现代信息科技

随着污染源自动监控系统的广泛应用，传统基于固定阈值和静态规则的异常识别方法，在应对“贴限排放”“恒值异常”“排放差距异常”和“标停未停”等复杂规避行为时，存在准确性不足的问题。为此，文章提出融合行为建模与统计特征分析的4类检测算法，分别针对排放值恒定、突发变化、未按规定停运以及持续贴限运行等行为进行建模识别。算法设计采用多窗口对比、趋势分析及区间判定等手段构建规则逻辑。同时，开发了基于大模型调用的自然语言问答模块，实现对污染数据的语义解析与智能查询，增强了数据交互能力。应用结果显示，该方法具有良好的检测准确性，为智能化环境监管提供了技术支撑。

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.