认证考核引擎技术架构与智能评估算法

{
  "examination": {
    "id": "string",
    "title": "string",
    "description": "string",
    "duration_minutes": 120,
    "total_score": 100,
    "pass_score": 60,
    "questions": [
      {
        "id": "string",
        "type": "single|multiple|judge|essay|coding|case",
        "content": "string",
        "score": 10,
        "time_limit": 5,
        "difficulty": "easy|medium|hard",
        "tags": ["tag1", "tag2"]
      }
    ],
    "rules": {
      "allow_retake": true,
      "max_retakes": 3,
      "anti_cheat": {
        "screen_monitor": true,
        "behavior_analysis": true,
        "ip_restriction": false
      }
    }
  }
}

def evaluate_essay_answer(correct_answer, user_answer):
    """
    基于BERT的简答题智能评估
    """
    # 预处理
    ref_embedding = bert_encode(correct_answer)
    user_embedding = bert_encode(user_answer)
    
    # 语义相似度计算
    semantic_score = cosine_similarity(ref_embedding, user_embedding)
    
    # 关键词匹配
    key_terms = extract_key_terms(correct_answer)
    matched_terms = [term for term in key_terms 
                    if term in user_answer]
    keyword_score = len(matched_terms) / len(key_terms)
    
    # 结构完整性分析
    structure_score = analyze_structure(user_answer, correct_answer)
    
    # 综合评分
    final_score = (
        0.5 * semantic_score + 
        0.3 * keyword_score + 
        0.2 * structure_score
    )
    
    return {
        "score": final_score,
        "semantic_similarity": semantic_score,
        "keyword_coverage": keyword_score,
        "structure_quality": structure_score,
        "feedback": generate_feedback(final_score)
    }

def evaluate_code_submission(submission, test_cases):
    """
    编程题多维度评估
    """
    # 代码执行测试
    execution_result = run_tests(submission.code, test_cases)
    
    # 测试通过率
    pass_rate = execution_result.passed / len(test_cases)
    
    # 性能评估
    performance_score = evaluate_performance(
        execution_result.time,
        execution_result.memory,
        test_cases
    )
    
    # 代码质量分析
    quality_score = analyze_code_quality(submission.code)
    
    # 综合评分
    final_score = (
        0.6 * pass_rate + 
        0.2 * performance_score + 
        0.2 * quality_score
    )
    
    return {
        "score": final_score,
        "test_results": execution_result.details,
        "time_used": execution_result.time,
        "memory_used": execution_result.memory,
        "code_quality": quality_score,
        "suggestions": generate_code_suggestions(submission)
    }

最终分数 = Σ(题目得分 × 题目权重) × 难度系数 × 状态系数

其中：
- 题目权重：由题目重要性决定
- 难度系数：easy=1.0, medium=1.2, hard=1.5
- 状态系数：根据答题稳定性动态调整

Mermaid 渲染失败: No diagram type detected matching given configuration for text: radar
    title 能力分析雷达图
    axis 知识掌握, 技能应用, 问题分析, 创新思维, 表达能力, 学习潜力
    curve 学员能力, [85, 78, 92, 70, 88, 95]
    curve 平均水平, [75, 72, 80, 68, 82, 85]

def detect_anomaly(attempt_data):
    """
    检测考核过程中的异常行为
    """
    anomalies = []
    
    # 检测快速答题
    if attempt_data.avg_answer_time < THRESHOLD_FAST:
        anomalies.append({
            "type": "rapid_answer",
            "severity": "high",
            "detail": "答题速度异常快"
        })
    
    # 检测频繁切换窗口
    window_switches = count_window_switches(attempt_data)
    if window_switches > THRESHOLD_SWITCH:
        anomalies.append({
            "type": "window_switch",
            "severity": "medium",
            "detail": f"窗口切换{window_switches}次"
        })
    
    # 检测IP异常
    if detect_ip_change(attempt_data):
        anomalies.append({
            "type": "ip_change",
            "severity": "critical",
            "detail": "检测到IP地址变更"
        })
    
    return anomalies

{
  "feedback_report": {
    "exam_id": "string",
    "user_id": "string",
    "total_score": 85,
    "pass_status": "passed",
    "dimension_analysis": {
      "knowledge": {
        "score": 90,
        "strengths": ["基础概念掌握牢固"],
        "weaknesses": ["进阶知识有待加强"]
      },
      "skill": {
        "score": 82,
        "strengths": ["编程能力强"],
        "weaknesses": ["算法效率需提升"]
      }
    },
    "question_feedback": [
      {
        "question_id": "q001",
        "user_answer": "string",
        "correct_answer": "string",
        "score": 10,
        "feedback": "答案正确，表达清晰",
        "suggestion": "可进一步优化解题思路"
      }
    ],
    "improvement_suggestions": [
      "建议深度学习《机器学习进阶》课程",
      "多做编程实践题，提升代码效率",
      "参加案例分析训练，强化应用能力"
    ]
  }
}