
+                percentile_5_smoothed=smoothed_l,
+                percentile_95_smoothed=smoothed_u,
+                record_count=len(all_raw),
+                min_raw=min(all_raw),
+                max_raw=max(all_raw),
+                avg_raw=sum(all_raw) / len(all_raw)
+            )
+
+        inserted = self._save_newconv_data(newconv_list)
+        self.logger.info("NCI calculation finished, inserted %d rows", inserted)
+
+        return {
+            'status': 'success',
+            'member_count': len(newconv_list),
+            'records_inserted': inserted
+        }
+
+    def _calculate_nci_scores(self, data: MemberNewconvData, params: Dict[str, float]) -> None:
+        """计算 NCI 分项与 Raw Score"""
+        activity = data.activity
+
+        # 1) 紧迫度
+        no_touch_days = float(params['no_touch_days_new'])
+        t2_target_days = float(params['t2_target_days'])
+        t2_max_days = t2_target_days * 2.0
+        if t2_max_days <= no_touch_days:
+            data.need_new = 0.0
+        else:
+            data.need_new = self._clip(
+                (activity.t_v - no_touch_days) / (t2_max_days - no_touch_days),
+                0.0, 1.0
+            )
+
+        # 2) Salvage（30-60天线性衰减）
+        salvage_start = float(params['salvage_start'])
+        salvage_end = float(params['salvage_end'])
+        if salvage_end <= salvage_start:
+            data.salvage_new = 0.0
+        elif activity.t_a <= salvage_start:
+            data.salvage_new = 1.0
+        elif activity.t_a >= salvage_end:
+            data.salvage_new = 0.0
+        else:
+            data.salvage_new = (salvage_end - activity.t_a) / (salvage_end - salvage_start)
+
+        # 3) 充值未回访压力
+        if activity.recharge_unconsumed == 1:
+            data.recharge_new = self.decay(activity.t_r, params['h_recharge'])
+        else:
+            data.recharge_new = 0.0
+
+        # 4) 价值分
+        m0 = float(params['amount_base_M0'])
+        b0 = float(params['balance_base_B0'])
+        spend_score = math.log1p(activity.spend_180d / m0) if m0 > 0 else 0.0
+        bal_score = math.log1p(activity.sv_balance / b0) if b0 > 0 else 0.0
+        data.value_new = float(params['value_w_spend']) * spend_score + float(params['value_w_bal']) * bal_score
+
+        # 5) 欢迎建联分：优先首访后立即触达
+        welcome_window_days = float(params.get('welcome_window_days', 3))
+        data.welcome_new = 0.0
+        if welcome_window_days > 0 and activity.visits_total <= 1 and activity.t_v <= welcome_window_days:
+            data.welcome_new = self._clip(1.0 - (activity.t_v / welcome_window_days), 0.0, 1.0)
+
+        # 6) 抑制高活跃新客在转化召回排名中的权重
+        active_visit_threshold = int(params.get('active_new_visit_threshold_14d', 2))
+        active_recency_days = float(params.get('active_new_recency_days', 7))
+        active_penalty = float(params.get('active_new_penalty', 0.2))
+        if activity.visits_14d >= active_visit_threshold and activity.t_v <= active_recency_days:
+            active_multiplier = self._clip(active_penalty, 0.0, 1.0)
+        else:
+            active_multiplier = 1.0
+
+        # 7) 价值/充值分主要在进入免打扰窗口后生效
+        if no_touch_days > 0:
+            touch_multiplier = self._clip(activity.t_v / no_touch_days, 0.0, 1.0)
+        else:
+            touch_multiplier = 1.0
+
+        data.raw_score_welcome = float(params.get('w_welcome', 1.0)) * data.welcome_new
+        data.raw_score_convert = active_multiplier * (
+            float(params['w_need']) * (data.need_new * data.salvage_new)
+            + float(params['w_re']) * data.recharge_new * touch_multiplier
+            + float(params['w_value']) * data.value_new * touch_multiplier
+        )
+        data.raw_score_welcome = max(0.0, data.raw_score_welcome)
+        data.raw_score_convert = max(0.0, data.raw_score_convert)
+        data.raw_score = data.raw_score_welcome + data.raw_score_convert
+
+        if data.raw_score < 0:
+            data.raw_score = 0.0
+
+    def _save_newconv_data(self, data_list: List[MemberNewconvData]) -> int:
+        """保存 NCI 数据"""
+        if not data_list:
+            return 0
+
+        site_id = data_list[0].activity.site_id
+        # 按门店全量刷新，避免因分群变化导致过期数据残留。
+        delete_sql = """
+            DELETE FROM dws.dws_member_newconv_index
+            WHERE site_id = %s
+        """
+        with self.db.conn.cursor() as cur:
+            cur.execute(delete_sql, (site_id,))
+
+        insert_sql = """
+            INSERT INTO dws.dws_member_newconv_index (
+                site_id, tenant_id, member_id,
+                status, segment,
+                member_create_time, first_visit_time, last_visit_time, last_recharge_time,
+                t_v, t_r, t_a,
+                visits_14d, visits_60d, visits_total,
+                spend_30d, spend_180d, sv_balance, recharge_60d_amt,
+                interval_count,
+                need_new, salvage_new, recharge_new, value_new,
+                welcome_new,
+                raw_score_welcome, raw_score_convert, raw_score,
+                display_score_welcome, display_score_convert, display_score,
+                last_wechat_touch_time,
+                calc_time, created_at, updated_at
+            ) VALUES (
+                %s, %s, %s,
+                %s, %s,
+                %s, %s, %s, %s,
+                %s, %s, %s,
+                %s, %s, %s,
+                %s, %s, %s, %s,
+                %s,
+                %s, %s, %s, %s,
+                %s,
+                %s, %s, %s,
+                %s, %s, %s,
+                %s,
+                NOW(), NOW(), NOW()
+            )
+        """
+
+        inserted = 0
+        with self.db.conn.cursor() as cur:
+            for data in data_list:
+                activity = data.activity
+                cur.execute(insert_sql, (
+                    activity.site_id, activity.tenant_id, activity.member_id,
+                    data.status, data.segment,
+                    activity.member_create_time, activity.first_visit_time, activity.last_visit_time, activity.last_recharge_time,
+                    activity.t_v, activity.t_r, activity.t_a,
+                    activity.visits_14d, activity.visits_60d, activity.visits_total,
