铁道机车车辆

为了满足动车组日常运维管理工作对列车运行数据日益增长的需求，针对动车组入所后，车地非实时数据文件传输量不断增加与列车的供电传输时长有限之间的矛盾，提出了一种基于相似性检测的动车组过程数据压缩算法。该算法利用海明距离实现对列车过程数据的相似性检测，并基于数据相似性进行去重后，再用传统压缩算法实现进一步压缩，大幅降低待传输的非实时数据文件大小，最终在地面还原原始数据。试验结果表明，动车组非实时数据文件记录的过程数据中，存在大量重复或相似数据，而该算法能应对较低的额外性能开销，将原始非实时数据文件减小近50%，即减少了一半的传输时间，能更好地适应列车入所后传输时间受限的工况，提高数据传输的完整性。

In response to the growing demand for train operation data in the daily maintenance and management of electric multiple units(EMUs), and addressing the contradiction between the increasing volume of non real time ground to train data files after EMUs enter the depot and the limited power supply duration for data transmission, a process data compression algorithm for EMUs based on similarity detection is proposed.This algorithm utilizes Hamming distance to detect the similarity of train process data. After removing duplicates based on data similarity, it further compresses data using traditional compression algorithms,which significantly reducing the size of non-real-time data files to be transmitted. Finally, the original data is restored on the ground system. The experimental results show that there is a large amount of duplicate or similar data in the process data recorded in the non real time data files of the EMU. With relatively low additional performance overhead, this algorithm can reduce the original non real time data files by nearly 50%, halve the transmission time. It can better adapt to the limited transmission time after the train enters the operation depot and improve the integrity of data transmission.