# -*- coding: utf-8 -*- """ 客户-助教亲密指数计算任务 功能说明： - 衡量客户与助教的关系强度和近期温度 - 用于助教约课精力分配和约课成功率预估 - 附加课权重 = 基础课的1.5倍 - 检测频率激增并放大权重 算法公式： Raw Score = (w_F × F + w_R × R + w_M × M + w_D × D) × mult 其中： - F = Σ(τ_i × decay(d_i, h_sess)) # 频次强度 - R = decay(d_last, h_last) # 最近温度 - M = Σ(ln(1+amt/A0) × decay(d_r, h_pay)) # 归因充值强度 - D = Σ(sqrt(dur/60) × τ × decay(d, h)) # 时长贡献 - mult = 1 + γ × burst # 激增放大 - burst = max(0, ln(1 + (F_short/F_long - 1))) 特殊逻辑： - 会话合并：同一客人对同一助教，间隔<4小时算同次服务 - 充值归因：服务结束后1小时内的充值算做该助教贡献 数据来源： - dwd_assistant_service_log: 服务记录 - dwd_recharge_order: 充值记录 更新频率：每4小时 作者：ETL团队 创建日期：2026-02-03 """ from __future__ import annotations import math 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, PercentileHistory from ..base_dws_task import TaskContext # ============================================================================= # 数据类定义 # ============================================================================= @dataclass class ServiceSession: """合并后的服务会话""" session_start: datetime session_end: datetime total_duration_minutes: int = 0 course_weight: float = 1.0 # 1.0=基础课, 1.5=附加课 is_incentive: bool = False # 是否为附加课 @dataclass class AttributedRecharge: """归因充值""" pay_time: datetime pay_amount: float days_ago: float @dataclass class MemberAssistantIntimacyData: """客户-助教亲密数据""" member_id: int assistant_no: str # 助教工号（字符串，如 "1", "2", "15"） assistant_nickname: str # 助教昵称 site_id: int tenant_id: int # 计算输入特征 session_count: int = 0 total_duration_minutes: int = 0 basic_session_count: int = 0 incentive_session_count: int = 0 days_since_last_session: Optional[int] = None attributed_recharge_count: int = 0 attributed_recharge_amount: float = 0.0 # 分项得分 score_frequency: float = 0.0 score_recency: float = 0.0 score_recharge: float = 0.0 score_duration: float = 0.0 burst_multiplier: float = 1.0 # 最终分数 raw_score: float = 0.0 display_score: float = 0.0 # 中间数据 sessions: List[ServiceSession] = field(default_factory=list) recharges: List[AttributedRecharge] = field(default_factory=list) # ============================================================================= # 亲密指数任务 # ============================================================================= class IntimacyIndexTask(BaseIndexTask): """ 客户-助教亲密指数计算任务 计算流程： 1. 提取近60天的助教服务记录 2. 按(member_id, assistant_id)分组，合并4小时内的服务 3. 提取归因充值（服务结束后1小时内） 4. 计算5项分数（频次、最近、充值、时长、激增） 5. 汇总Raw Score 6. 分位截断 + Log压缩 + MinMax映射到0-10 7. 写入DWS表 """ INDEX_TYPE = "INTIMACY" # 技能ID映射 SKILL_ID_BASIC = 2790683529513797 # 基础课 SKILL_ID_INCENTIVE = 2790683529513798 # 附加课/激励课 SKILL_ID_BOX = 3039912271463941 # 包厢课 # 默认参数 DEFAULT_PARAMS = { 'lookback_days': 60, 'halflife_session': 14.0, 'halflife_last': 10.0, 'halflife_recharge': 21.0, 'halflife_short': 7.0, 'halflife_long': 30.0, 'amount_base': 500.0, 'incentive_weight': 1.5, 'session_merge_hours': 4, 'recharge_attribute_hours': 1, 'weight_frequency': 2.0, 'weight_recency': 1.5, 'weight_recharge': 2.0, 'weight_duration': 0.5, 'burst_gamma': 0.6, 'percentile_lower': 5, 'percentile_upper': 95, } # ========================================================================== # 抽象方法实现 # ========================================================================== def get_task_code(self) -> str: return "DWS_INTIMACY_INDEX" def get_target_table(self) -> str: return "dws_member_assistant_intimacy" def get_primary_keys(self) -> List[str]: return ['site_id', 'member_id', 'assistant_id'] def get_index_type(self) -> str: