污染源自动监控数据智能分析方法研究

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中图分类号：TP391.3 文献标识码：A 文章编号：2096-4706（2026）04-0133-05

Abstract： With the wide application of automatic monitoring systems for pollution source，traditional anomaly identification methods basedon fixed thresholdand staticrules face the problemof insuffcient acuracywhen 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 typesof detection algorithm merging behavior modeling andstatisticalfeatureanalysis.Thesealgorithmstargetbehaviorsincludingconstantdischarge value，suddenchange，failureto stopaccording toregulations，andcontinuous limit-approachingoperationformodelingandidentifcation.Thealgorithmdesign employs multi-window comparison，trend analysis，and interval determination toconstructrule logic.Meanwhile，this paper develops a natural language question-answering module based on large modelcaling to realizesemantic parsing and inteligent queryofpolutiondata，whichenhancesdatainteractioncapability.Appicationresultsshowthathis methodhasgooddetection accuracy and provides technical support for intelligent environmental supervision.

Keywords： automatic monitoring of polution source;anomaly detection; limit-approaching production; declared stopbut not stopped; constant value anomaly; intelligent data questioning

0 引言

工业排放行为的智能化规避，使环境监管面临数据“表面正常”但实质违规的挑战[]。（剩余5906字）