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Real-time monitoring and early warning system and method for foundation settlement of ancient building
The invention discloses a real-time monitoring and early warning system and method for settlement of an ancient building foundation. The real-time monitoring and early warning system comprises the steps that settlement data, collected by multiple settlement sensor nodes, of the monitored ancient building foundation in multiple days within a preset time period are obtained; performing data structuring processing on the settlement data of the monitored ancient building foundation acquired by the plurality of settlement sensor nodes in multiple days in a preset time period to obtain a settlement data global matrix of the monitored ancient building foundation; carrying out settlement feature extraction on the monitored ancient building foundation settlement data global matrix to obtain a self-strengthening settlement data multi-scale correlation feature map; and determining whether an early warning prompt is generated or not based on the self-strengthening settlement data multi-scale correlation feature map. Therefore, the multi-scale correlation characteristics of the settlement data can be extracted through a deep learning technology, and then real-time monitoring and early warning of the settlement condition of the foundation of the ancient building are realized, so that technical support is provided for protection and repair of the ancient building.
一种古建筑地基沉降实时监测预警系统及方法,其获取由多个沉降传感器节点采集的被监控古建筑地基在预定时间周期内多天的沉降数据;对所述多个沉降传感器节点采集的被监控古建筑地基在预定时间周期内多天的沉降数据进行数据结构化处理以得到被监控古建筑地基沉降数据全局矩阵;对所述被监控古建筑地基沉降数据全局矩阵进行沉降特征提取以得到自强化沉降数据多尺度关联特征图;以及,基于所述自强化沉降数据多尺度关联特征图,确定是否产生预警提示。这样,可以通过深度学习技术提取沉降数据的多尺度关联特征,进而实现对古建筑地基沉降情况的实时监测和预警,从而为古建筑的保护和修复提供技术支持。
Real-time monitoring and early warning system and method for foundation settlement of ancient building
The invention discloses a real-time monitoring and early warning system and method for settlement of an ancient building foundation. The real-time monitoring and early warning system comprises the steps that settlement data, collected by multiple settlement sensor nodes, of the monitored ancient building foundation in multiple days within a preset time period are obtained; performing data structuring processing on the settlement data of the monitored ancient building foundation acquired by the plurality of settlement sensor nodes in multiple days in a preset time period to obtain a settlement data global matrix of the monitored ancient building foundation; carrying out settlement feature extraction on the monitored ancient building foundation settlement data global matrix to obtain a self-strengthening settlement data multi-scale correlation feature map; and determining whether an early warning prompt is generated or not based on the self-strengthening settlement data multi-scale correlation feature map. Therefore, the multi-scale correlation characteristics of the settlement data can be extracted through a deep learning technology, and then real-time monitoring and early warning of the settlement condition of the foundation of the ancient building are realized, so that technical support is provided for protection and repair of the ancient building.
一种古建筑地基沉降实时监测预警系统及方法,其获取由多个沉降传感器节点采集的被监控古建筑地基在预定时间周期内多天的沉降数据;对所述多个沉降传感器节点采集的被监控古建筑地基在预定时间周期内多天的沉降数据进行数据结构化处理以得到被监控古建筑地基沉降数据全局矩阵;对所述被监控古建筑地基沉降数据全局矩阵进行沉降特征提取以得到自强化沉降数据多尺度关联特征图;以及,基于所述自强化沉降数据多尺度关联特征图,确定是否产生预警提示。这样,可以通过深度学习技术提取沉降数据的多尺度关联特征,进而实现对古建筑地基沉降情况的实时监测和预警,从而为古建筑的保护和修复提供技术支持。
Real-time monitoring and early warning system and method for foundation settlement of ancient building
古建筑地基沉降实时监测预警系统及方法
06.12.2024
Patent
Elektronische Ressource
Chinesisch
IPC:
G08B
SIGNALLING OR CALLING SYSTEMS
,
Signalisier- oder Rufsysteme
/
E02D
FOUNDATIONS
,
Gründungen
/
G01C
Messen von Entfernungen, Höhen, Neigungen oder Richtungen
,
MEASURING DISTANCES, LEVELS OR BEARINGS
/
G06F
ELECTRIC DIGITAL DATA PROCESSING
,
Elektrische digitale Datenverarbeitung
/
G06N
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
,
Rechnersysteme, basierend auf spezifischen Rechenmodellen
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