The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.
The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.
Patent No.:
Date of Patent:
Aug. 12, 2025
Filed:
Mar. 27, 2025
Institute of Geology and Geophysics,cas, Beijing, CN;
Zan Wang, Beijing, CN;
Shengwen Qi, Beijing, CN;
Bowen Zheng, Beijing, CN;
Wang Zhang, Beijing, CN;
Bo Wan, Beijing, CN;
Xiaokun Hou, Beijing, CN;
Wei Lu, Beijing, CN;
Guangming Luo, Beijing, CN;
Lina Ma, Beijing, CN;
Yongchao Li, Beijing, CN;
Guoliang Li, Beijing, CN;
Yuran Zhang, Beijing, CN;
Weiwei Zhu, Beijing, CN;
Wenjie Hao, Beijing, CN;
Songfeng Guo, Beijing, CN;
Yu Zou, Beijing, CN;
Jianing Cong, Beijing, CN;
Chao Jin, Beijing, CN;
Tianming Huang, Beijing, CN;
Yanlong Kong, Beijing, CN;
Institute of Geology and Geophysics, CAS, Beijing, CN;
Abstract
A real-time quantitative characterization method, equipment and a medium for rock mass evolution are disclosed. The method includes: the multiphase field detection and monitoring data is fused; a deep learning model driven by physical principles is established based on the physical principles of fluid density; the deep learning model is trained using real-time fused data as input and corresponding evolution distribution images as output; a trained deep learning model is used to obtain an evolution distribution image based on multi-phase field detection and monitoring data of different time periods and types; a mathematical model is used to quantitatively characterize of physical and mechanical parameters in the whole process of progressive failure of the dynamic evolution of the rock mass based on the macroscopic mechanical parameters and evolution distribution images synchronized with multiphase field detection and monitoring data.