Deep Learning for Instant Parsing and Modeling of Structured Text in Power Systems

In response to the pre-warning needs for anomaly detection in power monitoring systems, this paper proposes an efficient anomaly detection model based on the E language. First, the E language is subjected to text classification, and the classified data is preprocessed to convert it into structured data usable by the model. Secondly, by combining the Isolation Forest algorithm with Bidirectional Long Short-Term Memory networks, a hybrid anomaly detection model is constructed to extract features from E language text and achieve anomaly judgment.Finally, through experimental validation, the model has shown significant advantages in key indicators such as precision and recall, fully demonstrating its effectiveness and practicality. This study provides a new technical approach for anomaly detection in power monitoring systems, with high application value.