Analysis On The Driving Cycle Of Trucks Based On Big Data Technology

In recent years,excessive consumption of petroleum resources and serious environmental pollution have all pointed to the transportation industry.However,with the development of e-commerce and trade,the demand for logistics transportation in China has greatly increased.In order to improve the operational efficiency of trucks,logistics and transportation companies need scientific management methods,and need to analyze a large number of real-life driving cycle of trucks.At the same time,in order to reduce the energy consumption of trucks and the emission of pollutants,auto manufacturers have increased the research and development of new energy vehicles,and the analysis of the driving cycle of trucks is the basis for the development and evaluation of new models.The research in this paper is based on the actual project of a car production company.It is of practical significance and research value to analyze the driving cycle of the truck using big data technology.The main content of this study:First of all,it consults a large number of domestic and foreign relevant literatures,and summarizes the current status of domestic and international analysis of driving cycle,including,the analysis of driving cycle in different regions,the analysis of driving cycle of different types of vehicles,the analysis of driving cycle using different data collection methods and the analysis of driving cycle using different analysis techniques.According to these summaries,a technical analysis route for truck driving cycle based on big data technology is developed.Then,the theory of truck driving cycle analysis based on big data technology is introduced.The principle of data collection for truck driving cycle(including the principles of vehicle-side and server-side principles)is described.Based on this principle,the data collection and transmission methods of the driving cycle data and the remote transmission method are formulated.The data conversion format and conversion method are defined based on the 808 industry standard of the Department of Transportation.Advanced encryption standards are used to encrypt data.The theoretical steps,advantages and disadvantages,and applicability of the K-means clustering algorithm are described in detail.In combination with the previous research on the driving cycle,the parameters and variables of the truck driving cycle analysis are defined.Using the membership function and K-means clustering algorithm,the attribute characteristic words of the truck driving cycle analysis are determined,the driving cycle analysis model of the truck is constructed,and the software function of the model is designed.And using a small part of the sample data,model examples are imported to verify the effectiveness of the model.Finally,based on the analysis model of truck driving cycle,the big data analysis technology is used to analyze the actual operation data of 200 trucks of an automobile manufacturing enterprise.The experiment is based on the theory of distributed file storage and uses the component of Hadoop Distributed File System(HDFS)to read and write the big data of truck driving cycle.Based on the theory of big data analysis,the Spark large-data computing framework is used to perform flow calculation and off-line analysis on a large number of truck data.The analysis results show that the operating efficiency analysis method of truck driving cycle using big data technology is significantly higher than the traditional technology.The experimental result data reflects the actual road condition information of these trucks and the running status of the vehicles.In summary,the research results of this paper provide a theoretical basis for controlling the energy consumption and pollutant emissions of trucks in logistics transportation,and provide data reference for the management of logistics transportation companies and the technology development and evaluation of new models of automobile manufacturers.