482 lines
22 KiB
Python
482 lines
22 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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月結分析路由模組
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包含：月份總表數據分析展示頁面與 API
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"""
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from datetime import datetime, timezone, timedelta
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from flask import Blueprint, request, jsonify, render_template
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from auth import login_required
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from sqlalchemy import func, desc, text, case
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from config import BASE_DIR, SYSTEM_VERSION, DATABASE_TYPE
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from database.manager import DatabaseManager
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from database.models import MonthlySummaryAnalysis
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from services.logger_manager import SystemLogger
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def get_group_concat_sql():
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"""根據資料庫類型回傳適當的聚合函數 SQL"""
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if DATABASE_TYPE == 'postgresql':
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return """
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SELECT
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STRING_AGG(DISTINCT year::TEXT, ',') as years,
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STRING_AGG(DISTINCT month::TEXT, ',') as months,
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STRING_AGG(DISTINCT division, ',') as divisions,
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STRING_AGG(DISTINCT pm_name, ',') as pms,
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STRING_AGG(DISTINCT area_name, ',') as areas,
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STRING_AGG(DISTINCT vendor_name, ',') as vendors,
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STRING_AGG(DISTINCT trade_type, ',') as trades
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FROM monthly_summary_analysis
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"""
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else:
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return """
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SELECT
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GROUP_CONCAT(DISTINCT year) as years,
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GROUP_CONCAT(DISTINCT month) as months,
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GROUP_CONCAT(DISTINCT division) as divisions,
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GROUP_CONCAT(DISTINCT pm_name) as pms,
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GROUP_CONCAT(DISTINCT area_name) as areas,
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GROUP_CONCAT(DISTINCT vendor_name) as vendors,
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GROUP_CONCAT(DISTINCT trade_type) as trades
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FROM monthly_summary_analysis
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"""
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# 時區設定
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TAIPEI_TZ = timezone(timedelta(hours=8))
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# Logger
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sys_log = SystemLogger("MonthlyRoutes").get_logger()
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# Blueprint 定義
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monthly_bp = Blueprint('monthly', __name__)
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# ==========================================
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# 頁面路由
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# ==========================================
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@monthly_bp.route('/monthly_summary_analysis')
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@login_required
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def monthly_summary_analysis_page():
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"""月份總表數據分析展示頁 (Phase 9)"""
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return render_template('monthly_summary_analysis.html',
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datetime_now=datetime.now(TAIPEI_TZ).strftime('%Y-%m-%d %H:%M:%S'),
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system_version=SYSTEM_VERSION,
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active_page='monthly')
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# ==========================================
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# API 路由
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# ==========================================
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@monthly_bp.route('/api/monthly_summary_data')
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@login_required
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def get_monthly_summary_data():
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"""API: 取得月份總表數據與分析指標 (Phase 9)"""
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year = request.args.get('year', type=int)
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month = request.args.get('month', type=int)
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division = request.args.get('division')
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pm_name = request.args.get('pm_name')
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brand_name = request.args.get('brand_name')
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vendor_name = request.args.get('vendor')
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area_name = request.args.get('area_name')
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trade_type = request.args.get('trade_type')
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limit = request.args.get('limit', default=1000, type=int)
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db = DatabaseManager()
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session = db.get_session()
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try:
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# 基礎查詢
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query = session.query(MonthlySummaryAnalysis)
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# 套用過濾
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if year:
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query = query.filter(MonthlySummaryAnalysis.year == year)
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if month:
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query = query.filter(MonthlySummaryAnalysis.month == month)
