CN114088097A

CN114088097A - Method for extracting marine vessel navigable manifold frame based on AIS big data

Info

Publication number: CN114088097A
Application number: CN202111337089.7A
Authority: CN
Inventors: 高邈; 张安民
Original assignee: Tianjin University
Current assignee: Tianjin University
Priority date: 2021-11-12
Filing date: 2021-11-12
Publication date: 2022-02-25

Abstract

The invention relates to the technical field of ship route planning, in particular to a method for extracting a marine ship navigable fairy form frame based on AIS big data, which comprises the following steps of: carrying out density statistics on data in the area based on AIS big data to obtain a complete ship density map, then carrying out rasterization processing, determining a corresponding density threshold value, and finally obtaining a data density search basic map; based on AIS big data, identifying and extracting track junction points of the ship, then determining corresponding track intersection angle thresholds, further screening the junction points, and finally obtaining a search starting point; and (3) performing search expansion by adopting the idea of manifold learning and taking the data density search basic graph as a searched base graph and taking track intersection points of different levels as starting points to finally obtain the marine vessel navigable manifold frame. The method can obtain an objective traffic flow frame in the region, and can be used as a part of an offshore space information facility foundation to be superposed and displayed on the electronic chart.

Description

Method for extracting marine vessel navigable manifold frame based on AIS big data

Technical Field

The invention relates to the technical field of ship route planning, in particular to a method for extracting a marine ship navigable fairy form frame based on AIS big data.

Background

In the past, the route planning of a ship and the calculation of marine navigation distance both directly calculate the Euclidean geometric straight-line distance on the marine geographic position, and the calculated route is not the actual ship advancing route, so that the distance has no practical application significance. The navigation habit of an actual ship is not considered in the original mode of calculating the marine distance, the planned path or the calculated voyage is far from the navigation path of the actual ship, the navigability of the actual marine ship and the habitual navigation method of a ship driver are neglected, the effect is poor when the practical application is carried out, and the guidance on the actual navigation is not strong.

Disclosure of Invention

In order to effectively solve the problems in the background art, the invention provides a method for extracting a marine vessel navigable fairform frame based on AIS big data.

The specific technical scheme is as follows;

a method for extracting a marine vessel navigable manifold frame based on AIS big data comprises the following steps:

step 1: carrying out density statistics on data in the area based on AIS big data to obtain a complete ship density map, then carrying out rasterization processing, determining a corresponding density threshold value, and finally obtaining a data density search basic map;

step 2: based on AIS big data, identifying and extracting track junction points of the ship, then determining corresponding track intersection angle thresholds, further screening the junction points, and finally obtaining a search starting point;

and step 3: and (3) performing search expansion by adopting the idea of manifold learning and taking the data density search basic graph as a searched base graph and taking track intersection points of different levels as starting points to finally obtain the marine vessel navigable manifold frame.

Preferably, in step 1, a portion of the low density data needs to be filtered out.

Preferably, in step 2, the screened points need to be subjected to density clustering and attached with corresponding weights and search levels.

Compared with the prior art, the invention has the beneficial effects that: according to the method, through the AIS big data extraction and mining, an objective traffic flow frame in the region can be obtained, the objective traffic flow frame can be displayed on an electronic chart as a part of an offshore space information facility foundation in an overlapping mode, then the manifold distance between two offshore points can be obtained based on the marine vessel navigable manifold frame, the manifold distance can be used as the basis of all subsequent similarity measurement, and the practical applicability of the measurement on the distances between the two offshore points is integrally improved.

Drawings

FIG. 1 is a schematic diagram of geodesic distances under the navigable fairform frame of a ship according to the invention;

FIG. 2 is a flow chart of the whole marine vessel navigable fairway frame extraction in the invention;

FIG. 3 is an AIS data grid density map in accordance with the present invention;

FIG. 4 is a basic diagram of data density search in the present invention;

FIG. 5 is a flowchart of the route intersection extraction of the present invention;

FIG. 6 is a trajectory intersection plot in the present invention;

FIG. 7 is a flow chart of density clustering in the present invention;

FIG. 8 is a schematic view of the intersection level of the present invention;

FIG. 9 is a schematic diagram of the initial search point in the present invention;

FIG. 10 is a view of a traffic flow frame search strategy according to the present invention;

FIG. 11 is a schematic diagram of search direction strategy in the present invention;

FIG. 12 is a schematic diagram of the framework search in the present invention.

