基于用户大数据的工况分类与构建方法

A Working Condition Classification and Construction Method Based on User Big Data

为提高可靠性试验所用工况与用户实际驾驶行为的相关性,提出一种基于用户大数据的用户工况分类及试验工况构建方法,通过主成分分析法对大量数据进行降维处理,利用K均值聚类方法对数据进行分类并选取代表片段,采用马尔可夫方法对数据进行排序,最终构建出符合用户实际驾驶情况的可靠性试验工况,并根据该计算流程对某大数据平台下20位用户的行驶数据进行实际计算,与目前普遍采用的工况相比,数据来源角度能更准确地描述用户实际驾驶状态。

To improve the correlation between reliability test condition and user actual driving behavior,this paper proposed a method of user condition classification and test condition construction based on user big data.In this method,the principal component analysis method was used to reduce the dimensionality of a large amount of data,and the K-means clustering method was used to classify the data and select representative segments,sort the data through the Markov method,and finally construct a reliability test condition conforming to the actual driving situation of the user.Driving data from twenty users under a big data platform was calculated according to this calculation process.Compared with the condition commonly used at present,the method from perspective of data source can describe user actual driving state more accurately.