+                    activity.spend_30d, activity.spend_180d, activity.sv_balance, activity.recharge_60d_amt,
+                    activity.interval_count,
+                    data.need_new, data.salvage_new, data.recharge_new, data.value_new,
+                    data.welcome_new,
+                    data.raw_score_welcome, data.raw_score_convert, data.raw_score,
+                    data.display_score_welcome, data.display_score_convert, data.display_score,
+                    None,
+                ))
+                inserted += cur.rowcount
+
+        self.db.conn.commit()
+        return inserted
+
+    def _clip(self, value: float, low: float, high: float) -> float:
+        return max(low, min(high, value))
+
+    def _map_compression(self, params: Dict[str, float]) -> str:
+        mode = int(params.get('compression_mode', 0))
+        if mode == 1:
+            return "log1p"
+        if mode == 2:
+            return "asinh"
+        return "none"
+
+    def _normalize_score_pairs(
+        self,
+        raw_scores: List[tuple[int, Optional[float]]],
+        params: Dict[str, float],
+        site_id: int,
+        use_smoothing: bool,
+    ) -> Dict[int, float]:
+        valid_scores = [(member_id, float(score)) for member_id, score in raw_scores if score is not None]
+        if not valid_scores:
+            return {}
+
+        # 全为0时直接返回，避免 MinMax 归一化退化
+        if all(abs(score) <= 1e-9 for _, score in valid_scores):
+            return {member_id: 0.0 for member_id, _ in valid_scores}
+
+        compression = self._map_compression(params)
+        normalized = self.batch_normalize_to_display(
+            valid_scores,
+            compression=compression,
+            percentile_lower=int(params['percentile_lower']),
+            percentile_upper=int(params['percentile_upper']),
+            use_smoothing=use_smoothing,
+            site_id=site_id
+        )
+        return {member_id: display for member_id, _, display in normalized}
+
+
+__all__ = ['NewconvIndexTask']
+

apps/etl/connectors/feiqiu/tasks/dws/index/relation_index_task.py

@@ -0,0 +1,695 @@
+# -*- coding: utf-8 -*-
+# AI_CHANGELOG [2026-02-13] 删除 _apply_last_touch_ml 方法及 source_mode/recharge_attribute_hours 参数；
+#   更新 docstring 移除 last-touch 备用路径描述；
+#   Prompt: "ML 只用人工台账，删除所有 last-touch 备用路径"
+"""
+关系指数任务（RS/OS/MS/ML）。
+
+设计说明：
+1. 单任务一次产出 RS / OS / MS / ML，写入统一关系表；
+2. RS/MS 复用服务日志 + 会话合并口径；
+3. ML 以人工台账窄表为唯一真源；
+4. RS/MS/ML 的 display 映射按 index_type 隔离分位历史。
+"""
+
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass, field
+from datetime import datetime, timedelta
+from decimal import Decimal
+from typing import Any, Dict, List, Optional, Tuple
+
+from .base_index_task import BaseIndexTask
+from ..base_dws_task import CourseType, TaskContext
+
+
+@dataclass
+class ServiceSession:
+    """合并后的服务会话。"""
+
+    session_start: datetime
+    session_end: datetime
+    total_duration_minutes: int
+    course_weight: float
+    is_incentive: bool
+
+
+@dataclass
+class RelationPairMetrics:
+    """单个 member-assistant 关系对的计算指标。"""
+
+    site_id: int
+    tenant_id: int
+    member_id: int
+    assistant_id: int
+
+    sessions: List[ServiceSession] = field(default_factory=list)
+    days_since_last_session: Optional[int] = None
+    session_count: int = 0
+    total_duration_minutes: int = 0
+    basic_session_count: int = 0
+    incentive_session_count: int = 0
+
+    rs_f: float = 0.0
+    rs_d: float = 0.0
+    rs_r: float = 0.0
+    rs_raw: float = 0.0
+    rs_display: float = 0.0
+
+    ms_f_short: float = 0.0
+    ms_f_long: float = 0.0
+    ms_raw: float = 0.0
+    ms_display: float = 0.0
+
+    ml_raw: float = 0.0
+    ml_display: float = 0.0
+    ml_order_count: int = 0
+    ml_allocated_amount: float = 0.0
+
+    os_share: float = 0.0
+    os_label: str = "POOL"
+    os_rank: Optional[int] = None
+
+
+class RelationIndexTask(BaseIndexTask):
+    """关系指数任务：单任务产出 RS / OS / MS / ML。"""
+
+    INDEX_TYPE = "RS"
+
+    DEFAULT_PARAMS_RS: Dict[str, float] = {
+        "lookback_days": 60,
+        "session_merge_hours": 4,
+        "incentive_weight": 1.5,
+        "halflife_session": 14.0,
+        "halflife_last": 10.0,
+        "weight_f": 1.0,
+        "weight_d": 0.7,
+        "gate_alpha": 0.6,
+        "percentile_lower": 5.0,
+        "percentile_upper": 95.0,
+        "compression_mode": 1.0,
+        "use_smoothing": 1.0,
+        "ewma_alpha": 0.2,
+    }
+    DEFAULT_PARAMS_OS: Dict[str, float] = {
+        "min_rs_raw_for_ownership": 0.05,
+        "min_total_rs_raw": 0.10,
+        "ownership_main_threshold": 0.60,
+        "ownership_comanage_threshold": 0.35,
+        "ownership_gap_threshold": 0.15,
+        "eps": 1e-6,
+    }
+    DEFAULT_PARAMS_MS: Dict[str, float] = {
+        "lookback_days": 60,
+        "session_merge_hours": 4,
+        "incentive_weight": 1.5,
+        "halflife_short": 7.0,
+        "halflife_long": 30.0,
+        "eps": 1e-6,
+        "percentile_lower": 5.0,
+        "percentile_upper": 95.0,
+        "compression_mode": 1.0,
+        "use_smoothing": 1.0,
+        "ewma_alpha": 0.2,
+    }
+    # CHANGE 2026-02-13 | intent: ML 仅使用人工台账，移除 source_mode / recharge_attribute_hours
+    DEFAULT_PARAMS_ML: Dict[str, float] = {
+        "lookback_days": 60,
+        "amount_base": 500.0,
+        "halflife_recharge": 21.0,
+        "percentile_lower": 5.0,
+        "percentile_upper": 95.0,
+        "compression_mode": 1.0,
+        "use_smoothing": 1.0,
+        "ewma_alpha": 0.2,
+    }
+
+    def get_task_code(self) -> str:
+        return "DWS_RELATION_INDEX"
+
+    def get_target_table(self) -> str:
+        return "dws_member_assistant_relation_index"
+
+    def get_primary_keys(self) -> List[str]:
+        return ["site_id", "member_id", "assistant_id"]
+
+    def get_index_type(self) -> str:
+        # 多指数任务保留一个默认 index_type，调用处应显式传 RS/MS/ML
+        return self.INDEX_TYPE
+
+    def execute(self, context: Optional[TaskContext]) -> Dict[str, Any]:
+        self.logger.info("开始计算关系指数（RS/OS/MS/ML）")
+
+        site_id = self._get_site_id(context)
+        tenant_id = self._get_tenant_id()
+        now = datetime.now(self.tz)
+
+        params_rs = self._load_params("RS", self.DEFAULT_PARAMS_RS)
+        params_os = self._load_params("OS", self.DEFAULT_PARAMS_OS)
