#!/usr/bin/env python # -*- coding: utf-8 -*- """ 基于extracted_scores.json生成完整的mockData.js """ import json import random from datetime import datetime, timedelta # 读取提取的评分数据 with open('分析报告/extracted_scores.json', 'r', encoding='utf-8') as f: scores_data = json.load(f) # 过滤出正确格式的学号（233061301XXX） valid_students = {} for sid, data in scores_data.items(): if sid.startswith('233061301') and len(sid) == 12: valid_students[sid] = data print(f'有效学生数: {len(valid_students)}') # 生成企业评价数据 def generate_company_evaluations(): companies = ['中信证券', '华泰证券', '招商证券', '国泰君安', '海通证券', '广发证券', '兴业证券', '东方证券'] evaluations = {} for idx, (sid, student_data) in enumerate(valid_students.items(), 1): student_id = int(sid[-2:]) # 取最后两位作为ID # 从6门课程中随机选一门的企业评分作为基准 courses = list(student_data['courses'].values()) if not courses: continue base_course = random.choice(courses) # 计算平均企业评分 company_scores = [] for key, val in base_course.items(): if key.endswith('_企业') and isinstance(val, (int, float)): company_scores.append(val) avg_score = sum(company_scores) / len(company_scores) if company_scores else 80 # 转换为5分制 attitude = round(avg_score / 20, 1) skills = round(avg_score / 20 + random.uniform(-0.3, 0.3), 1) communication = round(avg_score / 20 + random.uniform(-0.2, 0.2), 1) problemSolving = round(avg_score / 20 + random.uniform(-0.4, 0.4), 1) # 确定评价等级 if avg_score >= 90: overall = 'excellent' suggestion = f'{student_data["name"]}同学表现优异，专业技能扎实，金融数据分析能力突出。' elif avg_score >= 80: overall = 'good' suggestion = f'{student_data["name"]}同学表现良好，金融基础扎实，建议加强实践经验。' elif avg_score >= 70: overall = 'average' suggestion = f'{student_data["name"]}同学基础能力尚可，需要加强金融技能训练。' else: overall = 'below_average' suggestion = f'{student_data["name"]}同学需要加强金融基础知识学习和实践能力提升。' # 生成评价时间 days_ago = random.randint(30, 180) eval_date = (datetime.now() - timedelta(days=days_ago)).strftime('%Y-%m-%dT%H:%M:%S') evaluations[student_id] = { 'attitude': max(1.0, min(5.0, attitude)), 'skills': max(1.0, min(5.0, skills)), 'communication': max(1.0, min(5.0, communication)), 'problemSolving': max(1.0, min(5.0, problemSolving)), 'overall': overall, 'suggestions': suggestion, 'evaluatedAt': eval_date, 'evaluatorName': companies[idx % len(companies)] } return evaluations # 生成教师评价数据 def generate_teacher_evaluations(): teachers = ['刘澜涛', '王老师', '张老师', '赵老师', '陈老师', '李老师'] evaluations = {} for idx, (sid, student_data) in enumerate(valid_students.items(), 1): student_id = int(sid[-2:]) # 从课程中提取学校评分 courses = list(student_data['courses'].values()) if not courses: continue base_course = random.choice(courses) # 计算平均学校评分 school_scores = [] for key, val in base_course.items(): if key.endswith('_学校') and isinstance(val, (int, float)): school_scores.append(val) avg_score = sum(school_scores) / len(school_scores) if school_scores else 80 # 转换为5分制 theory = round(avg_score / 20, 1) practice = round(avg_score / 20 + random.uniform(-0.2, 0.2), 1) innovation = round(avg_score / 20 + random.uniform(-0.5, 0.3), 1) attitude = round(avg_score / 20 + random.uniform(-0.1, 0.3), 1) # 生成评语 if avg_score >= 90: comment = f'{student_data["name"]}同学表现卓越，金融理论功底深厚，实践能力强，创新思维活跃。' elif