统计分析模块

schema/statistics_schema.py

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+from pydantic import BaseModel,ConfigDict  # FastAPI 推荐用 pydantic 做数据校验和序列化
+from datetime import date               # 日期类型，用于 birthday、enrollment_date 等字段
+from typing import List                 # 类型提示，声明列表
+
+class StudentInfo1(BaseModel):
+    model_config = ConfigDict(from_attributes=True)
+    student_id: str             # 学号
+    student_name: str           # 姓名
+    gender: str                 # 性别（男/女）
+    id_card: str                # 身份证号
+    birthday: date              # 出生日期
+    ethnicity: str              # 民族
+    region_id: str              # 籍贯编码（关联 region表）
+    phone: str                  # 手机号
+    major: str                  # 专业
+    class_id: str               # 班级编号
+    enrollment_date: date       # 入学日期
+    graduation_date: date       # 毕业日期
+    student_status: str         # 学籍状态（在读/休学/退学/毕业）
+    education_level: str        # 学历（大专/本科/硕士/博士）
+
+class ResponseModel(BaseModel):
+    """统一响应格式 — 所有接口都返回这个结构，方便前端统一处理"""
+    code: int                  # 状态码，200=成功，400=参数错误
+    message: str = 'ok'        # 提示信息
+    total:int                  #总条数
+    total_pages:int            #总页数
+    data: List[StudentInfo1] # 真正的数据，这里是学员列表
+
+class StudentInfo2(BaseModel):
+    student_id: str
+    student_name: str
+    score:float
+
+class ResponseModel1(BaseModel):
+    """统一响应格式 — 所有接口都返回这个结构,方便前端统一处理"""
+    code: int                  # 状态码,200=成功,400=参数错误
+    message: str = 'ok'        # 提示信息
+    total:int                  #总条数
+    total_pages:int            #总页数
+    data: List[StudentInfo2] # 真正的数据,这里是学员列表
+
+class StudentInfo3(BaseModel):
+    student_id: str
+    student_name: str
+    fail_count: int
+
+class ResponseModel2(BaseModel):
+    code: int
+    message: str = 'ok'
+    total: int
+    total_pages: int
+    data: List[StudentInfo3]
+
+class ClassExamAvg(BaseModel):
+    course_id: str
+    class_id: str
+    class_name: str
+    avg_score: float
+
+class ResponseModel3(BaseModel):
+    code: int
+    message: str = 'ok'
+    total: int
+    total_pages: int
+    data: List[ClassExamAvg]
+
+class SalaryTop(BaseModel):
+    student_name: str
+    class_name: str
+    company_name: str
+    part_time: date
+    salary: float
+
+class ResponseModel4(BaseModel):
+    code: int
+    message: str = 'ok'
+    data: List[SalaryTop]
+
+class TimeSize(BaseModel):
+    student_id: str
+    student_name: str
+    offer_date: date
+    resume_open_date: date
+    time_size: int  # 就业时长（天数）
+
+class ResponseModel5(BaseModel):
+    code: int
+    message: str = 'ok'
+    total: int
+    total_pages: int
+    data: List[TimeSize]
+
+class ClassAvgTimeSize(BaseModel):
+    class_id: str
+    class_name: str
+    avg_time_size: float  # 平均就业时长（天数）
+
+class ResponseModel6(BaseModel):
+    code: int
+    message: str = 'ok'
+    total: int
+    total_pages: int
+    data: List[ClassAvgTimeSize]