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if division:
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query = query.filter(MonthlySummaryAnalysis.division == division)
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if pm_name:
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query = query.filter(MonthlySummaryAnalysis.pm_name == pm_name)
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if brand_name:
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query = query.filter(MonthlySummaryAnalysis.brand_name == brand_name)
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if vendor_name:
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query = query.filter(MonthlySummaryAnalysis.vendor_name == vendor_name)
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if area_name:
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if ',' in area_name:
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query = query.filter(MonthlySummaryAnalysis.area_name.in_(area_name.split(',')))
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else:
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query = query.filter(MonthlySummaryAnalysis.area_name == area_name)
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if trade_type:
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query = query.filter(MonthlySummaryAnalysis.trade_type == trade_type)
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# 取得統計數據 (KPIs)
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kpi_query = session.query(
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('total_sales'),
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func.sum(MonthlySummaryAnalysis.sales_amt_prev).label('total_sales_prev'),
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func.sum(MonthlySummaryAnalysis.sales_amt_yoa).label('total_sales_yoa'),
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func.sum(MonthlySummaryAnalysis.profit_amt_curr).label('total_profit'),
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func.sum(MonthlySummaryAnalysis.sales_vol_curr).label('total_vol'),
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func.sum(MonthlySummaryAnalysis.views_curr).label('total_views')
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)
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# 同樣套用過濾到 KPI
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if year:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.year == year)
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if month:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.month == month)
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if division:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.division == division)
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if pm_name:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.pm_name == pm_name)
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if brand_name:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.brand_name == brand_name)
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if vendor_name:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.vendor_name == vendor_name)
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if area_name:
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if ',' in area_name:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.area_name.in_(area_name.split(',')))
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else:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.area_name == area_name)
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if trade_type:
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kpi_query = kpi_query.filter(MonthlySummaryAnalysis.trade_type == trade_type)
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kpi_res = kpi_query.one()
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# 取得總筆數與月數
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total_rows = session.query(func.count(MonthlySummaryAnalysis.id))
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total_months_query = session.query(
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MonthlySummaryAnalysis.year, MonthlySummaryAnalysis.month
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).distinct()
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if year:
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total_rows = total_rows.filter(MonthlySummaryAnalysis.year == year)
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total_months_query = total_months_query.filter(MonthlySummaryAnalysis.year == year)
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if month:
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total_rows = total_rows.filter(MonthlySummaryAnalysis.month == month)
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total_rows = total_rows.scalar()
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total_months = total_months_query.count()
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# 取得趨勢數據 (按月加總)
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trend_query = session.query(
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MonthlySummaryAnalysis.year,
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MonthlySummaryAnalysis.month,
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales')
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).group_by(
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MonthlySummaryAnalysis.year, MonthlySummaryAnalysis.month
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).order_by(MonthlySummaryAnalysis.year, MonthlySummaryAnalysis.month)
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if division:
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trend_query = trend_query.filter(MonthlySummaryAnalysis.division == division)
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if pm_name:
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trend_query = trend_query.filter(MonthlySummaryAnalysis.pm_name == pm_name)
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if brand_name:
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trend_query = trend_query.filter(MonthlySummaryAnalysis.brand_name == brand_name)
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if vendor_name:
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trend_query = trend_query.filter(MonthlySummaryAnalysis.vendor_name == vendor_name)
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if area_name:
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if ',' in area_name:
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trend_query = trend_query.filter(MonthlySummaryAnalysis.area_name.in_(area_name.split(',')))
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else:
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trend_query = trend_query.filter(MonthlySummaryAnalysis.area_name == area_name)
