309 lines
15 KiB
Python
309 lines
15 KiB
Python
import json
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import os
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from Util.YuCeUtil import YuCeUtil
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class RuYuanZaiYuanModel:
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# 定义支持的教育阶段映射
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EDUCATION_STAGES = {
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'preschool': '学前',
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'primary': '小学',
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'junior': '初中',
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'senior': '高中',
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'vocational': '中职'
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}
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@staticmethod
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def load_student_data():
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try:
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# 加载招生数据(入园人数)
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with open("./Data/ZhaoShengCount.json", "r", encoding="utf-8") as f:
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enrollment_data = json.load(f)
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# 加载在校生数据(在园人数)
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with open("./Data/ZaiXiaoShengCount.json", "r", encoding="utf-8") as f:
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in_school_data = json.load(f)
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return enrollment_data, in_school_data
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except Exception as e:
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print(f"读取学生数据出错: {e}")
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return [], []
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@staticmethod
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def generate_preschool_education_config(education_stage='preschool', area_name='云南省'):
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# 验证教育阶段参数
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if education_stage not in RuYuanZaiYuanModel.EDUCATION_STAGES:
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education_stage = 'preschool' # 默认使用学前
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# 获取学前教育相关数据
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enrollment_data, in_school_data = RuYuanZaiYuanModel.load_student_data()
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# 提取指定区域数据
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area_enroll = next((item for item in enrollment_data if item["area_name"] == area_name), None)
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if not area_enroll:
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return {}
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# 构建学前教育数据
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urban_data = [] # 城区数据
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town_data = [] # 镇区数据
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rural_data = [] # 乡村数据
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total_enroll = [] # 总人数
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# 提取年份数据(2015-2035)
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years = [str(year) for year in range(2015, 2036)]
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# 初始化预测工具（只针对学前教育进行预测）
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forecast_util = None
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forecast_results = {}
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forecast_urban_enrollment = {}
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if education_stage == 'preschool':
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# 获取数据目录
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data_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'Data')
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# 创建预测实例，使用传入的区域名称
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forecast_util = YuCeUtil(data_directory, area_name)
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# 运行预测
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forecast_util.run_forecast()
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# 获取预测结果
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forecast_results = forecast_util.forecast_results
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forecast_urban_enrollment = forecast_util.forecast_urban_enrollment
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for year in years:
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# 使用传入的教育阶段参数
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if int(year) <= 2024:
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# 2015-2024年使用实际数据
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enroll_data = area_enroll["education_data"].get(education_stage, {}).get(year, {})
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# 特殊处理中职数据格式（只有total字段）
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if education_stage == 'vocational':
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total_value = enroll_data.get("total", 0)
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urban_data.append(0) # 中职没有城区数据
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town_data.append(0) # 中职没有镇区数据
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rural_data.append(0) # 中职没有乡村数据
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total_enroll.append(total_value / 10000) # 转换为万人
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else:
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urban_data.append(enroll_data.get("urban", 0) / 10000) # 转换为万人
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town_data.append(enroll_data.get("town", 0) / 10000) # 转换为万人
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rural_data.append(enroll_data.get("rural", 0) / 10000) # 转换为万人
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# 计算总和作为总人数
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calculated_total = enroll_data.get("urban", 0) + enroll_data.get("town", 0) + enroll_data.get("rural", 0)
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total_enroll.append(calculated_total / 10000) # 转换为万人
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else:
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# 2025-2035年使用预测数据
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if education_stage == 'preschool' and forecast_util:
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if year in forecast_results:
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total_value = forecast_results[year]
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# 获取城区招生数
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urban_value = forecast_urban_enrollment.get(int(year), {}).get('urban', 0)
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remaining_value = total_value - urban_value
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# 假设乡镇和农村按历史比例分配剩余部分
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# 查找最近一年的乡镇和农村比例
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recent_year = str(int(year) - 1)
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if recent_year in area_enroll["education_data"].get(education_stage, {}):
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recent_data = area_enroll["education_data"].get(education_stage, {}).get(recent_year, {})
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recent_town = recent_data.get("town", 0)
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recent_rural = recent_data.get("rural", 0)
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recent_remaining = recent_town + recent_rural
