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/app/services/statistics_service.py
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backend/app/services/statistics_service.py
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"""
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统计分析服务
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"""
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from datetime import datetime, timedelta
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from typing import List, Optional, Dict, Any, Tuple
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from sqlalchemy.ext.asyncio import AsyncSession
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from sqlalchemy import select, func, and_, or_, case, desc, distinct
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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.training import TrainingSession
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from app.core.logger import get_logger
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logger = get_logger(__name__)
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class StatisticsService:
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"""统计分析服务类"""
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@staticmethod
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def _get_date_range(period: str) -> Tuple[datetime, datetime]:
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"""
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根据period返回开始和结束日期
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Args:
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period: 时间范围 (week/month/quarter/halfYear/year)
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Returns:
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Tuple[datetime, datetime]: (开始日期, 结束日期)
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"""
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end_date = datetime.now()
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if period == "week":
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start_date = end_date - timedelta(days=7)
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elif period == "month":
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start_date = end_date - timedelta(days=30)
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elif period == "quarter":
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start_date = end_date - timedelta(days=90)
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elif period == "halfYear":
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start_date = end_date - timedelta(days=180)
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elif period == "year":
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start_date = end_date - timedelta(days=365)
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else:
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# 默认一个月
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start_date = end_date - timedelta(days=30)
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return start_date, end_date
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@staticmethod
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def _calculate_change_rate(current: float, previous: float) -> float:
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"""
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计算环比变化率
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Args:
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current: 当前值
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previous: 上期值
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Returns:
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float: 变化率（百分比）
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"""
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if previous == 0:
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return 0 if current == 0 else 100
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return round(((current - previous) / previous) * 100, 1)
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@staticmethod
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async def get_key_metrics(
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db: AsyncSession,
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user_id: int,
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course_id: Optional[int] = None,
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period: str = "month"
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) -> Dict[str, Any]:
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"""
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获取关键指标
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Args:
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db: 数据库会话
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user_id: 用户ID
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course_id: 课程ID（可选）
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period: 时间范围
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Returns:
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Dict: 包含学习效率、知识覆盖率、平均用时、进步速度的指标数据
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"""
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logger.info(f"获取关键指标 - user_id: {user_id}, course_id: {course_id}, period: {period}")
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start_date, end_date = StatisticsService._get_date_range(period)
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# 构建基础查询条件
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exam_conditions = [
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Exam.user_id == user_id,
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Exam.start_time >= start_date,
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Exam.start_time <= end_date,
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Exam.round1_score.isnot(None)
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]
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if course_id:
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exam_conditions.append(Exam.course_id == course_id)
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# 1. 学习效率 = (总题数 - 错题数) / 总题数
