CN115270920B - Ship target classical track generation method based on density spatial clustering - Google Patents
Ship target classical track generation method based on density spatial clustering Download PDFInfo
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Abstract
The invention provides a ship target classical track generation method based on density spatial clustering, which specifically comprises the following steps: step 1, initializing ship target activity track cluster analysis; step 2, generating a target frequent activity area; and step 3, generating a classical track of the ship target, and simultaneously, the invention also provides a classical track generation system of the ship target based on density spatial clustering. By using the method and the system, track association quality measurement evaluation and a large-density track data processing means based on large data are introduced, and the original algorithm is modified, so that the track association quality is used as a judgment condition of track association, and the track association is rapidly and accurately carried out.
Description
Technical Field
The invention relates to the field of machine learning, in particular to a ship target classical track generation method based on density spatial clustering.
Background
DBSCAN (Density-Based Spatial Clustering ofApplications withNoise, density-based clustering method with noise) is a Density-based spatial clustering algorithm, can find clusters of any shape, and has noise resistance. The algorithm divides regions of sufficient density into clusters and finds arbitrarily shaped clusters in the noisy spatial database, which defines clusters as the largest set of densely connected points. The algorithm exploits the concept of density-based clustering, i.e. requiring that the number of objects (points or other spatial objects) contained within a certain area in the clustering space is not less than a given threshold. The DBSCAN algorithm has the remarkable advantages of high clustering speed and capability of effectively processing noise points and finding spatial clusters with arbitrary shapes. However, since it directly operates on the entire database and uses a global density-characterizing parameter for clustering, it also has two distinct weaknesses: when the data volume is increased, the I/O consumption required by the larger memory is also large; when the density of the spatial clusters is uneven and the difference of the cluster spacing is large, the cluster quality is poor.
The clustering analysis in the data mining divides data according to the similarity degree, and aims to maximize the inter-cluster distance and minimize the intra-cluster distance, namely, the larger the similarity is, the better the difference is. The track clustering is the expansion of clustering analysis on space-time tracks, and aims to divide space-time objects with similar behaviors into one class based on the similarity of time or space, discover the movement mode of the objects through the clustering analysis, analyze the movement rule of the objects and predict the possible future movement behaviors. The track clustering algorithm based on the targets is mainly divided into two types, wherein the whole track is used as a research object for clustering, and the track is divided into a plurality of sub tracks for clustering. The similarity between tracks can be intuitively evaluated, the influence of input parameters is small, but local abnormal information is easy to ignore for complex tracks, and the clustering effect on high-dimensional track data is poor. The method can better identify local characteristics of the track, effectively process high-dimensional track data, and find track clusters with arbitrary shapes by combining a density-based clustering algorithm BSCAN, but with the increase of the data scale, the DBSCAN algorithm can cause low clustering efficiency due to the consumption of a large amount of I/O.
Disclosure of Invention
The present invention has been made in view of the above problems, and it is an object of the present invention to provide a solution to overcome or at least partially solve the above problems. Therefore, in one aspect of the invention, a method for generating a classical track of a ship target based on density spatial clustering is provided, and the method specifically comprises the following steps:
step 1, initializing ship target activity track cluster analysis;
step 2, generating a target frequent activity area;
namely, in the space block, forming a core point in the space block region and a boundary point in the space block region;
forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
then deleting the corresponding space blocks by judging the density accessibility between the residual traces;
forming a frequent activity area of a ship target and generating a track point pi of a space block;
and step 3, generating a classical track of the ship target.
Optionally, the step 1 specifically includes:
(1) Setting the size of a target point trace neighborhood radius Eps of an active area, and initially setting L0 sea;
(2) Setting the minimum point number MinPts included in each field, and initially setting MinPts as LY_PtMinNum;
(3) Setting the size of a space block divided by a target active region as w0 x h0;
(4) The target trace number threshold in the spatial block is set to KJK _PtMinNum.
Optionally, step 2 specifically includes:
(1) Acquiring historical activity track point data of a ship target;
(2) Counting the number of the stippling in each space block area according to the size of the set space block;
(3) According to the set threshold value, space blocks with track points smaller than the threshold value are filtered;
(4) In the space block, forming a core point in the space block area according to the fact that the Eps neighborhood of the track points at least contains the track points of the minimum number LY_PtMinNum;
(5) Forming boundary points in a space block area according to the neighborhood of a certain core point if the track is located in the space block, but not the core point;
(6) Forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
(7) Judging the density accessibility between the remaining traces, and if one trace chain p1, …, pi, …, pn exists in the remaining traces, satisfying p1=p and pn=q, pi is directly accessible from pi+1 about L0 and LY_PtMinNum densities, then trace p is accessible from trace q about L0 and LY_PtMinNum densities; thereby judging whether the space block has reachable density; if the density is not reachable, deleting the space block;
(8) Recording all the remaining space blocks to form a ship target frequent activity area;
(9) The spatial average of the densities of all core points in each spatial block is calculated as the track point pi of the spatial block.
