Delay Optimization Scheme For 5G Edge Computing Enabling Industrial AR Remote Operation and Maintenance

Abstract

With the advancement of Industry 4.0, augmented reality (AR) technologies face significant latency challenges in remote industrial maintenance due to high-bandwidth data transmission and real-time rendering requirements. This paper proposes a 5G edge computing-enabled latency optimization framework for industrial AR, integrating digital twin-driven predictive mechanisms and dynamic resource orchestration. The architecture employs a three-tier collaborative model (device-edge-cloud), where edge nodes deploy adaptive rendering engines to compress 4K video streams by 67% while maintaining defect recognition accuracy above 99%. A digital twin platform predicts equipment degradation trends 12 hours in advance, triggering pre-loading of AR maintenance guides at edge servers. The scheme introduces a hybrid scheduling algorithm combining deep reinforcement learning and fuzzy logic, achieving 31% latency reduction under dynamic workloads. Validated in a CNC machine tool maintenance scenario, the solution demonstrates 42% end-to-end latency reduction (from 78ms to 45ms) and 37% improvement in cache hit rate compared to conventional cloud-based approaches. The cross-border collaboration case shows a 9x efficiency gain in Germany-China joint troubleshooting via AR spatial annotation. These innovations provide a scalable paradigm for latency-sensitive industrial AR applications, while laying groundwork for 6G-empowered hyper-reliable maintenance systems.