diff --git a/Controller/RuYuanZaiYuanCountController.py b/Controller/RuYuanZaiYuanCountController.py index b52fd93..e43bc4e 100644 --- a/Controller/RuYuanZaiYuanCountController.py +++ b/Controller/RuYuanZaiYuanCountController.py @@ -9,16 +9,18 @@ router = APIRouter(prefix="/RuYuanZaiYuan", tags=["入校、在校人数统计"] @router.get("/school/chart") async def get_education_chart_config( education_stage: str = Query(default="preschool", pattern="^(preschool|primary|junior|senior|vocational)$", - description="教育阶段: preschool(学前), primary(小学), junior(初中), senior(高中), vocational(中职)") + description="教育阶段: preschool(学前), primary(小学), junior(初中), senior(高中), vocational(中职)"), + area_name: str = Query(default="云南省", description="区域名称，默认为云南省") ): - return RuYuanZaiYuanModel.generate_preschool_education_config(education_stage) + return RuYuanZaiYuanModel.generate_preschool_education_config(education_stage, area_name) @router.get("/school/inschool/chart") async def get_in_school_chart_config( education_stage: str = Query(default="preschool", pattern="^(preschool|primary|junior|senior|vocational)$", - description="教育阶段: preschool(学前), primary(小学), junior(初中), senior(高中), vocational(中职)") + description="教育阶段: preschool(学前), primary(小学), junior(初中), senior(高中), vocational(中职)"), + area_name: str = Query(default="云南省", description="区域名称，默认为云南省") ): - return RuYuanZaiYuanModel.generate_in_school_education_config(education_stage) + return RuYuanZaiYuanModel.generate_in_school_education_config(education_stage, area_name) diff --git a/Controller/__pycache__/RuYuanZaiYuanCountController.cpython-310.pyc b/Controller/__pycache__/RuYuanZaiYuanCountController.cpython-310.pyc index 0acb708..2ffd1ee 100644 Binary files a/Controller/__pycache__/RuYuanZaiYuanCountController.cpython-310.pyc and b/Controller/__pycache__/RuYuanZaiYuanCountController.cpython-310.pyc differ diff --git a/Model/RuYuanZaiYuanCountModel.py b/Model/RuYuanZaiYuanCountModel.py index 485cc69..93b2530 100644 --- a/Model/RuYuanZaiYuanCountModel.py +++ b/Model/RuYuanZaiYuanCountModel.py @@ -1,4 +1,6 @@ import json +import os +from Util.YuCeUtil import YuCeUtil class RuYuanZaiYuanModel: @@ -28,46 +30,118 @@ class RuYuanZaiYuanModel: return [], [] @staticmethod - def generate_preschool_education_config(education_stage='preschool'): + 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() - # 提取云南省级数据 - yunnan_enroll = next((item for item in enrollment_data if item["area_name"] == "云南省"), None) + # 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 yunnan_enroll: + if not area_enroll: return {} - # # 构建学前教育数据 + # 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(2015-2024) - years = [str(year) for year in range(2015, 2025)] + # 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 - enroll_data = yunnan_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 + 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: - 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 + # 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" @@ -75,7 +149,7 @@ class RuYuanZaiYuanModel: 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（2022-2024年） + # 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 @@ -95,7 +169,7 @@ class RuYuanZaiYuanModel: return data @staticmethod - def generate_in_school_education_config(education_stage='preschool'): + 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 @@ -103,10 +177,10 @@ class RuYuanZaiYuanModel: # 获取在校生相关数据 enrollment_data, in_school_data = RuYuanZaiYuanModel.load_student_data() - # 提取云南省级数据 - yunnan_in_school = next((item for item in in_school_data if item["area_name"] == "云南省"), None) + # 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 yunnan_in_school: + if not area_in_school: return {} # 构建在校生数据 @@ -115,27 +189,96 @@ class RuYuanZaiYuanModel: rural_data = [] # 乡村数据 total_in_school = [] # 总人数 - # 提取年份数据(2015-2024) - years = [str(year) for year in range(2015, 2025)] + # 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 - in_school_year_data = yunnan_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 + 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: - 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 + # 