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# 获取总题数
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total_questions_stmt = select(
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func.coalesce(func.sum(Exam.question_count), 0)
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).where(and_(*exam_conditions))
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total_questions = await db.scalar(total_questions_stmt) or 0
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# 获取错题数
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mistake_conditions = [ExamMistake.user_id == user_id]
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if course_id:
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mistake_conditions.append(
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ExamMistake.exam_id.in_(
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select(Exam.id).where(Exam.course_id == course_id)
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)
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)
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mistake_stmt = select(func.count(ExamMistake.id)).where(
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and_(*mistake_conditions)
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)
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mistake_count = await db.scalar(mistake_stmt) or 0
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# 计算学习效率
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learning_efficiency = 0.0
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if total_questions > 0:
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correct_questions = total_questions - mistake_count
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learning_efficiency = round((correct_questions / total_questions) * 100, 1)
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# 计算上期学习效率（用于环比）
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prev_start_date = start_date - (end_date - start_date)
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prev_exam_conditions = [
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Exam.user_id == user_id,
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Exam.start_time >= prev_start_date,
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Exam.start_time < start_date,
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Exam.round1_score.isnot(None)
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]
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if course_id:
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prev_exam_conditions.append(Exam.course_id == course_id)
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prev_total_questions = await db.scalar(
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select(func.coalesce(func.sum(Exam.question_count), 0)).where(
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and_(*prev_exam_conditions)
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)
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) or 0
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prev_mistake_count = await db.scalar(
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select(func.count(ExamMistake.id)).where(
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and_(
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ExamMistake.user_id == user_id,
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ExamMistake.exam_id.in_(
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select(Exam.id).where(and_(*prev_exam_conditions))
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)
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)
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)
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) or 0
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prev_efficiency = 0.0
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if prev_total_questions > 0:
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prev_correct = prev_total_questions - prev_mistake_count
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prev_efficiency = (prev_correct / prev_total_questions) * 100
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efficiency_change = StatisticsService._calculate_change_rate(
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learning_efficiency, prev_efficiency
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)
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# 2. 知识覆盖率 = 已掌握知识点数 / 总知识点数
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# 获取总知识点数
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kp_conditions = []
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if course_id:
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kp_conditions.append(KnowledgePoint.course_id == course_id)
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total_kp_stmt = select(func.count(KnowledgePoint.id)).where(
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and_(KnowledgePoint.is_deleted == False, *kp_conditions)
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)
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total_kp = await db.scalar(total_kp_stmt) or 0
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# 获取错误的知识点数（至少错过一次的）
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mistake_kp_stmt = select(
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func.count(distinct(ExamMistake.knowledge_point_id))
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).where(
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and_(
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ExamMistake.user_id == user_id,
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ExamMistake.knowledge_point_id.isnot(None),
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*([ExamMistake.exam_id.in_(
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select(Exam.id).where(Exam.course_id == course_id)
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)] if course_id else [])
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)
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)
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mistake_kp = await db.scalar(mistake_kp_stmt) or 0