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if trade_type:
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trend_query = trend_query.filter(MonthlySummaryAnalysis.trade_type == trade_type)
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# 取得排行榜 (Top 10 Brands)
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rank_query = session.query(
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MonthlySummaryAnalysis.brand_name,
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales')
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).group_by(MonthlySummaryAnalysis.brand_name)
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if year:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.year == year)
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if month:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.month == month)
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if division:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.division == division)
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if pm_name:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.pm_name == pm_name)
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if brand_name:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.brand_name == brand_name)
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if vendor_name:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.vendor_name == vendor_name)
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if area_name:
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if ',' in area_name:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.area_name.in_(area_name.split(',')))
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else:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.area_name == area_name)
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if trade_type:
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rank_query = rank_query.filter(MonthlySummaryAnalysis.trade_type == trade_type)
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rank_query = rank_query.order_by(desc('sales')).limit(10)
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# 取得明細資料
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rows_query = query.order_by(
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MonthlySummaryAnalysis.year.desc(),
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MonthlySummaryAnalysis.month.desc(),
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MonthlySummaryAnalysis.sales_amt_curr.desc()
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).limit(limit)
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# --- 進階分析子查詢 (Phase 17) ---
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def apply_filters(q, ignore_year=False):
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if year and not ignore_year:
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q = q.filter(MonthlySummaryAnalysis.year == year)
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if month:
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q = q.filter(MonthlySummaryAnalysis.month == month)
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if division:
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q = q.filter(MonthlySummaryAnalysis.division == division)
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if pm_name:
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q = q.filter(MonthlySummaryAnalysis.pm_name == pm_name)
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if brand_name:
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q = q.filter(MonthlySummaryAnalysis.brand_name == brand_name)
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if vendor_name:
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q = q.filter(MonthlySummaryAnalysis.vendor_name == vendor_name)
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if area_name:
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if ',' in area_name:
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q = q.filter(MonthlySummaryAnalysis.area_name.in_(area_name.split(',')))
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else:
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q = q.filter(MonthlySummaryAnalysis.area_name == area_name)
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if trade_type:
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q = q.filter(MonthlySummaryAnalysis.trade_type == trade_type)
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return q
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# 廠商排行 (Top 20, 分年度)
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vendor_rank_q = session.query(
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MonthlySummaryAnalysis.vendor_name,
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales'),
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func.sum(case((MonthlySummaryAnalysis.year == 2024, MonthlySummaryAnalysis.sales_amt_curr), else_=0)).label('sales_2024'),
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func.sum(case((MonthlySummaryAnalysis.year == 2025, MonthlySummaryAnalysis.sales_amt_curr), else_=0)).label('sales_2025'),
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func.sum(MonthlySummaryAnalysis.profit_amt_curr).label('profit'),
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func.sum(case((MonthlySummaryAnalysis.year == 2024, MonthlySummaryAnalysis.profit_amt_curr), else_=0)).label('profit_2024'),
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func.sum(case((MonthlySummaryAnalysis.year == 2025, MonthlySummaryAnalysis.profit_amt_curr), else_=0)).label('profit_2025'),
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).group_by(MonthlySummaryAnalysis.vendor_name)
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vendor_rank_q = apply_filters(vendor_rank_q, ignore_year=True)
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vendor_rank_q = vendor_rank_q.order_by(desc('sales')).limit(20)
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# 區域分佈 (按 area_name, Top 12, 分年度)
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div_dist_q = session.query(
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MonthlySummaryAnalysis.area_name,
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales'),
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func.sum(case((MonthlySummaryAnalysis.year == 2024, MonthlySummaryAnalysis.sales_amt_curr), else_=0)).label('sales_2024'),
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func.sum(case((MonthlySummaryAnalysis.year == 2025, MonthlySummaryAnalysis.sales_amt_curr), else_=0)).label('sales_2025')
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).group_by(MonthlySummaryAnalysis.area_name)
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div_dist_q = apply_filters(div_dist_q, ignore_year=True)
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div_dist_q = div_dist_q.order_by(desc('sales')).limit(12)