avg_score >= 80: comment = f'{student_data["name"]}同学理论基础扎实，实践能力良好，建议在创新思维方面多下功夫。' elif avg_score >= 70: comment = f'{student_data["name"]}同学基础扎实，但金融实践和创新能力有待提升，学习态度认真。' else: comment = f'{student_data["name"]}同学金融基础有待加强，需要更多的练习和指导。' days_ago = random.randint(20, 150) eval_date = (datetime.now() - timedelta(days=days_ago)).strftime('%Y-%m-%dT%H:%M:%S') evaluations[student_id] = { 'theory': max(1.0, min(5.0, theory)), 'practice': max(1.0, min(5.0, practice)), 'innovation': max(1.0, min(5.0, innovation)), 'attitude': max(1.0, min(5.0, attitude)), 'courseGrade': round(avg_score, 1), 'comments': comment, 'evaluatedAt': eval_date, 'evaluatorName': teachers[idx % len(teachers)] } return evaluations # 生成专家评价数据 def generate_expert_evaluations(): experts = ['张专家', '王专家', '李专家', '赵专家', '陈专家', '吴专家', '周专家'] evaluations = {} for idx, (sid, student_data) in enumerate(valid_students.items(), 1): student_id = int(sid[-2:]) # 综合学校和企业评分 all_scores = [] for course_data in student_data['courses'].values(): for key, val in course_data.items(): if (key.endswith('_学校') or key.endswith('_企业')) and isinstance(val, (int, float)): all_scores.append(val) avg_score = sum(all_scores) / len(all_scores) if all_scores else 75 # 转换为5分制 industryKnowledge = round(avg_score / 20 + random.uniform(-0.3, 0.2), 1) technicalDepth = round(avg_score / 20 + random.uniform(-0.2, 0.1), 1) applicationAbility = round(avg_score / 20 + random.uniform(-0.1, 0.3), 1) potential = round(avg_score / 20 + random.uniform(0, 0.4), 1) # 生成专家意见 if avg_score >= 85: advice = f'优秀的金融人才，行业洞察深刻，应用能力出色，强烈推荐重点培养。' recommendation = 'strongly_recommend' elif avg_score >= 75: advice = f'从金融行业发展角度看，{student_data["name"]}具备较强的理论基础和学习能力。' recommendation = 'recommend' elif avg_score >= 65: advice = f'基础扎实，需要加强对金融行业发展趋势的理解和把握。' recommendation = 'conditional_recommend' else: advice = f'基础能力一般，需要加强金融行业知识学习和实践能力提升。' recommendation = 'not_recommend' days_ago = random.randint(10, 120) eval_date = (datetime.now() - timedelta(days=days_ago)).strftime('%Y-%m-%dT%H:%M:%S') evaluations[student_id] = { 'industryKnowledge': max(1.0, min(5.0, industryKnowledge)), 'technicalDepth': max(1.0, min(5.0, technicalDepth)), 'applicationAbility': max(1.0, min(5.0, applicationAbility)), 'potential': max(1.0, min(5.0, potential)), 'professionalAdvice': advice, 'recommendation': recommendation, 'evaluatedAt': eval_date, 'evaluatorName': experts[idx % len(experts)] } return evaluations # 生成评价数据 print('\n生成企业评价数据...') company_evals = generate_company_evaluations() print(f'企业评价: {len(company_evals)}条') print('生成教师评价数据...') teacher_evals = generate_teacher_evaluations() print(f'教师评价: {len(teacher_evals)}条') print('生成专家评价数据...') expert_evals = generate_expert_evaluations() print(f'专家评价: {len(expert_evals)}条') # 保存为JSON便于后续处理 output_data = { 'company': company_evals, 'teacher': teacher_evals, 'expert': expert_evals } with open('分析报告/generated_evaluations.json', 'w', encoding='utf-8') as f: json.dump(output_data, f, ensure_ascii=False, indent=2) print(f'\n评价数据已生成: 分析报告/generated_evaluations.json') print('准备更新mockData.js...')