+        params_ms = self._load_params("MS", self.DEFAULT_PARAMS_MS)
+        params_ml = self._load_params("ML", self.DEFAULT_PARAMS_ML)
+
+        service_lookback_days = max(
+            int(params_rs.get("lookback_days", 60)),
+            int(params_ms.get("lookback_days", 60)),
+        )
+        service_start = now - timedelta(days=service_lookback_days)
+        merge_hours = max(
+            int(params_rs.get("session_merge_hours", 4)),
+            int(params_ms.get("session_merge_hours", 4)),
+        )
+
+        raw_services = self._extract_service_records(site_id, service_start, now)
+        pair_map = self._group_and_merge_sessions(
+            raw_services=raw_services,
+            merge_hours=merge_hours,
+            incentive_weight=max(
+                float(params_rs.get("incentive_weight", 1.5)),
+                float(params_ms.get("incentive_weight", 1.5)),
+            ),
+            now=now,
+            site_id=site_id,
+            tenant_id=tenant_id,
+        )
+        self.logger.info("服务关系对数量: %d", len(pair_map))
+
+        self._calculate_rs(pair_map, params_rs, now)
+        self._calculate_ms(pair_map, params_ms, now)
+        self._calculate_ml(pair_map, params_ml, site_id, now)
+        self._calculate_os(pair_map, params_os)
+
+        self._apply_display_scores(pair_map, params_rs, params_ms, params_ml, site_id)
+
+        inserted = self._save_relation_rows(site_id, list(pair_map.values()))
+        self.logger.info("关系指数计算完成，写入 %d 条记录", inserted)
+
+        return {
+            "status": "SUCCESS",
+            "records_inserted": inserted,
+            "pair_count": len(pair_map),
+        }
+
+    def _load_params(self, index_type: str, defaults: Dict[str, float]) -> Dict[str, float]:
+        params = dict(defaults)
+        params.update(self.load_index_parameters(index_type=index_type))
+        return params
+
+    def _extract_service_records(
+        self,
+        site_id: int,
+        start_datetime: datetime,
+        end_datetime: datetime,
+    ) -> List[Dict[str, Any]]:
+        """提取服务记录。"""
+        sql = """
+            SELECT
+                s.tenant_member_id AS member_id,
+                d.assistant_id AS assistant_id,
+                s.start_use_time AS start_time,
+                s.last_use_time AS end_time,
+                COALESCE(s.income_seconds, 0) / 60 AS duration_minutes,
+                s.skill_id
+            FROM dwd.dwd_assistant_service_log s
+            JOIN dwd.dim_assistant d
+              ON s.user_id = d.user_id
+             AND d.scd2_is_current = 1
+             AND COALESCE(d.is_delete, 0) = 0
+            WHERE s.site_id = %s
+              AND s.tenant_member_id > 0
+              AND s.user_id > 0
+              AND s.is_delete = 0
+              AND s.last_use_time >= %s
+              AND s.last_use_time < %s
+            ORDER BY s.tenant_member_id, d.assistant_id, s.start_use_time
+        """
+        rows = self.db.query(sql, (site_id, start_datetime, end_datetime))
+        return [dict(row) for row in (rows or [])]
+
+    def _group_and_merge_sessions(
+        self,
+        *,
+        raw_services: List[Dict[str, Any]],
+        merge_hours: int,
+        incentive_weight: float,
+        now: datetime,
+        site_id: int,
+        tenant_id: int,
+    ) -> Dict[Tuple[int, int], RelationPairMetrics]:
+        """按 (member_id, assistant_id) 分组并合并会话。"""
+        result: Dict[Tuple[int, int], RelationPairMetrics] = {}
+        if not raw_services:
+            return result
+
+        merge_threshold = timedelta(hours=max(0, merge_hours))
+        grouped: Dict[Tuple[int, int], List[Dict[str, Any]]] = {}
+        for row in raw_services:
+            member_id = int(row["member_id"])
+            assistant_id = int(row["assistant_id"])
+            grouped.setdefault((member_id, assistant_id), []).append(row)
+
+        for (member_id, assistant_id), records in grouped.items():
+            metrics = RelationPairMetrics(
+                site_id=site_id,
+                tenant_id=tenant_id,
+                member_id=member_id,
+                assistant_id=assistant_id,
+            )
+            sorted_records = sorted(records, key=lambda r: r["start_time"])
+
+            current: Optional[ServiceSession] = None
+            for svc in sorted_records:
+                start_time = svc["start_time"]
+                end_time = svc["end_time"]
+                duration = int(svc.get("duration_minutes") or 0)
+                skill_id = int(svc.get("skill_id") or 0)
+                course_type = self.get_course_type(skill_id)
+                is_incentive = course_type == CourseType.BONUS
+                weight = incentive_weight if is_incentive else 1.0
+
+                if current is None:
+                    current = ServiceSession(
+                        session_start=start_time,
+                        session_end=end_time,
+                        total_duration_minutes=duration,
+                        course_weight=weight,
+                        is_incentive=is_incentive,
+                    )
+                    continue
+
+                if start_time - current.session_end <= merge_threshold:
+                    current.session_end = max(current.session_end, end_time)
+                    current.total_duration_minutes += duration
+                    current.course_weight = max(current.course_weight, weight)
+                    current.is_incentive = current.is_incentive or is_incentive
+                else:
+                    metrics.sessions.append(current)
+                    current = ServiceSession(
+                        session_start=start_time,
+                        session_end=end_time,
+                        total_duration_minutes=duration,
+                        course_weight=weight,
+                        is_incentive=is_incentive,
+                    )
+
+            if current is not None:
+                metrics.sessions.append(current)
+
+            metrics.session_count = len(metrics.sessions)
+            metrics.total_duration_minutes = sum(s.total_duration_minutes for s in metrics.sessions)
+            metrics.basic_session_count = sum(1 for s in metrics.sessions if not s.is_incentive)
