Research on Big Data Analysis and Anomaly Detection Algorithms in Intelligent Operations and Maintenance

With the increasing complexity and scale of IT infrastructure, intelligent operations and maintenance (AIOps) has become the key to improving system reliability, reducing costs, and optimizing resource utilization. KPI anomaly detection is an important part of AIOps, which can detect potential failures in Internet services in a timely manner. This article introduces the basic concepts of KPI anomaly detection and proposes an anomaly detection method based on the LSTM Encoder Decoder model. This method detects anomalies by reconstructing normal KPI time series and introduces an Attention mechanism to improve the limitations of traditional Encoder Decoder models. The experimental section selected two types of operation and maintenance data from IT companies and insurance companies for testing, and the results showed that this method can effectively solve the problem of abnormal detection of operation and maintenance data, with high accuracy and recall rates. In addition, this method avoids the problem of requiring a large amount of manual labeling in traditional classification problem solving methods, providing new ideas and directions for intelligent operation and maintenance.