init: 项目初始提交 - NeoZQYY Monorepo 完整代码

2026-02-15 14:58:14 +08:00
commit ded6dfb9d8
769 changed files with 182616 additions and 0 deletions
--- a/apps/etl/pipelines/feiqiu/tasks/dws/finance_income_task.py
+++ b/apps/etl/pipelines/feiqiu/tasks/dws/finance_income_task.py
@@ -0,0 +1,412 @@
+# -*- coding: utf-8 -*-
+"""
+收入结构分析任务
+
+功能说明：
+    以"日期+区域/类型"为粒度，分析收入结构
+    
+数据来源：
+    - dwd_settlement_head: 结账单头表（台费、商品、助教正价）
+    - dwd_table_fee_log: 台费流水（区域关联）
+    - dwd_assistant_service_log: 助教服务流水（区域关联）
+    - cfg_area_category: 区域分类映射
+
+目标表：
+    billiards_dws.dws_finance_income_structure
+
+更新策略：
+    - 更新频率：每日更新
+    - 幂等方式：delete-before-insert（按日期+类型）
+    
+业务规则：
+    - 结构类型1（INCOME_TYPE）：按收入类型分析（台费/商品/助教基础课/助教附加课）
+    - 结构类型2（AREA）：按区域分析（普通台球区/VIP包厢/斯诺克/麻将/KTV等）
+    - 区域映射使用cfg_area_category配置
+
+作者：ETL团队
+创建日期：2026-02-01
+"""
+
+from __future__ import annotations
+
+from datetime import date, datetime, timedelta
+from decimal import Decimal
+from typing import Any, Dict, List, Optional, Tuple
+
+from .base_dws_task import BaseDwsTask, TaskContext
+
+
+class FinanceIncomeStructureTask(BaseDwsTask):
+    """
+    收入结构分析任务
+    
+    分析收入的两种维度：
+    1. INCOME_TYPE: 按收入类型（台费/商品/助教基础课/助教附加课）
+    2. AREA: 按区域（使用cfg_area_category映射）
+    """
+    
+    def get_task_code(self) -> str:
+        return "DWS_FINANCE_INCOME_STRUCTURE"
+    
+    def get_target_table(self) -> str:
+        return "dws_finance_income_structure"
+    
+    def get_primary_keys(self) -> List[str]:
+        return ["site_id", "stat_date", "structure_type", "category_code"]
+    
+    def extract(self, context: TaskContext) -> Dict[str, Any]:
+        """
+        抽取数据
+        
+        分两条路径抽取：
+        1. 按收入类型汇总（来自settlement_head）
+        2. 按区域汇总（来自table_fee_log和assistant_service_log）
+        """
+        start_date = context.window_start.date() if hasattr(context.window_start, 'date') else context.window_start
+        end_date = context.window_end.date() if hasattr(context.window_end, 'date') else context.window_end
+        site_id = context.store_id
+        
+        # 按收入类型汇总
+        income_by_type = self._extract_income_by_type(site_id, start_date, end_date)
+        
+        # 按区域汇总
+        income_by_area = self._extract_income_by_area(site_id, start_date, end_date)
+        
+        return {
+            'income_by_type': income_by_type,
+            'income_by_area': income_by_area,
+        }
+    
+    def _extract_income_by_type(
+        self, 
+        site_id: int, 
+        start_date: date, 
+        end_date: date
+    ) -> List[Dict[str, Any]]:
+        """
+        按收入类型汇总
+        
+        收入类型分类：
+        - TABLE_FEE: 台费收入 (table_charge_money)
+        - GOODS: 商品收入 (goods_money)
+        - ASSISTANT_BASE: 助教基础课 (assistant_pd_money)
+        - ASSISTANT_BONUS: 助教附加课 (assistant_cx_money)
+        """
+        sql = """
+            SELECT 
+                pay_time::DATE AS stat_date,
+                -- 台费收入
+                COALESCE(SUM(table_charge_money), 0) AS table_fee_income,
+                COUNT(CASE WHEN table_charge_money > 0 THEN 1 END) AS table_fee_orders,
+                -- 商品收入
+                COALESCE(SUM(goods_money), 0) AS goods_income,
+                COUNT(CASE WHEN goods_money > 0 THEN 1 END) AS goods_orders,
+                -- 助教基础课收入（PD=陪打）
+                COALESCE(SUM(assistant_pd_money), 0) AS assistant_base_income,
+                COUNT(CASE WHEN assistant_pd_money > 0 THEN 1 END) AS assistant_base_orders,
+                -- 助教附加课收入（CX=超休/促销）
+                COALESCE(SUM(assistant_cx_money), 0) AS assistant_bonus_income,
+                COUNT(CASE WHEN assistant_cx_money > 0 THEN 1 END) AS assistant_bonus_orders,
