Aiming at the requirements of massive equipment in the new power system for fine operation and maintenance control,high-frequency data acquisition,high-speed data transmission,and low-latency data processing,this paper firstly presents the cloud-edge collaborative data processing framework for intelligent operation and maintenance of power grid production equipment.Secondly,a cloud-edge collaborative data processing model is constructed to optimize the weighted sum of cloud-edge collaborative data processing delay and device data queue backlog.Finally,an optimization algorithm for cloud-edge collaborative data processing based on processing delay and backlog aware matching is proposed.The proposed algorithm constructs the preference list based on processing delay and backlog awareness and solves the resource competition problem by the iterative optimization of cloud-edge data processing matching.Simulation results show that compared with PDPRA and CSA algorithms,the proposed algorithm improves data processing delay by7.26%and12.18%,and reduces data queue backlog by 11.25%and 13.41%.The weighted sum of the data processing delay and the data queue backlog under the proposed algorithm can be effectively reduced in the edge server burst computation task and large-scale device access scenarios.It can meet the real-time data processing requirements of intelligent operation and maintenance for power grid production equipment.
关键词
电网生产设备/智能运维/云边协同数据处理/时延和积压感知/匹配降维
Key words
power grid production equipment/intelligent operation and maintenance/cloud-edge collaborative data processing/delay and backlog awareness/dimensionality reduction of matching
维普
万方数据