Article Details

Abstract

With the wide application of automatic monitoring systems for pollution source, traditional anomaly identification methods based on fixed threshold and static rules face the problem of insufficient accuracy when dealing with complex evasive behavior such as “limit-approaching discharge”“constant value anomaly”“discharge gap anomaly”and “declared stop but not stopped”. Therefore, this paper proposes four types of detection algorithm merging behavior modeling and statistical feature analysis. These algorithms target behaviors including constant discharge value, sudden change, failure to stop according to regulations, and continuous limit-approaching operation for modeling and identification. The algorithm design employs multi-window comparison, trend analysis, and interval determination to construct rule logic. Meanwhile, this paper develops a natural language question-answering module based on large model calling to realize semantic parsing and intelligent query of pollution data, which enhances data interaction capability. Application results show that this method has good detection accuracy and provides technical support for intelligent environmental supervision.

Keywords

automatic monitoring of pollution source; anomaly detection; limit-approaching production; declared stop but not stopped; constant value anomaly; intelligent data questioning

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About the authors

Hu Haitao (1985-), male, Han ethnicity, born in Xi'an, Shaanxi Province, is a senior engineer with a bachelor's degree. His research direction is environmental informatization; Corresponding author: Zhou Li (1976-), male, Han nationality, from Panzhihua, Sichuan, senior engineer, bachelor's degree, research direction: environmental informatization.