return self.INDEX_TYPE # ========================================================================== # 任务执行 # ========================================================================== def execute(self, context: Optional[TaskContext]) -> Dict[str, Any]: """执行亲密指数计算""" self.logger.info("开始计算客户-助教亲密指数") # 获取门店ID site_id = self._get_site_id(context) tenant_id = self._get_tenant_id() # 加载参数 params = self._load_params() lookback_days = int(params['lookback_days']) # 计算基准日期和时间 now = datetime.now(self.tz) base_date = now.date() start_datetime = now - timedelta(days=lookback_days) self.logger.info( "参数: lookback=%d天, h_sess=%.1f, h_last=%.1f, h_pay=%.1f, γ=%.2f", lookback_days, params['halflife_session'], params['halflife_last'], params['halflife_recharge'], params['burst_gamma'] ) # 1. 提取服务记录 raw_services = self._extract_service_records(site_id, start_datetime, now) self.logger.info("提取到 %d 条原始服务记录", len(raw_services)) if not raw_services: self.logger.warning("没有服务记录，跳过计算") return {'status': 'skipped', 'reason': 'no_data'} # 2. 按(member_id, assistant_id)分组并合并会话 pair_data = self._group_and_merge_sessions(raw_services, params, now) self.logger.info("合并为 %d 个客户-助教对", len(pair_data)) # 3. 提取归因充值 self._extract_attributed_recharges(site_id, pair_data, params, now) # 4. 计算每个pair的特征和分数 intimacy_data_list: List[MemberAssistantIntimacyData] = [] for key, data in pair_data.items(): data.site_id = site_id data.tenant_id = tenant_id # 计算分项得分 self._calculate_component_scores(data, params, now) # 汇总Raw Score base_score = ( params['weight_frequency'] * data.score_frequency + params['weight_recency'] * data.score_recency + params['weight_recharge'] * data.score_recharge + params['weight_duration'] * data.score_duration ) data.raw_score = base_score * data.burst_multiplier intimacy_data_list.append(data) self.logger.info("计算完成 %d 个pair的Raw Score", len(intimacy_data_list)) # 5. 归一化到Display Score（使用对数压缩） raw_scores = [((d.member_id, d.assistant_no), d.raw_score) for d in intimacy_data_list] normalized = self.batch_normalize_to_display( raw_scores, use_log=True, # 亲密指数建议使用对数压缩 percentile_lower=int(params['percentile_lower']), percentile_upper=int(params['percentile_upper']), use_smoothing=True, site_id=site_id ) # 更新display_score score_map = {key: (raw, display) for key, raw, display in normalized} for data in intimacy_data_list: key = (data.member_id, data.assistant_no) if key in score_map: _, data.display_score = score_map[key] # 6. 保存分位点历史 if intimacy_data_list: all_raw = [d.raw_score for d in intimacy_data_list] q_l, q_u = self.calculate_percentiles( all_raw, int(params['percentile_lower']), int(params['percentile_upper']) ) smoothed_l, smoothed_u = self._apply_ewma_smoothing(site_id, 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) ) # 7. 写入DWS表 inserted = self._save_intimacy_data(intimacy_data_list) self.logger.info("亲密指数计算完成，写入 %d 条记录", inserted) return { 'status': 'success', 'pair_count': len(intimacy_data_list), 'records_inserted': inserted } # ========================================================================== # 数据提取方法 # ========================================================================== def _extract_service_records( self, site_id: int, start_datetime: datetime, end_datetime: datetime ) -> List[Dict[str, Any]]: """ 提取服务记录 注意: 使用 assistant_no (助教工号) 作为助教标识，而不是 site_assistant_id 因为 site_assistant_id 在数据中是每次服务的唯一ID，不是助教的唯一标识 Returns: [{'member_id', 'assistant_no', 