YunNanProject/Model/RuYuanZaiYuanCountModel.py

import json
import os
from Util.YuCeUtil import YuCeUtil


class RuYuanZaiYuanModel:
    # 定义支持的教育阶段映射
    EDUCATION_STAGES = {
        'preschool': '学前',
        'primary': '小学',
        'junior': '初中',
        'senior': '高中',
        'vocational': '中职'
    }

    @staticmethod
    def load_student_data():
        try:
            # 加载招生数据(入园人数)
            with open("./Data/ZhaoShengCount.json", "r", encoding="utf-8") as f:
                enrollment_data = json.load(f)

            # 加载在校生数据(在园人数)
            with open("./Data/ZaiXiaoShengCount.json", "r", encoding="utf-8") as f:
                in_school_data = json.load(f)

            return enrollment_data, in_school_data
        except Exception as e:
            print(f"读取学生数据出错: {e}")
            return [], []

    @staticmethod
    def generate_preschool_education_config(education_stage='preschool', area_name='云南省'):
        # 验证教育阶段参数
        if education_stage not in RuYuanZaiYuanModel.EDUCATION_STAGES:
            education_stage = 'preschool'  # 默认使用学前

        # 获取学前教育相关数据
        enrollment_data, in_school_data = RuYuanZaiYuanModel.load_student_data()
        # 提取指定区域数据
        area_enroll = next((item for item in enrollment_data if item["area_name"] == area_name), None)

        if not area_enroll:
            return {}

        # 构建学前教育数据
        urban_data = []  # 城区数据
        town_data = []  # 镇区数据
        rural_data = []  # 乡村数据
        total_enroll = []  # 总人数

        # 提取年份数据(2015-2035)
        years = [str(year) for year in range(2015, 2036)]
        
        # 初始化预测工具（只针对学前教育进行预测）
        forecast_util = None
        forecast_results = {}
        forecast_urban_enrollment = {}
        
        if education_stage == 'preschool':
            # 获取数据目录
            data_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'Data')
            # 创建预测实例，使用传入的区域名称
            forecast_util = YuCeUtil(data_directory, area_name)
            # 运行预测
            forecast_util.run_forecast()
            # 获取预测结果
            forecast_results = forecast_util.forecast_results
            forecast_urban_enrollment = forecast_util.forecast_urban_enrollment

        for year in years:
            # 使用传入的教育阶段参数
            if int(year) <= 2024:
                # 2015-2024年使用实际数据
                enroll_data = area_enroll["education_data"].get(education_stage, {}).get(year, {})
                
                # 特殊处理中职数据格式（只有total字段）
                if education_stage == 'vocational':
                    total_value = enroll_data.get("total", 0)
                    urban_data.append(0)  # 中职没有城区数据
                    town_data.append(0)   # 中职没有镇区数据
                    rural_data.append(0)  # 中职没有乡村数据
                    total_enroll.append(total_value / 10000)  # 转换为万人
                else:
                    urban_data.append(enroll_data.get("urban", 0) / 10000)  # 转换为万人
                    town_data.append(enroll_data.get("town", 0) / 10000)  # 转换为万人
                    rural_data.append(enroll_data.get("rural", 0) / 10000)  # 转换为万人
                    # 计算总和作为总人数
                    calculated_total = enroll_data.get("urban", 0) + enroll_data.get("town", 0) + enroll_data.get("rural", 0)
                    total_enroll.append(calculated_total / 10000)  # 转换为万人
            else:
                # 2025-2035年使用预测数据
                if education_stage == 'preschool' and forecast_util:
                    if year in forecast_results:
                        total_value = forecast_results[year]
                        # 获取城区招生数
                        urban_value = forecast_urban_enrollment.get(int(year), {}).get('urban', 0)
                        remaining_value = total_value - urban_value
                        # 假设乡镇和农村按历史比例分配剩余部分
                        # 查找最近一年的乡镇和农村比例
                        recent_year = str(int(year) - 1)
                        if recent_year in area_enroll["education_data"].get(education_stage, {}):
                            recent_data = area_enroll["education_data"].get(education_stage, {}).get(recent_year, {})
                            recent_town = recent_data.get("town", 0)
                            recent_rural = recent_data.get("rural", 0)
                            recent_remaining = recent_town + recent_rural
                            if recent_remaining > 0:
                                town_value = int(remaining_value * (recent_town / recent_remaining))
                                rural_value = remaining_value - town_value
                            else:
                                town_value = int(remaining_value / 2)
                                rural_value = remaining_value - town_value
                        else:
                            town_value = int(remaining_value / 2)
                            rural_value = remaining_value - town_value
                        