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if recent_remaining > 0:
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town_value = int(remaining_value * (recent_town / recent_remaining))
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rural_value = remaining_value - town_value
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else:
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town_value = int(remaining_value / 2)
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rural_value = remaining_value - town_value
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else:
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town_value = int(remaining_value / 2)
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rural_value = remaining_value - town_value
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urban_data.append(urban_value / 10000)
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town_data.append(town_value / 10000)
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rural_data.append(rural_value / 10000)
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total_enroll.append(total_value / 10000)
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else:
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# 没有预测数据，使用前一年数据
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if len(total_enroll) > 0:
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urban_data.append(urban_data[-1])
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town_data.append(town_data[-1])
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rural_data.append(rural_data[-1])
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total_enroll.append(total_enroll[-1])
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else:
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urban_data.append(0)
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town_data.append(0)
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rural_data.append(0)
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total_enroll.append(0)
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else:
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# 非学前教育或没有预测工具，使用前一年数据
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if len(total_enroll) > 0:
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urban_data.append(urban_data[-1])
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town_data.append(town_data[-1])
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rural_data.append(rural_data[-1])
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total_enroll.append(total_enroll[-1])
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else:
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urban_data.append(0)
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town_data.append(0)
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rural_data.append(0)
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total_enroll.append(0)
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# 添加2022年基数的粉色折线
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base_year = "2022"
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# 找到2022年在years中的索引位置
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base_index = years.index(base_year) if base_year in years else 0
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# 获取2022年的总人数作为基数
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base_value = total_enroll[base_index] if base_index < len(total_enroll) else 0
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# 创建2022年基数折线数据（2022-2035年）
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base_2022_line = []
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for i, year in enumerate(years):
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# 只在2022年及之后显示基数线
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if i >= base_index:
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base_2022_line.append(base_value)
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else:
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base_2022_line.append(None) # 2022年之前不显示
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data = {
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"xAxis_data": years,
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"series_data_0": urban_data, # 城区
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"series_data_1": town_data, # 镇区
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"series_data_2": rural_data, # 乡村
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"series_data_3": total_enroll, # 总人数
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"series_data_4": base_2022_line, # 2022年基数
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"education_stage": RuYuanZaiYuanModel.EDUCATION_STAGES.get(education_stage, '学前') # 添加教育阶段名称
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}
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return data
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@staticmethod
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def generate_in_school_education_config(education_stage='preschool', area_name='云南省'):
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# 验证教育阶段参数
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if education_stage not in RuYuanZaiYuanModel.EDUCATION_STAGES:
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education_stage = 'preschool' # 默认使用学前
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# 获取在校生相关数据
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enrollment_data, in_school_data = RuYuanZaiYuanModel.load_student_data()
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# 提取指定区域数据
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area_in_school = next((item for item in in_school_data if item["area_name"] == area_name), None)
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if not area_in_school:
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return {}
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# 构建在校生数据
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urban_data = [] # 城区数据
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town_data = [] # 镇区数据
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rural_data = [] # 乡村数据
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total_in_school = [] # 总人数
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# 提取年份数据(2015-2035)
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years = [str(year) for year in range(2015, 2036)]
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# 初始化预测工具（只针对学前教育进行预测）
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forecast_util = None
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enrollment_in_school = {}
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if education_stage == 'preschool':
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# 获取数据目录
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data_directory = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'Data')
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# 创建预测实例，使用传入的区域名称
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forecast_util = YuCeUtil(data_directory, area_name)
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# 运行预测
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forecast_util.run_forecast()
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# 获取预测结果
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enrollment_in_school = forecast_util.enrollment_in_school
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for year in years:
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# 使用传入的教育阶段参数
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if int(year) <= 2024:
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# 2015-2024年使用实际数据
