265 lines
12 KiB
Python
265 lines
12 KiB
Python
"""灌入演示数据：顾问 / 老师 / 班级 / 学生 / 成绩 / 就业。
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用法：
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python -m app.scripts.seed_data # 已有数据就跳过
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python -m app.scripts.seed_data --reset # 清空业务数据后重灌
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数据是"设计过"的，不是随机糊上去的：
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* 4 个班、每班 12~16 人，男女比例有差异，方便看 2.6.1 的性别分布；
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* 5 次考核，成绩按"个人基础 + 波动"生成，必然产生：
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- 每次都在线以上的学霸（2.6.2 有结果）
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- 多次不及格的重点关注对象（红线预警有数据）
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- 成绩大起大落的人（2.7.2 波动分析有排名）
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* 就业数据让各班就业率、薪资区间都拉开档次（2.6.3 / 漏斗图有东西看）。
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"""
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from __future__ import annotations
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import argparse
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import random
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from datetime import date, timedelta
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from app.core.database import SessionLocal, engine, ensure_database_exists
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from app.dao.advisor_dao import AdvisorDao
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from app.dao.clazz_dao import ClazzDao
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from app.dao.student_dao import StudentDao
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from app.dao.teacher_dao import TeacherDao
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from app.model import Advisor, Base, Clazz, Employment, Score, Student, Teacher
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from app.service.student_service import StudentService
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random.seed(20260916) # 固定种子，每次灌出来的数据一样，便于对比
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SURNAMES = "赵钱孙李周吴郑王冯陈褚卫蒋沈韩杨朱秦尤许何吕施张孔曹严华金魏陶姜"
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GIVEN = [
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"伟", "芳", "娜", "敏", "静", "强", "磊", "洋", "艳", "勇", "军", "杰", "娟", "涛", "明",
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"超", "秀英", "霞", "平", "刚", "桂英", "文轩", "雨欣", "子豪", "思远", "梓涵", "浩宇",
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"若曦", "嘉怡", "天佑", "梦琪", "俊杰", "欣怡", "家豪", "雅静",
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]
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CITIES = ["广东深圳", "湖南长沙", "江西南昌", "广西南宁", "湖北武汉", "四川成都", "河南郑州", "福建福州", "安徽合肥", "山东济南"]
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SCHOOLS = [
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"深圳职业技术学院", "长沙民政职业技术学院", "江西现代职业技术学院", "南宁职业技术学院",
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"武汉船舶职业技术学院", "成都航空职业技术学院", "黄河水利职业技术学院",
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"福建信息职业技术学院", "安徽机电职业技术学院", "山东商业职业技术学院",
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]
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MAJORS = ["软件技术", "计算机应用技术", "大数据技术", "人工智能技术应用", "计算机网络技术", "数字媒体技术", "电子商务"]
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EDUCATIONS = ["大专", "大专", "大专", "本科", "中专"]
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COMPANIES = [
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"深圳华为技术", "腾讯科技（深圳）", "字节跳动", "广州网易", "杭州阿里巴巴", "比亚迪股份",
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"中兴通讯", "深圳大疆创新", "京东科技", "美团", "小米通讯", "海康威视", "OPPO 广东",
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"深信服科技", "金蝶软件", "用友网络", "软通动力", "中软国际",
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]
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CITY_WORK = ["深圳", "广州", "杭州", "北京", "上海", "成都", "武汉", "东莞"]
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POSITIONS = ["Java 开发工程师", "前端开发工程师", "测试工程师", "大数据开发工程师", "运维工程师", "算法工程师", "产品助理"]
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CLASS_PLAN = [
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{"class_no": "JAVA202601", "name": "Java 就业 2026 一班", "direction": "Java", "capacity": 40, "count": 16},
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{"class_no": "WEB202602", "name": "Web 前端 2026 一班", "direction": "Web", "capacity": 35, "count": 14},
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{"class_no": "BIGDATA202603", "name": "大数据 2026 一班", "direction": "BigData", "capacity": 30, "count": 12},
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{"class_no": "AI202604", "name": "人工智能 2026 一班", "direction": "AI", "capacity": 25, "count": 10},
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]
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TEACHER_PLAN = [
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("陈志远", 1, "高级讲师", "Java", "2019-03-01"),
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("林晓雯", 2, "讲师", "Web 前端", "2020-07-15"),
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("黄建国", 1, "教研组长", "大数据", "2017-09-01"),
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("苏婉清", 2, "讲师", "人工智能", "2021-04-20"),
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("郑海涛", 1, "高级讲师", "Java", "2018-11-05"),
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]
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ADVISOR_PLAN = [
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("李慧敏", 2, "招生一部", "13900000001"),
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("王志强", 1, "招生二部", "13900000002"),
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("周雅丽", 2, "就业服务部", "13900000003"),
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]
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EXAM_NAMES = ["阶段一考试", "阶段二考试", "阶段三考试", "阶段四考试", "结课答辩"]
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def clear_business_data(db) -> None:
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for model in (Employment, Score, Student, Clazz, Teacher, Advisor):
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db.query(model).delete()
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from app.model import class_teachers
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db.execute(class_teachers.delete())
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db.commit()
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print("已清空业务数据（账号保留）")
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def seed(reset: bool = False) -> None:
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ensure_database_exists()
