考虑多点监测数据的混凝土坝智能预警分析方法

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关键词：混凝土坝；多点变形监测;预警指标;K-means聚类法;ConvLSTM模型;3-Sigma原则中图分类号：TV62 文献标志码：Adoi：10.3969/j.issn.1000-1379.2025.07.024引用格式：，李炎隆，张野，等.考虑多点监测数据的混凝土坝智能预警分析方法[J].人民黄河，2025，47（7）：150-155.

Intelligent Early Warning Analysis Method for Concrete Dams Considering Multi-Point Monitoring Data

ZHONG Wen¹，LI Yanlong1， ZHANG Ye¹， ZHOU Tao²， KANG Xinyu¹，YANG Tao³， LI Kangping4 （1.StateKeyLboratoryofWaterEgeringEolodEnviontinAidreaXi'nUivesityfToloXi'na; 2.Huanghe HydropowerDevelopmentCo.，Ltd.，，Xining 810o，China；3.ChinaYangtzePowerCo.，Ltd.，Yibin 644612，China; 4.Power China Northwest Engineering Corporation Limited，Xi’an 71OO65，China）

Abstract：Inodertohancetheaccacyofarlywaringincocretedmsafetymonitoring，isstudypropedanintellgentearlyainganalysismetdbasedonulti-poitmitoringdataimingtoovercoetesuseptibilityoftraditioalsingle-poitmetodstoo structuralinteferec.FirstlyK-eanslusterngmethodasusedtoartiomoitoringontsihsiilaeforationpatesn ConvLSTMmodelwasemployed toextractthespatial-temporalfeaturesoftedformationsequenesfromeachclusterandmakepredictions. Byanalyzingtheresidualsequencesanddeterminingtheearlywarning treshldbasedonthe3-Sigmapriciple，single-pointearlywaing results weregenerated.Finall，teearlywaringresultsfromallusterswereitegratedtoensurethatanearlywaingwastriggdonly whenallmoitorgpoitswitinaustereibitaomalssiultaneoslyattesaetie.Experimentalresultsshowatteproposd methodreducesthefalsealamsandmiseddetectioscausedbyextealdisturbancesinsingle-pointearlywaingmetodsbyintegatig multi-point information，thereby improving the reliabilityand stabilityof the early warning system.

Keywords：concretedam；multi-ointdeforationmonitoring;arlywaingindicators；K-meansclustering method;onSTMmodel;3 Sigma principle

0 引言

大坝作为重要的水利设施，其安全性直接关系到人民生命财产和生态环境的安全[1-2]。（剩余7194字）