Research On Remote Monitoring And Management System Of New Energy Vehicle Based On Data Mining

At present,the remote monitoring and management system of new energy vehicles only displays the collected data,and does not excavate the deep-seated information of the data.Data mining technology can analyze the implicit information between data,which is conducive to enterprise management and further research and development of vehicles.It is an inevitable trend to combine remote monitoring and management system with data mining technology.This thesis mainly studies the construction of the remote monitoring and management system for new energy vehicles.Java language is used to realize the system function,and the data display,positioning,historical trajectory query and other functions of the monitoring and management system are retained.The data mining technology is combined with the monitoring and management system,and the data mining module of the monitoring and management system is taken as the research focus.Data mining module is mainly divided into three aspects : driving style recognition,driving condition recognition and driving behavior comprehensive evaluation.The kmeans algorithm and Gaussian mixture clustering algorithm are used to classify the driver’s driving style and driving conditions,so as to establish the database of driving style and driving conditions.The BP neural network recognition algorithm is implemented in eclipse software using Java language,and the accuracy of the recognition algorithm is verified.The accuracy of the recognition results is about 90 %,indicating that the accuracy of the algorithm is good.According to the BP neural network algorithm and the driving style and driving condition database,the driving style and driving condition of the driver are identified.In the comprehensive evaluation function of driving behavior,the driver’s driving behavior is first identified,and the recognition method of speeding behavior is improved.The recognition of speeding behavior is combined with the recognition of vehicle driving conditions to improve the accuracy of speeding behavior recognition.Then according to the analytic hierarchy process,the subjective weight of driving behavior is given,and the entropy weight method is used to objectively weight it.According to the subjective weight and objective weight results of driving behavior,the comprehensive weight calculation is completed,and the driving behavior of the driver is comprehensively scored.Finally,the display interface of the remote monitoring and management system for new energy vehicles is improved,and the above data mining modules are embedded to complete the overall design of the remote monitoring and management system for new energy vehicles.In addition to the functions of driving data display,positioning and historical trajectory query,the system realizes the functions of driving style recognition,driving condition recognition,driving behavior recognition and comprehensive evaluation of driving behavior.