基于分层贝叶斯自适应稀疏性的多目标任务资源分配方法,用于低压电站的边缘计算

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS IET Cyber-Physical Systems: Theory and Applications Pub Date : 2023-07-03 DOI:10.1049/cps2.12067
Yupeng Liu, Bofeng Yan, Jia Yu
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引用次数: 0

摘要

为了实现更高效、更优化的资源调度,本研究开展了基于分层贝叶斯自适应稀疏性的低压站边缘计算多目标任务资源分配方法。基于分层贝叶斯自适应稀疏性,建立了低压站边缘计算的多目标任务资源分配技术框架,该框架由终端管道边缘云组成;在架构端采集配电设备、变电站终端、输电终端等的实时运行数据后,传输到终端管道边缘云。在架构端采集配电设备、变电站终端、输电终端等实时运行数据后,通过边缘物联网代理传输到云端物联网管理平台的数据中间平台和服务中间平台;设置并求解约束条件,建立多类型柔性负荷分层优化分配模型;利用低压台区边缘计算多目标任务资源异常区域拓扑识别子模块,识别当前低压台区异常区域拓扑;以此为输入,分配边缘计算多目标任务资源,在差分进化算法下实现低压台区边缘计算多目标任务资源分配方法。实验结果表明,所提方法收敛效果好,分配能力强,能耗增加相对平缓,计算结果与实际值基本一致,具有较好的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Multi objective task resource allocation method based on hierarchical Bayesian adaptive sparsity for edge computing in low voltage stations

In order to achieve more efficient and optimised resource scheduling, this research carried out a multi-objective task resource allocation method for low-voltage station edge computing based on hierarchical Bayesian adaptive sparsity. Based on hierarchical Bayesian adaptive sparsity, the multi-objective task resource allocation technical framework for edge computing in low-voltage stations is established, which is composed of end pipe edge cloud; After collecting real-time operation data of power distribution equipment, substation terminals, transmission terminals, etc. in the architecture end, it is transmitted to the data middle platform and service middle platform of the Internet of Things management platform in the cloud through the edge Internet of Things agent; Set and solve the constraint conditions, and build a multi type flexible load hierarchical optimal allocation model; The abnormal area topology identification sub module of multi-objective task resource of low-voltage station area edge computing is used to identify the abnormal area topology of the current low-voltage station area; Taking it as input, the multi-objective task resources of edge computing are allocated, and the multi-objective task resources allocation method of edge computing in low pressure platform area is realized under the differential evolution algorithm. The experimental results show that the proposed method has good convergence effect, strong distribution ability, relatively gentle increase in energy consumption, and the calculated results are basically consistent with the actual values, with good effectiveness.

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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
期刊最新文献
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