                        urban_data.append(urban_value / 10000)
                        town_data.append(town_value / 10000)
                        rural_data.append(rural_value / 10000)
                        total_enroll.append(total_value / 10000)
                    else:
                        # 没有预测数据，使用前一年数据
                        if len(total_enroll) > 0:
                            urban_data.append(urban_data[-1])
                            town_data.append(town_data[-1])
                            rural_data.append(rural_data[-1])
                            total_enroll.append(total_enroll[-1])
                        else:
                            urban_data.append(0)
                            town_data.append(0)
                            rural_data.append(0)
                            total_enroll.append(0)
                else:
                    # 非学前教育或没有预测工具，使用前一年数据
                    if len(total_enroll) > 0:
                        urban_data.append(urban_data[-1])
                        town_data.append(town_data[-1])
                        rural_data.append(rural_data[-1])
                        total_enroll.append(total_enroll[-1])
                    else:
                        urban_data.append(0)
                        town_data.append(0)
                        rural_data.append(0)
                        total_enroll.append(0)

        # 添加2022年基数的粉色折线
        base_year = "2022"
        # 找到2022年在years中的索引位置
        base_index = years.index(base_year) if base_year in years else 0
        # 获取2022年的总人数作为基数
        base_value = total_enroll[base_index] if base_index < len(total_enroll) else 0
        # 创建2022年基数折线数据（2022-2035年）
        base_2022_line = []
        for i, year in enumerate(years):
            # 只在2022年及之后显示基数线
            if i >= base_index:
                base_2022_line.append(base_value)
            else:
                base_2022_line.append(None)  # 2022年之前不显示
        data = {
            "xAxis_data": years,
            "series_data_0": urban_data,  # 城区
            "series_data_1": town_data,  # 镇区
            "series_data_2": rural_data,  # 乡村
            "series_data_3": total_enroll,  # 总人数
            "series_data_4": base_2022_line,  # 2022年基数
            "education_stage": RuYuanZaiYuanModel.EDUCATION_STAGES.get(education_stage, '学前')  # 添加教育阶段名称
        }
        return data

    @staticmethod
    def generate_in_school_education_config(education_stage='preschool', area_name='云南省'):
        # 验证教育阶段参数
        if education_stage not in RuYuanZaiYuanModel.EDUCATION_STAGES:
            education_stage = 'preschool'  # 默认使用学前

        # 获取在校生相关数据
        enrollment_data, in_school_data = RuYuanZaiYuanModel.load_student_data()

        # 提取指定区域数据
        area_in_school = next((item for item in in_school_data if item["area_name"] == area_name), None)

        if not area_in_school:
            return {}

        # 构建在校生数据
        urban_data = []  # 城区数据
        town_data = []  # 镇区数据
        rural_data = []  # 乡村数据
        total_in_school = []  # 总人数

        # 提取年份数据(2015-2035)
        years = [str(year) for year in range(2015, 2036)]
        
        # 初始化预测工具（只针对学前教育进行预测）
        forecast_util = None
        enrollment_in_school = {}
        
        if education_stage == 'preschool':
            # 获取数据目录
            data_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'Data')
            # 创建预测实例，使用传入的区域名称
            forecast_util = YuCeUtil(data_directory, area_name)
            # 运行预测
            forecast_util.run_forecast()
            # 获取预测结果
            enrollment_in_school = forecast_util.enrollment_in_school

        for year in years:
            # 使用传入的教育阶段参数
            if int(year) <= 2024:
                # 2015-2024年使用实际数据
                in_school_year_data = area_in_school["student_data"].get(education_stage, {}).get(year, {})
                
