摘要
当前国内疫情逐步转向了常态化防控,各高校开学后将严格实施封闭式管理。利用智慧校园中各应用系统中的学生状态数据,通过大数据分析技术,对学生不在校状态进行识别,能有效提升学校的安全管理效率。首先确立学生在校状态的数据源,并对其进行数据治理;其次建立学生不在校状态的判定模型;最后制定不在校学生的预警策略,系统按照预警机制自动输出不在校学生名单,可用于疫情期间高校对学生的安全管理。
The current domestic epidemic has gradually shifted to normalized prevention and control,and universities will strictly implement closed management after the opening of the school year.Using the student status data from each application system in the smart campus,the identification of student absence through big data analysis technology can effectively improve the safety management efficiency of the school.Firstly,the data source of the student’s school status is established and data governance is carried out;secondly,the judgment model of the student’s non-school status is established;finally,the early warning strategy of the non-school students is formulated,and the system automatically outputs the list of students out of school according to the early warning mechanism,which can be used for the safety management of students in colleges and universities during the epidemic period.
作者
涂旭东
蒲飞
赵正辉
陈苗
Tu Xudong;Pu Fei;Zhao Zhenghui;Chen Miao(Chongqing Medical College,Chongqing,401331)
出处
《电子测试》
2020年第12期62-64,共3页
Electronic Test
基金
重庆市科技局技术创新与应用发展专项重点项目(cstc2019jscx-dxwtBX0013-1)
重庆市教委科学技术研究重点项目(KJZD-K201902802)
重庆市教育科学“十三五”规划项目(2018-GX-462)
重庆医药高等专科学校自然科学基金项目(ygz2017110)。
关键词
疫情期间
大数据
不在校预警系统
安全管理
outbreak period
big data
early warning of absence from school
security management
