ZhangQiPro/generate_report_data.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
生成mockReportData - 基于真实评分数据生成报告趋势数据
"""

import json
from datetime import datetime, timedelta
import random

# 读取已生成的评价数据
with open('分析报告/generated_evaluations.json', 'r', encoding='utf-8') as f:
    evaluations = json.load(f)

# 读取画像数据（用于能力分析）
with open('分析报告/generated_portrait_data.json', 'r', encoding='utf-8') as f:
    portrait_data = json.load(f)

# 6门课程对应的月份（2024年9月-2025年2月）
COURSE_MONTHS = [
    '2024-09',  # SQL金融数据处理
    '2024-10',  # Python金融分析
    '2024-11',  # Matlab金融建模
    '2024-12',  # 金融数据可视化
    '2025-01',  # 证券模拟投资竞赛实战
    '2025-02',  # 期货模拟交易大赛实战
]

COURSE_NAMES = [
    'SQL金融数据处理',
    'Python金融分析',
    'Matlab金融建模',
    '金融数据可视化',
    '证券模拟投资竞赛实战',
    '期货模拟交易大赛实战'
]

def calculate_overall_score(company_score, teacher_score, expert_score):
    """计算综合评分（统一百分制）"""
    # 所有分数已经是百分制
    # 加权平均：企业30%，教师40%，专家30%
    overall = company_score * 0.3 + teacher_score * 0.4 + expert_score * 0.3
    return round(overall, 1)

def generate_development_trends():
    """生成发展趋势数据"""
    trends = {}

    for student_id in range(1, 41):
        student_id_str = str(student_id)

        # 获取该学生的评价数据
        company_eval = evaluations['company'].get(student_id_str, {})
        teacher_eval = evaluations['teacher'].get(student_id_str, {})
        expert_eval = evaluations['expert'].get(student_id_str, {})

        # 基础评分（公司、教师、专家的平均分）
        company_base = (company_eval.get('attitude', 3.5) +
                       company_eval.get('skills', 3.5) +
                       company_eval.get('communication', 3.5) +
                       company_eval.get('problemSolving', 3.5)) / 4 * 20  # 转为百分制

        teacher_base = (teacher_eval.get('theory', 3.5) +
                       teacher_eval.get('practice', 3.5) +
                       teacher_eval.get('innovation', 3.5) +
                       teacher_eval.get('attitude', 3.5)) / 4 * 20

        expert_base = (expert_eval.get('industryKnowledge', 3.5) +
                      expert_eval.get('technicalDepth', 3.5) +
                      expert_eval.get('applicationAbility', 3.5) +
                      expert_eval.get('potential', 3.5)) / 4 * 20

        # 生成6个月的趋势数据（模拟学习进步曲线）
        monthly_scores = []
        for i, month in enumerate(COURSE_MONTHS):
            # 初始分数基于基础评分，随着时间逐步提升
            progress_factor = 1 + (i * 0.03)  # 每月提升3%

            company_score = round(company_base * progress_factor, 1)
            teacher_score = round(teacher_base * progress_factor, 1)
            expert_score = round(expert_base * progress_factor, 1)

            # 计算综合分数（已经是百分制）
            overall = calculate_overall_score(
                company_score,
                teacher_score,
                expert_score
            )

            # 互评分数（基于综合分数±5分波动）
            peer_score = round(overall + random.uniform(-5, 5), 1)
            peer_score = max(60, min(100, peer_score))  # 限制在60-100范围

            monthly_scores.append({
                'month': month,
                'overall': overall,
                'company': company_score,
                'teacher': teacher_score,
                'expert': expert_score,
                'peer': peer_score
            })

        trends[student_id] = {
            'monthlyScores': monthly_scores
        }

    return trends

def generate_milestones():
    """生成里程碑数据"""
    milestones = {}

    # 里程碑事件模板（基于6门课程）
    milestone_templates = [
        {
            'courses': [0, 1],  # SQL和Python课程
            'events': [
                '完成{course}课程项目',
                '{course}期末答辩优秀',
                '{course}实践报告获得好评'
            ]
        },
        {
            'courses': [2, 3],  # Matlab和数据可视化
            'events': [
                '完成{course}课程设计',
                '{course}作业获得满分',
                '{course}小组项目表现突出'
            ]
        },
        {
            'courses': [4, 5],  # 两个竞赛
            'events': [
                '参与{course}并获得名次',
                '{course}表现优异',
                '{course}获得证书'
            ]
        }
    ]

    for student_id in range(1, 41):
        student_milestones = []

