基于相关性的CAN信号解析技术研究及应用

The Research and Application of CAN Signal Analysis Technology based on Correlation

本文针对汽车行业中海量CAN数据的高效处理与精准分析需求,提出并开发了一种基于同源相关性原理的CAN信号逆向分析技术及相应软件。该技术通过利用同源信号的相关性,以诊断信号为基准,将广播信号转换为二进制格式,并根据位长、类型、系数、偏移量、符号位及相似度等参数,将数据拆分为多个对比信号数据源。随后,通过设定相似度阈值,快速筛选出符合条件的信号。研究结果表明,该技术显著提升了CAN信号的获取效率,缩短了解析周期,提高了工作效率,并为汽车行业的发展提供了重要支持。

In response to the automotive industry's need for efficient processing and precise analysis of massive CAN data,this paper proposes and develops a reverse analysis technology for CAN signals and corresponding software based on the principle of homology similarity.This technology leverages the similarity of homologous signals,using diagnostic signals as a benchmark,to convert broadcast signals into binary format.The data is then split into multiple comparison signal data sources based on parameters such as bit length,type,coefficient,offset,sign bit,and similarity.Subsequently,by setting a similarity threshold,signals that meet the criteria are quickly filtered out.The research results show that this technology significantly improves the efficiency of acquiring CAN signals,shortens the analysis cycle,enhances work efficiency,and provides crucial support for the development of the automotive industry.