+            metrics.incentive_session_count = sum(1 for s in metrics.sessions if s.is_incentive)
+            if metrics.sessions:
+                last_session = max(metrics.sessions, key=lambda s: s.session_end)
+                metrics.days_since_last_session = (now - last_session.session_end).days
+
+            result[(member_id, assistant_id)] = metrics
+
+        return result
+
+    def _calculate_rs(
+        self,
+        pair_map: Dict[Tuple[int, int], RelationPairMetrics],
+        params: Dict[str, float],
+        now: datetime,
+    ) -> None:
+        lookback_days = int(params.get("lookback_days", 60))
+        halflife_session = float(params.get("halflife_session", 14.0))
+        halflife_last = float(params.get("halflife_last", 10.0))
+        weight_f = float(params.get("weight_f", 1.0))
+        weight_d = float(params.get("weight_d", 0.7))
+        gate_alpha = max(0.0, float(params.get("gate_alpha", 0.6)))
+
+        for metrics in pair_map.values():
+            f_score = 0.0
+            d_score = 0.0
+            for session in metrics.sessions:
+                days_ago = min(
+                    lookback_days,
+                    max(0.0, (now - session.session_end).total_seconds() / 86400.0),
+                )
+                decay_factor = self.decay(days_ago, halflife_session)
+                f_score += session.course_weight * decay_factor
+                d_score += (
+                    math.sqrt(max(session.total_duration_minutes, 0) / 60.0)
+                    * session.course_weight
+                    * decay_factor
+                )
+
+            if metrics.days_since_last_session is None:
+                r_score = 0.0
+            else:
+                r_score = self.decay(min(lookback_days, metrics.days_since_last_session), halflife_last)
+
+            base = weight_f * f_score + weight_d * d_score
+            gate = math.pow(r_score, gate_alpha) if r_score > 0 else 0.0
+
+            metrics.rs_f = f_score
+            metrics.rs_d = d_score
+            metrics.rs_r = r_score
+            metrics.rs_raw = max(0.0, base * gate)
+
+    def _calculate_ms(
+        self,
+        pair_map: Dict[Tuple[int, int], RelationPairMetrics],
+        params: Dict[str, float],
+        now: datetime,
+    ) -> None:
+        lookback_days = int(params.get("lookback_days", 60))
+        halflife_short = float(params.get("halflife_short", 7.0))
+        halflife_long = float(params.get("halflife_long", 30.0))
+        eps = float(params.get("eps", 1e-6))
+
+        for metrics in pair_map.values():
+            f_short = 0.0
+            f_long = 0.0
+            for session in metrics.sessions:
+                days_ago = min(
+                    lookback_days,
+                    max(0.0, (now - session.session_end).total_seconds() / 86400.0),
+                )
+                f_short += session.course_weight * self.decay(days_ago, halflife_short)
+                f_long += session.course_weight * self.decay(days_ago, halflife_long)
+            ratio = (f_short + eps) / (f_long + eps)
+            metrics.ms_f_short = f_short
+            metrics.ms_f_long = f_long
+            metrics.ms_raw = max(0.0, self.safe_log(ratio, 0.0))
+
+    def _calculate_ml(
+        self,
+        pair_map: Dict[Tuple[int, int], RelationPairMetrics],
+        params: Dict[str, float],
+        site_id: int,
+        now: datetime,
+    ) -> None:
+        lookback_days = int(params.get("lookback_days", 60))
+        amount_base = float(params.get("amount_base", 500.0))
+        halflife_recharge = float(params.get("halflife_recharge", 21.0))
+        start_time = now - timedelta(days=lookback_days)
+
+        # CHANGE 2026-02-13 | intent: ML 仅使用人工台账，移除 last-touch 备用路径
+        manual_rows = self._extract_manual_alloc(site_id, start_time, now)
+        for row in manual_rows:
+            member_id = int(row["member_id"])
+            assistant_id = int(row["assistant_id"])
+            key = (member_id, assistant_id)
+            if key not in pair_map:
+                pair_map[key] = RelationPairMetrics(
+                    site_id=site_id,
+                    tenant_id=pair_map[next(iter(pair_map))].tenant_id if pair_map else self._get_tenant_id(),
+                    member_id=member_id,
+                    assistant_id=assistant_id,
+                )
+            metrics = pair_map[key]
+            amount = float(row.get("allocated_amount") or 0.0)
+            pay_time = row.get("pay_time")
+            if amount <= 0 or pay_time is None:
+                continue
+            days_ago = min(lookback_days, max(0.0, (now - pay_time).total_seconds() / 86400.0))
+            metrics.ml_raw += math.log1p(amount / max(amount_base, 1e-6)) * self.decay(
+                days_ago,
+                halflife_recharge,
+            )
+            metrics.ml_order_count += 1
+            metrics.ml_allocated_amount += amount
+
+    def _extract_manual_alloc(
+        self,
+        site_id: int,
+        start_time: datetime,
+        end_time: datetime,
+    ) -> List[Dict[str, Any]]:
+        sql = """
+            SELECT
+                member_id,
+                assistant_id,
+                pay_time,
+                allocated_amount
+            FROM dws.dws_ml_manual_order_alloc
+            WHERE site_id = %s
+              AND pay_time >= %s
+              AND pay_time < %s
+        """
+        rows = self.db.query(sql, (site_id, start_time, end_time))
+        return [dict(row) for row in (rows or [])]
+
+
+    def _calculate_os(
+        self,
+        pair_map: Dict[Tuple[int, int], RelationPairMetrics],
+        params: Dict[str, float],
+    ) -> None:
+        min_rs = float(params.get("min_rs_raw_for_ownership", 0.05))
+        min_total = float(params.get("min_total_rs_raw", 0.10))
+        main_threshold = float(params.get("ownership_main_threshold", 0.60))
+        comanage_threshold = float(params.get("ownership_comanage_threshold", 0.35))
+        gap_threshold = float(params.get("ownership_gap_threshold", 0.15))
+
+        member_groups: Dict[int, List[RelationPairMetrics]] = {}
+        for metrics in pair_map.values():
