Research on Abnormal Data Identification and Early Warning Model for Continuous Automatic Monitoring and Control of Ecological Environment

Abstract

In the context of digital transformation in ecological environment governance, continuous automatic monitoring systems have become key technical support for pollution tracing, quality assessment, and risk warning. However, monitoring data are easily affected by instrument failures, environmental interference, transmission anomalies, and other factors, which restricts data availability and the scientific validity of early-warning decisions. To address the problems of insufficient robustness, delayed warning, and weak generalization in traditional anomaly identification methods, this paper proposes an integrated solution based on multi-feature fusion, hierarchical recognition, and dynamic warning. A four-dimensional anomaly feature system covering data integrity, temporal stability, physical constraints, and spatial correlation is first constructed. Sliding-window statistics and multi-scale feature extraction are used to represent anomaly patterns. A hierarchical recognition model combining improved Isolation Forest and LSTM is then designed, where the improved IForest rapidly screens significant anomalies and the LSTM captures temporal dependencies for precise classification. The proposed method improves anomaly recognition and warning reliability, and provides technical references for environmental sensing networks, electromagnetic interference-resistant monitoring, and intelligent signal processing.