基于模式检测的嵌入式系统流式日志压缩算法OA

With the widespread deployment of embedded systems in IoT,industrial control,and intelligent devices,log recording as a critical means for system runtime and fault diagnosis,faces dual challenges of limited storage capacity and stringent real-time requirements.This paper proposes a Streaming Log Compression algorithm based on Pattern Detection(SLC-PD),which employs an adaptive sliding window mechanism to analyze repetitive pattern characteristics in log streams in real time,and achieves efficient online compression through finite state machine control.Theoretical analysis demonstrates that the algorithm achieves O(1)time complexity and O(W)space complexity,where W denotes the window size.Experimental results indicate that SLC-PD maintains low processing latency while achieving compression ratio of up to 80%,with memory overhead less than 18 KB,making it suitable for diverse embedded application scenarios.