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" @@ -143,7 +286,7 @@ class RuYuanZaiYuanModel: 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（2022-2024年） + # 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 diff --git a/Model/__pycache__/RuYuanZaiYuanCountModel.cpython-310.pyc b/Model/__pycache__/RuYuanZaiYuanCountModel.cpython-310.pyc index cb6dba5..acc5a69 100644 Binary files a/Model/__pycache__/RuYuanZaiYuanCountModel.cpython-310.pyc and b/Model/__pycache__/RuYuanZaiYuanCountModel.cpython-310.pyc differ diff --git a/Test/__pycache__/__init__.cpython-310.pyc b/Test/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000..a6f147f Binary files /dev/null and b/Test/__pycache__/__init__.cpython-310.pyc differ diff --git a/Test/__pycache__/test_ruyuan_zaiyuan_interfaces.cpython-310.pyc b/Test/__pycache__/test_ruyuan_zaiyuan_interfaces.cpython-310.pyc new file mode 100644 index 0000000..702807f Binary files /dev/null and b/Test/__pycache__/test_ruyuan_zaiyuan_interfaces.cpython-310.pyc differ diff --git a/Test/education_resource_forecast.py b/Test/education_resource_forecast.py deleted file mode 100644 index 3133940..0000000 --- a/Test/education_resource_forecast.py +++ /dev/null @@ -1,149 +0,0 @@ -import os - -class EducationResourceForecast: - def __init__(self): - # 数据文件路径 - self.data_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), 'Data') - # 教育阶段映射 - self.education_stages = { - 'preschool': '学前', - 'primary': '小学', - 'junior': '初中', - 'senior': '高中', - 'vocational': '中职' - } - # 预测年份范围 - self.forecast_years = list(range(2023, 2030)) # 2023-2029年 - # 加载数据 - self.teacher_data = self._load_data('TeacherCount.json') - self.school_area_data = self._load_data('SchoolArea.json') - # 存储预测结果 - self.forecast_results = {stage: [] for stage in self.education_stages.keys()} - - def _load_data(self, filename): - """加载JSON数据文件""" - import json - file_path = os.path.join(self.data_dir, filename) - try: - with open(file_path, 'r', encoding='utf-8') as f: - data = json.load(f) - # 数据文件是数组格式，我们只需要云南省的数据 - for item in data: - if item.get('area_name') == '云南省': - return item - return {} - except Exception as e: - print(f"加载文件 {filename} 失败: {e}") - return {} - - def _get_teacher_data_by_stage(self, stage): - """获取特定教育阶段的教师数据""" - # 教师数据字段映射 - teacher_field_map = { - 'preschool': 'preschool_teachers', - 'primary': 'primary_teachers', - 'junior': 'junior_teachers', - 'senior': 'senior_teachers', - 'vocational': 'vocational_teachers' - } - - field = teacher_field_map.get(stage) - if not field or field not in self.teacher_data: - return [] - - teacher_data = [] - # 提取2015-2024年的专任教师数据 - for year in range(2015, 2025): - year_data = self.teacher_data[field].get(str(year), {}) - # 优先使用专任教师数量 - teacher_count = year_data.get('total_teacher', 0) - teacher_data.append({ - 'year': year, - 'teacher_count': teacher_count - }) - - return teacher_data - - def _get_school_area_data_by_stage(self, stage): - """获取特定教育阶段的学校面积数据""" - # 学校面积字段映射 - area_field_map = { - 'preschool': 'preschool_area', - 'primary': 'primary_area', - 'junior': 'junior_high_area', # 修正字段名 - 'senior': 'senior_high_area', # 修正字段名 - 'vocational': 'vocational_area' - } - - field = area_field_map.get(stage) - if not field or field not in self.school_area_data: - return [] - - area_data = [] - # 提取2015-2024年的学校面积数据 - for year in range(2015, 2025): - year_data = self.school_area_data[field].get(str(year), {}) - # 优先使用教学面积，如果为0则使用学校总面积 - teaching_area = year_data.get('teaching_total', 0) - if teaching_area == 0: - teaching_area = year_data.get('school_total', 0) - area_data.append({ - 'year': year, - 'teaching_area': teaching_area - }) - - return area_data - - def forecast(self): - """执行预测""" - for stage in self.education_stages.keys(): - # 获取历史数据 - teacher_history = self._get_teacher_data_by_stage(stage) - area_history = self._get_school_area_data_by_stage(stage) - - # 生成最终结果 - for year in self.forecast_years: - if year <= 2024: - # 2023-2024年使用实际数据 - # 查找实际教师数据 - teacher_item = next((item for item in teacher_history if item['year'] == year), None) - teacher_count = teacher_item['teacher_count'] if teacher_item else 0 - - # 查找实际面积数据 - area_item = next((item for item in area_history