基于核密度估计的电力物联网并发业务计算负荷建模与任务分配策略  

Load Modeling and Task Allocation Strategy of Concurrent Business Computing in Power Internet of Things Based on Kernel Density Estimation

作　　者：匡佩 刘文泽[1] 岑伯维 屈径 蔡泽祥[1] 康逸群 Kuang Pei;Liu Wenze;Cen Bowei;Qu Jing;Cai Zexiang;Kang Yiqun(School of Electric Power Engineering,South China University of Technology,Guangzhou Guangdong 510640,China)

机构地区：[1]华南理工大学电力学院,广东广州510640

出　　处：《电气自动化》2024年第2期7-10,共4页Electrical Automation

基　　金：国家自然科学基金项目(51577073);广东省重点领域研发计划(2019B111109002)。

摘　　要：在电力物联网背景下大量并发业务给边缘计算终端处理能力带来了挑战。为此,提出了基于核密度估计的电力物联网并发业务计算负荷建模与任务分配策略。基于核密度估计理论建立了并发业务覆盖等级及计算负荷模型,根据业务覆盖等级决策边缘计算终端的资源配置,以最小化处理延时为目标决策云边协同的任务分配,以电动汽车有序充电业务为例进行仿真分析。结果表明,所提模型和方法能提升系统整体计算资源使用效率、降低业务延时,提高电力物联网应对并发业务处理需求的能力。In the context of the Power Internet of Things,a large number of concurrent services pose a challenge to the processing capacity of edge computing terminals.Therefore,a computing load modelling and task allocation strategy for concurrent services in the Power Internet of Things based on kernel density estimation was proposed.Based on the kernel density estimation theory,a model of concurrent service coverage level and computing load was established.According to the service coverage level,the resource allocation of edge computing terminals was determined,and the task allocation of cloud side collaboration was determined with the goal of minimizing processing delay.The orderly charging service of electric vehicles was taken as an example for simulation analysis.The results show that the proposed model and method can improve the overall efficiency of the system computing resources,reduce the business delay,and improve the ability of the Power Internet of Things to deal with concurrent business processing requirements.

关 键 词：电力物联网 核密度估计 计算负荷 任务分配 有序充电 

分 类 号：TM727[电气工程—电力系统及自动化]