'assistant_nickname', 'start_time', 'end_time', 'duration_minutes', 'skill_id'}, ...] """ sql = """ SELECT tenant_member_id AS member_id, assistant_no, nickname AS assistant_nickname, start_use_time, last_use_time, COALESCE(income_seconds, 0) / 60 AS duration_minutes, skill_id FROM billiards_dwd.dwd_assistant_service_log WHERE site_id = %s AND tenant_member_id > 0 -- 排除散客 AND is_delete = 0 AND assistant_no IS NOT NULL -- 确保有助教工号 AND last_use_time >= %s AND last_use_time < %s ORDER BY tenant_member_id, assistant_no, start_use_time """ rows = self.db.query(sql, (site_id, start_datetime, end_datetime)) result = [] for row in (rows or []): row_dict = dict(row) # 使用 assistant_no 作为助教标识 assistant_no = row_dict['assistant_no'] if assistant_no: result.append({ 'member_id': int(row_dict['member_id']), 'assistant_no': str(assistant_no), # 助教工号（字符串） 'assistant_nickname': row_dict['assistant_nickname'] or '', 'start_time': row_dict['start_use_time'], 'end_time': row_dict['last_use_time'], 'duration_minutes': int(row_dict['duration_minutes'] or 0), 'skill_id': int(row_dict['skill_id'] or 0) }) return result def _group_and_merge_sessions( self, raw_services: List[Dict[str, Any]], params: Dict[str, float], now: datetime ) -> Dict[Tuple[int, str], MemberAssistantIntimacyData]: """ 按(member_id, assistant_no)分组并合并会话 合并逻辑：同一客人对同一助教，间隔<4小时算同次服务 """ merge_threshold_hours = int(params['session_merge_hours']) merge_threshold = timedelta(hours=merge_threshold_hours) incentive_weight = params['incentive_weight'] pair_data: Dict[Tuple[int, str], MemberAssistantIntimacyData] = {} # 按pair分组（使用assistant_no） pair_services: Dict[Tuple[int, str], List[Dict[str, Any]]] = {} for svc in raw_services: key = (svc['member_id'], svc['assistant_no']) if key not in pair_services: pair_services[key] = [] pair_services[key].append(svc) # 对每个pair合并会话 for key, services in pair_services.items(): member_id, assistant_no = key # 取第一个服务记录的昵称 assistant_nickname = services[0]['assistant_nickname'] if services else '' data = MemberAssistantIntimacyData( member_id=member_id, assistant_no=assistant_no, assistant_nickname=assistant_nickname, site_id=0, # 稍后填充 tenant_id=0 ) # 按开始时间排序 sorted_services = sorted(services, key=lambda x: x['start_time']) # 合并会话 current_session: Optional[ServiceSession] = None for svc in sorted_services: start_time = svc['start_time'] end_time = svc['end_time'] duration = svc['duration_minutes'] skill_id = svc['skill_id'] # 判断课型 is_incentive = (skill_id == self.SKILL_ID_INCENTIVE) weight = incentive_weight if is_incentive else 1.0 if current_session is None: # 开始新会话 current_session = ServiceSession( session_start=start_time, session_end=end_time, total_duration_minutes=duration, course_weight=weight, is_incentive=is_incentive ) elif start_time - current_session.session_end <= merge_threshold: # 合并到当前会话 current_session.session_end = max(current_session.session_end, end_time) current_session.total_duration_minutes += duration # 同次服务取最高权重 current_session.course_weight = max(current_session.course_weight, weight) current_session.is_incentive = current_session.is_incentive or is_incentive else: # 保存当前会话，开始新会话 data.sessions.append(current_session) current_session = ServiceSession( session_start=start_time, session_end=end_time, total_duration_minutes=duration, course_weight=weight, is_incentive=is_incentive ) # 保存最后一个会话 if current_session is not None: data.sessions.append(current_session) # 统计特征 