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in_school_year_data = area_in_school["student_data"].get(education_stage, {}).get(year, {})
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# 特殊处理中职数据格式（只有total字段）
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if education_stage == 'vocational':
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total_value = in_school_year_data.get("total", 0)
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urban_data.append(0) # 中职没有城区数据
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town_data.append(0) # 中职没有镇区数据
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rural_data.append(0) # 中职没有乡村数据
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total_in_school.append(total_value / 10000) # 转换为万人
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else:
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urban_data.append(in_school_year_data.get("urban", 0) / 10000) # 转换为万人
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town_data.append(in_school_year_data.get("town", 0) / 10000) # 转换为万人
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rural_data.append(in_school_year_data.get("rural", 0) / 10000) # 转换为万人
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# 计算总和作为总人数
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calculated_total = in_school_year_data.get("urban", 0) + in_school_year_data.get("town", 0) + in_school_year_data.get("rural", 0)
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total_in_school.append(calculated_total / 10000) # 转换为万人
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else:
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# 2025-2035年使用预测数据
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if education_stage == 'preschool' and forecast_util:
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if int(year) in enrollment_in_school:
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total_value = enrollment_in_school[int(year)]
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# 获取城区比例（使用最近一年的城区比例）
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recent_year = str(int(year) - 1)
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if recent_year in area_in_school["student_data"].get(education_stage, {}):
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recent_data = area_in_school["student_data"].get(education_stage, {}).get(recent_year, {})
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recent_urban = recent_data.get("urban", 0)
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recent_total = recent_data.get("urban", 0) + recent_data.get("town", 0) + recent_data.get("rural", 0)
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if recent_total > 0:
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urban_ratio = recent_urban / recent_total
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else:
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urban_ratio = 0.5
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else:
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urban_ratio = 0.5
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# 计算城乡分布
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urban_value = int(total_value * urban_ratio)
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remaining_value = total_value - urban_value
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# 假设乡镇和农村按5:5分配剩余部分
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town_value = int(remaining_value * 0.5)
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rural_value = remaining_value - town_value
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urban_data.append(urban_value / 10000)
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town_data.append(town_value / 10000)
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rural_data.append(rural_value / 10000)
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total_in_school.append(total_value / 10000)
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else:
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# 没有预测数据，使用前一年数据
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if len(total_in_school) > 0:
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urban_data.append(urban_data[-1])
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town_data.append(town_data[-1])
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rural_data.append(rural_data[-1])
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total_in_school.append(total_in_school[-1])
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else:
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urban_data.append(0)
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town_data.append(0)
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rural_data.append(0)
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total_in_school.append(0)
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else:
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# 非学前教育或没有预测工具，使用前一年数据
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if len(total_in_school) > 0:
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urban_data.append(urban_data[-1])
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town_data.append(town_data[-1])
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rural_data.append(rural_data[-1])
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total_in_school.append(total_in_school[-1])
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else:
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urban_data.append(0)
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town_data.append(0)
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rural_data.append(0)
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total_in_school.append(0)
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# 添加2022年基数的粉色折线
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base_year = "2022"
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# 找到2022年在years中的索引位置
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base_index = years.index(base_year) if base_year in years else 0
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# 获取2022年的总人数作为基数
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base_value = total_in_school[base_index] if base_index < len(total_in_school) else 0
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# 创建2022年基数折线数据（2022-2035年）
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base_2022_line = []
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for i, year in enumerate(years):
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# 只在2022年及之后显示基数线
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if i >= base_index:
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base_2022_line.append(base_value)
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else:
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base_2022_line.append(None) # 2022年之前不显示
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data = {
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"xAxis_data": years,
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"series_data_0": urban_data,
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"series_data_1": town_data,
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"series_data_2": rural_data,
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"series_data_3": total_in_school,
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"series_data_4": base_2022_line,
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"education_stage": RuYuanZaiYuanModel.EDUCATION_STAGES.get(education_stage, '学前') # 添加教育阶段名称
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}
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return data
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