基于车联网信息的公交客车行驶工况数据处理方法

Data processing method of bus driving cycle based on vehicular network information

受技术、成本和环境等因素的影响,当前车联网平台数据在传输过程中不可避免地存在缺失和数据噪声问题。针对车联网数据传输优化需进一步改进的现实问题,基于某商用客车企业车联网平台,获取固定线路运行的公交客车行驶工况数据,分析研究车联网平台数据特点及其存在的问题,采用插补缺失与神经网络集成方法对行驶工况缺失数据进行估计,并基于小波变换对行驶工况噪声数据进行清洗。最后,给出了行驶工况数据处理方法的评价指标,合理说明了评价本文数据处理方法的效果。结果表明:本文提出的缺失数据轨迹方法,可较好地估计还原车辆真实工况的变化情况,基于小波变换的方法对噪声数据进行了有效处理,两者误差均在合理范围内。本文开展的研究内容可为车联网领域的数据传输优化改进、海量高并发数据的处理方法提供实际参考及借鉴。

Under the influence of technology,cost,environment and other adverse factors,data missing and data noise problems inevitably exist in the data transmission process of the vehicle networking platform.In view of the practical problems that need to be further improved in the data transmission optimization of the Internet of vehicles,based on the vehicle networking platform of a commercial bus enterprise,this paper obtains the driving cycle data of the bus running on the fixed line.According to the characteristics and problems of the data of the vehicle networking platform,the missing data of driving cycle is estimated by the integration method of interpolation and neural network,and the noise data of driving cycle is cleaned based on wavelet transform.Finally,the evaluation index of the data processing method of driving cycle is given,and the effect of the data processing method is explained reasonably.The results show that the missing data track method can better estimate and restore the change of the real working condition of the vehicle.The method based on wavelet transform effectively processes the noise data,and the errors of both methods are within a reasonable range.The research content of this paper can provide practical reference and reference for the optimization of data transmission and the processing methods of massive and high concurrent data in the field of Internet of vehicles.