摘要
为提升数据中心机房设备耗电监控效果及可视化监控系统的实时性,提出基于数字孪生的数据中心机房设备耗电可视化监控系统设计。首先,在数字孪生技术的基础上完成系统架构及功能的设计;其次,构建源数据矩阵,计算矩阵的欧氏距离,并通过K-means聚类算法实现设备耗电的聚类处理;最后,利用数字孪生技术将映射真实物理车间到虚拟车间,引入孪生实体概念表达机房部件,融合数据中心机房设备故障数据,构建网络神经模型训练样本数据,生成新的故障诊断模型,实现基于数字孪生的数据中心机房设备耗电可视化监控。结果表明,所提方法的数据中心机房设备耗电监控系统的可视化效果好、设备耗电监控结果好,可为数据管理提供可靠的技术支撑。
To improve the power consumption monitoring effect of data center equipment and the real-time performance of the visual monitoring system,this article proposes a design of a data center equipment power consumption visual monitoring system based on digital twins.Firstly,based on digital twin technology,complete the design of system architecture and functions.Secondly,construct a source data matrix,calculate the Euclidean distance of the matrix,and use the K-means clustering algorithm to achieve clustering processing of device power consumption.Finally,using digital twin technology to map the real physical workshop to the virtual workshop,the concept of twin entity is introduced to express the components of the data center equipment,fuse the fault data of the data center equipment,construct a network neural model to train sample data,generate a new fault diagnosis model,and achieve visual monitoring of power consumption of data center equipment based on digital twin.The results show that the proposed method has good visualization effect and equipment power consumption monitoring results in the data center equipment power consumption monitoring system,which can provide reliable technical support for data management.
作者
申子明
SHEN Zi-ming(Information and Electronic Engineering College,Lu'an Vocational Technical College,Anhui Lu'an 232001,China)
出处
《齐齐哈尔大学学报（自然科学版）》
2024年第2期45-50,共6页
Journal of Qiqihar University(Natural Science Edition)
基金
2020年度安徽省高校优秀人才支持计划项目“基于区块链的分布式系统研究与应用”(gxyqZD2020061)。
关键词
数字孪生
数据中心机房
可视化监控
系统构架
虚拟模型
digital twins
data center data room
visual monitoring
system architecture
virtual model