+                -- 总订单数
+                COUNT(*) AS total_orders
+            FROM billiards_dwd.dwd_settlement_head
+            WHERE site_id = %(site_id)s
+              AND pay_time >= %(start_date)s
+              AND pay_time < %(end_date)s + INTERVAL '1 day'
+              AND settle_status = 1  -- 已结账
+            GROUP BY pay_time::DATE
+            ORDER BY stat_date
+        """
+        rows = self.db.query(sql, {
+            'site_id': site_id,
+            'start_date': start_date,
+            'end_date': end_date,
+        })
+        return [dict(row) for row in rows] if rows else []
+    
+    def _extract_income_by_area(
+        self, 
+        site_id: int, 
+        start_date: date, 
+        end_date: date
+    ) -> List[Dict[str, Any]]:
+        """
+        按区域汇总收入
+        
+        关联dim_table获取区域名称，再映射到cfg_area_category
+        """
+        sql = """
+            WITH area_orders AS (
+                SELECT 
+                    tfl.pay_time::DATE AS stat_date,
+                    dt.site_table_area_name AS area_name,
+                    tfl.order_settle_id,
+                    COALESCE(tfl.ledger_amount, 0) AS income_amount,
+                    COALESCE(tfl.ledger_time_seconds, 0) AS duration_seconds
+                FROM billiards_dwd.dwd_table_fee_log tfl
+                LEFT JOIN billiards_dwd.dim_table dt 
+                    ON dt.site_table_id = tfl.site_table_id
+                WHERE tfl.site_id = %(site_id)s
+                  AND tfl.pay_time >= %(start_date)s
+                  AND tfl.pay_time < %(end_date)s + INTERVAL '1 day'
+                  AND COALESCE(tfl.is_delete, 0) = 0
+                
+                UNION ALL
+                
+                SELECT 
+                    asl.start_use_time::DATE AS stat_date,
+                    dt.site_table_area_name AS area_name,
+                    asl.order_settle_id,
+                    COALESCE(asl.ledger_amount, 0) AS income_amount,
+                    COALESCE(asl.income_seconds, 0) AS duration_seconds
+                FROM billiards_dwd.dwd_assistant_service_log asl
+                LEFT JOIN billiards_dwd.dim_table dt 
+                    ON dt.site_table_id = asl.site_table_id
+                WHERE asl.site_id = %(site_id)s
+                  AND asl.start_use_time >= %(start_date)s
+                  AND asl.start_use_time < %(end_date)s + INTERVAL '1 day'
+                  AND asl.is_delete = 0
+            )
+            SELECT 
+                stat_date,
+                area_name,
+                COALESCE(SUM(income_amount), 0) AS income_amount,
+                COALESCE(SUM(duration_seconds), 0) AS duration_seconds,
+                COUNT(DISTINCT order_settle_id) AS order_count
+            FROM area_orders
+            GROUP BY stat_date, area_name
+            ORDER BY stat_date, area_name
+        """
+        rows = self.db.query(sql, {
+            'site_id': site_id,
+            'start_date': start_date,
+            'end_date': end_date,
+        })
+        return [dict(row) for row in rows] if rows else []
+    
+    def transform(self, data: Dict[str, Any], context: TaskContext) -> List[Dict[str, Any]]:
+        """
+        转换数据
+        
+        将抽取的数据转换为目标表格式：
+        1. 按收入类型展开（每种类型一条记录）
+        2. 按区域展开（每个区域一条记录）
+        3. 计算占比
+        """
+        site_id = context.store_id
+        tenant_id = self.config.get("app.tenant_id", site_id)
+        
+        records = []
+        
+        # 处理按收入类型的数据
+        income_type_records = self._transform_income_by_type(
+            data.get('income_by_type', []),
+            site_id,
+            tenant_id
+        )
+        records.extend(income_type_records)
+        
+        # 处理按区域的数据
