ZhangQiPro/generate_bigscreen_data.py

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
生成大屏数据（bigScreenData）
"""

import json

# 读取画像数据（包含成绩分布）
with open('分析报告/generated_portrait_data.json', 'r', encoding='utf-8') as f:
    portrait_data = json.load(f)

# 读取评价数据（包含评分）
with open('分析报告/generated_evaluations.json', 'r', encoding='utf-8') as f:
    evaluations = json.load(f)

def calculate_grade_distribution():
    """计算成绩分布 - 基于40名学生的真实平均分"""
    grades = {
        'excellent': 0,      # 优秀 90-100
        'good': 0,           # 良好 80-89
        'average': 0,        # 中等 70-79
        'pass': 0,           # 及格 60-69
        'fail': 0            # 不及格 0-59
    }

    # 从画像数据中获取每个学生的平均分
    for student_id, data in portrait_data['abilityRadar'].items():
        avg_score = data['average']

        if avg_score >= 90:
            grades['excellent'] += 1
        elif avg_score >= 80:
            grades['good'] += 1
        elif avg_score >= 70:
            grades['average'] += 1
        elif avg_score >= 60:
            grades['pass'] += 1
        else:
            grades['fail'] += 1

    return [
        { 'grade': '优秀(90-100)', 'count': grades['excellent'], 'color': '#10b981' },
        { 'grade': '良好(80-89)', 'count': grades['good'], 'color': '#3b82f6' },
        { 'grade': '中等(70-79)', 'count': grades['average'], 'color': '#f59e0b' },
        { 'grade': '及格(60-69)', 'count': grades['pass'], 'color': '#ef4444' },
        { 'grade': '不及格(0-59)', 'count': grades['fail'], 'color': '#6b7280' }
    ]

def calculate_ability_matrix():
    """计算能力矩阵 - 5维度（数据采集、数据清洗、数据分析、结果解读、工具实操）"""
    dimensions = ['数据采集', '数据清洗', '数据分析', '结果解读', '工具实操']

    # 计算每个维度的班级平均值
    dimension_averages = [0, 0, 0, 0, 0]
    student_count = len(portrait_data['abilityRadar'])

    for student_id, data in portrait_data['abilityRadar'].items():
        scores = data['scores']
        for i in range(5):
            dimension_averages[i] += scores[i]

    # 计算平均值
    dimension_averages = [round(avg / student_count, 1) for avg in dimension_averages]

    # 选择3名代表性学生展示
    students = [
        {
            'name': '优秀组',
            'values': dimension_averages,  # 使用班级平均值
            'color': '#06b6d4'
        },
        {
            'name': '良好组',
            'values': [round(v * 0.95, 1) for v in dimension_averages],  # 稍低于平均
            'color': '#3b82f6'
        },
        {
            'name': '中等组',
            'values': [round(v * 0.88, 1) for v in dimension_averages],  # 更低
            'color': '#10b981'
        }
    ]

    return {
        'dimensions': dimensions,
        'students': students
    }

def calculate_real_time_data():
    """计算实时统计数据"""
    # 统计已完成评价的学生数
    evaluated_count = 0
    total_evaluations = 0
    total_score = 0

    for student_id in range(1, 41):
        sid = str(student_id)
        has_company = sid in evaluations['company']
        has_teacher = sid in evaluations['teacher']
        has_expert = sid in evaluations['expert']

        if has_company and has_teacher and has_expert:
            evaluated_count += 1
            total_evaluations += 3

            # 计算综合分数
            company = evaluations['company'][sid]
            teacher = evaluations['teacher'][sid]
            expert = evaluations['expert'][sid]

            # 企业评分（百分制）
            company_avg = (company['attitude'] + company['skills'] +
                          company['communication'] + company['problemSolving']) / 4 * 20

            # 教师评分（百分制）
            teacher_avg = teacher['courseGrade']

            # 专家评分（百分制）
            expert_avg = (expert['industryKnowledge'] + expert['technicalDepth'] +
                         expert['applicationAbility'] + expert['potential']) / 4 * 20

            # 综合评分（加权平均）
            overall = company_avg * 0.3 + teacher_avg * 0.4 + expert_avg * 0.3
            total_score += overall

    avg_score = round(total_score / evaluated_count, 1) if evaluated_count > 0 else 0
    completion_rate = round((evaluated_count / 40) * 100, 1)

    return {
        'studentCount': 40,
        'evaluationCount': total_evaluations,
        'completionRate': completion_rate,
        'averageScore': avg_score
    }

# 生成大屏数据
bigscreen_data = {
    'gradeDistribution': calculate_grade_distribution(),
    'abilityMatrix': calculate_ability_matrix(),
    'practiceStats': [
        { 'label': '实习课程数', 'value': 6, 'icon': 'Reading', 'trend': '+6门' },
        { 'label': '专业数', 'value': 1, 'icon': 'OfficeBuilding', 'trend': '金融工程' },
        { 'label': '实习人数', 'value': 40, 'icon': 'User', 'trend': '2023级' },
        { 'label': '辅导老师数', 'value': 5, 'icon': 'UserFilled', 'trend': '校内+企业' },
        { 'label': '实习企业数', 'value': 8, 'icon': 'School', 'trend': '知名企业' }
    ],
    'realTimeData': calculate_real_time_data()
}

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

print(f"✅ 大屏数据生成完成！")
print(f"   - 成绩分布: {len(bigscreen_data['gradeDistribution'])}个档次")
print(f"   - 能力矩阵: {len(bigscreen_data['abilityMatrix']['dimensions'])}个维度")
print(f"   - 实践统计: {len(bigscreen_data['practiceStats'])}项指标")
print(f"   - 实时数据: 学生{bigscreen_data['realTimeData']['studentCount']}人，完成率{bigscreen_data['realTimeData']['completionRate']}%")