+            member_groups.setdefault(metrics.member_id, []).append(metrics)
+
+        for _, rows in member_groups.items():
+            eligible = [row for row in rows if row.rs_raw >= min_rs]
+            sum_rs = sum(row.rs_raw for row in eligible)
+            if sum_rs < min_total:
+                for row in rows:
+                    row.os_share = 0.0
+                    row.os_label = "UNASSIGNED"
+                    row.os_rank = None
+                continue
+
+            for row in rows:
+                if row.rs_raw >= min_rs:
+                    row.os_share = row.rs_raw / sum_rs
+                else:
+                    row.os_share = 0.0
+
+            sorted_eligible = sorted(
+                eligible,
+                key=lambda item: (
+                    -item.os_share,
+                    -item.rs_raw,
+                    item.days_since_last_session if item.days_since_last_session is not None else 10**9,
+                    item.assistant_id,
+                ),
+            )
+            for idx, row in enumerate(sorted_eligible, start=1):
+                row.os_rank = idx
+
+            top1 = sorted_eligible[0]
+            top2_share = sorted_eligible[1].os_share if len(sorted_eligible) > 1 else 0.0
+            gap = top1.os_share - top2_share
+            has_main = top1.os_share >= main_threshold and gap >= gap_threshold
+
+            if has_main:
+                for row in rows:
+                    if row is top1:
+                        row.os_label = "MAIN"
+                    elif row.os_share >= comanage_threshold:
+                        row.os_label = "COMANAGE"
+                    else:
+                        row.os_label = "POOL"
+            else:
+                for row in rows:
+                    if row.os_share >= comanage_threshold and row.rs_raw >= min_rs:
+                        row.os_label = "COMANAGE"
+                    else:
+                        row.os_label = "POOL"
+
+            # 非 eligible 不赋 rank
+            for row in rows:
+                if row.rs_raw < min_rs:
+                    row.os_rank = None
+
+    def _apply_display_scores(
+        self,
+        pair_map: Dict[Tuple[int, int], RelationPairMetrics],
+        params_rs: Dict[str, float],
+        params_ms: Dict[str, float],
+        params_ml: Dict[str, float],
+        site_id: int,
+    ) -> None:
+        pair_items = list(pair_map.items())
+
+        rs_map = self._normalize_and_record(
+            raw_pairs=[(key, item.rs_raw) for key, item in pair_items],
+            params=params_rs,
+            index_type="RS",
+            site_id=site_id,
+        )
+        ms_map = self._normalize_and_record(
+            raw_pairs=[(key, item.ms_raw) for key, item in pair_items],
+            params=params_ms,
+            index_type="MS",
+            site_id=site_id,
+        )
+        ml_map = self._normalize_and_record(
+            raw_pairs=[(key, item.ml_raw) for key, item in pair_items],
+            params=params_ml,
+            index_type="ML",
+            site_id=site_id,
+        )
+
+        for key, item in pair_items:
+            item.rs_display = rs_map.get(key, 0.0)
+            item.ms_display = ms_map.get(key, 0.0)
+            item.ml_display = ml_map.get(key, 0.0)
+
+    def _normalize_and_record(
+        self,
+        *,
+        raw_pairs: List[Tuple[Any, float]],
+        params: Dict[str, float],
+        index_type: str,
+        site_id: int,
+    ) -> Dict[Any, float]:
+        if not raw_pairs:
+            return {}
+        if all(abs(score) <= 1e-9 for _, score in raw_pairs):
+            return {entity: 0.0 for entity, _ in raw_pairs}
+
+        percentile_lower = int(params.get("percentile_lower", 5))
+        percentile_upper = int(params.get("percentile_upper", 95))
+        use_smoothing = int(params.get("use_smoothing", 1)) == 1
+        compression = self._map_compression(params)
+
+        normalized = self.batch_normalize_to_display(
+            raw_scores=raw_pairs,
+            compression=compression,
+            percentile_lower=percentile_lower,
+            percentile_upper=percentile_upper,
+            use_smoothing=use_smoothing,
+            site_id=site_id,
+            index_type=index_type,
+        )
+        display_map = {entity: display for entity, _, display in normalized}
+
+        raw_values = [float(score) for _, score in raw_pairs]
+        q_l, q_u = self.calculate_percentiles(raw_values, percentile_lower, percentile_upper)
+        if use_smoothing:
+            smoothed_l, smoothed_u = self._apply_ewma_smoothing(
+                site_id=site_id,
+                current_p5=q_l,
+                current_p95=q_u,
+                index_type=index_type,
+            )
+        else:
+            smoothed_l, smoothed_u = q_l, q_u
+        self.save_percentile_history(
+            site_id=site_id,
+            percentile_5=q_l,
+            percentile_95=q_u,
+            percentile_5_smoothed=smoothed_l,
+            percentile_95_smoothed=smoothed_u,
+            record_count=len(raw_values),
+            min_raw=min(raw_values),
+            max_raw=max(raw_values),
+            avg_raw=sum(raw_values) / len(raw_values),
+            index_type=index_type,
+        )
+        return display_map
+
+    @staticmethod
+    def _map_compression(params: Dict[str, float]) -> str:
+        mode = int(params.get("compression_mode", 0))
+        if mode == 1:
+            return "log1p"
+        if mode == 2:
+            return "asinh"
+        return "none"
+
+    def _save_relation_rows(self, site_id: int, rows: List[RelationPairMetrics]) -> int:
+        with self.db.conn.cursor() as cur:
+            cur.execute(
+                "DELETE FROM dws.dws_member_assistant_relation_index WHERE site_id = %s",
+                (site_id,),
+            )
+
+            if not rows:
+                self.db.conn.commit()
+                return 0
+
+            insert_sql = """
+                INSERT INTO dws.dws_member_assistant_relation_index (
+                    site_id, tenant_id, member_id, assistant_id,
+                    session_count, total_duration_minutes, basic_session_count, incentive_session_count,
+                    days_since_last_session,
