Storage medium, navigation monitoring method, and navigation monitoring device

Patent metadata

Field	Value
Publication number	US-11945560-B2
Application number	US-202217850870-A
Country	US
Kind code	B2
Filing date	Jun 27, 2022
Priority date	Oct 18, 2021
Publication date	Apr 2, 2024
Grant date	Apr 2, 2024

Abstract

A non-transitory computer-readable storage medium storing a navigation monitoring program that causes at least one computer to execute a process, the process includes acquiring direction information that indicates a direction of a vessel and position information that indicates a position of the vessel; and predicting whether or not the vessel navigates along a course by inputting the acquired direction information and the acquired position information to a prediction model generated by machine learning by using direction information and position information for each of a plurality of vessels that has navigated in the past and a correct answer label that indicates whether or not each of the plurality of vessels navigates along a course.

First claim

What is claimed is:

1. A non-transitory computer-readable storage medium storing a navigation monitoring program that causes at least one computer to execute a process, the process comprising:
acquiring direction information that indicates a direction of a vessel and position information that indicates a position of the vessel; and
predicting whether or not the vessel navigates along a course by inputting the acquired direction information and the acquired position information to a prediction model generated by machine learning by using direction information and position information for each of a plurality of vessels that has navigated in the past and a correct answer label that indicates whether or not each of the plurality of vessels navigates along the course,
wherein the position information includes section information that indicates a section where a vessel is positioned among a plurality of sections set by dividing a course area with a certain width along the course into polygonal shapes with respect to a navigation direction,
wherein the correct answer label is set to a value that indicates a correct answer in a case where a corresponding vessel among the plurality of vessels reaches a section that is a certain number of sections ahead from a section where the vessel is currently positioned of the plurality of sections with respect to the navigation direction.

2. A navigation monitoring method for a computer to execute a process comprising:
acquiring direction information that indicates a direction of a vessel and position information that indicates a position of the vessel; and
predicting whether or not the vessel navigates along a course by inputting the acquired direction information and the acquired position information to a prediction model generated by machine learning by using direction information and position information for each of a plurality of vessels that has navigated in the past and a correct answer label that indicates whether or not each of the plurality of vessels navigates along the course,
wherein the position information includes section information that indicates a section where a vessel is positioned among a plurality of sections set by dividing a course area with a certain width along the course into polygonal shapes with respect to a navigation direction,
wherein the correct answer label is set to a value that indicates a correct answer in a case where a corresponding vessel among the plurality of vessels reaches a section that is a certain number of sections ahead from a section where the vessel is currently positioned of the plurality of sections with respect to the navigation direction.

3. The non-transitory computer-readable storage medium according to claim 1 , wherein the position information includes the section information and at least one of a latitude and a longitude.

4. A navigation monitoring device comprising:
one or more memories; and
one or more processors coupled to the one or more memories and the one or more processors configured to:
acquire direction information that indicates a direction of a vessel and position information that indicates a position of the vessel, and
predict whether or not the vessel navigates along a course by inputting the acquired direction information and the acquired position information to a prediction model generated by machine learning by using direction information and position information for each of a plurality of vessels that has navigated in the past and a correct answer label that indicates whether or not each of the plurality of vessels navigates along the course,
wherein the position information includes section information that indicates a section where a vessel is positioned among a plurality of sections set by dividing a course area with a certain width along the course into polygonal shapes with respect to a navigation direction,
wherein the correct answer label is set to a value that indicates a correct answer in a case where a corresponding vessel among the plurality of vessels reaches a section that is a certain number of sections ahead from a section where the vessel is currently positioned of the plurality of sections with respect to the navigation direction.

5. The non-transitory computer-readable storage medium according to claim 1 , wherein the direction information includes course over ground.

6. The non-transitory computer-readable storage medium according to claim 1 , wherein the prediction model is generated by using destination information that indicates a destination of a vessel, wherein the predicting includes inputting the destination information regarding the vessel to the prediction model.

7. The non-transitory computer-readable storage medium according to claim 1 , wherein the prediction model is generated by using size information that indicates a size of a vessel, wherein the predicting includes inputting the size information regarding the vessel to the prediction model.

Assignees

• Fujitsu Ltd

Inventors

• Watanabe Isamu
• Ito Shinji
• Fujimoto Hiraku
• Ikeda Ryusei
• Usui Kenta

Classifications

• B63B79/40 Primary

  for controlling the operation of vessels, e.g. monitoring their speed, routing or maintenance schedules · CPC title

• B63B79/20

  using models or simulation, e.g. statistical models or stochastic models · CPC title

• G08G3/02

  Anti-collision systems · CPC title

• G06F16/29

  Geographical information databases · CPC title

• G01C21/203 Primary

  specially adapted for water-borne vessels · CPC title