电力物联网边缘智能:概念、架构、技术及应用

Edge Intelligence to Power Internet of Things:Concept,Architecture,Technology and Application

近年来,随着传感器、采集装置、感知终端的规模化部署,以及人工智能、5G、北斗等新技术的融合应用,智能巡检、在线监测、需求响应等电力物联网应用产生海量感知数据,数据上传至云端服务器会占用大量通信带宽,为网络通道和云端资源带来巨大压力,处理分析的实时性与时效性也不满足应用要求。为解决上述问题,考虑将边缘计算和人工智能赋予电力物联网,电力物联网边缘智能技术应运而生。电力物联网边缘智能通过在边缘侧嵌入人工智能算法,在靠近数据产生源处对数据进行预处理、本地计算、推理研判,从而减少上传到云端的通信带宽需求,降低传输时延和传输功耗,为上述问题的解决提供一种有效技术路径。首先,阐释电力物联网边缘智能的概念与演进,提出电力物联网边缘智能体系架构;其次,从边缘侧芯片、边缘计算操作系统、边缘计算框架3个层次分析电力物联网边缘智能软硬件基础,同时从云边协同、模型压缩、模型加速、群体智能、联邦学习5个方面讨论电力物联网边缘智能关键技术;然后,从“发输变配用”5个环节探讨电力物联网边缘智能应用场景;最后,分析电力物联网边缘智能应用的机遇和挑战。

In recent years,with the large-scale deployment of sensors,acquisition devices,and perception terminals,as well as the integration of new technologies such as Artificial Intelligence,5G,and BeiDou,there are many intelligent applications in Power Internet of Things(PIoT),such as intelligent inspection,online monitoring,and demand response.These applications have generated massive amounts of sensory data.Uploading the data to cloud servers consumes a significant amount of communication bandwidth,placing immense pressure on network channels and cloud resources.Moreover,the data process cannot meet the real-time requirements of these applications.To address these issues,the concept of edge intelligence to PIoT has emerged.By combining edge computing with artificial intelligence,edge intelligence to PIoT uses AI algorithms to perform preprocessing,local computation,and inference near the data source.This approach reduces the communication bandwidth,and decreases transmission latency and power consumption.Edge Intelligence to PIoT provides an effective solution to the above problems.This article first explains the concept and evolution of edge intelligence to PIoT.Then,the architecture of edge intelligence in smart grid IoT is proposed,and the hardware and software foundations of edge intelligence to PIoT are analyzed from three levels:edge-side AI chips,edge computing operating systems,and edge computing frameworks.Next,key technologies in edge intelligence to PIoT are discussed from five aspects:cloud-edge collaboration,model compression,model acceleration,swarm intelligence,and federated learning.Furthermore,application scenarios for edge intelligence to PIoT are explored in the five aspects of“generation,transmission,transformation,distribution,and utilization”.Finally,the opportunities and challenges of edge intelligence to PIoT are analyzed.