+                    rs_f, rs_d, rs_r, rs_raw, rs_display,
+                    os_share, os_label, os_rank,
+                    ms_f_short, ms_f_long, ms_raw, ms_display,
+                    ml_order_count, ml_allocated_amount, ml_raw, ml_display,
+                    calc_time, created_at, updated_at
+                ) VALUES (
+                    %s, %s, %s, %s,
+                    %s, %s, %s, %s,
+                    %s,
+                    %s, %s, %s, %s, %s,
+                    %s, %s, %s,
+                    %s, %s, %s, %s,
+                    %s, %s, %s, %s,
+                    NOW(), NOW(), NOW()
+                )
+            """
+            inserted = 0
+            for row in rows:
+                cur.execute(
+                    insert_sql,
+                    (
+                        row.site_id,
+                        row.tenant_id,
+                        row.member_id,
+                        row.assistant_id,
+                        row.session_count,
+                        row.total_duration_minutes,
+                        row.basic_session_count,
+                        row.incentive_session_count,
+                        row.days_since_last_session,
+                        row.rs_f,
+                        row.rs_d,
+                        row.rs_r,
+                        row.rs_raw,
+                        row.rs_display,
+                        row.os_share,
+                        row.os_label,
+                        row.os_rank,
+                        row.ms_f_short,
+                        row.ms_f_long,
+                        row.ms_raw,
+                        row.ms_display,
+                        row.ml_order_count,
+                        row.ml_allocated_amount,
+                        row.ml_raw,
+                        row.ml_display,
+                    ),
+                )
+                inserted += max(cur.rowcount, 0)
+        self.db.conn.commit()
+        return inserted
+
+    def _get_site_id(self, context: Optional[TaskContext]) -> int:
+        if context and getattr(context, "store_id", None):
+            return int(context.store_id)
+        site_id = self.config.get("app.default_site_id") or self.config.get("app.store_id")
+        if site_id is not None:
+            return int(site_id)
+        sql = "SELECT DISTINCT site_id FROM dwd.dwd_assistant_service_log WHERE site_id IS NOT NULL LIMIT 1"
+        rows = self.db.query(sql)
+        if rows:
+            return int(dict(rows[0]).get("site_id") or 0)
+        self.logger.warning("无法确定门店ID，使用 0 继续执行")
+        return 0
+
+    def _get_tenant_id(self) -> int:
+        tenant_id = self.config.get("app.tenant_id")
+        if tenant_id is not None:
+            return int(tenant_id)
+        sql = "SELECT DISTINCT tenant_id FROM dwd.dwd_assistant_service_log WHERE tenant_id IS NOT NULL LIMIT 1"
+        rows = self.db.query(sql)
+        if rows:
+            return int(dict(rows[0]).get("tenant_id") or 0)
+        self.logger.warning("无法确定租户ID，使用 0 继续执行")
+        return 0
+
+
+__all__ = ["RelationIndexTask", "RelationPairMetrics", "ServiceSession"]

apps/etl/connectors/feiqiu/tasks/dws/index/winback_index_task.py

@@ -0,0 +1,421 @@
+# -*- coding: utf-8 -*-
+# AI_CHANGELOG [2026-02-13] 修复 STOP_HIGH_BALANCE 会员不参与评分的逻辑缺陷；
+#   Prompt: "STOP_HIGH_BALANCE 应该参与 WBI 评分"
+"""
+老客挽回指数（WBI）计算任务。"""
+from __future__ import annotations
+
+import math
+from dataclasses import dataclass
+from datetime import date, timedelta
+from typing import Any, Dict, List, Optional, Tuple
+
+from .member_index_base import MemberActivityData, MemberIndexBaseTask
+from ..base_dws_task import TaskContext
+
+
+@dataclass
+class MemberWinbackData:
+    activity: MemberActivityData
+    status: str
+    segment: str
+
+    overdue_old: float = 0.0
+    overdue_cdf_p: float = 0.0
+    drop_old: float = 0.0
+    recharge_old: float = 0.0
+    value_old: float = 0.0
+    ideal_interval_days: Optional[float] = None
+    ideal_next_visit_date: Optional[date] = None
+
+    raw_score: Optional[float] = None
+    display_score: Optional[float] = None
+
+
+class WinbackIndexTask(MemberIndexBaseTask):
+    """老客挽回指数（WBI）计算任务。"""
+
+    INDEX_TYPE = "WBI"
+
+    DEFAULT_PARAMS = {
+        # 通用参数
+        'lookback_days_recency': 60,
+        'visit_lookback_days': 180,
+        'percentile_lower': 5,
+        'percentile_upper': 95,
+        'compression_mode': 0,
+        'use_smoothing': 1,
+        'ewma_alpha': 0.2,
+        # 分流参数
+        'new_visit_threshold': 2,
+        'new_days_threshold': 30,
+        'recharge_recent_days': 14,
+        'new_recharge_max_visits': 10,
+        'recency_hard_floor_days': 14,
+        'recency_gate_days': 14,
+        'recency_gate_slope_days': 3,
+        # WBI参数
+        'overdue_alpha': 2.0,
+        'overdue_weight_halflife_days': 30,
+        'overdue_weight_blend_min_samples': 8,
+        'h_recharge': 7,
+        'amount_base_M0': 300,
+        'balance_base_B0': 500,
+        'value_w_spend': 1.0,
+        'value_w_bal': 1.0,
+        'w_over': 2.0,
+        'w_drop': 1.0,
+        'w_re': 0.4,
+        'w_value': 1.2,
+        # STOP高余额例外（默认关闭）
+        'enable_stop_high_balance_exception': 0,
+        'high_balance_threshold': 1000,
+    }
+
+    def get_task_code(self) -> str:
+        return "DWS_WINBACK_INDEX"
+
+    def get_target_table(self) -> str:
+        return "dws_member_winback_index"
+
+    def get_primary_keys(self) -> List[str]:
+        return ['site_id', 'member_id']
+
+    def get_index_type(self) -> str:
+        return self.INDEX_TYPE
+
+    # CHANGE 2026-07-25 | intent: 迁移至 MemberIndexBaseTask 模板方法
+    # 原 execute() 拆分为 _calculate_scores() + _save_results()，
+    # 由基类 execute() 统一编排。
+
+    def _calculate_scores(
+        self,
+        activities: Dict[int, MemberActivityData],
+        params: Dict[str, float],
+        site_id: int,
+        tenant_id: int,
+    ) -> List[MemberWinbackData]:
+        """对会员活动数据计算 WBI 原始评分。
+
+        Returns:
+            评分后的 MemberWinbackData 列表
+        """
+        winback_list: List[MemberWinbackData] = []
+        for activity in activities.values():
+            segment, status, in_scope = self.classify_segment(activity, params)
