摘要
随着5G、人工智能等技术的不断发展,工业大数据通过边缘计算提供终端智能服务的需求愈加强烈。笔者首先分析了工业大数据服务面临的技术挑战以及企业、产业链发展的特殊需求,进而对工业大数据及模型进行全生命周期管理和精简优化,最后从平台开发管理、部署运营服务、产业技术发展3个方面提出实施策略。
With the continuous development of 5G,artificial intelligence and other technologies,the demand for industrial big data to provide terminal intelligent services through edge computing becomes more and more intense.The author first analyzes the technical challenges faced by the industrial big data service and the special needs of the development of enterprises and industrial chains,then carries out the whole life cycle management and simplification optimization of the industrial big data and models,and finally puts forward the implementation strategies from the three aspects of platform development management,deployment of operation services and industrial technology development.
作者
孙立
Sun Li(School of Economics and Management,Yancheng Institute of Technology,Yancheng Jiangsu 224051,China)
出处
《信息与电脑》
2020年第10期133-135,共3页
Information & Computer
基金
教育部人文社会科学研究青年基金资助项目“面向智能制造产业一体化的新一代信息技术赋能机理与实施路径研究——以长三角为例”(项目编号:20YJC790121)
全国统计科学研究项目“工业大数据环境下多源异构数据融合与应用研究”(项目编号:2018LY98)。
关键词
边缘智能
工业大数据
“云—边—端”产业协同框架
数字孪生
edge intelligence
industrial big data
“cloud side end”industrial coordination framework
digital twins