                # 特殊处理中职数据格式（只有total字段）
                if education_stage == 'vocational':
                    total_value = in_school_year_data.get("total", 0)
                    urban_data.append(0)  # 中职没有城区数据
                    town_data.append(0)   # 中职没有镇区数据
                    rural_data.append(0)  # 中职没有乡村数据
                    total_in_school.append(total_value / 10000)  # 转换为万人
                else:
                    urban_data.append(in_school_year_data.get("urban", 0) / 10000)  # 转换为万人
                    town_data.append(in_school_year_data.get("town", 0) / 10000)  # 转换为万人
                    rural_data.append(in_school_year_data.get("rural", 0) / 10000)  # 转换为万人
                    # 计算总和作为总人数
                    calculated_total = in_school_year_data.get("urban", 0) + in_school_year_data.get("town", 0) + in_school_year_data.get("rural", 0)
                    total_in_school.append(calculated_total / 10000)  # 转换为万人
            else:
                # 2025-2035年使用预测数据
                if education_stage == 'preschool' and forecast_util:
                    if int(year) in enrollment_in_school:
                        total_value = enrollment_in_school[int(year)]
                        # 获取城区比例（使用最近一年的城区比例）
                        recent_year = str(int(year) - 1)
                        if recent_year in area_in_school["student_data"].get(education_stage, {}):
                            recent_data = area_in_school["student_data"].get(education_stage, {}).get(recent_year, {})
                            recent_urban = recent_data.get("urban", 0)
                            recent_total = recent_data.get("urban", 0) + recent_data.get("town", 0) + recent_data.get("rural", 0)
                            if recent_total > 0:
                                urban_ratio = recent_urban / recent_total
                            else:
                                urban_ratio = 0.5
                        else:
                            urban_ratio = 0.5
                        
                        # 计算城乡分布
                        urban_value = int(total_value * urban_ratio)
                        remaining_value = total_value - urban_value
                        # 假设乡镇和农村按5:5分配剩余部分
                        town_value = int(remaining_value * 0.5)
                        rural_value = remaining_value - town_value
                        
                        urban_data.append(urban_value / 10000)
                        town_data.append(town_value / 10000)
                        rural_data.append(rural_value / 10000)
                        total_in_school.append(total_value / 10000)
                    else:
                        # 没有预测数据，使用前一年数据
                        if len(total_in_school) > 0:
                            urban_data.append(urban_data[-1])
                            town_data.append(town_data[-1])
                            rural_data.append(rural_data[-1])
                            total_in_school.append(total_in_school[-1])
                        else:
                            urban_data.append(0)
                            town_data.append(0)
                            rural_data.append(0)
                            total_in_school.append(0)
                else:
                    # 非学前教育或没有预测工具，使用前一年数据
                    if len(total_in_school) > 0:
                        urban_data.append(urban_data[-1])
                        town_data.append(town_data[-1])
                        rural_data.append(rural_data[-1])
                        total_in_school.append(total_in_school[-1])
                    else:
                        urban_data.append(0)
                        town_data.append(0)
                        rural_data.append(0)
                        total_in_school.append(0)

        # 添加2022年基数的粉色折线
        base_year = "2022"
        # 找到2022年在years中的索引位置
        base_index = years.index(base_year) if base_year in years else 0
        # 获取2022年的总人数作为基数
        base_value = total_in_school[base_index] if base_index < len(total_in_school) else 0
        # 创建2022年基数折线数据（2022-2035年）
        base_2022_line = []
        for i, year in enumerate(years):
            # 只在2022年及之后显示基数线
            if i >= base_index:
                base_2022_line.append(base_value)
            else:
                base_2022_line.append(None)  # 2022年之前不显示

        data = {
            "xAxis_data": years,
            "series_data_0": urban_data,
            "series_data_1": town_data,
            "series_data_2": rural_data,
            "series_data_3": total_in_school,
            "series_data_4": base_2022_line,
            "education_stage": RuYuanZaiYuanModel.EDUCATION_STAGES.get(education_stage, '学前')  # 添加教育阶段名称
        }

        return data