Optimizing the Efficiency of Distributed Log Data Analysis Using a Multi-Scale Convolutional Attention Mechanism

Abstract

As smart manufacturing and networked sensing systems continue to evolve, distributed cloud platforms generate massive volumes of log data whose efficient analysis is essential for reliable monitoring and intelligent decision-making. Such capabilities also provide valuable support for communication-oriented and electromagnetic sensing infrastructures requiring real-time system awareness. To address the limitations of existing distributed log analysis methods, including insufficient feature extraction, weak capture of critical information, and the difficulty of balancing efficiency and accuracy, this paper proposes a distributed log analysis approach based on a Multi-Scale Convolutional Attention Mechanism (MS-CAM). A structured preprocessing pipeline is first established to perform log transformation and noise filtering. A multi-scale convolutional module is then employed to extract features at different granularities, capturing both local critical information and global semantic relationships, while an attention mechanism further enhances key feature representation through adaptive weight allocation. Experimental results demonstrate that the proposed method effectively improves analytical performance and provides an efficient solution for distributed intelligent systems with potential value for real-time monitoring and signal-aware computing applications.