feat: 初始化考培练系统项目
- 从服务器拉取完整代码 - 按框架规范整理项目结构 - 配置 Drone CI 测试环境部署 - 包含后端(FastAPI)、前端(Vue3)、管理端 技术栈: Vue3 + TypeScript + FastAPI + MySQL
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backend/scripts/seed_statistics_demo_data.py
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408
backend/scripts/seed_statistics_demo_data.py
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#!/usr/bin/env python
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"""
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为统计分析页面生成轻医美场景的模拟数据
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生成的数据包括：
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1. 考试记录（exams）- 不同时间段、不同课程、不同分数
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2. 错题记录（exam_mistakes）- 不同难度、不同知识点
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3. 陪练会话（practice_sessions）- 不同时间的陪练记录
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4. 知识点数据（knowledge_points）- 轻医美相关知识点
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目标：确保统计分析页面的每个模块都能显示数据
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"""
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import sys
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import asyncio
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from datetime import datetime, timedelta
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import random
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from pathlib import Path
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# 添加项目路径
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project_root = Path(__file__).parent.parent
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sys.path.insert(0, str(project_root))
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from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession
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from sqlalchemy.orm import sessionmaker
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from sqlalchemy import select, func
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from app.models.exam import Exam, Question
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from app.models.exam_mistake import ExamMistake
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from app.models.course import Course, KnowledgePoint
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from app.models.practice import PracticeSession
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from app.models.user import User
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from app.core.logger import get_logger
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logger = get_logger(__name__)
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# 数据库连接（开发测试环境）
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DATABASE_URL = "mysql+aiomysql://root:nj861021@localhost:3306/kaopeilian"
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# 轻医美知识点数据
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BEAUTY_KNOWLEDGE_POINTS = {
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"皮肤生理学基础": [
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"皮肤结构与层次",
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"皮肤类型与特征",
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"皮肤屏障功能",
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"皮肤老化机制",
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"皮肤色素形成",
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"皮肤水分平衡"
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],
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"医美产品知识与应用": [
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"透明质酸的应用",
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"肉毒素的作用机理",
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"光子嫩肤原理",
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"果酸焕肤技术",
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"维生素C美白",
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"胶原蛋白补充"
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],
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"美容仪器操作与维护": [
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"超声刀操作流程",
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"热玛吉治疗参数",
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"皮秒激光使用",
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"射频美容仪器",
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"冷光美肤仪",
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"水光注射仪"
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],
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"轻医美销售技巧": [
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"客户需求分析",
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"项目推荐话术",
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"价格异议处理",
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"成交技巧",
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"客户关系维护",
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"套餐设计方法"
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],
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"客户服务与投诉处理": [
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"服务标准流程",
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"投诉应对技巧",
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"客户期望管理",
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"售后跟踪服务",
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"客户满意度提升",
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"危机公关处理"
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]
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}
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# 难度级别
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DIFFICULTY_LEVELS = ["easy", "medium", "hard"]
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async def clear_old_demo_data(db: AsyncSession, user_id: int):
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"""清理旧的演示数据"""
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logger.info(f"清理用户 {user_id} 的旧演示数据...")
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# 清理考试记录（会级联删除错题记录）
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from sqlalchemy import delete
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await db.execute(delete(Exam).where(Exam.user_id == user_id))
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# 清理陪练会话
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await db.execute(delete(PracticeSession).where(PracticeSession.user_id == user_id))
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await db.commit()
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logger.info("旧数据清理完成")
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async def get_or_create_knowledge_points(db: AsyncSession, course_id: int, course_name: str) -> list:
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"""获取或创建知识点"""
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# 检查是否已有知识点
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result = await db.execute(
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select(KnowledgePoint).where(
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KnowledgePoint.course_id == course_id,
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KnowledgePoint.is_deleted == False
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)
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)
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existing_kps = result.scalars().all()
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if existing_kps:
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logger.info(f"课程 {course_name} 已有 {len(existing_kps)} 个知识点")
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return existing_kps
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# 创建新知识点
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knowledge_points = []
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if course_name in BEAUTY_KNOWLEDGE_POINTS:
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for kp_name in BEAUTY_KNOWLEDGE_POINTS[course_name]:
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kp = KnowledgePoint(
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course_id=course_id,
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name=kp_name,
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description=f"{course_name}中的重要知识点：{kp_name}",
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type="核心概念",
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source=0, # 手动创建
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is_deleted=False
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)
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db.add(kp)
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knowledge_points.append(kp)
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await db.commit()
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# 刷新以获取ID
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for kp in knowledge_points:
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await db.refresh(kp)
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logger.info(f"为课程 {course_name} 创建了 {len(knowledge_points)} 个知识点")
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return knowledge_points
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async def create_exam_records(db: AsyncSession, user_id: int, courses: list):
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"""创建考试记录"""
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logger.info("创建考试记录...")
