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