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# 價格帶貢獻 (分年度)
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price_cont_q = session.query(
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case(
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(MonthlySummaryAnalysis.unit_price < 500, '0-499'),
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(MonthlySummaryAnalysis.unit_price < 1000, '500-999'),
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(MonthlySummaryAnalysis.unit_price < 2000, '1,000-1,999'),
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(MonthlySummaryAnalysis.unit_price < 5000, '2,000-4,999'),
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(MonthlySummaryAnalysis.unit_price < 10000, '5,000-9,999'),
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else_='10,000+'
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).label('price_range'),
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales'),
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func.sum(case((MonthlySummaryAnalysis.year == 2024, MonthlySummaryAnalysis.sales_amt_curr), else_=0)).label('sales_2024'),
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func.sum(case((MonthlySummaryAnalysis.year == 2025, MonthlySummaryAnalysis.sales_amt_curr), else_=0)).label('sales_2025')
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).group_by('price_range')
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price_cont_q = apply_filters(price_cont_q, ignore_year=True)
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# BCG 矩陣 (品牌 x 區域)
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bcg_q = session.query(
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MonthlySummaryAnalysis.brand_name,
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MonthlySummaryAnalysis.area_name,
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func.sum(MonthlySummaryAnalysis.sales_vol_curr).label('vol'),
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales'),
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func.sum(MonthlySummaryAnalysis.profit_amt_curr).label('profit')
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).group_by(MonthlySummaryAnalysis.brand_name, MonthlySummaryAnalysis.area_name)\
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.having(func.sum(MonthlySummaryAnalysis.sales_amt_curr) > 0)
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bcg_q = apply_filters(bcg_q)
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bcg_q = bcg_q.order_by(desc('sales')).limit(100)
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# 熱力圖 (月份 x 分類)
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div_dist_results = div_dist_q.all()
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top_12_areas = [r.area_name for r in div_dist_results]
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heatmap_q = session.query(
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MonthlySummaryAnalysis.year,
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MonthlySummaryAnalysis.month,
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MonthlySummaryAnalysis.area_name,
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales')
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).filter(MonthlySummaryAnalysis.area_name.in_(top_12_areas))\
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.group_by(MonthlySummaryAnalysis.year, MonthlySummaryAnalysis.month, MonthlySummaryAnalysis.area_name)\
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.order_by(MonthlySummaryAnalysis.year, MonthlySummaryAnalysis.month)
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heatmap_q = apply_filters(heatmap_q, ignore_year=True)
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# Highlights (Top 3)
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def get_highlights_q(metric_col):
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q = session.query(MonthlySummaryAnalysis.brand_name, func.sum(metric_col).label('val'))
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q = apply_filters(q)
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q = q.group_by(MonthlySummaryAnalysis.brand_name).order_by(desc('val')).limit(3)
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return q
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rev_top_q = get_highlights_q(MonthlySummaryAnalysis.sales_amt_curr)
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profit_top_q = get_highlights_q(MonthlySummaryAnalysis.profit_amt_curr)
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vol_top_q = get_highlights_q(MonthlySummaryAnalysis.sales_vol_curr)
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# 區域排行
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area_rank_q = session.query(
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MonthlySummaryAnalysis.area_name,
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales'),
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func.sum(case((MonthlySummaryAnalysis.year == 2024, MonthlySummaryAnalysis.sales_amt_curr), else_=0)).label('sales_2024'),
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func.sum(case((MonthlySummaryAnalysis.year == 2025, MonthlySummaryAnalysis.sales_amt_curr), else_=0)).label('sales_2025')
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).group_by(MonthlySummaryAnalysis.area_name)
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area_rank_q = apply_filters(area_rank_q, ignore_year=True)
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area_rank_q = area_rank_q.order_by(desc('sales'))
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# 年度對比趨勢
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yoy_trend_q = session.query(
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MonthlySummaryAnalysis.year,
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MonthlySummaryAnalysis.month,
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func.sum(MonthlySummaryAnalysis.sales_amt_curr).label('sales_curr'),
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func.sum(MonthlySummaryAnalysis.sales_amt_yoa).label('sales_yoa')
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)
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yoy_trend_q = apply_filters(yoy_trend_q)
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yoy_trend_q = yoy_trend_q.group_by(
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MonthlySummaryAnalysis.year, MonthlySummaryAnalysis.month
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).order_by(MonthlySummaryAnalysis.year, MonthlySummaryAnalysis.month)
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rows = []
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for r in rows_query.all():
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rows.append({
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'year': r.year,
