Prediction system, prediction apparatus, and prediction method

Patent metadata

Field	Value
Publication number	US-2024193796-A1
Application number	US-202318528447-A
Country	US
Kind code	A1
Filing date	Dec 4, 2023
Priority date	Dec 9, 2022
Publication date	Jun 13, 2024
Grant date	—

Abstract

An acquisition means repeatedly acquires three-dimensional data of a monitoring target surface on a time axis. A deformation detection means detects, based on a plurality of pieces of the three-dimensional data, a first deformation occurring at a first time, and a second deformation occurring at a position different from an occurrence position of the first deformation at a second time being after the first time. A deformation prediction means predicts, based on a detection result by the deformation detection means, that a third deformation may occur in a future at a position different from occurrence positions of the first deformation and the second deformation.

First claim

What is claimed is:

1 . A prediction system comprising:
at least one memory storing instructions, and at least one processor configured to execute the instructions to; repeatedly acquire three-dimensional data of a monitoring target surface on a time axis; detect, based on a plurality of pieces of the three-dimensional data, a first deformation occurring at a first time, and a second deformation occurring at a position different from an occurrence position of the first deformation at a second time being after the first time; and predict, based on a detection result, that a third deformation occurs in a future at a position different from occurrence positions of the first deformation and the second deformation.

2 . The prediction system according to claim 1 , wherein the at least one processor is further configured to execute the instructions to predict that the third deformation occurs on an extension line of a line segment connecting a first occurrence position as an occurrence position of the first deformation and a second occurrence position as an occurrence position of the second deformation.

3 . The prediction system according to claim 2 , wherein the at least one processor is further configured to execute the instructions to predict a third time as a time at which the third deformation occurs and a third occurrence position as a position at which the third deformation occurs, based on a time difference between the first time and the second time, and a spatial difference between the first occurrence position and the second occurrence position.

4 . The prediction system according to claim 3 , wherein the at least one processor is further configured to execute the instructions to calculate deformation propagation velocity, based on the time difference and the spatial difference, and predict the third time and the third occurrence position, based on the second time, the second occurrence position, and the deformation propagation velocity.

5 . The prediction system according to claim 3 , wherein the at least one processor is further configured to execute the instructions to output the third occurrence position in an image format.

6 . The prediction system according to claim 3 , the at least one processor is further configured to execute the instructions to output the first occurrence position, the second occurrence position, and the third occurrence position in an image format.

7 . The prediction system according to claim 1 , wherein the three-dimensional data are point cloud data being output from a three-dimensional LiDAR scanner.

8 . A prediction apparatus comprising:
at least one memory storing instructions, and at least one processor configured to execute the instructions to; repeatedly acquire three-dimensional data of a monitoring target surface on a time axis; detect, based on a plurality of pieces of the three-dimensional data, a first deformation occurring at a first time, and a second deformation occurring at a position different from an occurrence position of the first deformation at a second time being after the first time; and predict, based on a detection result, that a third deformation occurs in a future at a position different from occurrence positions of the first deformation and the second deformation.

9 . The prediction apparatus according to claim 8 , wherein the at least one processor is further configured to execute the instructions to predict that the third deformation occurs on an extension line of a line segment connecting a first occurrence position as an occurrence position of the first deformation and a second occurrence position as an occurrence position of the second deformation.

10 . The prediction apparatus according to claim 9 , wherein the at least one processor is further configured to execute the instructions to predict a third time as a time at which the third deformation occurs and a third occurrence position as a position at which the third deformation occurs, based on a time difference between the first time and the second time, and a spatial difference between the first occurrence position and the second occurrence position.

11 . The prediction apparatus according to claim 10 , wherein the at least one processor is further configured to execute the instructions to calculate deformation propagation velocity, based on the time difference and the spatial difference, and predict the third time and the third occurrence position, based on the second time, the second occurrence position, and the deformation propagation velocity.

12 . The prediction apparatus according to claim 10 , wherein the at least one processor is further configured to execute the instructions to output the third occurrence position in an image format.

13 . The prediction apparatus according to claim 10 , the at least one processor is further configured to execute the instructions to output the first occurrence position, the second occurrence position, and the third occurrence position in an image format.

14 . The prediction apparatus according to claim 8 , wherein the three-dimensional data are point cloud data being output from a three-dimensional LiDAR scanner.

15 . A prediction method comprising,
repeatedly acquiring three-dimensional data of a monitoring target surface on a time axis; detecting, based on a plurality of pieces of the three-dimensional data, a first deformation occurring at a first time, and a second deformation occurring at a position different from an occurrence position of the first deformation at a second time being after the first time; and predicting, based on a detection result, that a third deformation occurs in a future at a position different from occurrence positions of the first deformation and the second deformation.

16 . The prediction method according to claim 15 ,
in the predicting, further comprises predicting that the third deformation occurs on an extension line of a line segment connecting a first occurrence position as an occurrence position of the first deformation and a second occurrence position as an occurrence position of the second deformation.

17 . The prediction method according to claim 16 ,
in the predicting, further comprises predicting a third time as a time at which the third deformation occurs and a third occurrence position as a position at which the third deformation occurs, based on a time difference between the first time and the second time, and a spatial difference between the first occurrence position and the second occurrence position.

18 . The prediction method according to claim 17 , in the predicting, further comprises;
calculating deformation propagation velocity, based on the time difference and the space difference; and predicting the third time and the third occurrence position, based on the second time, the second occurrence position, and the deformation propagation velocity.

19 . The prediction method according to claim 17 , further comprising outputting the third occurrence position in an image format.

20 . The prediction method according to claim 17 , further comprising outputting the first occurrence position, the second occurrence position, and the third occurrence position in an image format.

Assignees

• Nec Corp

Inventors

• Yutani Katsuhiro

Classifications

• G01S17/89 Primary

  for mapping or imaging · CPC title

• G06T7/564

  from contours · CPC title

• G06T7/521

  from laser ranging, e.g. using interferometry; from the projection of structured light · CPC title

• G06T2207/10028

  Range image; Depth image; 3D point clouds · CPC title

• G06T7/251 Primary

  involving models · CPC title