if item['year'] == year), None) - teaching_area = area_item['teaching_area'] if area_item else 0 - else: - # 2025-2029年预测数据设为0 - teacher_count = 0 - teaching_area = 0 - - # 添加结果，师资缺口/富余字段暂时设为0 - self.forecast_results[stage].append({ - 'year': year, - 'teacher_count': round(teacher_count / 10000, 2), # 转换为万人 - 'teacher_gap': 0, # 师资缺口/富余暂时设为0 - 'teaching_area': round(teaching_area / 10000, 2), # 转换为万平方米 - 'is_forecast': year >= 2025 # 标记是否为预测数据 - }) - - def print_results(self): - """打印预测结果""" - for stage, chinese_name in self.education_stages.items(): - print(f"\n{chinese_name}教育阶段资源配置预测（2023-2029年）:") - print("{:<8} {:<12} {:<16} {:<20} {:<10}".format("年份", "专任教师(万人)", "师资缺口/富余", "教学及辅助用房面积(万㎡)", "数据类型")) - for item in self.forecast_results[stage]: - data_type = "预测" if item['is_forecast'] else "实际" - print("{:<8} {:<12.2f} {:<16.2f} {:<20.2f} {:<10}".format( - item['year'], - item['teacher_count'], - item['teacher_gap'], - item['teaching_area'], - data_type - )) - -if __name__ == "__main__": - print("开始执行教育资源配置发展预测...") - forecast = EducationResourceForecast() - forecast.forecast() - forecast.print_results() - print("\n预测完成！") \ No newline at end of file diff --git a/Test/population_statistics.py b/Test/population_statistics.py deleted file mode 100644 index b0bcc84..0000000 --- a/Test/population_statistics.py +++ /dev/null @@ -1,274 +0,0 @@ -import json -import os -from datetime import datetime - -class PopulationStatistics: - def __init__(self, data_file): - """初始化人口统计类，加载数据文件""" - self.data_file = data_file - self.data = self.load_data() - self.years = list(range(2015, 2025)) # 2015-2024年 - - def load_data(self): - """加载人口数据JSON文件""" - try: - with open(self.data_file, 'r', encoding='utf-8') as f: - return json.load(f) - except FileNotFoundError: - print(f"错误：找不到数据文件 {self.data_file}") - return [] - except json.JSONDecodeError: - print(f"错误：数据文件 {self.data_file} 格式不正确") - return [] - - def get_province_data(self): - """获取云南省全省数据""" - for item in self.data: - if item['area_name'] == '云南省' and item['area_code'] == '530000000': - return item - return None - - def get_city_data(self, city_name): - """获取指定城市的数据""" - for item in self.data: - if item['area_name'] == city_name: - return item - return None - - def get_all_cities(self): - """获取所有地市级城市列表""" - cities = [] - # 地市级城市的area_code格式为53XX00000 - for item in self.data: - if len(item['area_code']) == 9 and item['area_code'][-5:] == '00000' and item['area_code'] != '530000000': - cities.append(item) - return cities - - def calculate_total_population_by_year(self, level='province'): - """按年份计算总人口 - level: 'province'(全省), 'city'(昆明市), 'all_cities'(所有地级市) - """ - result = {} - - if level == 'province': - province_data = self.get_province_data() - if province_data: - result = province_data['total_population'] - - elif level == 'city': - city_data = self.get_city_data('昆明市') - if city_data: - result = city_data['total_population'] - - elif level == 'all_cities': - cities = self.get_all_cities() - for year in self.years: - total = 0 - for city in cities: - if str(year) in city['total_population']: - total += city['total_population'][str(year)] - result[str(year)] = total - - return result - - def calculate_urbanization_trend(self, area_name='云南省'): - """计算指定地区的城镇化率趋势""" - area_data = None - if area_name == '云南省': - area_data = self.get_province_data() - else: - area_data = self.get_city_data(area_name) - - if area_data and 'urbanization_rate' in area_data: - return area_data['urbanization_rate'] - return {} - - def compare_city_populations(self, year=2023): - """比较指定年份各城市的人口数量""" - cities = self.get_all_cities() - result = {} - - for city in cities: - if str(year) in city['total_population']: - result[city['area_name']] = city['total_population'][str(year)] - - # 按人口数量排序 - return dict(sorted(result.items(), key=lambda x: x[1], reverse=True)) - - def calculate_population_growth(self, area_name='云南省', start_year=2020, end_year=2023): - """计算指定地区在指定时间段内的人口增长率""" - area_data = None - if area_name == '云南省': - area_data = self.get_province_data() - else: - area_data = self.get_city_data(area_name) - - if not area_data: - return None - - start_pop = area_data['total_population'].get(str(start_year), 