Implementation of data analysis algorithm for identifying abnormal states in environmental monitoring system
For the urban environment monitoring scenario, this paper constructs a multi-source time-series data feature system and preprocessing process. Through exponential smoothing and linear interpolation, noise is suppressed and missing data is repaired. On this basis, a joint anomaly identification framework combining traditional supervised models and LSTM time-series models is designed. Further, a multi-model output weighted fusion and adaptive weight update mechanism based on batch performance indicators is proposed. The fusion results are embedded into the monitoring business process and real-time data stream interface to achieve online anomaly determination. Experimental results show that in typical monitoring scenarios, the accuracy of anomaly identification in this system reaches 97.3%, which is approximately 2.5 and 1.7 percentage points higher than that of the two comparison systems (94.8% and 95.6%), respectively. The false alarm rate is reduced to 3.1%, and the end-to-end average latency is about 180 ms. This verifies the effectiveness of the proposed mechanism in balancing recognition accuracy and real-time performance, providing a feasible technical path for engineering environment anomaly monitoring.