data.session_count = len(data.sessions) data.total_duration_minutes = sum(s.total_duration_minutes for s in data.sessions) data.basic_session_count = sum(1 for s in data.sessions if not s.is_incentive) data.incentive_session_count = sum(1 for s in data.sessions if s.is_incentive) # 最近一次服务 if data.sessions: last_session = max(data.sessions, key=lambda s: s.session_end) data.days_since_last_session = (now - last_session.session_end).days pair_data[key] = data return pair_data def _extract_attributed_recharges( self, site_id: int, pair_data: Dict[Tuple[int, int], MemberAssistantIntimacyData], params: Dict[str, float], now: datetime ) -> None: """ 提取归因充值 归因逻辑：服务结束后1小时内的充值算做该助教贡献 """ attribution_hours = int(params['recharge_attribute_hours']) attribution_window = timedelta(hours=attribution_hours) # 获取所有相关会员ID member_ids = set(key[0] for key in pair_data.keys()) if not member_ids: return member_ids_str = ','.join(str(m) for m in member_ids) # 查询充值记录 sql = f""" SELECT member_id, pay_time, pay_amount FROM billiards_dwd.dwd_recharge_order WHERE site_id = %s AND member_id IN ({member_ids_str}) AND settle_type = 5 -- 充值订单 AND pay_time >= %s """ lookback_days = int(params['lookback_days']) start_datetime = now - timedelta(days=lookback_days) rows = self.db.query(sql, (site_id, start_datetime)) # 为每个充值找到归因助教 for row in (rows or []): row_dict = dict(row) member_id = int(row_dict['member_id']) pay_time = row_dict['pay_time'] pay_amount = float(row_dict['pay_amount'] or 0) if pay_amount <= 0: continue # 查找该会员在pay_time前1小时内结束服务的助教 for key, data in pair_data.items(): if key[0] != member_id: continue for session in data.sessions: # 服务结束后1小时内的充值 if (session.session_end <= pay_time and pay_time - session.session_end <= attribution_window): # 归因给这个助教 data.attributed_recharge_count += 1 data.attributed_recharge_amount += pay_amount data.recharges.append(AttributedRecharge( pay_time=pay_time, pay_amount=pay_amount, days_ago=(now - pay_time).total_seconds() / 86400 )) break # 一笔充值只归因给一个助教 # ========================================================================== # 分数计算方法 # ========================================================================== def _calculate_component_scores( self, data: MemberAssistantIntimacyData, params: Dict[str, float], now: datetime ) -> None: """计算5项分数""" epsilon = 1e-6 h_sess = params['halflife_session'] h_last = params['halflife_last'] h_pay = params['halflife_recharge'] h_short = params['halflife_short'] h_long = params['halflife_long'] A0 = params['amount_base'] gamma = params['burst_gamma'] # 1. 频次强度 F = Σ(τ_i × decay(d_i, h_sess)) F = 0.0 for session in data.sessions: days_ago = (now - session.session_end).total_seconds() / 86400 F += session.course_weight * self.decay(days_ago, h_sess) data.score_frequency = F # 2. 最近温度 R = decay(d_last, h_last) if data.days_since_last_session is not None: data.score_recency = self.decay(data.days_since_last_session, h_last) else: data.score_recency = 0.0 # 3. 归因充值强度 M = Σ(ln(1+amt/A0) × decay(d_r, h_pay)) M = 0.0 for recharge in data.recharges: m_amt = math.log1p(recharge.pay_amount / A0) M += m_amt * self.decay(recharge.days_ago, h_pay) data.score_recharge = M # 4. 时长贡献 D = Σ(sqrt(dur/60) × τ × decay(d, h_sess)) D = 0.0 for session in data.sessions: days_ago = (now - session.session_end).total_seconds() / 86400 dur_hours = session.total_duration_minutes / 60.0 D += math.sqrt(dur_hours) * session.course_weight * self.decay(days_ago, h_sess) data.score_duration = D # 5. 