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# 计算知识覆盖率（掌握的知识点 = 总知识点 - 错误知识点）
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knowledge_coverage = 0.0
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if total_kp > 0:
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mastered_kp = max(0, total_kp - mistake_kp)
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knowledge_coverage = round((mastered_kp / total_kp) * 100, 1)
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# 上期知识覆盖率（简化：假设知识点总数不变）
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prev_mistake_kp = await db.scalar(
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select(func.count(distinct(ExamMistake.knowledge_point_id))).where(
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and_(
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ExamMistake.user_id == user_id,
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ExamMistake.knowledge_point_id.isnot(None),
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ExamMistake.exam_id.in_(
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select(Exam.id).where(and_(*prev_exam_conditions))
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)
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)
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)
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) or 0
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prev_coverage = 0.0
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if total_kp > 0:
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prev_mastered = max(0, total_kp - prev_mistake_kp)
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prev_coverage = (prev_mastered / total_kp) * 100
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coverage_change = StatisticsService._calculate_change_rate(
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knowledge_coverage, prev_coverage
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)
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# 3. 平均用时 = 总考试时长 / 总题数
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total_duration_stmt = select(
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func.coalesce(func.sum(Exam.duration_minutes), 0)
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).where(and_(*exam_conditions))
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total_duration = await db.scalar(total_duration_stmt) or 0
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avg_time_per_question = 0.0
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if total_questions > 0:
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avg_time_per_question = round((total_duration / total_questions), 1)
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# 上期平均用时
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prev_total_duration = await db.scalar(
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select(func.coalesce(func.sum(Exam.duration_minutes), 0)).where(
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and_(*prev_exam_conditions)
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)
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) or 0
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prev_avg_time = 0.0
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if prev_total_questions > 0:
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prev_avg_time = prev_total_duration / prev_total_questions
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# 平均用时的环比是负增长表示好（时间减少）
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time_change = StatisticsService._calculate_change_rate(
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avg_time_per_question, prev_avg_time
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)
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# 4. 进步速度 = (本期平均分 - 上期平均分) / 上期平均分
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avg_score_stmt = select(func.avg(Exam.round1_score)).where(
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and_(*exam_conditions)
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)
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avg_score = await db.scalar(avg_score_stmt) or 0
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prev_avg_score_stmt = select(func.avg(Exam.round1_score)).where(
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and_(*prev_exam_conditions)
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)
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prev_avg_score = await db.scalar(prev_avg_score_stmt) or 0
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progress_speed = StatisticsService._calculate_change_rate(
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float(avg_score), float(prev_avg_score)
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)
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return {
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"learningEfficiency": {
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"value": learning_efficiency,
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"unit": "%",
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"change": efficiency_change,
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"description": "正确题数/总练习题数"
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},
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"knowledgeCoverage": {
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"value": knowledge_coverage,
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"unit": "%",
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"change": coverage_change,
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"description": "已掌握知识点/总知识点"
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},
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"avgTimePerQuestion": {
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"value": avg_time_per_question,
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"unit": "分/题",
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"change": time_change,
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"description": "平均每道题的答题时间"
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},
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"progressSpeed": {
