Real-time Fault Detection and Stability Enhancement Mechanism Based on Large Models

This paper proposes a framework for real-time fault detection using large models, which rapidly identifies potential faults through system log and operational data analysis, triggering stability enhancement mechanisms. The research designs a self-supervised learning algorithm that enables large models to continuously improve detection accuracy in dynamic environments. The method adopts a transformer architecture and attention mechanism to capture temporal dependencies and complex patterns in system behavior. Through comparison with traditional methods, we validate the advantages of the proposed method in terms of accuracy and real-time performance. Experiments conducted across various fault scenarios demonstrate that the method significantly reduces fault response time in high-concurrency systems, decreasing average detection latency by 47.3% and shortening system recovery time by 35.8%, thereby improving overall stability. Additionally, the self-supervised nature of the method enables continuous adaptation to new fault patterns, providing an innovative solution for reliability assurance in distributed systems.