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'month': r.month,
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'division': r.division,
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'pm_name': r.pm_name,
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'area_name': r.area_name,
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'brand_name': r.brand_name,
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'vendor_name': r.vendor_name,
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'trade_type': r.trade_type,
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'sales_amt_curr': r.sales_amt_curr,
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'sales_amt_yoa': r.sales_amt_yoa,
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'sales_vol_curr': r.sales_vol_curr,
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'profit_amt_curr': r.profit_amt_curr,
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'views_curr': r.views_curr
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})
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# 使用單一 SQL 查詢取得所有不重複的維度列表 (支援 PostgreSQL/SQLite)
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with db.engine.connect() as conn:
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filters_result = conn.execute(text(get_group_concat_sql())).fetchone()
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years_list = [int(x) for x in (filters_result[0] or '').split(',') if x]
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months_list = [int(x) for x in (filters_result[1] or '').split(',') if x]
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divisions_list = [x for x in (filters_result[2] or '').split(',') if x]
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pms_list = [x for x in (filters_result[3] or '').split(',') if x]
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areas_list = [x for x in (filters_result[4] or '').split(',') if x]
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vendors_list = [x for x in (filters_result[5] or '').split(',') if x]
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trades_list = [x for x in (filters_result[6] or '').split(',') if x]
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# 預先執行所有查詢
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area_rank_results = area_rank_q.all()
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vendor_rank_results = vendor_rank_q.all()
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price_cont_results = price_cont_q.all()
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bcg_results = bcg_q.all()
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heatmap_results = heatmap_q.all()
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trend_results = trend_query.all()
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yoy_trend_results = yoy_trend_q.all()
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rank_results = rank_query.all()
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rev_top_results = rev_top_q.all()
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profit_top_results = profit_top_q.all()
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vol_top_results = vol_top_q.all()
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return jsonify({
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'status': 'success',
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'total_rows': total_rows,
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'total_months': total_months,
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'kpis': {
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'sales': int(kpi_res.total_sales or 0),
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'sales_prev': int(kpi_res.total_sales_prev or 0),
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'sales_yoa': int(kpi_res.total_sales_yoa or 0),
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'profit': int(kpi_res.total_profit or 0),
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'vol': int(kpi_res.total_vol or 0),
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'views': int(kpi_res.total_views or 0),
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'margin': round((kpi_res.total_profit / kpi_res.total_sales * 100), 2) if kpi_res.total_sales and kpi_res.total_profit else 0
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},
|
|
'trend': [{'date': f"{r.year}/{r.month}", 'sales': int(r.sales or 0)} for r in trend_results],
|
|
'yoy_trend': [{'date': f"{r.year}/{r.month}", 'curr': int(r.sales_curr or 0), 'yoa': int(r.sales_yoa or 0)} for r in yoy_trend_results],
|
|
'rankings': [{'brand': r.brand_name, 'sales': int(r.sales or 0)} for r in rank_results],
|
|
'area_ranking': [
|
|
{
|
|
'name': r.area_name,
|
|
'sales': int(r.sales or 0),
|
|
'sales_2024': int(r.sales_2024 or 0),
|
|
'sales_2025': int(r.sales_2025 or 0)
|
|
}
|
|
for r in area_rank_results
|
|
],
|
|
'vendor_ranking': [
|
|
{
|
|
'name': r.vendor_name,
|
|
'sales': int(r.sales or 0),
|
|
'sales_2024': int(r.sales_2024 or 0),
|
|
'sales_2025': int(r.sales_2025 or 0),
|
|
'profit': int(r.profit or 0),
|
|
'profit_2024': int(r.profit_2024 or 0),
|
|
'profit_2025': int(r.profit_2025 or 0),
|
|
'margin': round((r.profit / r.sales * 100), 2) if r.sales and r.profit else 0
|
|
}
|
|
for r in vendor_rank_results
|
|
],
|
|
'division_dist': [
|
|
{
|
|
'name': r.area_name,
|
|
'value': int(r.sales or 0),
|
|
'sales_2024': int(r.sales_2024 or 0),
|
|
'sales_2025': int(r.sales_2025 or 0)
|
|
}
|
|
for r in div_dist_results
|
|
],
|
|
'price_contribution': [
|
|
{
|
|
'range': r.price_range,
|
|
'sales': int(r.sales or 0),
|
|
'sales_2024': int(r.sales_2024 or 0),
|
|
'sales_2025': int(r.sales_2025 or 0)
|
|
}
|
|
for r in price_cont_results
|
|
],
|
|
'bcg_data': [
|
|
{
|
|
'name': f"{r.brand_name}-{r.area_name}",
|
|
'qty': int(r.vol or 0),
|
|
'margin': round((r.profit / r.sales * 100), 2) if r.sales and r.profit else 0,
|
|
'sales': int(r.sales or 0)
|
|
}
|
|
for r in bcg_results
|
|
],
|
|
'heatmap_data': [
|
|
{'year': r.year, 'month': r.month, 'category': r.area_name, 'sales': int(r.sales or 0)}
|
|
for r in heatmap_results
|
|
],
|
|
'highlights': {
|
|
'rev_top': [{'name': r.brand_name, 'value': int(r.val or 0)} for r in rev_top_results],
|
|
'profit_top': [{'name': r.brand_name, 'value': int(r.val or 0)} for r in profit_top_results],
|
|
'vol_top': [{'name': r.brand_name, 'value': int(r.val or 0)} for r in vol_top_results]
|
|
},
|
|
'filters': {
|
|
'years': sorted(years_list, reverse=True),
|
|
'months': sorted(months_list),
|
|
'divisions': sorted(divisions_list),
|
|
'pms': sorted(pms_list),
|
|
'areas': sorted(areas_list),
|
|
'vendors': sorted(vendors_list),
|
|
'trades': sorted(trades_list)
|
|
},
|
|
'rows': rows
|
|
})
|
|
|
|
except Exception as e:
|
|
sys_log.error(f"取得月份總表數據失敗: {e}")
|
|
return jsonify({'status': 'error', 'message': str(e)}), 500
|
|
finally:
|
|
session.close()
|