系统架构设计

采用Spring Boot+MyBatis框架组合，前端使用Vue.js或Thymeleaf模板引擎。数据库设计需包含以下核心表：

• 题目表(question)：存储题目内容、类型、难度、知识点等
• 试卷表(exam_paper)：存储组卷规则和试卷元信息
• 考试记录表(exam_record)：存储考生答题数据
• 成绩表(score)：存储统计结果和分析指标

随机组卷实现

核心算法使用MySQL的RAND()函数配合权重计算：

-- 按知识点和难度随机抽题
SELECT * FROM question 
WHERE knowledge_point='函数' AND difficulty=3 
ORDER BY RAND() LIMIT 5;

-- 带权重的随机抽样（难度系数作为权重）
SELECT q.*, RAND() * (1/difficulty) as weight 
FROM question q WHERE course_id=1 
ORDER BY weight DESC LIMIT 20;

Java层实现组卷策略模式：

public interface PaperGenerationStrategy {
    List<Question> generatePaper(PaperRule rule);
}

// 随机组卷实现
@Component
public class RandomStrategy implements PaperGenerationStrategy {
    @Override
    public List<Question> generatePaper(PaperRule rule) {
        return questionMapper.selectByRandom(
            rule.getKnowledgePoints(), 
            rule.getDifficultyDistribution()
        );
    }
}

成绩统计分析

MySQL统计查询示例：

-- 各分数段人数统计
SELECT 
    FLOOR(score/10)*10 as score_range,
    COUNT(*) as count 
FROM exam_record 
GROUP BY score_range
ORDER BY score_range;

-- 题目正确率分析
SELECT 
    q.id,
    q.content,
    SUM(CASE WHEN r.is_correct=1 THEN 1 ELSE 0 END)/COUNT(*) as correct_rate
FROM question q JOIN exam_record r ON q.id = r.question_id
GROUP BY q.id;

Java实现分析模块：

public class ScoreAnalyzer {
    public AnalysisResult analyze(Long examId) {
        List<ScoreDistribution> distributions = scoreMapper
            .selectScoreDistribution(examId);
        
        DoubleSummaryStatistics stats = recordMapper
            .selectByExam(examId)
            .stream()
            .mapToDouble(ExamRecord::getScore)
            .summaryStatistics();
        
        return new AnalysisResult(
            stats.getAverage(),
            stats.getMax(),
            stats.getMin(),
            distributions
        );
    }
}

性能优化方案

使用Redis缓存高频访问的题目数据，对统计分析结果进行预计算：

@Cacheable(value = "hotQuestions", key = "#courseId")
public List<Question> getHotQuestions(Long courseId) {
    return questionMapper.selectHotQuestions(courseId);
}

// 定时任务预计算统计结果
@Scheduled(cron = "0 0 2 * * ?")
public void preCalculateStatistics() {
    examIds.forEach(id -> {
        AnalysisResult result = scoreAnalyzer.analyze(id);
        redisTemplate.opsForValue().set(
            "exam:stats:" + id, 
            result
        );
    });
}