+        area_records = self._transform_income_by_area(
+            data.get('income_by_area', []),
+            site_id,
+            tenant_id
+        )
+        records.extend(area_records)
+        
+        return records
+    
+    def _transform_income_by_type(
+        self,
+        income_data: List[Dict[str, Any]],
+        site_id: int,
+        tenant_id: int
+    ) -> List[Dict[str, Any]]:
+        """
+        转换按收入类型的数据
+        
+        将每日汇总数据展开为4条记录（台费/商品/基础课/附加课）
+        """
+        # 收入类型定义
+        income_types = [
+            ('TABLE_FEE', '台费收入', 'table_fee_income', 'table_fee_orders'),
+            ('GOODS', '商品收入', 'goods_income', 'goods_orders'),
+            ('ASSISTANT_BASE', '助教基础课', 'assistant_base_income', 'assistant_base_orders'),
+            ('ASSISTANT_BONUS', '助教附加课', 'assistant_bonus_income', 'assistant_bonus_orders'),
+        ]
+        
+        records = []
+        
+        for daily_data in income_data:
+            stat_date = daily_data.get('stat_date')
+            
+            # 计算当日总收入（用于计算占比）
+            total_income = sum(
+                self.safe_decimal(daily_data.get(field, 0))
+                for _, _, field, _ in income_types
+            )
+            
+            # 为每种收入类型生成一条记录
+            for type_code, type_name, income_field, order_field in income_types:
+                income_amount = self.safe_decimal(daily_data.get(income_field, 0))
+                order_count = daily_data.get(order_field, 0) or 0
+                
+                # 计算占比（避免除零）
+                income_ratio = (income_amount / total_income) if total_income > 0 else Decimal('0')
+                
+                records.append({
+                    'site_id': site_id,
+                    'tenant_id': tenant_id,
+                    'stat_date': stat_date,
+                    'structure_type': 'INCOME_TYPE',
+                    'category_code': type_code,
+                    'category_name': type_name,
+                    'income_amount': income_amount,
+                    'income_ratio': round(income_ratio, 4),
+                    'order_count': order_count,
+                    'duration_minutes': 0,  # 收入类型维度不统计时长
+                })
+        
+        return records
+    
+    def _transform_income_by_area(
+        self,
+        area_data: List[Dict[str, Any]],
+        site_id: int,
+        tenant_id: int
+    ) -> List[Dict[str, Any]]:
+        """
+        转换按区域的数据
+        
+        将区域名称映射到cfg_area_category的category_code
+        """
+        records = []
+        
+        # 加载区域分类配置
+        self.load_config_cache()
+        
+        # 按日期分组计算总收入（用于计算占比）
+        daily_totals = {}
+        for row in area_data:
+            stat_date = row.get('stat_date')
+            income = self.safe_decimal(row.get('income_amount', 0))
+            daily_totals[stat_date] = daily_totals.get(stat_date, Decimal('0')) + income
+        
+        # 按日期+区域聚合（相同category_code需要合并）
+        aggregated = {}
+        
+        for row in area_data:
+            stat_date = row.get('stat_date')
+            area_name = row.get('area_name') or '未知区域'
+            income_amount = self.safe_decimal(row.get('income_amount', 0))
+            duration_seconds = row.get('duration_seconds', 0) or 0
+            order_count = row.get('order_count', 0) or 0
+            
+            # 映射区域名称到分类代码
+            category = self.get_area_category(area_name)
+            category_code = category.get('category_code', 'OTHER')
+            category_name = category.get('category_name', '其他区域')
+            
+            # 聚合键
+            key = (stat_date, category_code)
+            
+            if key not in aggregated:
+                aggregated[key] = {
+                    'stat_date': stat_date,
+                    'category_code': category_code,
+                    'category_name': category_name,
+                    'income_amount': Decimal('0'),
+                    'duration_seconds': 0,
+                    'order_count': 0,
+                }
+            
+            aggregated[key]['income_amount'] += income_amount
+            aggregated[key]['duration_seconds'] += duration_seconds
+            aggregated[key]['order_count'] += order_count
+        
+        # 生成记录
+        for key, agg_data in aggregated.items():
+            stat_date = agg_data['stat_date']
+            total_income = daily_totals.get(stat_date, Decimal('1'))
+            income_amount = agg_data['income_amount']
+            
+            # 计算占比
+            income_ratio = (income_amount / total_income) if total_income > 0 else Decimal('0')
+            
+            records.append({
+                'site_id': site_id,
+                'tenant_id': tenant_id,
+                'stat_date': stat_date,
+                'structure_type': 'AREA',
+                'category_code': agg_data['category_code'],
+                'category_name': agg_data['category_name'],
+                'income_amount': income_amount,
+                'income_ratio': round(income_ratio, 4),
+                'order_count': agg_data['order_count'],
+                'duration_minutes': agg_data['duration_seconds'] // 60,
+            })
+        
+        return records
+    
+    def _map_area_to_category(
+        self,
+        area_name: str,
+        area_categories: Dict[str, Dict[str, Any]]
+    ) -> Dict[str, Any]:
+        """
+        兼容旧逻辑的映射方法（当前使用 get_area_category）
+        """
+        return self.get_area_category(area_name)
+    
+    def load(self, records: List[Dict[str, Any]], context: TaskContext) -> Dict[str, Any]:
+        """
+        加载数据到目标表
+        
+        使用幂等方式：delete-before-insert（按日期范围）
+        """
+        if not records:
+            return {'inserted': 0, 'deleted': 0}
+        
+        site_id = context.store_id
+        start_date = context.window_start.date() if hasattr(context.window_start, 'date') else context.window_start
+        end_date = context.window_end.date() if hasattr(context.window_end, 'date') else context.window_end
+        
+        # 删除窗口内的旧数据
+        delete_sql = """
+            DELETE FROM billiards_dws.dws_finance_income_structure
+            WHERE site_id = %(site_id)s
+              AND stat_date >= %(start_date)s
+              AND stat_date <= %(end_date)s
+        """
+        deleted = self.db.execute(delete_sql, {
+            'site_id': site_id,
+            'start_date': start_date,
+            'end_date': end_date,
+        })
+        
+        # 批量插入新数据
+        insert_sql = """
+            INSERT INTO billiards_dws.dws_finance_income_structure (
+                site_id, tenant_id, stat_date,
+                structure_type, category_code, category_name,
+                income_amount, income_ratio,
+                order_count, duration_minutes,
+                created_at, updated_at
+            ) VALUES (
+                %(site_id)s, %(tenant_id)s, %(stat_date)s,
+                %(structure_type)s, %(category_code)s, %(category_name)s,
+                %(income_amount)s, %(income_ratio)s,
+                %(order_count)s, %(duration_minutes)s,
+                NOW(), NOW()
+            )
+        """
+        
+        inserted = 0
+        for record in records:
+            self.db.execute(insert_sql, record)
+            inserted += 1
+        
+        return {
+            'deleted': deleted or 0,
+            'inserted': inserted,
+        }