Relying on its technology and deployment advantages at the edge of the network, edge computing can collect the running status of terminal equipment in real time, and complete the upload of data to the cloud platform on demand, effectively alleviating the working pressure of the cloud processor and reducing the delay of real-time data return. However, the inherent characteristics of a single edge node with limited resources may still be unable to meet the delay requirements of large-scale accessed new power businesses. In this regard, this paper studies the workload distribution strategy that meets the lower service delay. Considering the network delay and calculation delay, a workload distribution model based on multiple edge computing nodes is constructed to minimize the service delay in the new power system scenario. In addition, in order to further reduce the service delay of multi-service terminals, a workload distribution mechanism has been developed. In the initialization phase, the task balancing algorithm is used to improve the workload balance among edge nodes. In order to effectively ensure the rationality of resource allocation, an improved particle swarm optimization algorithm is adopted to realize the reasonable allocation of CPU resources of edge nodes. Based on the semi-definite planning algorithm, network resources are allocated more effectively. Finally, the simulation experiment shows that the allocation strategy proposed in this paper is more effective.