摘要
由于现行的数据共享平台响应性能与数据安全性能较差,因此提出了双碳背景下基于机器学习的工业互联网大数据共享平台设计。采用面向服务的架构(Service Oriented Architecture,SOA)模式搭建平台,在系统软件方面建立数据集成中心,以实现工业互联网大数据共享平台的设计。实验证明,当共享数据量为700 GB时,平台响应时间仅为0.41s。当运行时间为70h时,平台丢包率没有超过0.5%。在多次实验下,所设计平台的最高入侵率未超过1%,提高了共享的安全性。因此,设计平台数据共享服务具有良好的工业互联网大数据共享效果。
Due to the poor response and data security performance of the current data sharing platform,the design of the industrial internet big data sharing platform based on machine learning under the dual-carbon background is proposed.The platform is built using the Service Oriented Architecture(SOA) model,and the data integration center is established in the system software to realize the design of the big data sharing platform of the industrial internet.Experiments show that when the amount of shared data is 700 GB,the platform response time is only 0.41 s.When the running time is 70 hours,the packet loss rate of the platform does not exceed 0.5%.In many experiments,the maximum intrusion rate of the designed platform is less than 1%,which improves the security of sharing.Therefore,the data sharing service of the design platform has a good effect on big data sharing of the industrial internet.
作者
丁洪朋
崔建军
管亚威
翟梽锦
高广慧
DING Hongpeng;CUI Jianjun;GUAN Yawei;ZHAI Zhijin;GAO Guanghui(Huanghe Jiaotong University,Jiaozuo Henan 454950,China;Henan Engineering Technology Research Center on Intelligent Manufacturing Technology and Equipment,Jiaozuo Henan 454950,China)
出处
《信息与电脑》
2022年第23期126-128,共3页
Information & Computer
基金
黄河交通学院2022年度校级“双碳”专项课题(项目编号:HhjtZX2022-14)。
关键词
双碳背景
机器学习
互联网
大数据
共享平台
double carbon background
machine learning
internet
big data
sharing platform
