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