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end_date = datetime.now()
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exams_created = 0
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# 在过去60天内创建考试记录
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for days_ago in range(60, 0, -1):
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exam_date = end_date - timedelta(days=days_ago)
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# 跳过一些日期（不是每天都考试）
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if random.random() > 0.3: # 30%的日子有考试
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continue
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# 每天可能考1-2次
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num_exams = random.choice([1, 1, 1, 2])
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for _ in range(num_exams):
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# 随机选择课程
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course = random.choice(courses)
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# 生成考试分数（呈现进步趋势）
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# 早期分数较低，后期分数较高
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progress_factor = (60 - days_ago) / 60 # 0 到 1
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base_score = 60 + (progress_factor * 20) # 60-80分基础
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score_variance = random.uniform(-10, 15)
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round1_score = max(50, min(100, base_score + score_variance))
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# 创建考试记录
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exam = Exam(
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user_id=user_id,
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course_id=course.id,
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exam_name=f"{course.name}测试",
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question_count=10,
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total_score=100.0,
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pass_score=60.0,
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start_time=exam_date,
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end_time=exam_date + timedelta(minutes=random.randint(15, 45)),
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duration_minutes=random.randint(15, 45),
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round1_score=round(round1_score, 1),
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round2_score=None,
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round3_score=None,
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score=round(round1_score, 1),
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is_passed=round1_score >= 60,
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status="submitted"
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)
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db.add(exam)
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exams_created += 1
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await db.commit()
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logger.info(f"创建了 {exams_created} 条考试记录")
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return exams_created
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async def create_exam_mistakes(db: AsyncSession, user_id: int, courses: list):
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"""创建错题记录"""
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logger.info("创建错题记录...")
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# 获取用户的所有考试
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result = await db.execute(
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select(Exam).where(Exam.user_id == user_id).order_by(Exam.start_time)
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)
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exams = result.scalars().all()
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mistakes_created = 0
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for exam in exams:
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# 找到对应的课程
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course = next((c for c in courses if c.id == exam.course_id), None)
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if not course:
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continue
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# 获取该课程的知识点
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result = await db.execute(
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select(KnowledgePoint).where(
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KnowledgePoint.course_id == course.id,
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KnowledgePoint.is_deleted == False
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)
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)
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knowledge_points = result.scalars().all()
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if not knowledge_points:
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continue
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# 根据分数决定错题数（分数越低，错题越多）
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score = exam.round1_score or 70
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mistake_rate = (100 - score) / 100 # 0.0 到 0.5
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num_mistakes = int(exam.question_count * mistake_rate)
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num_mistakes = max(1, min(num_mistakes, exam.question_count - 1))
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# 创建错题
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for i in range(num_mistakes):
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# 随机选择知识点
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kp = random.choice(knowledge_points)
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# 随机选择题型
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question_types = ["single_choice", "multiple_choice", "true_false", "fill_blank", "essay"]
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question_type = random.choice(question_types)
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mistake = ExamMistake(
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user_id=user_id,
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exam_id=exam.id,
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question_id=None, # AI生成的题目
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knowledge_point_id=kp.id,
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question_content=f"关于{kp.name}的问题{i+1}",
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correct_answer="正确答案",
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user_answer="用户错误答案",
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question_type=question_type
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)
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db.add(mistake)
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mistakes_created += 1
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await db.commit()
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logger.info(f"创建了 {mistakes_created} 条错题记录")
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return mistakes_created
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async def create_practice_sessions(db: AsyncSession, user_id: int):
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"""创建陪练会话记录"""
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logger.info("创建陪练会话记录...")