0) - end_pop = area_data['total_population'].get(str(end_year), 0) - - if start_pop == 0: - return None - - growth_rate = ((end_pop - start_pop) / start_pop) * 100 - return growth_rate - - def analyze_birth_rate(self, area_name='云南省'): - """分析指定地区的出生率趋势""" - area_data = None - if area_name == '云南省': - area_data = self.get_province_data() - else: - area_data = self.get_city_data(area_name) - - if not area_data or 'birth_population' not in area_data or 'total_population' not in area_data: - return {} - - birth_rates = {} - for year in self.years: - year_str = str(year) - if year_str in area_data['birth_population'] and year_str in area_data['total_population']: - birth_pop = area_data['birth_population'][year_str] - total_pop = area_data['total_population'][year_str] - - # 注意：总人口单位是万人，出生人口单位是个，需要统一单位 - if total_pop > 0 and birth_pop > 0: - birth_rate = (birth_pop / (total_pop * 10000)) * 1000 # 千分比 - birth_rates[year] = birth_rate - - return birth_rates - - def save_results_to_file(self, results, filename): - """将统计结果保存到文件""" - # 创建结果目录 - result_dir = os.path.join(os.path.dirname(self.data_file), '../Results') - os.makedirs(result_dir, exist_ok=True) - - # 生成带时间戳的文件名 - timestamp = datetime.now().strftime('%Y%m%d_%H%M%S') - full_filename = f"{filename}_{timestamp}.txt" - full_path = os.path.join(result_dir, full_filename) - - # 保存结果 - with open(full_path, 'w', encoding='utf-8') as f: - f.write(f"人口统计结果 - 生成时间：{datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n\n") - for key, value in results.items(): - if isinstance(value, dict): - f.write(f"{key}:\n") - for k, v in value.items(): - f.write(f" {k}: {v}\n") - else: - f.write(f"{key}: {value}\n") - - print(f"统计结果已保存至：{full_path}") - return full_path - -# 主函数演示如何使用这个类 -if __name__ == "__main__": - # 数据文件路径 - data_file = os.path.join(os.path.dirname(__file__), '../Data/RenKou.json') - - # 创建统计类实例 - stats = PopulationStatistics(data_file) - - # 检查数据是否成功加载 - if not stats.data: - print("无法加载数据，程序退出。") - exit(1) - - # 主菜单 - print("云南省人口数据统计分析工具") - print("========================") - - while True: - print("\n请选择要执行的统计分析：") - print("1. 查看云南省历年总人口统计") - print("2. 查看昆明市历年总人口统计") - print("3. 分析云南省城镇化率趋势") - print("4. 分析昆明市城镇化率趋势") - print("5. 比较2023年各城市人口数量") - print("6. 计算云南省2020-2023年人口增长率") - print("7. 分析云南省出生率趋势") - print("8. 分析昆明市出生率趋势") - print("9. 导出所有统计结果") - print("0. 退出程序") - - choice = input("请输入您的选择（0-9）：") - - if choice == '0': - print("感谢使用，再见！") - break - - elif choice == '1': - result = stats.calculate_total_population_by_year('province') - print("\n云南省历年总人口统计（单位：万人）：") - for year, population in result.items(): - print(f"{year}年: {population}") - - elif choice == '2': - result = stats.calculate_total_population_by_year('city') - print("\n昆明市历年总人口统计（单位：万人）：") - for year, population in result.items(): - print(f"{year}年: {population}") - - elif choice == '3': - result = stats.calculate_urbanization_trend('云南省') - print("\n云南省历年城镇化率（单位：%）：") - for year, rate in result.items(): - print(f"{year}年: {rate}") - - elif choice == '4': - result = stats.calculate_urbanization_trend('昆明市') - print("\n昆明市历年城镇化率（单位：%）：") - for year, rate in result.items(): - print(f"{year}年: {rate}") - - elif choice == '5': - result = stats.compare_city_populations(2023) - print("\n2023年各城市人口数量排名（单位：万人）：") - for i, (city, population) in enumerate(result.items(), 1): - print(f"{i}. {city}: {population}") - - elif choice == '6': - rate = stats.calculate_population_growth('云南省', 2020, 2023) - if rate is not None: - print(f"\n云南省2020-2023年人口增长率: {rate:.2f}%") - else: - print("\n无法计算人口增长率") - - elif choice == '7': - result = stats.analyze_birth_rate('云南省') - print("\n云南省历年出生率（单位：‰）：") - for year, rate in result.items(): - print(f"{year}年: {rate:.2f}") - - elif choice == '8': - result = stats.analyze_birth_rate('昆明市') - print("\n昆明市历年出生率（单位：‰）：") - for year, rate in result.items(): - print(f"{year}年: {rate:.2f}") - - elif choice == '9': - # 收集所有统计结果 - all_results = { - "云南省历年总人口统计（万人）": stats.calculate_total_population_by_year('province'), - "昆明市历年总人口统计（万人）": stats.calculate_total_population_by_year('city'), - "云南省历年城镇化率（%）": stats.calculate_urbanization_trend('云南省'), - "昆明市历年城镇化率（%）": stats.calculate_urbanization_trend('昆明市'), - "2023年各城市人口数量排名（万人）": stats.compare_city_populations(2023), - "云南省2020-2023年人口增长率（%）": stats.calculate_population_growth('云南省', 