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"value": abs(progress_speed),
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"unit": "%",
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"change": progress_speed,
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"description": "成绩提升速度"
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}
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}
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@staticmethod
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async def get_score_distribution(
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db: AsyncSession,
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user_id: int,
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course_id: Optional[int] = None,
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period: str = "month"
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) -> Dict[str, Any]:
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"""
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获取成绩分布统计
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Args:
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db: 数据库会话
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user_id: 用户ID
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course_id: 课程ID（可选）
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period: 时间范围
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Returns:
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Dict: 成绩分布数据（优秀、良好、中等、及格、不及格）
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"""
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logger.info(f"获取成绩分布 - user_id: {user_id}, course_id: {course_id}, period: {period}")
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start_date, end_date = StatisticsService._get_date_range(period)
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# 构建查询条件
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conditions = [
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Exam.user_id == user_id,
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Exam.start_time >= start_date,
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Exam.start_time <= end_date,
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Exam.round1_score.isnot(None)
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]
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if course_id:
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conditions.append(Exam.course_id == course_id)
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# 使用case when统计各分数段的数量
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stmt = select(
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func.count(case((Exam.round1_score >= 90, 1))).label("excellent"),
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func.count(case((and_(Exam.round1_score >= 80, Exam.round1_score < 90), 1))).label("good"),
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func.count(case((and_(Exam.round1_score >= 70, Exam.round1_score < 80), 1))).label("medium"),
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func.count(case((and_(Exam.round1_score >= 60, Exam.round1_score < 70), 1))).label("pass_count"),
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func.count(case((Exam.round1_score < 60, 1))).label("fail")
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).where(and_(*conditions))
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result = await db.execute(stmt)
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row = result.one()
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return {
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"excellent": row.excellent or 0, # 优秀(90-100)
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"good": row.good or 0, # 良好(80-89)
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"medium": row.medium or 0, # 中等(70-79)
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"pass": row.pass_count or 0, # 及格(60-69)
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"fail": row.fail or 0 # 不及格(<60)
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}
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@staticmethod
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async def get_difficulty_analysis(
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db: AsyncSession,
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user_id: int,
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course_id: Optional[int] = None,
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period: str = "month"
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) -> Dict[str, Any]:
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"""
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获取题目难度分析
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Args:
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db: 数据库会话
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user_id: 用户ID
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course_id: 课程ID（可选）
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period: 时间范围
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Returns:
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Dict: 各难度题目的正确率统计
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"""
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logger.info(f"获取难度分析 - user_id: {user_id}, course_id: {course_id}, period: {period}")
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start_date, end_date = StatisticsService._get_date_range(period)
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# 获取用户在时间范围内的考试
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exam_conditions = [
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Exam.user_id == user_id,
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Exam.start_time >= start_date,
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Exam.start_time <= end_date
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]
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if course_id:
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exam_conditions.append(Exam.course_id == course_id)
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exam_ids_stmt = select(Exam.id).where(and_(*exam_conditions))
