公开(公告)号：US11619487B2

公开(公告)日：2023-04-04

申请号：US16991389

申请日：2020-08-12

申请人： China Institute of Water Resources and Hydropower Research ,  Peking Remote Sensing Wisdom Technology Co., LTD.

发明人： Tianjie Lei ,  Jing Qin ,  Geng Sun ,  Lingyun Zhao ,  Weiwei Wang ,  Li Zhang ,  Mingming Zhu ,  Lu Wang ,  Ruihu Yao ,  Xiangyu Li ,  Jiabao Wang ,  Huaidong Zhao

IPC分类号： G01B21/32 ,  G06T7/00 ,  B64C39/02 ,  G01S13/86 ,  G06V20/10

摘要： A dam slope deformation monitoring system and method are provided. The monitoring system monitors an entire dam in a reservoir area by using an unmanned aerial vehicle (UAV) photogrammetry system, and determines an encrypted monitoring area (steep slope) with the relatively large deformation and a relatively large digital elevation difference; determines, in the intensive monitoring area, a first level key monitoring area with the larger deformation by using a ground-based radar interferometry measurement system; determines, in the first level key monitoring area, a second level key monitoring area with the larger deformation by using a ground-based three-dimensional lidar measurement system; determines, in the second level key monitoring area, a key monitoring particle with a high deformation speed by using a global navigation satellite system (GNSS). The core chip stack is used to monitor and warn the collapse process in the area where the key monitoring particles are located.

公开(公告)号：US11676076B2

公开(公告)日：2023-06-13

申请号：US16993770

申请日：2020-08-14

申请人： China Institute of Water Resources and Hydropower Research

发明人： Jing Qin ,  Tianjie Lei ,  Geng Sun ,  Lingyun Zhao ,  Wenlong Niu ,  Mingming Zhu ,  Yanhong Wang ,  Xiaomin Guo ,  Qian Wang ,  Jiabao Wang ,  Xiangyu Li ,  Yazhen Zhang ,  Li Zhang ,  Haoyu Yang

IPC分类号： G06N20/10 ,  G06N3/006 ,  G06N20/00

CPC分类号： G06N20/10 ,  G06N3/006 ,  G06N20/00

摘要： The present invention provides a prediction method and system of high slope deformation. First, historical deformation data of each period of each part of a high slope is obtained as sample data; the sample data is divided into training samples and test samples; then a parameter group of a Support Vector Machine (SVM) model is optimized by using the training samples and a particle Swarm Optimization (PSO) algorithm to determine an optimal parameter group of the SVM model, to obtain a trained SVM model; whether the trained SVM model satisfies a condition is verified by using the test samples, and when the SVM model does not satisfy the condition, an optimal parameter group of the SVM model is re-determined; and finally the deformation of each area of the high slope is predicted by using the SVM model that satisfies the condition.