2020, 2023), - "云南省历年出生率（‰）": stats.analyze_birth_rate('云南省'), - "昆明市历年出生率（‰）": stats.analyze_birth_rate('昆明市') - } - - # 保存结果到文件 - file_path = stats.save_results_to_file(all_results, "population_statistics") - print(f"\n所有统计结果已保存至文件：{file_path}") - - else: - print("\n无效的选择，请重新输入。") - - # 等待用户按Enter键继续 - input("\n按Enter键继续...") \ No newline at end of file diff --git a/Test/read_population_data.py b/Test/read_population_data.py deleted file mode 100644 index 5d6fec3..0000000 --- a/Test/read_population_data.py +++ /dev/null @@ -1,52 +0,0 @@ -import json -import os - -# 获取脚本所在目录 -script_dir = os.path.dirname(os.path.abspath(__file__)) -# 构建数据文件的绝对路径 -file_path = os.path.join(script_dir, '../Data/RenKou.json') - -# 读取JSON数据 -with open(file_path, 'r', encoding='utf-8') as f: - population_data = json.load(f) - -# 查找云南省的数据 -for area in population_data: - if area.get('area_name') == '云南省': - yunnan_data = area - break - -# 提取2023年的人口数据 -year = '2023' - -# 总人口（万人） -total_population = yunnan_data.get('total_population', {}).get(year, 0) - -# 新生人口（万人，注意原数据是整数，需要转换为万人） -birth_population = yunnan_data.get('birth_population', {}).get(year, 0) -# 转换为万人（如果是整数） -if isinstance(birth_population, int): - birth_population = round(birth_population / 10000, 1) - -# 城镇人口（万人） -urban_population = yunnan_data.get('urban_population', {}).get(year, 0) - -# 乡村人口（万人） -rural_population = yunnan_data.get('rural_population', {}).get(year, 0) - -# 城镇化率（%） -urbanization_rate = yunnan_data.get('urbanization_rate', {}).get(year, 0) - -# 打印结果 -print("云南省 2023年人口概览") -print("-------------------") -print(f"总人口 {total_population:,} 万人") -print(f"新生人口 {birth_population} 万人") -print(f"城镇人口 {urban_population:,} 万人") -print(f"乡村人口 {rural_population:,} 万人") -print(f"城镇化率 {urbanization_rate:.2f} %") - -# 验证数据一致性 -if abs(total_population - (urban_population + rural_population)) > 0.01: - print("\n注意：总人口与城镇人口+乡村人口存在差异") - print(f"差异值: {abs(total_population - (urban_population + rural_population)):.2f} 万人") \ No newline at end of file diff --git a/Test/test_education_interfaces.py b/Test/test_education_interfaces.py deleted file mode 100644 index 62ef328..0000000 --- a/Test/test_education_interfaces.py +++ /dev/null @@ -1,167 +0,0 @@ -import requests -import json -import time - -# API基础URL -BASE_URL = "http://localhost:8100/RuYuanZaiYuan" -EDUCATION_DATA_BASE_URL = "http://localhost:8100/EducationData" - -# 支持的教育阶段 -test_education_stages = [ - ("preschool", "学前"), - ("primary", "小学"), - ("junior", "初中"), - ("senior", "高中"), - ("vocational", "中职") # 添加中职教育阶段 -] - -# 测试年份 -test_years = [2023, 2022, 2024] - - -def test_school_chart_interface(): - """测试获取教育阶段图表数据接口""" - print("\n===== 测试 /school/chart 接口 =====") - - for stage_code, stage_name in test_education_stages: - url = f"{BASE_URL}/school/chart?education_stage={stage_code}" - print(f"\n测试 {stage_name}({stage_code}) 数据:") - - try: - response = requests.get(url) - response.raise_for_status() # 如果状态码不是200，抛出异常 - - data = response.json() - print(f"响应状态码: {response.status_code}") - print(f"响应数据: {json.dumps(data, ensure_ascii=False, indent=2)}") - - # 验证返回数据的基本结构 - assert "xAxis_data" in data, "返回数据缺少xAxis_data字段" - assert "series_data_0" in data, "返回数据缺少series_data_0字段(城区数据)" - assert "series_data_1" in data, "返回数据缺少series_data_1字段(镇区数据)" - assert "series_data_2" in data, "返回数据缺少series_data_2字段(乡村数据)" - assert "series_data_3" in data, "返回数据缺少series_data_3字段(总人数数据)" - assert "series_data_4" in data, "返回数据缺少series_data_4字段(2022年基数数据)" - assert "education_stage" in data, "返回数据缺少education_stage字段" - assert data["education_stage"] == stage_name, f"教育阶段名称不匹配，期望: {stage_name}，实际: {data['education_stage']}" - - print(f"✅ {stage_name} 数据验证通过") - - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - except AssertionError as e: - print(f"❌ 数据验证失败: {e}") - - # 添加短暂延迟，避免请求过于频繁 - time.sleep(0.5) - - -# @router.get("/school/inschool/chart") -# async def get_in_school_chart_config( -# education_stage: str = Query(default="preschool", pattern="^(preschool|primary|junior|senior)$", -# description="教育阶段: preschool(学前), primary(小学), junior(初中), senior(高中)") -# ): -# return RuYuanZaiYuanModel.generate_in_school_education_config(education_stage) -# - - -def test_education_data_by_year(): - """测试获取指定年份教育数据接口""" - print("\n===== 测试 /EducationData/byYear 接口 =====") - - # 测试不同年份的数据 - for year in test_years: - url = f"{EDUCATION_DATA_BASE_URL}/byYear?year={year}" - print(f"\n测试 {year} 年数据:") - - try: - response = requests.get(url) - response.raise_for_status() # 如果状态码不是200，抛出异常 - - data = response.json() - print(f"响应状态码: {response.status_code}") - print(f"响应数据: {json.dumps(data, ensure_ascii=False, indent=2)}") - - # 验证返回数据的基本结构 - assert "code" in data, "返回数据缺少code字段" - assert "message" in data, "返回数据缺少message字段" - assert "data" in data, "返回数据缺少data字段" - assert data["code"] == 200, f"状态码不匹配，期望: 200，实际: {data['code']}" - assert isinstance(data["data"], list), "data字段应为列表类型" - - # 验证数据内容 - if data["data"]: - # 检查每个数据项的结构 - for item in data["data"]: - assert "education_stage" in item, "数据项缺少education_stage字段" - assert "school_count" in item, "数据项缺少school_count字段" - assert "teacher_count_10k" in item, "数据项缺少teacher_count_10k字段" - assert "enrollment_count_10k" in item, "数据项缺少enrollment_count_10k字段" - assert "admission_count_10k" in item, "数据项缺少admission_count_10k字段" - - # 检查数值类型 - assert isinstance(item["school_count"], (int, float)), "school_count应为数值类型" - assert isinstance(item["teacher_count_10k"], (int, float)), "teacher_count_10k应为数值类型" - assert isinstance(item["enrollment_count_10k"], (int, float)), "enrollment_count_10k应为数值类型" - assert isinstance(item["admission_count_10k"], (int, float)), "admission_count_10k应为数值类型" - - print(f"✅ {year} 年数据验证通过，共包含 {len(data['data'])} 个教育阶段的数据") - else: - print(f"⚠️ {year} 年未返回数据") - - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - except AssertionError as e: - print(f"❌ 数据验证失败: {e}") - - # 添加短暂延迟，避免请求过于频繁 - time.sleep(0.5) - - # 测试边界值 - print("\n===== 测试边界值 =====") - - # 测试最小值年份 - min_year = 2015 - url = f"{EDUCATION_DATA_BASE_URL}/byYear?year={min_year}" - print(f"\n测试最小值年份 {min_year}:") - try: - response = requests.get(url) - response.raise_for_status() - print(f"响应状态码: {response.status_code}") - print(f"✅ 最小值年份测试通过") - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - - # 测试最大值年份 - max_year = 2028 - url = f"{EDUCATION_DATA_BASE_URL}/byYear?year={max_year}" - print(f"\n测试最大值年份 {max_year}:") - try: - response = requests.get(url) - response.raise_for_status() - print(f"响应状态码: {response.status_code}") - print(f"✅ 最大值年份测试通过") - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - - # 测试超出范围的年份 - invalid_year = 2030 - url = f"{EDUCATION_DATA_BASE_URL}/byYear?year={invalid_year}" - print(f"\n测试超出范围的年份 {invalid_year}:") - try: - response = requests.get(url) - if response.status_code == 422: # FastAPI的验证错误状态码 - print(f"响应状态码: {response.status_code}") - print(f"✅ 超出范围年份正确返回验证错误") - else: - print(f"❌ 期望状态码422，实际: {response.status_code}") - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - - -if __name__ == "__main__": - # 测试原有接口 - # test_school_chart_interface() - - # 测试新添加的教育数据接口 - test_education_data_by_year() \ No newline at end of file diff --git a/Test/test_population_data_interface.py b/Test/test_population_data_interface.py deleted file mode 100644 index 74879ad..0000000 --- a/Test/test_population_data_interface.py +++ /dev/null @@ -1,124 +0,0 @@ -import requests -import json -import time - -# API基础URL -EDUCATION_DATA_BASE_URL = "http://localhost:8100/EducationData" - -# 测试年份 -test_years = [2023, 2022, 2024] - - -def test_population_data_by_year(): - """测试获取指定年份人口数据接口""" - print("\n===== 测试 /EducationData/populationByYear 接口 =====") - - # 测试不同年份的数据 - for year in test_years: - url = f"{EDUCATION_DATA_BASE_URL}/populationByYear?year={year}" - print(f"\n测试 {year} 年人口数据:") - - try: - response = requests.get(url) - response.raise_for_status() # 如果状态码不是200，抛出异常 - - data = response.json() - print(f"响应状态码: {response.status_code}") - print(f"响应数据: {json.dumps(data, ensure_ascii=False, indent=2)}") - - # 验证返回数据的基本结构 - assert "code" in data, "返回数据缺少code字段" - assert "message" in data, "返回数据缺少message字段" - assert "data" in data, "返回数据缺少data字段" - assert