水利学报2025，Vol.56Issue(9)：1132-1142,11.DOI:10.13243/j.cnki.slxb.20240841
基于多源异构数据融合的高坝泄流结构安全智能监测预警方法
Intelligent monitoring and early warning method for high dam discharge structure safety based on multi-source heterogeneous data fusion
摘要
Abstract
The discharge structure of high dams will inevitably be damaged during the long operation and mainte-nance period.It is urgent to implement effective safety monitoring and early warning to avoid local abnormalities from expanding into safety accidents.Since the monitoring items,including air sound pressure and flow pattern images,are very sensitive to abnormal operating states of high dams,they are monitored synchronously with the low-frequency vibration displacement to enrich the types of monitoring data and improve effective information.A feature-level fusion is proposed to splice the time-frequency images of vibration and sound pressure with the segmented and cropped flow pattern images in the additional dimension,so as to retain and fuse the key features of the above multi-source heterogeneous data as much as possible.Based on the framework of autoencoder,a deep learning network is built,Inception and GRU modules are embedded to improve the spatial and temporal feature learning capabilities of the model,and then the Autoencoder-Inception-GRU single-classification anomaly recognition model is proposed.Absolute mean error percentage and Euclidean distance are used as the reconstruction error function of the model,and 95％of their maximum values are set as the anomaly threshold.Based on the prototype monitoring experiment,a multi-source heterogeneous database of vibration-sound-image was constructed,and the performance of the Autoencoder-Inception-GRU model was analyzed in detail.The accuracy,robustness and generalization ability of the proposed model were tested and investigated by case studies under various conditions.The results show that the pro-posed approach achieves excellent performance,which provides key technical support for engineering application.关键词
高坝泄流/监测预警/单分类异常识别/多源异构数据融合/原型监测Key words
high dam flood discharge/monitoring and early warning/single classification anomaly recognition/multi-source heterogeneous data fusion/prototype monitoring分类
建筑与水利引用本文复制引用
马斌,彭志,梁超..基于多源异构数据融合的高坝泄流结构安全智能监测预警方法[J].水利学报,2025,56(9):1132-1142,11.基金项目
国家重点研发计划项目(2022YFB4200704) （2022YFB4200704）
天津市应用基础研究项目(22JCYBJC01180) （22JCYBJC01180）
国家自然科学基金项目(51909185) （51909185）
天津市科技计划项目全国重点实验室重大专项(24ZXZSSS00450) （24ZXZSSS00450）
华能集团科技项目(HNKJ24-H165) （HNKJ24-H165）
云南省科技人才与平台计划项目(202405AK340002) （202405AK340002）
