摘要
随着高校后勤信息化建设推进,资源节约集约型的智慧化后勤建设成为今后的研究方向。目前,高校智慧后勤建设以基础硬件设备建设为主。设备有效管控、风险预测等方面还需要进一步探索。文章通过智慧后勤综合管理平台采集实时数据,提出一种集成学习模型,结合常用的梯度学习算法,使用BPTT算法对高校智慧后勤风险进行有效预测。
With the advance of logistics information construction in colleges and universities,resource⁃conserving and intensive intelligent logistics construction has become the research direction in the future.At present,the construction of intelligent logistics in colleges and universities mainly focuses on the construction of basic hardware equipment.The effective control of equipment and risk prediction need to be further explored.This paper collects real⁃time data through the intelligent logistics integrated management platform,and proposes an integrated learning model.Combined with the commonly used gradient learning algorithm,BPTT algorithm is used to effectively predict the risk of intelligent logistics in colleges and universities.
作者
张军
刘亚茹
Zhang Jun;Liu Yaru(Henan Vocational College of Water Conservancy and Environment,Zhengzhou 450008,China)
出处
《无线互联科技》
2022年第10期25-27,共3页
Wireless Internet Technology
基金
2022年河南省重点研发与推广专项科技攻关支持项目,项目名称:基于5G的高校后勤服务智能化风险预测与处置关键技术研究与应用,项目编号:222102320062
河南水利与环境职业学院院内课题,项目名称:信息技术在职业院校后勤管理中的应用研究,项目编号:SHKYXM2130。
关键词
管理平台
集成学习
BPTT算法
management platform
integrated learning
BPTT algorithm