data["code"] == 200, f"状态码不匹配，期望: 200，实际: {data['code']}" - - # 验证数据内容 - if data["data"]: - # 检查数据项的结构 - population_data = data["data"] - assert "year" in population_data, "数据缺少year字段" - assert "total_population" in population_data, "数据缺少total_population字段" - assert "birth_population" in population_data, "数据缺少birth_population字段" - assert "urban_population" in population_data, "数据缺少urban_population字段" - assert "rural_population" in population_data, "数据缺少rural_population字段" - assert "urbanization_rate" in population_data, "数据缺少urbanization_rate字段" - - # 检查数值类型 - assert isinstance(population_data["year"], int), "year应为整数类型" - assert isinstance(population_data["total_population"], (int, float)), "total_population应为数值类型" - assert isinstance(population_data["birth_population"], (int, float)), "birth_population应为数值类型" - assert isinstance(population_data["urban_population"], (int, float)), "urban_population应为数值类型" - assert isinstance(population_data["rural_population"], (int, float)), "rural_population应为数值类型" - assert isinstance(population_data["urbanization_rate"], (int, float)), "urbanization_rate应为数值类型" - - # 检查年份是否匹配 - assert population_data["year"] == year, f"年份不匹配，期望: {year}，实际: {population_data['year']}" - - print(f"✅ {year} 年人口数据验证通过") - else: - print(f"⚠️ {year} 年未返回人口数据") - - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - except AssertionError as e: - print(f"❌ 数据验证失败: {e}") - - # 添加短暂延迟，避免请求过于频繁 - time.sleep(0.5) - - # 测试边界值 - print("\n===== 测试边界值 =====") - - # 测试最小值年份 - min_year = 2015 - url = f"{EDUCATION_DATA_BASE_URL}/populationByYear?year={min_year}" - print(f"\n测试最小值年份 {min_year}:") - try: - response = requests.get(url) - response.raise_for_status() - data = response.json() - print(f"响应状态码: {response.status_code}") - if data["code"] == 200 or data["code"] == 404: - print(f"✅ 最小值年份测试通过") - else: - print(f"❌ 状态码异常: {data['code']}") - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - - # 测试最大值年份 - max_year = 2028 - url = f"{EDUCATION_DATA_BASE_URL}/populationByYear?year={max_year}" - print(f"\n测试最大值年份 {max_year}:") - try: - response = requests.get(url) - response.raise_for_status() - data = response.json() - print(f"响应状态码: {response.status_code}") - if data["code"] == 200 or data["code"] == 404: - print(f"✅ 最大值年份测试通过") - else: - print(f"❌ 状态码异常: {data['code']}") - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - - # 测试超出范围的年份 - invalid_year = 2030 - url = f"{EDUCATION_DATA_BASE_URL}/populationByYear?year={invalid_year}" - print(f"\n测试超出范围的年份 {invalid_year}:") - try: - response = requests.get(url) - if response.status_code == 422: # FastAPI的验证错误状态码 - print(f"响应状态码: {response.status_code}") - print(f"✅ 超出范围年份正确返回验证错误") - else: - data = response.json() - print(f"响应状态码: {response.status_code}") - print(f"响应数据: {json.dumps(data, ensure_ascii=False, indent=2)}") - print(f"❌ 期望状态码422，实际: {response.status_code}") - except requests.exceptions.RequestException as e: - print(f"❌ 请求失败: {e}") - - -if __name__ == "__main__": - # 测试新添加的人口数据接口 - test_population_data_by_year() \ No newline at end of file diff --git a/Test/test_ruyuan_zaiyuan_interfaces.py b/Test/test_ruyuan_zaiyuan_interfaces.py new file mode 100644 index 0000000..f4567ea --- /dev/null +++ b/Test/test_ruyuan_zaiyuan_interfaces.py @@ -0,0 +1,162 @@ +import unittest +import requests + + +class TestRuYuanZaiYuanInterfaces(unittest.TestCase): + # 配置API基础URL + BASE_URL = "http://localhost:8100" # 根据实际运行的服务地址和端口修改 + + def setUp(self): + # 测试前的初始化工作 + pass + + def tearDown(self): + # 测试后的清理工作 + pass + + def test_get_education_chart_config_default(self): + """测试默认参数下的入园/招生数据接口""" + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/chart" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + # 验证返回数据格式 + self.assertIn("xAxis_data", data) + self.assertIn("series_data_0", data) + self.assertIn("series_data_1", data) + self.assertIn("series_data_2", data) + self.assertIn("series_data_3", data) + self.assertIn("series_data_4", data) + self.assertIn("education_stage", data) + # 默认应该是学前教育 + self.assertEqual(data["education_stage"], "学前") + # 年份应该包含2015-2035 + self.assertIn("2015", data["xAxis_data"]) + self.assertIn("2024", data["xAxis_data"]) + self.assertIn("2035", data["xAxis_data"]) + + def