import json from sqlalchemy.orm import Session from database import engine, SessionLocal, Base import models import main # 导入原有假数据 import logging # 设置日志 logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # 默认仪表盘数据（如果main.py中没有定义） default_dashboard_data = { "paperCount": 3500, "patentCount": 2000, "highImpactPapers": 100, "keyProjects": 50, "fundingAmount": "500万元", "researcherStats": { "academician": 12, "chiefScientist": 28, "distinguishedProfessor": 56, "youngScientist": 120 }, "newsData": [ {"title": "我校科研团队在量子计算领域取得重大突破", "date": "2023-05-15"}, {"title": "张教授团队论文被Nature收录", "date": "2023-04-28"}, {"title": "校长率团访问美国麻省理工学院商讨合作事宜", "date": "2023-04-15"}, {"title": "我校获批3项国家重点研发计划", "date": "2023-03-20"}, {"title": "2023年度国家自然科学基金申请工作启动", "date": "2023-02-10"} ] } # 创建所有表 def create_tables(): Base.metadata.create_all(bind=engine) logger.info("数据库表已创建") # 导入用户数据 def import_users(db: Session): # 检查是否已存在用户 existing_users = db.query(models.User).count() if existing_users == 0: for username, user_data in main.fake_users_db.items(): db_user = models.User( username=user_data["username"], email=user_data.get("email"), full_name=user_data.get("full_name"), hashed_password=user_data["hashed_password"], disabled=user_data.get("disabled", False) ) db.add(db_user) db.commit() logger.info("用户数据已导入") else: logger.info("用户数据已存在，跳过导入") # 导入人才数据 def import_talents(db: Session): # 检查是否已存在人才数据 existing_talents = db.query(models.Talent).count() if existing_talents == 0: for talent_data in main.talents: # 处理evaluationData字段 - 确保是JSON格式 evaluation_data = talent_data.get("evaluationData") db_talent = models.Talent( id=talent_data["id"], name=talent_data["name"], gender=talent_data.get("gender"), birthDate=talent_data.get("birthDate"), title=talent_data.get("title"), position=talent_data.get("position"), education=talent_data.get("education"), address=talent_data.get("address"), academicDirection=talent_data.get("academicDirection"), talentPlan=talent_data.get("talentPlan"), officeLocation=talent_data.get("officeLocation"), email=talent_data.get("email"), phone=talent_data.get("phone"), tutorType=talent_data.get("tutorType"), papers=talent_data.get("papers"), projects=talent_data.get("projects"), photo=talent_data.get("photo"), eduWorkHistory=talent_data.get("eduWorkHistory"), researchDirection=talent_data.get("researchDirection"), recentProjects=talent_data.get("recentProjects"), representativePapers=talent_data.get("representativePapers"), patents=talent_data.get("patents"), evaluationData=evaluation_data ) db.add(db_talent) db.commit() logger.info("人才数据已导入") else: logger.info("人才数据已存在，跳过导入") # 导入工程研究中心数据 def import_labs(db: Session): # 检查是否已存在工程研究中心数据 existing_labs = db.query(models.Lab).count() if existing_labs == 0: for lab_data in main.labs: # 处理evaluationData字段 - 确保是JSON格式 evaluation_data = lab_data.get("evaluationData") db_lab = models.Lab( id=lab_data["id"], name=lab_data["name"], personnel=lab_data.get("personnel"), nationalProjects=lab_data.get("nationalProjects"), otherProjects=lab_data.get("otherProjects"), achievements=lab_data.get("achievements"), labAchievements=lab_data.get("labAchievements"), image=lab_data.get("image"), score=lab_data.get("score"), evaluationData=evaluation_data ) db.add(db_lab) db.commit() logger.info("工程研究中心数据已导入") else: logger.info("工程研究中心数据已存在，跳过导入") # 导入仪表盘数据 def import_dashboard(db: Session): # 检查是否已存在仪表盘数据 existing_dashboard = db.query(models.DashboardData).count() if existing_dashboard == 0: # 优先使用main.py中的数据，如果不存在则使用默认数据 dashboard_data = getattr(main, "dashboard_data", default_dashboard_data) # 创建仪表盘记录 db_dashboard = models.DashboardData( id=1, # 主键ID设为1 paperCount=dashboard_data["paperCount"], patentCount=dashboard_data["patentCount"], highImpactPapers=dashboard_data["highImpactPapers"], keyProjects=dashboard_data["keyProjects"], fundingAmount=dashboard_data["fundingAmount"], researcherStats=dashboard_data["researcherStats"] ) db.add(db_dashboard) db.flush() # 立即写入数据库，获取ID # 创建新闻数据记录 for news_item in dashboard_data["newsData"]: db_news = models.News( title=news_item["title"], date=news_item["date"], dashboard_id=db_dashboard.id ) db.add(db_news) db.commit() logger.info("仪表盘和新闻数据已导入") else: logger.info("仪表盘数据已存在，跳过导入") # 导入默认维度 def import_dimensions(db: Session): # 人才评估维度 talent_dimensions = [ {"name": "学术成果", "weight": 1.0, "category": "talent", "description": "包括论文发表、专著、专利等"}, {"name": "科研项目", "weight": 1.0, "category": "talent", "description": "承担的科研项目数量和级别"}, {"name": "人才引进", "weight": 1.0, "category": "talent", "description": "引进的人才数量和质量"}, {"name": "学术影响力", "weight": 1.0, "category": "talent", "description": "学术引用和影响力指标"}, {"name": "教学质量", "weight": 1.0, "category": "talent", "description": "教学评估和学生反馈"}, {"name": "社会服务", "weight": 1.0, "category": "talent", "description": "社会服务和贡献"} ] # 工程研究中心评估维度 lab_dimensions = [ {"name": "科研产出", "weight": 1.0, "category": "lab", "description": "工程研究中心科研成果产出"}, {"name": "人才培养", "weight": 1.0, "category": "lab", "description": "培养的研究生和博士后数量"}, {"name": "项目承担", "weight": 1.0, "category": "lab", "description": "承担的科研项目数量和级别"}, {"name": "设备利用", "weight": 1.0, "category": "lab", "description": "设备利用率和效益"}, {"name": "学术交流", "weight": 1.0, "category": "lab", "description": "国内外学术交流和合作"}, {"name": "社会服务", "weight": 1.0, "category": "lab", "description": "社会服务和贡献"} ] # 检查是否已存在维度 existing_dimensions = db.query(models.Dimension).count() if existing_dimensions == 0: # 添加人才评估维度 for dim in talent_dimensions: db_dimension = models.Dimension(**dim) db.add(db_dimension) # 添加工程研究中心评估维度 for dim in lab_dimensions: db_dimension = models.Dimension(**dim) db.add(db_dimension) db.commit() logger.info("默认评估维度已导入") else: logger.info("评估维度已存在，跳过导入") # 主函数 def init_db(): # 创建表结构 create_tables() # 获取数据库会话 db = SessionLocal() try: # 导入各类数据 import_users(db) import_talents(db) import_labs(db) import_dashboard(db) import_dimensions(db) logger.info("数据库初始化完成！") except Exception as e: logger.error(f"数据库初始化失败: {e}") finally: db.close() # 直接运行脚本时执行初始化 if __name__ == "__main__": init_db()