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Base.metadata.create_all(bind=engine)
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with SessionLocal() as db:
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if reset:
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clear_business_data(db)
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elif StudentDao.count(db) > 0:
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print("已有学生数据，跳过灌数据（要重灌请加 --reset）")
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return
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# ---------------- 顾问 ----------------
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advisors = []
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for idx, (name, gender, dept, phone) in enumerate(ADVISOR_PLAN, start=1):
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advisor = Advisor(
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advisor_no=f"A{idx:04d}", name=name, gender=gender, dept=dept, phone=phone,
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email=f"advisor{idx}@wolin.com",
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)
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db.add(advisor)
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advisors.append(advisor)
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db.flush()
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# ---------------- 老师 ----------------
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teachers = []
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for idx, (name, gender, title, subject, hire) in enumerate(TEACHER_PLAN, start=1):
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teacher = Teacher(
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teacher_no=f"T2026{idx:03d}", name=name, gender=gender, title=title, subject=subject,
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hire_date=date.fromisoformat(hire), phone=f"1380000{idx:04d}",
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email=f"teacher{idx}@wolin.com",
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)
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db.add(teacher)
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teachers.append(teacher)
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db.flush()
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# ---------------- 班级 ----------------
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classes = []
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for idx, plan in enumerate(CLASS_PLAN):
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klass = Clazz(
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class_no=plan["class_no"],
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name=plan["name"],
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direction=plan["direction"],
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open_date=date(2026, 3, 2) + timedelta(days=idx * 7),
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close_date=date(2026, 9, 30) + timedelta(days=idx * 7),
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classroom=f"A{201 + idx}",
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capacity=plan["capacity"],
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status=1,
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head_teacher_id=teachers[idx % len(teachers)].id,
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advisor_id=advisors[idx % len(advisors)].id,
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description=f"{plan['direction']} 方向就业班，{plan['count']} 人",
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)
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# 授课老师：本方向 + 一位公共课老师
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klass.teachers = [teachers[idx % len(teachers)], teachers[(idx + 1) % len(teachers)]]
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db.add(klass)
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classes.append(klass)
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db.flush()
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# ---------------- 学生 ----------------
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students: list[Student] = []
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used_names: set[str] = set()
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for c_idx, (klass, plan) in enumerate(zip(classes, CLASS_PLAN)):
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for i in range(plan["count"]):
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while True:
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name = random.choice(SURNAMES) + random.choice(GIVEN)
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if name not in used_names:
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used_names.add(name)
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break
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gender = 1 if random.random() < 0.62 else 2
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age = random.randint(19, 27)
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enroll = date(2026, 3, 2) + timedelta(days=c_idx * 7 + i)
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student = Student(
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stu_no=StudentService.build_stu_no(db, klass.id, enroll),
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name=name,
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gender=gender,
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birth_date=date(2026 - age, random.randint(1, 12), random.randint(1, 28)),
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birth_date_estimated=0,
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native_place=random.choice(CITIES),
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graduate_school=random.choice(SCHOOLS),
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major=random.choice(MAJORS),
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education=random.choice(EDUCATIONS),
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enroll_date=enroll,
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graduate_date=enroll + timedelta(days=random.randint(500, 900)),
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phone=f"1{random.randint(3, 9)}{random.randint(10**8, 10**9 - 1)}",
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class_id=klass.id,
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advisor_id=advisors[(c_idx + i) % len(advisors)].id,
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status=1,
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)
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db.add(student)
