Design And Development Of A New On-board Weighing System

With the continuous increase of traffic volume,freight carriers tend to reduce transportation costs and improve transportation efficiency by overloading,which makes the phenomenon of overloading very common.Therefore,the relevant institutions have strengthened the research on weighing system.At present,the commonly used weighing systems are divided into static weighing and dynamic weighing.Although static weighing has high weighing accuracy,it needs to stop the vehicle to be measured,so it takes a long time and has low efficiency.The dynamic weighing improves weighing efficiency,the accuracy of measurement cannot be effectively guaranteed.In view of the shortcomings of the existing weighing system,this paper designs and develops a new on-board weighing system based on three-stage linear transfer.The construction methods of vehicle weighing prediction models under static and dynamic conditions were studied respectively.The software and hardware of the on-board weighing system were designed and developed,and the cloud platform was built to complete data receiving and calculation of vehicle load.The specific research contents are described as follows:First of all,a construction method of on-board weighing prediction model based on three-level linear transfer is proposed.The static simulation analysis of three types of leaf springs of semi-trailer(front axle of tractor,rear axle of tractor and rear axle of trailer)is carried out,and the linear relationship curve between load and displacement of leaf spring is obtained.Based on the three-level linear transmission relationship of leaf spring,soft spring and parallel beam sensor,the load bearing prediction model of a single leaf spring is constructed,and then the vehicle weight prediction model is constructed.The validity of the weight prediction model is verified by experiments.Secondly,a dynamic vehicle weighing prediction model based on Kalman filter and BP neural network is constructed.The dynamic characteristics of leaf springs of semi-trailer are simulated and analyzed,and the nonlinear relationship between the load and displacement of the leaf spring is obtained.Based on Trucksim,a parameterized vehicle model is built and its dynamic simulation analysis is carried out.The data of leaf spring displacement,vehicle acceleration and driving speed are obtained.The Kalman filter algorithm is used to filter and pre-process leaf springs displacement,and the dynamic weighing prediction model based on single and double hidden layer BP neural network were construct.The influence of different driving speed and acceleration corresponding to different road grade on vehicle dynamic load is studied.And then,the software and hardware of on-board weighing system are designed and developed.The hardware structure and work flow of the data acquisition terminal and the vehicle weighing terminal in on-board weighing system are introduced.The components required for the data acquisition terminal and the vehicle weighing terminal was select,and the corresponding hardware circuit is designed and the PCB is drawn.The software integrated development environment is built,and the program compilation and debugging are completed.Finally,the on-board weighing cloud platform is designed and developed.The structure and data processing flow of the cloud platform are introduced.Based on TCP/IP protocol,a server is built to receive the data of vehicle weighing terminal,and the received data is stored in Redis database.A neural network model is built based on Tensor Flow architecture to predict the displacement of leaf springs.The load calculation is completed in the calculation module and write it into the My SQL database.By adding data sources in Grafana,the weighing results are synchronized on the visualization interface.The new on-board weighing system designed in this paper can be directly installed on the vehicle,making the whole weighing process simple and improving the weighing efficiency.The on-board weighing system can solve the problem of overloading and exceeding the limit of vehicles from the source and enable relevant departments to effectively manage vehicles.