<ul><li>Proposal of an anomaly detection and early warning mechanism for intelligent monitoring systems in multi-cloud environments based on Large-Scale Language Model (LLM).</li><li>Introduction of a multi-level feature extraction method combining LLM's natural language processing with traditional machine learning for enhanced anomaly detection accuracy and real-time response efficiency.</li><li>Dynamic adaptation to various cloud service providers and environments by utilizing LLM's contextual understanding capabilities for improved abnormal pattern detection and failure prediction.</li><li>Experimental results demonstrate the model's superiority over traditional systems in terms of detection accuracy, latency, resilience, and active management ability in cloud infrastructure.</li></ul>

Anomaly Detection and Early Warning Mechanism for Intelligent Monitoring Systems in Multi-Cloud Environments Based on LLM

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