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result = await db.execute(exam_ids_stmt)
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exam_ids = [row[0] for row in result.all()]
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if not exam_ids:
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# 没有考试数据，返回默认值
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return {
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"easy": 100.0,
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"medium": 100.0,
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"hard": 100.0,
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"综合题": 100.0,
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"应用题": 100.0
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}
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# 统计各难度的总题数和错题数
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difficulty_stats = {}
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for difficulty in ["easy", "medium", "hard"]:
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# 总题数：从exams的questions字段中统计（这里简化处理）
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# 由于questions字段是JSON，我们通过question_count估算
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# 实际应用中可以解析JSON或通过exam_results表统计
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# 错题数：从exam_mistakes通过question_id关联查询
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mistake_stmt = select(func.count(ExamMistake.id)).select_from(
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ExamMistake
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).join(
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Question, ExamMistake.question_id == Question.id
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).where(
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and_(
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ExamMistake.user_id == user_id,
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ExamMistake.exam_id.in_(exam_ids),
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Question.difficulty == difficulty
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)
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)
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mistake_count = await db.scalar(mistake_stmt) or 0
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# 总题数：该难度的题目在用户考试中出现的次数
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# 简化处理：假设每次考试平均包含该难度题目的比例
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total_questions_stmt = select(
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func.coalesce(func.sum(Exam.question_count), 0)
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).where(and_(*exam_conditions))
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total_count = await db.scalar(total_questions_stmt) or 0
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total_count = int(total_count) # 转换为int避免Decimal类型问题
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# 简化算法：假设easy:medium:hard = 3:2:1
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if difficulty == "easy":
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estimated_count = int(total_count * 0.5)
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elif difficulty == "medium":
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estimated_count = int(total_count * 0.3)
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else: # hard
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estimated_count = int(total_count * 0.2)
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# 计算正确率
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if estimated_count > 0:
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correct_count = max(0, estimated_count - mistake_count)
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accuracy = round((correct_count / estimated_count) * 100, 1)
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else:
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accuracy = 100.0
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difficulty_stats[difficulty] = accuracy
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# 综合题和应用题使用中等和困难题的平均值
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difficulty_stats["综合题"] = round((difficulty_stats["medium"] + difficulty_stats["hard"]) / 2, 1)
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difficulty_stats["应用题"] = round((difficulty_stats["medium"] + difficulty_stats["hard"]) / 2, 1)
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return {
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"简单题": difficulty_stats["easy"],
|
||||
"中等题": difficulty_stats["medium"],
|
||||
"困难题": difficulty_stats["hard"],
|
||||
"综合题": difficulty_stats["综合题"],
|
||||
"应用题": difficulty_stats["应用题"]
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
async def get_knowledge_mastery(
|
||||
db: AsyncSession,
|
||||
user_id: int,
|
||||
course_id: Optional[int] = None
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取知识点掌握度
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
user_id: 用户ID
|
||||
course_id: 课程ID（可选）
|
||||
|
||||
Returns:
|
||||
List[Dict]: 知识点掌握度列表
|
||||
"""
|
||||
logger.info(f"获取知识点掌握度 - user_id: {user_id}, course_id: {course_id}")
|
||||
|
||||
# 获取知识点列表
|
||||
kp_conditions = [KnowledgePoint.is_deleted == False]
|
||||
if course_id:
|
||||
kp_conditions.append(KnowledgePoint.course_id == course_id)
|
||||
|
||||
kp_stmt = select(KnowledgePoint).where(and_(*kp_conditions)).limit(10)
|
||||
result = await db.execute(kp_stmt)
|
||||
knowledge_points = result.scalars().all()
|
||||
|
||||
if not knowledge_points:
|
||||
# 没有知识点数据，返回默认数据
|
||||
return [
|
||||
{"name": "基础概念", "mastery": 85.0},
|
||||
{"name": "核心知识", "mastery": 72.0},
|
||||
{"name": "实践应用", "mastery": 68.0},
|
||||
{"name": "综合运用", "mastery": 58.0},
|
||||
{"name": "高级技巧", "mastery": 75.0},
|
||||
{"name": "案例分析", "mastery": 62.0}
|
||||
]
|
||||
|
||||
mastery_list = []
|
||||
|
||||
for kp in knowledge_points:
|
||||
# 统计该知识点的错误次数
|
||||
mistake_stmt = select(func.count(ExamMistake.id)).where(
|
||||
and_(
|
||||
ExamMistake.user_id == user_id,
|
||||
ExamMistake.knowledge_point_id == kp.id
|
||||
)
|
||||
)
|
||||
mistake_count = await db.scalar(mistake_stmt) or 0
|
||||
|
||||
# 假设每个知识点平均被考查10次（简化处理）
|