+            if not in_scope:
+                continue
+
+            if segment != "OLD" and status != "STOP_HIGH_BALANCE":
+                continue
+
+            data = MemberWinbackData(activity=activity, status=status, segment=segment)
+
+            # CHANGE 2026-02-13 | intent: STOP_HIGH_BALANCE 也参与评分
+            if segment == "OLD" or status == "STOP_HIGH_BALANCE":
+                self._calculate_wbi_scores(data, params)
+            winback_list.append(data)
+
+        return winback_list
+
+    def _save_results(
+        self,
+        raw_scores: List[MemberWinbackData],
+        site_id: int,
+        tenant_id: int,
+        params: Dict[str, float],
+        context: Optional[TaskContext],
+    ) -> Dict[str, Any]:
+        """归一化 WBI 评分并持久化到数据库。"""
+        winback_list = raw_scores
+
+        if not winback_list:
+            self.logger.warning("No old-member rows to calculate")
+            return {'status': 'skipped', 'reason': 'no_old_members'}
+
+        # 归一化 Display Score
+        score_pairs = [
+            (d.activity.member_id, d.raw_score)
+            for d in winback_list
+            if d.raw_score is not None
+        ]
+        if score_pairs:
+            compression = self._map_compression(params)
+            use_smoothing = int(params.get('use_smoothing', 1)) == 1
+            normalized = self.batch_normalize_to_display(
+                score_pairs,
+                compression=compression,
+                percentile_lower=int(params['percentile_lower']),
+                percentile_upper=int(params['percentile_upper']),
+                use_smoothing=use_smoothing,
+                site_id=site_id
+            )
+            score_map = {member_id: display for member_id, _, display in normalized}
+            for data in winback_list:
+                if data.activity.member_id in score_map:
+                    data.display_score = score_map[data.activity.member_id]
+
+            # 保存分位点历史
+            all_raw = [float(score) for _, score in score_pairs]
+            q_l, q_u = self.calculate_percentiles(
+                all_raw,
+                int(params['percentile_lower']),
+                int(params['percentile_upper'])
+            )
+            if use_smoothing:
+                smoothed_l, smoothed_u = self._apply_ewma_smoothing(site_id, q_l, q_u)
+            else:
+                smoothed_l, smoothed_u = q_l, q_u
+            self.save_percentile_history(
+                site_id=site_id,
+                percentile_5=q_l,
+                percentile_95=q_u,
+                percentile_5_smoothed=smoothed_l,
+                percentile_95_smoothed=smoothed_u,
+                record_count=len(all_raw),
+                min_raw=min(all_raw),
+                max_raw=max(all_raw),
+                avg_raw=sum(all_raw) / len(all_raw)
+            )
+
+        inserted = self._save_winback_data(winback_list)
+        self.logger.info("WBI calculation finished, inserted %d rows", inserted)
+
+        return {
+            'status': 'success',
+            'member_count': len(winback_list),
+            'records_inserted': inserted
+        }
+
+    def _weighted_cdf(
+        self,
+        samples: List[Tuple[float, int]],
+        t_v: float,
+        halflife_days: float,
+        blend_min_samples: int,
+    ) -> float:
+        if not samples:
+            return 0.5
+
+        if halflife_days <= 0:
+            p_equal = sum(1.0 for interval, _ in samples if interval <= t_v) / len(samples)
+            return self._clip(p_equal, 0.0, 1.0)
+
+        ln2 = math.log(2.0)
+        weighted_hit = 0.0
+        weight_sum = 0.0
+        equal_hit = 0.0
+        for interval, age_days in samples:
+            weight = math.exp(-ln2 * float(age_days) / halflife_days)
+            indicator = 1.0 if interval <= t_v else 0.0
+            weighted_hit += weight * indicator
+            weight_sum += weight
+            equal_hit += indicator
+
+        p_weighted = 0.5 if weight_sum <= 0 else (weighted_hit / weight_sum)
+        p_equal = equal_hit / len(samples)
+        lam = min(1.0, float(len(samples)) / float(max(1, blend_min_samples)))
+        p_final = lam * p_weighted + (1.0 - lam) * p_equal
+        return self._clip(p_final, 0.0, 1.0)
+
+    def _weighted_quantile(
+        self,
+        samples: List[Tuple[float, int]],
+        quantile: float,
+        halflife_days: float,
+        blend_min_samples: int,
+    ) -> Optional[float]:
+        if not samples:
+            return None
+
+        q = self._clip(quantile, 0.0, 1.0)
+        equal_weight = 1.0 / float(len(samples))
+        if halflife_days <= 0:
+            weighted = [(interval, equal_weight) for interval, _ in samples]
+        else:
+            ln2 = math.log(2.0)
+            raw_weighted: List[Tuple[float, float]] = []
+            total = 0.0
+            for interval, age_days in samples:
+                w = math.exp(-ln2 * float(age_days) / halflife_days)
+                raw_weighted.append((interval, w))
+                total += w
+            if total <= 0:
+                weighted = [(interval, equal_weight) for interval, _ in samples]
+            else:
+                weighted = [(interval, w / total) for interval, w in raw_weighted]
+
+        # 对小样本混合加权分布与等权分布。
+        lam = min(1.0, float(len(samples)) / float(max(1, blend_min_samples)))
+        blended: List[Tuple[float, float]] = []
+        for (interval_w, w), (interval_e, _) in zip(weighted, samples):
+            _ = interval_e  # keep tuple alignment explicit
+            blended_weight = lam * w + (1.0 - lam) * equal_weight
+            blended.append((interval_w, blended_weight))
+
+        blended.sort(key=lambda item: item[0])
+        cumulative = 0.0
+        for interval, weight in blended:
+            cumulative += weight
+            if cumulative >= q:
+                return float(interval)
+        return float(blended[-1][0])
+
+    def _calculate_wbi_scores(self, data: MemberWinbackData, params: Dict[str, float]) -> None:
+        """计算 WBI 分项与 Raw Score"""
+        activity = data.activity
+
+        # 1) 超期紧急性（基于近期加权经验CDF）
+        overdue_alpha = float(params['overdue_alpha'])
