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METHOD AND APPRATUS FOR DETECTING ANOMALY OF VEHICLE BASED ON EUCLIDEAN DISTANCE MEASURE

机译:基于欧氏距离测量的车辆异常检测方法及装置

摘要

Disclosed are a method and a device for detecting abnormality of a vehicle based on a euclidean distance measurement technique, capable of rapidly and effectively detecting an abnormal vehicle state. The method for detecting abnormality of a vehicle based on the euclidean distance measurement technique includes: a step of being executed by a vehicle abnormality detecting device and collecting vehicle state data on an n (n is a natural number of 1 or greater) number of vehicles (k) in a predetermined section; a step of filtering the data in which there is no change of a data value among the vehicle state data and generating the filtered vehicle state data; a step of extracting an m (m is the natural number of 2 or greater) number of details in which a dimension is reduced by factor analysis of the filtered vehicle state data and generating the state data on each of the details; a step of extracting a reference value for detecting the abnormality of the vehicle by using traffic situation information; a step of calculating a euclidean distance (D_k_, called ′a first distance′) from the state data on a whole of the details of a vehicle to be analyzed and the reference value; a step of detecting the abnormality by using the first distance; a step of calculating a detail-based euclidean distance (called ′a second distance′) from the state data on each of the details of the vehicle to be analyzed and the reference value; a step of detecting the abnormality on the each of the details by using the second distance; and a step of providing an abnormality detection alarm including information on a cause of the abnormality of the vehicle when the abnormality of the vehicle is detected.
机译:本发明公开了一种基于欧几里德距离测量技术的车辆异常检测方法和装置,能够快速有效地检测车辆异常状态。基于欧几里德距离测量技术的用于检测车辆异常的方法包括:由车辆异常检测装置执行并收集关于n个(n为自然数1或更大)车辆的车辆状态数据的步骤。 (k)在预定段中;过滤在车辆状态数据中数据值没有变化的数据并生成过滤后的车辆状态数据的步骤;提取m个细节(m是2的自然数或更大)的细节的步骤,其中,通过对滤波后的车辆状态数据进行因子分析来减小尺寸,并针对每个细节生成状态数据;通过使用交通状况信息提取用于检测车辆异常的参考值的步骤;根据待分析车辆的全部细节和参考值的状态数据计算欧几里德距离(D_k_,称为“第一距离”)的步骤;利用第一距离检测异常的步骤;根据待分析车辆的每个细节的状态数据和基准值计算基于细节的欧氏距离(称为“第二距离”)的步骤;通过使用第二距离来检测每个细节上的异常的步骤;当检测到车辆异常时,提供包括关于车辆异常原因的信息的异常检测警报的步骤。

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