        # 为每个学生随机生成2-4个里程碑
        num_milestones = random.randint(2, 4)
        selected_indices = random.sample(range(6), num_milestones)

        for idx in sorted(selected_indices):
            month = COURSE_MONTHS[idx]
            course_name = COURSE_NAMES[idx]

            # 选择对应的事件模板
            if idx in [0, 1]:
                event_template = random.choice(milestone_templates[0]['events'])
            elif idx in [2, 3]:
                event_template = random.choice(milestone_templates[1]['events'])
            else:
                event_template = random.choice(milestone_templates[2]['events'])

            event = event_template.format(course=course_name)

            # 分数基于学生该课程的表现（从portrait数据获取）
            portrait = portrait_data['abilityRadar'].get(str(student_id), {})
            base_score = portrait.get('average', 75)
            score = round(base_score + random.uniform(-5, 10), 1)
            score = max(60, min(100, score))

            student_milestones.append({
                'date': month,
                'event': event,
                'score': score
            })

        milestones[student_id] = student_milestones

    return milestones

def generate_development_suggestions():
    """生成发展建议数据"""
    suggestions = {}

    # 优势模板
    strengths_templates = [
        '金融理论基础扎实，数据分析能力强',
        '学习态度认真，能够主动思考问题',
        '团队协作意识好，沟通能力较强',
        '实践能力突出，善于将理论应用于实际',
        '创新思维活跃，勇于尝试新方法',
        '代码能力扎实，熟练掌握金融工具',
        '逻辑思维清晰，问题分析能力强',
        '自学能力强，能够快速掌握新知识'
    ]

    # 劣势模板
    weaknesses_templates = [
        '量化投资实践经验需要积累',
        '创新思维有待提升',
        '风险识别能力需要更多练习',
        '金融工具使用熟练度需要提高',
        '理论知识体系需要进一步完善',
        '数据处理效率有待提升',
        '复杂问题分析能力需要加强',
        '金融市场敏感度需要培养'
    ]

    # 建议模板
    suggestions_templates = [
        '建议加强量化投资模型的实践学习',
        '多参与金融创新项目，培养创新思维',
        '定期参加投资论坛，提升专业表达能力',
        '深入学习金融科技前沿技术',
        '加强金融市场实时跟踪和分析',
        '参与更多实战项目，积累实践经验',
        '系统学习风险管理理论和方法',
        '提升编程能力，掌握更多金融工具'
    ]

    for student_id in range(1, 41):
        # 获取学生能力数据
        portrait = portrait_data['abilityRadar'].get(str(student_id), {})
        scores = portrait.get('scores', [75] * 5)
        avg_score = portrait.get('average', 75)

        # 根据平均分确定优劣势数量
        if avg_score >= 85:
            num_strengths = 4
            num_weaknesses = 2
        elif avg_score >= 75:
            num_strengths = 3
            num_weaknesses = 3
        else:
            num_strengths = 2
            num_weaknesses = 4

        suggestions[student_id] = {
            'strengths': random.sample(strengths_templates, min(num_strengths, 3)),
            'weaknesses': random.sample(weaknesses_templates, min(num_weaknesses, 3)),
            'suggestions': random.sample(suggestions_templates, 3)
        }

    return suggestions

def main():
    print("开始生成报告数据...")

    # 生成三部分数据
    development_trends = generate_development_trends()
    milestones = generate_milestones()
    development_suggestions = generate_development_suggestions()

    # 组装完整数据
    report_data = {
        'developmentTrends': development_trends,
        'milestones': milestones,
        'developmentSuggestions': development_suggestions
    }

    # 保存到文件
    output_file = '分析报告/generated_report_data.json'
    with open(output_file, 'w', encoding='utf-8') as f:
        json.dump(report_data, f, ensure_ascii=False, indent=2)

    print(f"✅ 报告数据生成完成！")
    print(f"   - 发展趋势: 40名学生 × 6个月")
    print(f"   - 里程碑: 40名学生 × 2-4个事件")
    print(f"   - 发展建议: 40名学生")
    print(f"   - 输出文件: {output_file}")

if __name__ == '__main__':
    main()