An OpenHarmony-Based AI-Driven Edge Intelligence Framework for Manufacturing Disruption Monitoring and Resilient Response
DOI:
https://doi.org/10.4108/eetsis.13806Keywords:
Manufacturing resilience, disruption monitoring, edge intelligence, industrial patrol, OpenHarmony, SparkLink SLEAbstract
INTRODUCTION: Delayed awareness of hazards and route restrictions can weaken manufacturing continuity.
OBJECTIVES: To develop an OpenHarmony-based edge-intelligence framework for disruption monitoring and resilient patrol response.
METHODS: A WS63 controller performs local sensing and PID control; SparkLink SLE transmits compact frames to a Jetson Nano running a GSConv- and MSAA-enhanced YOLOv10-N detector; an ArkTS/ArkUI application presents warnings and fallback status.
RESULTS: At 3 m line of sight, SLE achieved 4.8 ± 1.2 ms RTT, 67.1% lower than matched Wi-Fi/UDP and 72.1% lower than BLE. The detector reached 83.96% mAP50, 51.74% mAP50:95, 28.3 FPS, and 3.08 FPS/W. In 500 trials, recognition accuracy was 94.0% and mean alert latency was 97.9 ± 22.6 ms.
CONCLUSION: The prototype supports timely warning and continuity-oriented fallback, but evidence is limited to one robot, short-range line-of-sight tests, and traffic-sign-derived proxy events.
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