Research on Adaptive Segmentation and Collaborative Computing of Edge Intelligence Model in Power Edge Gateway

This paper proposes an edge intelligence model adaptive segmentation and collaborative computing method for power edge gateways to improve computing efficiency and resource utilization. First, an adaptive segmentation algorithm is studied and designed to effectively optimize the computational overhead and delay by adjusting the segmentation strategy of the model in real time. Next, the design of the collaborative computing framework realizes efficient data interaction between edge nodes, and uses advanced data exchange protocols to improve the reliability and speed of communication between nodes. Through empirical analysis, it has been verified that the model has significant performance improvements in application scenarios such as real-time fault detection, load forecasting, and smart meter reading analysis. Compared with traditional methods, the performance improvement rate reaches more than 20%. Experimental results show that the model proposed in this paper has high computational efficiency, resource utilization and task response capabilities, and can adapt to diverse power system application requirements. In summary, this article provides theoretical and practical support for the application of edge intelligence in power gateways and verifies its effectiveness in practical application scenarios.