Ship trajectory prediction method based on machine learning and AIS data

A ship trajectory and machine learning technology, applied in the field of ship shipping safety, can solve the problems of accuracy influence, ignoring the prior information of the water area, and not considering the historical trajectory, so as to reduce the accuracy requirement and improve the accuracy rate

Pending Publication Date: 2021-08-20
DALIAN MARITIME UNIVERSITY
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Problems solved by technology

[0003] At present, the methods for predicting ship voyage trajectories based on AIS data mainly include two types: based on ship kinematics model and based on machine learning model. The former only considers the current trajectory of the ship, but does not consider the historical trajectory, ignoring the prior information of the current waters. , while the latter has higher requirements on the trajectory data, which requires

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  • Ship trajectory prediction method based on machine learning and AIS data
  • Ship trajectory prediction method based on machine learning and AIS data
  • Ship trajectory prediction method based on machine learning and AIS data

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[0057] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments It is a part of embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0058] Such as figure 1 As shown, the present embodiment provides a ship track prediction method based on machine learning and AIS data, including:

[0059] 101. Preprocessing the ship historical AIS data set;

[0060] Specifically, the obvious erroneous data refers to MMSI errors, excessive speed errors, and course out-of-bounds errors. Static...

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Abstract

The invention discloses a ship trajectory prediction method based on machine learning and AIS data. The method comprises the following steps: preprocessing a historical AIS data set of a ship; dividing the pre-processed AIS data set according to tracks to obtain multiple pieces of trajectory feature data; detecting and deleting error data in the trajectory feature data; performing gridding processing on each piece of trajectory feature data, and dividing prediction targets of trajectory points in each piece of trajectory feature data into eight neighborhood grid directions; and establishing a trajectory prediction model based on an xgboost algorithm, and predicting the trajectory points through a trajectory prediction model. Therefore, the accuracy of ship motion direction prediction is improved. Meanwhile, when the data set is selected, the data can be considered to be valid as long as returned AIS data position information is required to be within a grid precision allowable range, so that the accuracy requirement on the AIS data is reduced.

Description

technical field [0001] The invention relates to the technical field of ship shipping safety, in particular to a ship track prediction method based on machine learning and AIS data. Background technique [0002] AIS is an automatic tracking system installed on ships, which can send characteristic information of ships under navigation in real time, such as speed, course, heading, etc. By analyzing the AIS data obtained in the past, combined with the current voyage trajectory of the ship, the voyage trajectory can be predicted, and it provides assistance for the monitoring and dispatching of the port ships. [0003] At present, the methods for predicting ship voyage trajectories based on AIS data mainly include two types: based on ship kinematics model and based on machine learning model. The former only considers the current trajectory of the ship, but does not consider the historical trajectory, ignoring the prior information of the current waters. , while the latter has hig...

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Application Information

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IPC IPC(8): G06Q10/04G06F16/29G06N5/00
CPCG06Q10/04G06F16/29G06N5/01
Inventor 马宝山熊桐张新宇高宗江
Owner DALIAN MARITIME UNIVERSITY
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