Method and system for abnormal detection of sewage treatment process based on hybrid expert model

Abstract

This invention relates to a method and system for detecting anomalies in wastewater treatment processes based on a hybrid expert model. The method includes: constructing a multimodal input vector for the wastewater treatment plant; inputting the multimodal input vector into a hybrid expert model, and performing anomaly inference through multiple expert networks selected by sparse gating to obtain anomaly prediction results; dynamically calculating the weight coefficients of each expert network based on the feature distribution of the multimodal input vector, and using the weight coefficients to perform weighted fusion of the anomaly prediction results to generate a preliminary comprehensive detection result; performing consistency verification on the anomaly prediction results, and triggering a thought chain inference mechanism for analysis if the verification fails, and updating the preliminary comprehensive detection result based on the analysis results; performing multi-level risk assessment and decision-making on the optimized comprehensive detection result, and generating a process anomaly report including risk level and disposal recommendations. This invention can reduce false alarms and false negatives, and improve the accuracy and efficiency of anomaly identification and decision-making in wastewater treatment plants.

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