||||
estimated_total = 10
|
||||
|
||||
# 计算掌握度
|
||||
if estimated_total > 0:
|
||||
correct_count = max(0, estimated_total - mistake_count)
|
||||
mastery = round((correct_count / estimated_total) * 100, 1)
|
||||
else:
|
||||
mastery = 100.0
|
||||
|
||||
mastery_list.append({
|
||||
"name": kp.name[:10], # 限制名称长度
|
||||
"mastery": mastery
|
||||
})
|
||||
|
||||
return mastery_list[:6] # 最多返回6个知识点
|
||||
|
||||
@staticmethod
|
||||
async def get_study_time_stats(
|
||||
db: AsyncSession,
|
||||
user_id: int,
|
||||
course_id: Optional[int] = None,
|
||||
period: str = "month"
|
||||
) -> Dict[str, Any]:
|
||||
"""
|
||||
获取学习时长统计
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
user_id: 用户ID
|
||||
course_id: 课程ID（可选）
|
||||
period: 时间范围
|
||||
|
||||
Returns:
|
||||
Dict: 学习时长和练习时长的日期分布数据
|
||||
"""
|
||||
logger.info(f"获取学习时长统计 - user_id: {user_id}, course_id: {course_id}, period: {period}")
|
||||
|
||||
start_date, end_date = StatisticsService._get_date_range(period)
|
||||
|
||||
# 获取天数
|
||||
days = (end_date - start_date).days
|
||||
if days > 30:
|
||||
days = 30 # 最多显示30天
|
||||
|
||||
# 生成日期列表
|
||||
date_list = []
|
||||
for i in range(days):
|
||||
date = end_date - timedelta(days=days - i - 1)
|
||||
date_list.append(date.date())
|
||||
|
||||
# 初始化数据
|
||||
study_time_data = {str(d): 0.0 for d in date_list}
|
||||
practice_time_data = {str(d): 0.0 for d in date_list}
|
||||
|
||||
# 统计考试时长（学习时长）
|
||||
exam_conditions = [
|
||||
Exam.user_id == user_id,
|
||||
Exam.start_time >= start_date,
|
||||
Exam.start_time <= end_date
|
||||
]
|
||||
if course_id:
|
||||
exam_conditions.append(Exam.course_id == course_id)
|
||||
|
||||
exam_stmt = select(
|
||||
func.date(Exam.start_time).label("date"),
|
||||
func.sum(Exam.duration_minutes).label("total_minutes")
|
||||
).where(
|
||||
and_(*exam_conditions)
|
||||
).group_by(
|
||||
func.date(Exam.start_time)
|
||||
)
|
||||
|
||||
exam_result = await db.execute(exam_stmt)
|
||||
for row in exam_result.all():
|
||||
date_str = str(row.date)
|
||||
if date_str in study_time_data:
|
||||
study_time_data[date_str] = round(float(row.total_minutes) / 60, 1)
|
||||
|
||||
# 统计陪练时长（练习时长）
|
||||
practice_conditions = [
|
||||
PracticeSession.user_id == user_id,
|
||||
PracticeSession.start_time >= start_date,
|
||||
PracticeSession.start_time <= end_date,
|
||||
PracticeSession.status == "completed"
|
||||
]
|
||||
|
||||
practice_stmt = select(
|
||||
func.date(PracticeSession.start_time).label("date"),
|
||||
func.sum(PracticeSession.duration_seconds).label("total_seconds")
|
||||
).where(
|
||||
and_(*practice_conditions)
|
||||
).group_by(
|
||||
func.date(PracticeSession.start_time)
|
||||
)
|
||||
|
||||
practice_result = await db.execute(practice_stmt)
|
||||
for row in practice_result.all():
|
||||
date_str = str(row.date)
|
||||
if date_str in practice_time_data:
|
||||
practice_time_data[date_str] = round(float(row.total_seconds) / 3600, 1)
|
||||
|
||||
# 如果period是week，返回星期几标签
|
||||
if period == "week":
|
||||
weekday_labels = ["周一", "周二", "周三", "周四", "周五", "周六", "周日"]
|
||||
labels = weekday_labels[:len(date_list)]
|
||||
else:
|
||||
# 其他情况返回日期
|
||||
labels = [d.strftime("%m-%d") for d in date_list]
|
||||
|
||||
study_values = [study_time_data[str(d)] for d in date_list]
|
||||
practice_values = [practice_time_data[str(d)] for d in date_list]
|
||||
|
||||
return {
|
||||
"labels": labels,
|
||||
"studyTime": study_values,
|
||||
"practiceTime": practice_values
|
||||
}
|
||||
|
||||
@staticmethod
|
||||
async def get_detail_data(
|
||||
db: AsyncSession,
|
||||
user_id: int,
|
||||
course_id: Optional[int] = None,
|
||||
period: str = "month"
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
获取详细统计数据（按日期）
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
user_id: 用户ID
|
||||
course_id: 课程ID（可选）
|
||||
period: 时间范围
|
||||
|
||||
Returns:
|
||||
List[Dict]: 每日详细统计数据
|
||||
"""
|
||||
logger.info(f"获取详细数据 - user_id: {user_id}, course_id: {course_id}, period: {period}")
|
||||
|
||||
start_date, end_date = StatisticsService._get_date_range(period)
|
||||
|
||||
# 构建查询条件
|
||||
exam_conditions = [
|
||||
Exam.user_id == user_id,
|
||||
Exam.start_time >= start_date,
|
||||
Exam.start_time <= end_date,
|
||||
Exam.round1_score.isnot(None)
|
||||
]
|
||||
if course_id:
|
||||
exam_conditions.append(Exam.course_id == course_id)
|
||||
|
||||
# 按日期分组统计
|
||||
stmt = select(
|
||||
func.date(Exam.start_time).label("date"),
|
||||
func.count(Exam.id).label("exam_count"),
|
||||
func.avg(Exam.round1_score).label("avg_score"),
|
||||
func.sum(Exam.duration_minutes).label("total_duration"),
|
||||
func.sum(Exam.question_count).label("total_questions")
|
||||
).where(
|
||||
and_(*exam_conditions)
|
||||
).group_by(
|
||||
func.date(Exam.start_time)
|
||||
).order_by(
|
||||
desc(func.date(Exam.start_time))
|
||||
).limit(10) # 最多返回10条
|
||||
|
||||
result = await db.execute(stmt)
|
||||
rows = result.all()
|
||||
|
||||
detail_list = []
|
||||
|
||||
for row in rows:
|
||||
date_str = row.date.strftime("%Y-%m-%d")
|
||||
exam_count = row.exam_count or 0
|
||||
avg_score = round(float(row.avg_score or 0), 1)
|
||||
study_time = round(float(row.total_duration or 0) / 60, 1)
|
||||
question_count = row.total_questions or 0
|
||||
|
||||
# 统计当天的错题数
|
||||
mistake_stmt = select(func.count(ExamMistake.id)).where(
|
||||
and_(
|
||||
ExamMistake.user_id == user_id,
|
||||
ExamMistake.exam_id.in_(
|
||||
select(Exam.id).where(
|
||||
and_(
|
||||
Exam.user_id == user_id,
|
||||
func.date(Exam.start_time) == row.date
|
||||
)
|
||||
)
|
||||
)
|
||||
)
|
||||
)
|
||||
mistake_count = await db.scalar(mistake_stmt) or 0
|
||||
|
||||
# 计算正确率
|
||||
accuracy = 0.0
|
||||
if question_count > 0:
|
||||
correct_count = question_count - mistake_count
|
||||
accuracy = round((correct_count / question_count) * 100, 1)
|
||||
|
||||
# 计算进步指数（基于平均分）
|
||||
improvement = min(100, max(0, int(avg_score)))
|
||||
|
||||
detail_list.append({
|
||||
"date": date_str,
|
||||
"examCount": exam_count,
|
||||
"avgScore": avg_score,
|
||||
"studyTime": study_time,
|
||||
"questionCount": question_count,
|
||||
"accuracy": accuracy,
|
||||
"improvement": improvement
|
||||
})
|
||||
|
||||
return detail_list
|
||||
|
||||
Reference in New Issue
Block a user