频率激增放大 mult = 1 + γ × burst # F_short = Σ(τ × decay(d, h_short)) # F_long = Σ(τ × decay(d, h_long)) F_short = 0.0 F_long = 0.0 for session in data.sessions: days_ago = (now - session.session_end).total_seconds() / 86400 F_short += session.course_weight * self.decay(days_ago, h_short) F_long += session.course_weight * self.decay(days_ago, h_long) # burst = max(0, ln(1 + (F_short/F_long - 1))) ratio = F_short / (F_long + epsilon) if ratio > 1: burst = self.safe_ln1p(ratio - 1) else: burst = 0.0 data.burst_multiplier = 1 + gamma * burst # ========================================================================== # 数据保存方法 # ========================================================================== def _save_intimacy_data(self, data_list: List[MemberAssistantIntimacyData]) -> int: """保存亲密数据到DWS表""" if not data_list: return 0 # 先删除已存在的记录 site_id = data_list[0].site_id # 构建删除条件（使用assistant_no） # 注意：assistant_id字段在数据库中存储assistant_no的整数形式 keys = [(d.member_id, d.assistant_no) for d in data_list] conditions = " OR ".join( f"(member_id = {m} AND assistant_id = {int(a)})" for m, a in keys ) delete_sql = f""" DELETE FROM billiards_dws.dws_member_assistant_intimacy WHERE site_id = %s AND ({conditions}) """ with self.db.conn.cursor() as cur: cur.execute(delete_sql, (site_id,)) # 插入新记录 # 使用assistant_no的整数值作为assistant_id insert_sql = """ INSERT INTO billiards_dws.dws_member_assistant_intimacy ( site_id, tenant_id, member_id, assistant_id, session_count, total_duration_minutes, basic_session_count, incentive_session_count, days_since_last_session, attributed_recharge_count, attributed_recharge_amount, score_frequency, score_recency, score_recharge, score_duration, burst_multiplier, raw_score, display_score, 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, NOW(), NOW(), NOW() ) """ inserted = 0 with self.db.conn.cursor() as cur: for data in data_list: # 将assistant_no转为整数作为assistant_id assistant_id = int(data.assistant_no) if data.assistant_no.isdigit() else 0 cur.execute(insert_sql, ( data.site_id, data.tenant_id, data.member_id, assistant_id, data.session_count, data.total_duration_minutes, data.basic_session_count, data.incentive_session_count, data.days_since_last_session, data.attributed_recharge_count, data.attributed_recharge_amount, data.score_frequency, data.score_recency, data.score_recharge, data.score_duration, data.burst_multiplier, data.raw_score, data.display_score )) inserted += cur.rowcount # 提交事务 self.db.conn.commit() return inserted # ========================================================================== # 辅助方法 # ========================================================================== def _load_params(self) -> Dict[str, float]: """加载参数，缺失时使用默认值""" params = self.load_index_parameters() result = dict(self.DEFAULT_PARAMS) result.update(params) return result 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') if site_id: return int(site_id) sql = "SELECT DISTINCT site_id FROM billiards_dwd.dwd_assistant_service_log LIMIT 1" rows = self.db.query(sql) if rows: return int(dict(rows[0])['site_id']) raise ValueError("无法确定门店ID") def _get_tenant_id(self) -> int: """获取租户ID""" tenant_id = self.config.get('app.tenant_id') if tenant_id: return int(tenant_id) sql = "SELECT DISTINCT tenant_id FROM billiards_dwd.dwd_assistant_service_log LIMIT 1" rows = self.db.query(sql) if rows: return int(dict(rows[0])['tenant_id']) return 0