基于卡尔曼滤波预测的电动汽车分时租赁监测数据去人为干预技术研究

Study of eliminating human intervention for electric vehicle time-sharing leasing monitoring data based on a modified Kalman filter prediction algorithm

随着社会环保意识的增强,大力推广采用清洁能源的电动汽车,电动汽车分时租赁业务随之不断拓展,多级管理平台纷纷建立。总管理平台所采集的电动汽车参数日益积累,为大数据分析研究奠定了坚实的基础。预处理技术在数据采集过程中起着至关重要的作用。特别地,从各运营商子平台所采集的监测数据存在人为干预风险。为此,文章提出采用人为干预概率曲线量化监测数据与协方差比间关系,将人为干预概率曲线区间和观测量关系作为输入,建立了回归对象决策树并引入传统卡尔曼滤波算法,从而提出基于决策树分析的卡尔曼滤波预测方法,从而减少人为篡改的干预影响,以达到对运营总平台所采集的数据去人为干预的目的。将该算法应用于车速预测领域,得到可信度更高的车速预测数据,为有效地实现大数据分析奠定坚实的技术支撑。

With the enhancement of social environmental protection consciousness,electric vehicles using clean energy are promoted widely. The scope of time-sharing leasing business for the electric vehicle keeps expanding,and multi-level management platforms are set up one after another. Electric vehicle parameters collected by main management platform are accumulated continually,which lays a firm foundation for big data analysis research. Preprocessing technology plays an important role on the process of data acquisition. Especially,monitoring data collected from the each sub-platform of operators may have the risk of being tampered manually. Therefore,in this paper,human intervention probability curve is used to quantify the relationship between monitoring data and covariance ratio,and both of human intervention probability curve interval and observation data relationship are treated as input,so that the regression object decision tree is established and is introduced to the traditional Kalman filter algorithm. Therefore,a modified Kalman filter prediction method based on decision tree analysis is presented which can be used to reduce the influence of human tampering,and achieve preprocessing function of main operating platform for collecting data. In this paper,this modified method is successfully used in the field of vehicle speed prediction,as well as to get speed data with higher credibility,which provides a strong technical support for large data analysis.