Research on Intelligent Early Warning Algorithm of System Anomaly Detection Using Multi-Modal Data Fusion Technology

System anomaly detection and early warning are the key technologies to ensure the safe and stable operation of the system in the complex and changeable environment. Traditional anomaly detection methods often rely on a single data source, and it is difficult to capture the multi-dimensional anomaly characteristics of the system. To solve this problem, an intelligent early warning algorithm based on multi-modal data fusion technology is proposed in this paper. The algorithm uses multi-source heterogeneous data, including sensor data, log data, network traffic data, etc., and constructs a multi-modal feature fusion model to realize efficient detection of abnormal behavior of the system. Specifically, we first preprocess and extract features from all kinds of data, and then use deep learning models for feature fusion to build a unified multimodal feature representation. Then, a method combining graph neural network and time series analysis is used to detect and warn the fused features. The experimental results show that the proposed method has higher detection accuracy and lower false positive rate on multiple real world data sets, which has significant advantages over traditional methods. The research results show that multi-modal data fusion technology has broad application prospects in the field of anomaly detection, and provides a new idea for the construction of intelligent and accurate early warning system.