Edge-enhanced AR-AI framework for intelligent terminal-assisted field operation and equipment management in power distribution networks

Abstract

This paper presents an edge-enhanced AR–AI framework for intelligent terminal-assisted field operation and equipment management in power distribution networks. The proposed system combines augmented reality (AR) interfaces with distributed edge computing and lightweight artificial intelligence (AI) models to achieve real-time situational awareness and context-sensitive decision-making. Unlike traditional centralized or cloud-dependent AR systems, the framework leverages terminal-level processing and cross-node model coordination to support low-latency diagnostics, adaptive maintenance, and predictive equipment monitoring. A hybrid inference pipeline enables continuous synchronization between on-site terminals and edge servers, effectively balancing computational load and communication overhead. Extensive experimental results demonstrate that the proposed approach enhances task responsiveness, reliability, and data privacy while maintaining scalability in dynamic network environments. These findings highlight the potential of integrating edge intelligence and AR technologies to advance the digital transformation of power distribution operations.