+        half_life_days = float(params.get('overdue_weight_halflife_days', 30))
+        blend_min_samples = int(params.get('overdue_weight_blend_min_samples', 8))
+        if activity.interval_count <= 0:
+            p = 0.5
+            ideal_interval = None
+        else:
+            if len(activity.interval_ages_days) == activity.interval_count:
+                samples = list(zip(activity.intervals, activity.interval_ages_days))
+            else:
+                samples = [(interval, 0) for interval in activity.intervals]
+            p = self._weighted_cdf(
+                samples=samples,
+                t_v=activity.t_v,
+                halflife_days=half_life_days,
+                blend_min_samples=blend_min_samples,
+            )
+            ideal_interval = self._weighted_quantile(
+                samples=samples,
+                quantile=0.5,
+                halflife_days=half_life_days,
+                blend_min_samples=blend_min_samples,
+            )
+        data.overdue_cdf_p = p
+        data.overdue_old = math.pow(p, overdue_alpha)
+        data.ideal_interval_days = ideal_interval
+        if ideal_interval is not None and activity.last_visit_time is not None:
+            ideal_days = max(0, int(round(ideal_interval)))
+            data.ideal_next_visit_date = activity.last_visit_time.date() + timedelta(days=ideal_days)
+        else:
+            data.ideal_next_visit_date = None
+
+        # 2) 降频分
+        expected14 = activity.visits_60d * 14.0 / 60.0
+        data.drop_old = self._clip((expected14 - activity.visits_14d) / (expected14 + 1), 0.0, 1.0)
+
+        # 3) 充值未回访压力
+        if activity.recharge_unconsumed == 1:
+            data.recharge_old = self.decay(activity.t_r, params['h_recharge'])
+        else:
+            data.recharge_old = 0.0
+
+        # 4) 价值分
+        m0 = float(params['amount_base_M0'])
+        b0 = float(params['balance_base_B0'])
+        spend_score = math.log1p(activity.spend_180d / m0) if m0 > 0 else 0.0
+        bal_score = math.log1p(activity.sv_balance / b0) if b0 > 0 else 0.0
+        data.value_old = float(params['value_w_spend']) * spend_score + float(params['value_w_bal']) * bal_score
+
+        data.raw_score = (
+            float(params['w_over']) * data.overdue_old
+            + float(params['w_drop']) * data.drop_old
+            + float(params['w_re']) * data.recharge_old
+            + float(params['w_value']) * data.value_old
+        )
+
+        hard_floor_days = float(params.get('recency_hard_floor_days', 0))
+        gate_days = float(params.get('recency_gate_days', 14))
+        slope_days = float(params.get('recency_gate_slope_days', 3))
+        if hard_floor_days > 0 and activity.t_v < hard_floor_days:
+            suppression = 0.0
+        elif slope_days <= 0:
+            suppression = 1.0 if activity.t_v >= gate_days else 0.0
+        else:
+            x = (activity.t_v - gate_days) / slope_days
+            x = self._clip(x, -60.0, 60.0)
+            suppression = 1.0 / (1.0 + math.exp(-x))
+        data.raw_score *= suppression
+
+        # 限制在 0 以上
+        if data.raw_score < 0:
+            data.raw_score = 0.0
+
+    def _save_winback_data(self, data_list: List[MemberWinbackData]) -> int:
+        """保存 WBI 数据"""
+        if not data_list:
+            return 0
+
+        site_id = data_list[0].activity.site_id
+        # 按门店全量刷新，避免因分群变化导致过期数据残留。
+        delete_sql = """
+            DELETE FROM dws.dws_member_winback_index
+            WHERE site_id = %s
+        """
+        with self.db.conn.cursor() as cur:
+            cur.execute(delete_sql, (site_id,))
+
+        insert_sql = """
+            INSERT INTO dws.dws_member_winback_index (
+                site_id, tenant_id, member_id,
+                status, segment,
+                member_create_time, first_visit_time, last_visit_time, last_recharge_time,
+                t_v, t_r, t_a,
+                visits_14d, visits_60d, visits_total,
+                spend_30d, spend_180d, sv_balance, recharge_60d_amt,
+                interval_count,
+                overdue_old, overdue_cdf_p, drop_old, recharge_old, value_old,
+                ideal_interval_days, ideal_next_visit_date,
+                raw_score, display_score,
+                last_wechat_touch_time,
+                calc_time, created_at, updated_at
+            ) VALUES (
+                %s, %s, %s,
+                %s, %s,
+                %s, %s, %s, %s,
+                %s, %s, %s,
+                %s, %s, %s,
+                %s, %s, %s, %s,
+                %s,
+                %s, %s, %s, %s, %s,
+                %s, %s,
+                %s, %s,
+                %s,
+                NOW(), NOW(), NOW()
+            )
+        """
+
+        inserted = 0
+        with self.db.conn.cursor() as cur:
+            for data in data_list:
+                activity = data.activity
+                cur.execute(insert_sql, (
+                    activity.site_id, activity.tenant_id, activity.member_id,
+                    data.status, data.segment,
+                    activity.member_create_time, activity.first_visit_time, activity.last_visit_time, activity.last_recharge_time,
+                    activity.t_v, activity.t_r, activity.t_a,
+                    activity.visits_14d, activity.visits_60d, activity.visits_total,
+                    activity.spend_30d, activity.spend_180d, activity.sv_balance, activity.recharge_60d_amt,
+                    activity.interval_count,
+                    data.overdue_old, data.overdue_cdf_p, data.drop_old, data.recharge_old, data.value_old,
+                    data.ideal_interval_days, data.ideal_next_visit_date,
+                    data.raw_score, data.display_score,
+                    None,
+                ))
+                inserted += cur.rowcount
+
+        self.db.conn.commit()
+        return inserted
+
+    def _clip(self, value: float, low: float, high: float) -> float:
+        return max(low, min(high, value))
+
+    def _map_compression(self, params: Dict[str, float]) -> str:
+        mode = int(params.get('compression_mode', 0))
+        if mode == 1:
+            return "log1p"
+        if mode == 2:
+            return "asinh"
+        return "none"
+
+
+__all__ = ['WinbackIndexTask']
+