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end_date = datetime.now()
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sessions_created = 0
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# 在过去60天内创建陪练记录
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for days_ago in range(60, 0, -1):
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session_date = end_date - timedelta(days=days_ago)
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# 跳过一些日期
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if random.random() > 0.25: # 25%的日子有陪练
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continue
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# 每次陪练的时长（秒）
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duration_seconds = random.randint(600, 1800) # 10-30分钟
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# 场景类型
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scene_types = ["电话销售", "面对面咨询", "客户投诉处理", "售后服务", "产品介绍"]
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scene_name = random.choice(scene_types)
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session = PracticeSession(
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session_id=f"session_{user_id}_{int(session_date.timestamp())}",
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user_id=user_id,
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scene_id=random.randint(1, 5),
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scene_name=scene_name,
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scene_type=scene_name,
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start_time=session_date,
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end_time=session_date + timedelta(seconds=duration_seconds),
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duration_seconds=duration_seconds,
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turns=random.randint(10, 30),
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status="completed",
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is_deleted=False
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)
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db.add(session)
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sessions_created += 1
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await db.commit()
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logger.info(f"创建了 {sessions_created} 条陪练会话记录")
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return sessions_created
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async def main():
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"""主函数"""
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logger.info("=" * 60)
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logger.info("开始为统计分析页面生成轻医美场景的模拟数据")
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logger.info("=" * 60)
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# 创建数据库连接
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engine = create_async_engine(DATABASE_URL, echo=False)
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async_session = sessionmaker(engine, class_=AsyncSession, expire_on_commit=False)
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try:
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async with async_session() as db:
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# 1. 获取测试用户（admin或第一个用户）
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result = await db.execute(
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select(User).where(User.username == "admin")
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)
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user = result.scalar_one_or_none()
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if not user:
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# 如果没有admin，使用第一个用户
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result = await db.execute(select(User).limit(1))
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user = result.scalar_one_or_none()
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if not user:
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logger.error("❌ 未找到用户，请先创建用户")
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return
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logger.info(f"📝 使用用户: {user.username} (ID: {user.id})")
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# 2. 获取轻医美相关课程
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result = await db.execute(
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select(Course).where(
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Course.is_deleted == False,
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Course.status == "published"
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)
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)
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courses = result.scalars().all()
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if not courses:
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logger.error("❌ 未找到已发布的课程")
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return
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logger.info(f"📚 找到 {len(courses)} 门课程")
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# 3. 清理旧数据（可选）
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clear_old = input("\n是否清理该用户的旧数据？(y/n): ").lower()
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if clear_old == 'y':
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await clear_old_demo_data(db, user.id)
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# 4. 为每门课程创建知识点
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logger.info("\n" + "=" * 60)
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logger.info("步骤 1/3: 创建知识点")
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logger.info("=" * 60)
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for course in courses:
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await get_or_create_knowledge_points(db, course.id, course.name)
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# 5. 创建考试记录
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logger.info("\n" + "=" * 60)
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logger.info("步骤 2/3: 创建考试记录")
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logger.info("=" * 60)
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exams_count = await create_exam_records(db, user.id, courses)
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# 6. 创建错题记录
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logger.info("\n" + "=" * 60)
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logger.info("步骤 3/3: 创建错题记录")
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logger.info("=" * 60)
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mistakes_count = await create_exam_mistakes(db, user.id, courses)
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# 7. 创建陪练会话记录
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logger.info("\n" + "=" * 60)
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logger.info("步骤 4/4: 创建陪练会话记录")
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logger.info("=" * 60)
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sessions_count = await create_practice_sessions(db, user.id)
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# 8. 统计信息
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logger.info("\n" + "=" * 60)
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logger.info("✅ 数据生成完成！")
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logger.info("=" * 60)
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logger.info(f"用户: {user.username} (ID: {user.id})")
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logger.info(f"考试记录: {exams_count} 条")
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logger.info(f"错题记录: {mistakes_count} 条")
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logger.info(f"陪练记录: {sessions_count} 条")
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logger.info("=" * 60)
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logger.info("\n现在可以访问统计分析页面查看数据：")
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logger.info("http://localhost:5173/analysis/statistics")
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logger.info("=" * 60)
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except Exception as e:
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logger.error(f"❌ 生成数据失败: {e}")
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import traceback
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traceback.print_exc()
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finally:
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await engine.dispose()
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if __name__ == "__main__":
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asyncio.run(main())
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