Node.js REPL自定义与高级调试技巧
2026/8/9 4:59:56
Flask + MySQL + ECharts 电商用户行为数据分析平台
这是一个非常适合作为毕业设计的选题，技术栈主流、数据可视化直观、业务逻辑清晰
📊 核心分析指标
指标 | 含义 | SQL计算方式 |
|---|---|---|
PV (Page View) | 页面访问量 |
|
UV (Unique Visitor) | 独立访客数 |
|
转化漏斗 | 浏览→加购→下单→支付 | 各阶段人数逐层统计 |
🗄️ 数据库设计 (MySQL)
-- 用户行为表 CREATE TABLE user_behavior ( id INT AUTO_INCREMENT PRIMARY KEY, user_id VARCHAR(50), item_id INT, category_id INT, behavior_type ENUM('pv','cart','fav','buy'), timestamp DATETIME, INDEX idx_user (user_id), INDEX idx_time (timestamp) ); -- 商品表 CREATE TABLE items ( item_id INT PRIMARY KEY, category_id INT, price DECIMAL(10,2) );🔧 Flask后端核心代码
1. 项目结构
ecommerce_analysis/ ├── app.py # Flask主程序 ├── models.py # 数据库操作 ├── templates/ │ ├── index.html # 仪表盘主页 │ └── funnel.html # 漏斗图页面 └── static/ └── js/ # ECharts配置2. API接口示例 (app.py)
from flask import Flask, jsonify, render_template from flask_sqlalchemy import SQLAlchemy from datetime import datetime, timedelta app = Flask(__name__) app.config['SQLALCHEMY_DATABASE_URI'] = 'mysql://root:password@localhost/ecommerce' db = SQLAlchemy(app) class UserBehavior(db.Model): __tablename__ = 'user_behavior' id = db.Column(db.Integer, primary_key=True) user_id = db.Column(db.String(50)) behavior_type = db.Column(db.String(20)) timestamp = db.Column(db.DateTime) @app.route('/api/pv_uv') def get_pv_uv(): """获取每日PV/UV数据""" today = datetime.now().date() results = db.session.execute(""" SELECT DATE(timestamp) as date, COUNT(*) as pv, COUNT(DISTINCT user_id) as uv FROM user_behavior WHERE timestamp >= :start_date GROUP BY DATE(timestamp) ORDER BY date """, {'start_date': today - timedelta(days=30)}).fetchall() return jsonify([{ 'date': str(r.date), 'pv': r.pv, 'uv': r.uv } for r in results]) @app.route('/api/funnel') def get_funnel(): """获取转化漏斗数据""" stages = ['pv', 'cart', 'fav', 'buy'] funnel_data = [] for stage in stages: count = db.session.execute(""" SELECT COUNT(DISTINCT user_id) FROM user_behavior WHERE behavior_type = :stage """, {'stage': stage}).scalar() funnel_data.append({'stage': stage, 'count': count}) return jsonify(funnel_data) @app.route('/') def dashboard(): return render_template('index.html') if __name__ == '__main__': app.run(debug=True)📈 ECharts前端可视化
折线图 - PV/UV趋势 (templates/index.html)
<!DOCTYPE html> <html> <head> <script src="https://cdn.jsdelivr.net/npm/echarts@5/dist/echarts.min.js"></script> </head> <body> <div id="pvuvChart" style="width: 100%; height: 400px;"></div> <div id="funnelChart" style="width: 600px; height: 450px;"></div> <script> // PV/UV折线图 fetch('/api/pv_uv') .then(res => res.json()) .then(data => { const chart = echarts.init(document.getElementById('pvuvChart')); chart.setOption({ title: { text: '30天PV/UV趋势' }, tooltip: { trigger: 'axis' }, legend: { data: ['PV', 'UV'] }, xAxis: { type: 'category', data: data.map(d => d.date) }, yAxis: [ { type: 'value', name: 'PV' }, { type: 'value', name: 'UV' } ], series: [ { name: 'PV', type: 'line', data: data.map(d => d.pv), smooth: true }, { name: 'UV', type: 'line', data: data.map(d => d.uv), yAxisIndex: 1, smooth: true } ] }); }); // 漏斗图 fetch('/api/funnel') .then(res => res.json()) .then(data => { const chart = echarts.init(document.getElementById('funnelChart')); chart.setOption({ title: { text: '用户转化漏斗' }, tooltip: { trigger: 'item', formatter: '{b} : {c}' }, series: [{ type: 'funnel', left: '10%', top: 60, bottom: 40, width: '80%', min: 0, max: Math.max(...data.map(d => d.count)), minSize: '0%', maxSize: '100%', sort: 'descending', gap: 2, label: { show: true, position: 'inside' }, data: data.map(d => ({ name: d.stage === 'pv' ? '浏览' : d.stage === 'cart' ? '加入购物车' : d.stage === 'fav' ? '收藏' : '购买', value: d.count })) }] }); }); </script> </body> </html>🚀 扩展功能建议（加分项）
功能模块 | 实现思路 | 技术点 |
|---|---|---|
实时监控 | WebSocket推送最新数据 | Flask-SocketIO |
用户画像 | RFM模型分析用户价值 | Pandas聚合计算 |
热力图 | 按小时+星期展示活跃度 | ECharts热力图 |
导出报告 | 生成PDF分析报告 | ReportLab/PyPDF2 |
预测趋势 | ARIMA/LSTM预测未来流量 | Prophet/Sklearn |
📦 数据集推荐
淘宝用户行为数据集 (UserBehavior.csv) - 约1亿条记录
天猫用户行为数据 (Tianchi竞赛数据)
自己写脚本模拟生成测试数据