Vehicle Load Monitoring Method Based on NBIOT

Vehicle load monitoring plays an important role in vehicle safety and vehicle scheduling optimization. In order to ensure the basic safety of vehicle operation and improve the level of traffic management, real-time monitoring of the load of transport vehicles is needed. In this paper, structural mechanics is used for vehicle load detection, and a monitoring method based on Narrow Band Internet of Things (NBIOT) is designed. Firstly, aiming at the problem of low accuracy of existing self-loading detection methods, a new detection method is proposed. The structural mechanics analysis of vehicle leaf spring is carried out, and the corresponding relationship between leaf spring deformation and vehicle load is obtained; Secondly, in order to suit the load conditions of different models, the radial basis function (RBF) neural network is used to calibrate the load on the cloud server, and in most cases, the fitting error is less than 0.6%; Finally, load monitoring is implemented through NBIOT and WeChat mini program. The load monitoring method proposed in this paper has the characteristics of simple sensor installation, no damage to the vehicle structure, good real-time performance, low application cost and wide coverage. The method plays an important role in further improving the level of intelligent traffic.