Detailed Description

Spatially relative terms, such as "above … …," "above … …," "above … …," "above," and the like, may be used herein for ease of description to describe one device or feature's spatial relationship to another device or feature as illustrated in the figures. It will be understood that the spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the figures. For example, if a device in the figures is turned over, devices described as "above" or "on" other devices or configurations would then be oriented "below" or "under" the other devices or configurations. Thus, the exemplary term "above … …" can include both an orientation of "above … …" and "below … …". The device may also be oriented 90 degrees or at other orientations and the spatially relative descriptors used herein interpreted accordingly.

The following detailed description of the preferred embodiments will be made with reference to the accompanying drawings. According to the method, high-density path search is carried out based on AIS data, the AIS big data of the ship is analyzed from the AIS big data, the navigable manifold frame of the ship at sea is extracted, and a new navigable manifold geodesic distance at sea can be calculated based on the frame. As shown in fig. 1, the solid line represents the marine navigable manifold geodesic distance obtained based on the marine vessel navigable manifold frame, and the dotted line represents the traditional euclidean geometric linear distance, so that the geodesic distance can be seen to better conform to the navigation rule of the actual vessel, be more reasonable, and be closer to the application scene of the actual navigation.

The method comprises the steps of carrying out grating processing on an offshore area on the basis of AIS big data, carrying out statistics on the density of offshore traffic flow, extracting a high-density area of the traffic flow as a frame structure of the offshore traffic flow, taking a high-density point of an offshore traffic flow gathering area extracted in advance as an initial point, carrying out extension search along the direction of high density, finally obtaining a basic frame of navigation of the offshore traffic flow, and finally providing a concept of the offshore geodesic distance on the basis of the basic frame of the traffic flow. The method is more approximate to the actual navigation rule and the actual navigation habit at sea, and is more reasonable and universal than the method for calculating the Euclidean geometric distance between two points in a simple two-dimensional space.

The marine navigable pattern distance frame obtained by mining the AIS big data can objectively embody the driving habit of the ship in the water area and good navigation conditions near the route, and can provide navigation safety suggestions for the ship sailing in the water area. The frame also well reflects the accessibility among the route nodes and provides safe and reliable route planning for the sailing ships. In addition, the method has important reference value for relevant maritime affair administration authorities to carry out supervision and set recommended navigation channels.

In view of the defects of the current research, a ship position density graph of ship AIS data is taken as a search basis of a traffic network, and an intersection point of ship tracks after ship clustering is taken as a search starting point, so that a marine ship navigable manifold frame is obtained.

The method specifically comprises the following steps:

1. data cleaning is performed based on the AIS big data.

2. And performing grid treatment on the research water area.

3. AIS big data was subjected to density statistical analysis (fig. 3).

4. And (4) carrying out threshold value screening on the density data subjected to the statistical analysis to obtain a data density search basic graph (figure 4).

5. The intersection point of the ship track is extracted based on the AIS big data, and the extraction flow chart is shown in FIG. 5.

6. The extracted route collection points are shown in fig. 6.

7. Then, the intersection scatter points are aggregated into an intersection center point by means of density clustering, and the clustering process is shown in fig. 7.

8. The grade of the intersection point is divided into four grades according to the weight of the cluster center point, and a schematic diagram of the intersection point grade is shown in fig. 8.

9. The intersection points obtained by clustering are used as initial search points, density direction search is performed based on a search basic graph (fig. 4), and a schematic diagram of initial point search is shown in fig. 9.

10. The traffic flow frame search strategy is shown in fig. 10.

11. The actual search process and the search direction are schematically shown in fig. 11.

12. The final search results are shown in fig. 12.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims

1. A method for extracting a marine vessel navigable manifold frame based on AIS big data is characterized by comprising the following steps: the method comprises the following steps: step 1: carrying out density statistics on data in the area based on AIS big data to obtain a complete ship density map, then carrying out rasterization processing, determining a corresponding density threshold value, and finally obtaining a data density search basic map;

2. The method for extracting the marine vessel navigable fairy form frame based on AIS big data according to claim 1, characterized in that: in step 1, a portion of the low density data needs to be filtered out.

3. The method for extracting the marine vessel navigable fairy form frame based on AIS big data according to claim 1, characterized in that: in step 2, the screened points need to be subjected to density clustering and attached with corresponding weights and search levels.