水力发电2025，Vol.51Issue(4)：48-53,90,7.
融合物理机制深度学习的混凝土浇筑仓温度监测数据去噪方法
A Method for Denoising Temperature Monitoring Data of Concrete Pouring Block Using Deep Learning Combined with Physical Mechanisms
摘要
Abstract
Considering the physical mechanism behind the temperature monitoring data of concrete pouring block,a data denoising method based on physical information neural network is proposed,in which,the heat conduction equation followed by the spatio-temporal variation of concrete pouring block temperature is used,and on the basis of the conventional data-driven denoising of concrete pouring block temperature monitoring data by neural network fitting,the constraints of the control equation of the spatio-temporal variation of the concrete pouring block temperature are added to control the smoothness and reasonableness of the neural network function fitting,so as to realize the smooth denoising of the temperature monitoring data of the concrete pouring block.The method is validated through the analysis of engineering examples,and the results show that using this method to denoise the temperature monitoring data of the dam concrete pouring block preserves the local features of the data while overall being relatively smooth.The method can effectively remove the noise in the temperature monitoring data of concrete pouring block,and has a good generalization fitting ability,which provides a new idea for the smooth denoising processing of the temperature monitoring data of concrete pouring block.关键词
混凝土浇筑仓温度监测/监测数据/数据去噪/物理机制/物理信息神经网络Key words
concrete pouring block temperature monitoring/monitoring data/data denoising/physical mechanisms/physical information neural network分类
建筑与水利引用本文复制引用
李如尧,张毅,何瑞良,张磊..融合物理机制深度学习的混凝土浇筑仓温度监测数据去噪方法[J].水力发电,2025,51(4):48-53,90,7.基金项目
国家自然科学基金资助项目(51779277) （51779277）
国家重点研发计划项目(2018YFC0406703) （2018YFC0406703）
流域水循环模拟与调控国家重点实验室资助项目(SKL2020ZY10,SS0112B102016) （SKL2020ZY10,SS0112B102016）
