A navigation mark intelligent collision avoidance method and system

Abstract

The invention discloses an intelligent collision avoidance method for a navigation mark, which realizes intelligent prediction and early warning of the ship-to-navigation mark collision risk by predicting the ship's track, constructing a 3D scene, and estimating the collision risk; predicting the ship's track, according to the ship's feature point model, according to the ship's Real-time and historical ship position and heading prediction Ship position, heading and track of ship feature points, wherein the ship track is predicted according to the Kalman filter method; build a 3D scene, and perform a three-dimensional visual display of the distribution of navigation marks. The three-dimensional The visual display includes a schematic diagram of the buoy layout; collision risk estimation, calculation of the closest encounter distance between the buoy and the ship feature point model and the driving distance required for the ship to reach the meeting point, real-time estimation of the collision risk, and prediction of the ship and the buoy Whether to collide. The invention also discloses a device based on the above method. The invention has the advantage that it can effectively realize the intelligent prediction and early warning of the ship-to-navigation mark collision risk.

Description

technical field [0001] The invention relates to the technical field of ship track prediction, in particular to an intelligent method and system for collision avoidance of navigation marks. Background technique [0002] In the prior art, in the field of ship navigation prediction and control, various intelligent prediction algorithms have been used to predict the ship's navigation track, thereby ensuring the safety of the ship's navigation. In the prior art, a method for predicting a ship's navigation trajectory is to use a Kalman filter for calculation. However, the traditional Kalman filter uses the same sampling time interval throughout the filtering process. However, due to the time interval of AIS position update report varies with ship speed, communication blockage between coast station and buoy and network delay, sometimes correct position data may not be received, or position report may not be updated in time. In the prior art, the Kalman filter has been introduced ...

AI Technical Summary

Problems solved by technology

However, the traditional Kalman filter uses the same sampling time interval throughout the filtering process

However, because the AIS position update report time interval varies with different ship speeds, communication blockage and network delay between the coast station and the buoy, sometimes the correct position data may not be received, or the position report may not be updated in time

In the prior art, there is also an adaptive mechanism that has introduced the Kalman filter to predict the ship's track, but there are still problems that the position data is not updated in time or the estimation error is large

[0003] In addition, in the existing technology, especially in the field of navigation aids and ship navigation prediction, there is a lack of methods for the visualization of navigation aids and the intelligent prediction and early warning of ship-to-navigation collision risks

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