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db.flush() # 立刻落库，保证下一个学号能看到它
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students.append(student)
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db.flush()
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# ---------------- 成绩 ----------------
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score_count = 0
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for student in students:
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# 每个人的"基础水平"，决定他是学霸还是重点关注对象
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base = random.gauss(76, 12)
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volatility = random.choice([2, 3, 4, 6, 9, 13]) # 有人稳、有人大起大落
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for seq in range(1, 6):
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value = max(20.0, min(100.0, random.gauss(base, volatility)))
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if random.random() < 0.06: # 偶尔缺考/失手
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value = max(20.0, value - random.randint(15, 30))
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db.add(
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Score(
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stu_id=student.id,
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exam_seq=seq,
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exam_name=EXAM_NAMES[seq - 1],
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exam_date=date(2026, 3, 2) + timedelta(days=seq * 45 + random.randint(-3, 3)),
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score=round(value, 1),
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flag=1 if value < 60 else 0,
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remark="系统生成" if value < 60 else None,
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)
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)
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score_count += 1
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db.flush()
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# ---------------- 就业 ----------------
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emp_count = 0 # 已拿到 offer
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open_count = 0 # 只开放了就业、还没 offer
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OFFER_RATE = [0.88, 0.79, 0.67, 0.50] # 就业率按班级拉开差距：Java 班最猛，AI 班最慢
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SALARY_BASE = [13500, 11500, 14000, 15500]
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for student in students:
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klass_idx = next(i for i, c in enumerate(classes) if c.id == student.class_id)
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rate = OFFER_RATE[klass_idx % len(OFFER_RATE)]
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if random.random() > rate:
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# 没就业的学生里，一部分"已开放就业"，状态推到"进入就业"
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if random.random() < 0.6:
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open_d = date(2026, 9, 30) + timedelta(days=random.randint(0, 20))
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db.add(Employment(stu_id=student.id, class_id=student.class_id, open_date=open_d))
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student.status = 2
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open_count += 1
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continue
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open_d = date(2026, 9, 30) + timedelta(days=random.randint(0, 25))
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wait = random.randint(5, 70)
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offer_d = open_d + timedelta(days=wait)
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# 薪资：Java/大数据偏高，AI 班样本少但更极端
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salary_base = SALARY_BASE[klass_idx % len(SALARY_BASE)]
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salary = max(6000, int(random.gauss(salary_base, 3200)))
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if random.random() < 0.08:
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salary = int(salary * random.uniform(1.3, 1.6)) # 少数高薪 offer
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db.add(
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Employment(
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stu_id=student.id,
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class_id=student.class_id,
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open_date=open_d,
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offer_date=offer_d,
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company=random.choice(COMPANIES),
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salary=salary,
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position=random.choice(POSITIONS),
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city=random.choice(CITY_WORK),
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remark="就业老师推荐" if random.random() < 0.3 else None,
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)
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)
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student.status = 3
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emp_count += 1
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if random.random() < 0.75:
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# 拿到 offer 的学生大多有对应阶段的成绩
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student.graduate_date = offer_d + timedelta(days=random.randint(20, 60))
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db.commit()
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print("演示数据灌入完成：")
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print(f" 顾问 {len(advisors)} 人")
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print(f" 老师 {len(teachers)} 人")
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print(f" 班级 {len(classes)} 个")
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print(f" 学生 {len(students)} 人")
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print(f" 成绩 {score_count} 条")
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print(f" 就业记录 {emp_count + open_count} 条（已拿 offer {emp_count} / 仅开放就业 {open_count}）")
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="灌入沃林学生管理系统演示数据")
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parser.add_argument("--reset", action="store_true", help="先清空业务数据再灌")
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args = parser.parse_args()
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seed(reset=args.reset)
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