公开(公告)号：US12115731B2

公开(公告)日：2024-10-15

申请号：US17779550

申请日：2020-11-06

Applicant: SHANDONG UNIVERSITY

Inventor： Jing Wang ,  Zhengfang Wang ,  Peng Jiang ,  Kefu Chen ,  Yanfei Yu ,  Wei Guo ,  Qingmei Sui

IPC: B29C64/393 ,  B25J9/16 ,  B25J11/00 ,  B29C64/236 ,  B29C73/02 ,  B29C73/24 ,  B33Y30/00 ,  B33Y50/02 ,  G01S17/89

CPC classification number: B29C64/393 ,  B25J9/1694 ,  B25J11/005 ,  B29C64/236 ,  B29C73/02 ,  B29C73/24 ,  B33Y30/00 ,  B33Y50/02 ,  G01S17/89

Abstract: A surface disease repair system and method for an infrastructure based on climbing robots are provided. The system includes a detection and marking climbing robot and a repair climbing robot. In the process of moving on a surface of an infrastructure to be detected, the detection and marking climbing robot collects a front surface image in real time through a binocular camera arranged at a front end, detects a disease on the basis of the front surface image, and performs localization and map reconstruction at the same time; when a disease is detected, the position of the disease is recorded, and a marking device is controlled to mark the disease; after detection and marking are completed, the position of the disease and the map are sent to the repair climbing robot; and the repair climbing robot receives the map and the position of the disease, reaches the position of the disease, and repairs the disease according to the mark by using a repair device.

公开(公告)号：US11828894B2

公开(公告)日：2023-11-28

申请号：US18031289

申请日：2021-12-14

Applicant: SHANDONG UNIVERSITY

Inventor： Bin Liu ,  Yuxiao Ren ,  Peng Jiang ,  Senlin Yang ,  Qingyang Wang ,  Xinji Xu ,  Duo Li

IPC: G01V1/30

CPC classification number: G01V1/303 ,  G01V2210/6222 ,  G01V2210/66

Abstract: A multi-scale unsupervised seismic velocity inversion method based on an autoencoder for observation data. Large-scale information is extracted by the autoencoder, which is used for guiding an inversion network to complete the recovery of different-scale features in a velocity model, thereby reducing the non-linearity degree of inversion. A trained encoder part is embedded into the network to complete the extraction of seismic observation data information at the front end, so it can better analyze the information contained in seismic data, the mapping relationship between the data and velocity model is established better, then the inversion method is unsupervised, and location codes are added to the observation data to assist the network in perceiving the layout form of an observation system, which facilitates practical engineering application. Thus a relatively accurate inversion result of the seismic velocity model when no real geological model serves as a network training label can be achieved.

公开(公告)号：US12013485B2

公开(公告)日：2024-06-18

申请号：US17289139

申请日：2020-09-30

Applicant: SHANDONG UNIVERSITY

Inventor： Shucai Li ,  Bin Liu ,  Zhengfang Wang ,  Peng Jiang ,  Fengkai Zhang ,  Hanchi Liu

IPC: G01S7/41 ,  B25J5/00 ,  B25J15/00 ,  F16L101/30 ,  G01M3/38 ,  G01N23/203 ,  G01N29/06 ,  G01S13/88 ,  G05D1/00 ,  G06N3/08

CPC classification number: G01S7/41 ,  B25J5/007 ,  B25J15/0019 ,  G01M3/38 ,  G01N23/203 ,  G01N29/069 ,  G01S13/885 ,  G05D1/0212 ,  G06N3/08 ,  F16L2101/30 ,  G01N2223/628 ,  G01N2223/646 ,  G01N2291/0289

Abstract: A multi-scale inspection and intelligent diagnosis system and method for tunnel structural defects includes: a traveling section; a supporting section, disposed on the traveling section, and including a rotatable telescopic platform, where two mechanical arms working in parallel are disposed on the rotatable telescopic platform; an inspection section, mounted on the supporting section, and configured to perform multi-scale inspection on surface defects and internal defects in different depth ranges of a same position of a tunnel structure, and transmit inspected defect information to a control section; and the control section, configured to: construct a deep neural network-based defect diagnosis model; construct a data set by using historical surface defect and internal defect information, and train the deep neural network-based defect diagnosis model; and receive multi-scale inspection information in real time, and automatically recognize types, positions, contours, and dielectric attributes of the internal and surface defects.

公开(公告)号：US12031922B2

公开(公告)日：2024-07-09

申请号：US17289280

申请日：2020-09-30

Applicant: SHANDONG UNIVERSITY

Inventor： Bin Liu ,  Zhengfang Wang ,  Peng Jiang ,  Wenqiang Kang ,  Hanchi Liu ,  Jiaqi Zhang ,  Qingmei Sui

IPC: G01N21/954 ,  F16L55/40 ,  G01N21/88 ,  G01N22/02 ,  G01N23/20008 ,  G01N23/203 ,  G06N20/00 ,  F16L101/30

CPC classification number: G01N21/954 ,  F16L55/40 ,  G01N21/8851 ,  G01N22/02 ,  G01N23/20008 ,  G01N23/203 ,  G06N20/00 ,  F16L2101/30 ,  G01N2021/9544

Abstract: A multi-arm robot used for tunnel lining inspection and defect diagnosis in an operation period, including a moving platform, where an environment detection device and a defect infection device are disposed on the moving platform, the defect infection device is disposed on the moving platform by using a multi-degree-of-freedom mechanical arm, and an attitude detection module is disposed on each multi-degree-of-freedom mechanical arm; a controller receives environmental data and mechanical arm attitude data sensed by the environment detection device and the attitude detection module, and sends a control instruction to the moving platform and the multi-degree-of-freedom mechanical arm according to the environmental data, to implement movement of the robot; and the controller receives tunnel lining structural data sensed by the defect infection device, and performs defect diagnosis. Overall automatic inspection can be implemented both on the surface and inside of the tunnel lining.