(untitled) (International Journal of Communication Systems and Network Technologies, 2025)
To address the bottlenecks of hidden and sudden structural damage to highway bridges under long-term heavy traffic, the inability of traditional central cloud monitoring to process massive amounts of vehicle-mounted mobile data in real time, and the difficulty in multi-source heterogeneous data fusion, this paper constructs a health monitoring and vehicle-mounted intelligent operation and maintenance system driven by edge computing and joint clustering. First, a three-layer collaborative architecture of "vehicle-roadside-edge cloud" is designed. Then, a distributed multi-view joint clustering algorithm is proposed, simultaneously optimizing bidirectional clustering of sample and sensor features, and exchanging centroids only through federated averaging between edge nodes. Finally, a vehicle-mounted terminal is developed to map damage level and location in real time to graded early warning and maintenance suggestions. Experiments show that the anomaly detection accuracy is 94.7%, alarm latency is 87ms, transmission volume is 0.41MB, a reduction rate of over 99.6%, and strong noise robustness. The integration of edge computing and joint clustering solves the problems of real-time performance and data silos, achieving low-latency and high-precision vehicle-mounted structural health operation and maintenance.