test_get_in_school_chart_config_default(self): + """测试默认参数下的在园/在校数据接口""" + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/inschool/chart" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + # 验证返回数据格式 + self.assertIn("xAxis_data", data) + self.assertIn("series_data_0", data) + self.assertIn("series_data_1", data) + self.assertIn("series_data_2", data) + self.assertIn("series_data_3", data) + self.assertIn("series_data_4", data) + self.assertIn("education_stage", data) + # 默认应该是学前教育 + self.assertEqual(data["education_stage"], "学前") + # 年份应该包含2015-2035 + self.assertIn("2015", data["xAxis_data"]) + self.assertIn("2024", data["xAxis_data"]) + self.assertIn("2035", data["xAxis_data"]) + + def test_with_different_education_stages(self): + """测试不同教育阶段参数""" + stages = ["preschool", "primary", "junior", "senior", "vocational"] + stage_names = {"preschool": "学前", "primary": "小学", "junior": "初中", "senior": "高中", "vocational": "中职"} + + for stage in stages: + # 测试入园/招生接口 + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/chart?education_stage={stage}" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["education_stage"], stage_names[stage]) + + # 测试在园/在校接口 + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/inschool/chart?education_stage={stage}" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["education_stage"], stage_names[stage]) + + def test_with_different_area_names(self): + """测试不同区域名称参数""" + # 注意：这里使用的区域名称需要在数据文件中存在，否则会返回空数据 + # 以下测试假设"云南省"和"昆明市"在数据中存在 + areas = ["云南省", "昆明市"] + + for area in areas: + # 测试入园/招生接口 + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/chart?area_name={area}" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + # 如果区域不存在，会返回空字典 + if data: # 只在数据存在时进行验证 + self.assertIn("xAxis_data", data) + self.assertIn("series_data_3", data) # 总人数数据 + + # 测试在园/在校接口 + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/inschool/chart?area_name={area}" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + # 如果区域不存在，会返回空字典 + if data: # 只在数据存在时进行验证 + self.assertIn("xAxis_data", data) + self.assertIn("series_data_3", data) # 总人数数据 + + def test_with_invalid_parameters(self): + """测试无效参数""" + # 测试无效的教育阶段 + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/chart?education_stage=invalid_stage" + response = requests.get(url) + # FastAPI会自动验证参数格式，不符合pattern的参数会返回422 + self.assertEqual(response.status_code, 422) + + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/inschool/chart?education_stage=invalid_stage" + response = requests.get(url) + self.assertEqual(response.status_code, 422) + + # 测试不存在的区域名称 + # 注意：不存在的区域名称不会返回422，而是返回空数据 + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/chart?area_name=不存在的区域" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data, {}) + + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/inschool/chart?area_name=不存在的区域" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data, {}) + + def test_data_consistency(self): + """测试数据一致性""" + # 测试相同参数下两次请求返回相同数据 + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/chart" + response1 = requests.get(url) + response2 = requests.get(url) + self.assertEqual(response1.json(), response2.json()) + + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/inschool/chart" + response1 = requests.get(url) + response2 = requests.get(url) + self.assertEqual(response1.json(), response2.json()) + + def test_with_combined_parameters(self): + """测试组合参数""" + # 同时指定教育阶段和区域名称 + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/chart?education_stage=primary&area_name=云南省" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["education_stage"], "小学") + + url = f"{self.BASE_URL}/RuYuanZaiYuan/school/inschool/chart?education_stage=junior&area_name=云南省" + response = requests.get(url) + self.assertEqual(response.status_code, 200) + data = response.json() + self.assertEqual(data["education_stage"], "初中") + + +if __name__ == "__main__": + unittest.main() \ No newline at end of file diff --git a/Util/__pycache__/YuCeUtil.cpython-310.pyc b/Util/__pycache__/YuCeUtil.cpython-310.pyc new file mode 100644 index 0000000..7f2dd9d Binary files /dev/null and b/Util/__pycache__/YuCeUtil.cpython-310.pyc differ