Ship track clustering analysis method based on improved dbscan algorithm

A ship trajectory and clustering analysis technology, applied in the field of ship navigation safety, can solve the problems of long time consumption of ship trajectory clustering, inaccurate trajectory classification, poor robustness and adaptability, etc. Adaptability, reducing time consumption, avoiding the effect of accidents

Active Publication Date: 2021-07-13
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

[0003] The DBSCAN algorithm is usually used for cluster analysis of ship trajectories, but the accuracy of the existing DBSCAN algorithm measurement is low, the obtained trajectory classification is not particularly accurate, and the clustering of ship trajectories takes a long time, and its robustness and adaptability sex is also poor

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  • Ship track clustering analysis method based on improved dbscan algorithm
  • Ship track clustering analysis method based on improved dbscan algorithm
  • Ship track clustering analysis method based on improved dbscan algorithm

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Embodiment Construction

[0047] In order to facilitate those skilled in the art to better understand the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments. The following is only exemplary and does not limit the protection scope of the present invention.

[0048] like figure 1 As shown, a kind of ship trajectory cluster analysis method based on the improved DBSCAN algorithm described in this embodiment comprises the following steps:

[0049] Step S1, extract valid ship trajectory data from the AIS database.

[0050] Step S2, using the fusion distance MD to measure the trajectory similarity, and obtain the shortest super-trajectory after the fusion between the trajectories.

[0051] Specifically, such as figure 2 As shown, the specific method of using the fusion distance MD to measure the trajectory similarity to obtain the shortest supertrajectory after fusion between trajectories is as follows: A...

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Abstract

The invention discloses a ship trajectory clustering analysis method based on the improved DBSCAN algorithm, comprising the following steps: S1, extracting effective ship trajectory data from the AIS database; S2, using the fusion distance MD to measure the trajectory similarity, and obtaining the trajectory The shortest hypertrajectory after fusion between; S3, adopt the length of the shortest supertrajectory of secondary time O (mn); S4, obtain fusion distance MD from the length of trajectory; S5, determine the global parameter of the improved DBSCAN algorithm; S6 1. Scan the entire trajectory segment data set through the improved DBSCAN algorithm to obtain a collection of clusters; S7. For the acquired spatial motion mode, if a new trajectory conforms to one of the ship operating modes, then the trajectory is considered to be a normal state track. This method improves the measurement accuracy, reduces the time consumption compared with the traditional DBSCAN algorithm, and can handle multi-density data sets well, has better robustness and adaptability, and correctly classifies the trajectory .

Description

technical field [0001] The invention relates to the technical field of ship navigation safety, in particular to a ship track cluster analysis method based on an improved DBSCAN algorithm. Background technique [0002] With the development of the economy, the enhancement of waterway transportation and international trade has led to the rise of maritime traffic, and at the same time, a large number of ship motion trajectories have been generated, which is of great significance to the analysis and supervision of global maritime transportation. Nowadays, with the development of technology, the Automatic Identification System (AIS) is introduced to track ships and monitor maritime affairs. It can cooperate with the Global Positioning System (GPS) to exchange a series of ship dynamic and static information such as position, course, and speed with other ships and AIS base stations. Facilitates tracking and monitoring of vessel movements at sea. Through these AIS data, mining the s...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/906G06K9/62G06Q50/30
CPCG06F16/906G06Q50/30G06F18/2321G06F18/22G06F18/25
Inventor 陈姚节桂飞周海徐进
Owner WUHAN UNIV OF SCI & TECH
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