Optionally, the step 3 specifically includes:
(1) According to the principle of nearest distance, carrying out connection between track points pi and p (i+1) in a space block, and processing by using a passing point curve;
(2) Setting an entry point and an exit point of a target in a space region;
(3) And loading all target active track point data on the map platform, and simultaneously loading the formed track point curve to perform manual adjustment to form a ship target classical track point.
The invention also provides a ship target classical track generation system based on density spatial clustering, which specifically comprises an initialization module, an active area generation module and a target classical track generation module;
the initialization module is used for initializing the cluster analysis of the ship target activity track;
the active region generation module is configured to generate a target frequent active region,
namely, the active region generating module forms a core point in the space block region and a boundary point in the space block region in the space block;
forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
then the active region generation module deletes the corresponding space blocks by judging the density accessibility between the residual traces; forming a frequent activity area of a ship target and generating a track point pi of a space block;
the target classical track generation module is used for generating a ship target classical track.
Optionally, the initialization module is specifically configured to:
(1) Setting the size of a target point trace neighborhood radius Eps of an active area, and initially setting L0 sea;
(2) Setting the minimum point number MinPts included in each field, and initially setting MinPts as LY_PtMinNum;
(3) Setting the size of a space block divided by a target active region as w0 x h0;
(4) The target trace number threshold in the spatial block is set to KJK _PtMinNum.
Optionally, the active area generating module is specifically configured to:
(1) Acquiring historical activity track point data of a ship target;
(2) Counting the number of the stippling in each space block area according to the size of the set space block;
(3) According to the set threshold value, space blocks with track points smaller than the threshold value are filtered;
(4) In the space block, forming a core point in the space block area according to the fact that the Eps neighborhood of the track points at least contains the track points of the minimum number LY_PtMinNum;
(5) Forming boundary points in a space block area according to the neighborhood of a certain core point if the track is located in the space block, but not the core point;
(6) Forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
(7) Judging the density accessibility between the remaining traces, and if one trace chain p1, …, pi, …, pn exists in the remaining traces, satisfying p1=p and pn=q, pi is directly accessible from pi+1 about L0 and LY_PtMinNum densities, then trace p is accessible from trace q about L0 and LY_PtMinNum densities; thereby judging whether the space block has reachable density; if the density is not reachable, deleting the space block;
(8) Recording all the remaining space blocks to form a ship target frequent activity area;
(9) The spatial average of the densities of all core points in each spatial block is calculated as the track point pi of the spatial block.
Optionally, the target classical track generation module is specifically configured to:
(1) According to the principle of nearest distance, carrying out connection between track points pi and p (i+1) in a space block, and processing by using a passing point curve;
(2) Setting an entry point and an exit point of a target in a space region;
(3) And loading all target active track point data on the map platform, and simultaneously loading the formed track point curve to perform manual adjustment to form a ship target classical track point.
The technical scheme provided in the embodiment of the application has at least the following technical effects or advantages:
in order to improve the found track quality, a track association quality measurement and evaluation and a large-density track data processing means based on large data are introduced, and an original algorithm is modified to take the track association quality as a judgment condition of track association, so that the track association is rapidly and accurately carried out.
The foregoing description is only an overview of the technical solutions of the present invention, and may be implemented according to the content of the specification in order to make the technical means of the present invention more clearly understood, and in order to make the technical solutions of the present invention and the objects, features and advantages thereof more clearly understood, the following specific embodiments of the present invention will be specifically described.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to designate like parts throughout the figures. In the drawings:
FIG. 1 shows a flow chart of a method for generating a classical track of a ship target based on density spatial clustering;
FIG. 2 shows an exemplary initial model build schematic;
FIG. 3 illustrates an exemplary initial target track point location schematic;
FIG. 4 illustrates a schematic diagram of an exemplary filtered target track point less spatial block;
FIG. 5 illustrates an exemplary core point determination schematic;
FIG. 6 illustrates an exemplary boundary point determination schematic;
FIG. 7 illustrates an exemplary noise point determination schematic;
FIG. 8 shows a schematic diagram of an exemplary spatial block with noise point residuals removed;
FIG. 9 illustrates an exemplary density unreachable spatial block determination schematic;
FIG. 10 shows an exemplary residual space block schematic;
FIG. 11 illustrates an exemplary residual spatial block density center generation schematic;
FIG. 12 illustrates an exemplary residual block track point link schematic;
FIG. 13 illustrates an exemplary human intervention generation target classical track schematic;
fig. 14 shows a schematic diagram of a ship target classical trajectory generation system based on density spatial clustering.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The invention aims at solving the problem that the DBSCAN algorithm can cause low clustering efficiency due to the consumption of a large amount of I/O along with the increase of the data scale, and provides a ship target classical track generation method based on density space clustering, which specifically comprises the following steps as shown in figure 1:
and step 1, constructing a ship target activity track clustering analysis initialization model.
(1) Setting the size of a target point trace neighborhood radius Eps of an active area, and initially setting L0 sea;
(2) Setting the minimum point number MinPts included in each field, and initially setting MinPts as LY_PtMinNum;
(3) Setting the size of a space block divided by a target active region as w0 x h0;
(4) Setting a target trace number threshold value in a space block as KJK _PtMinNum;
(5) And forming a ship target activity track cluster analysis initialization model based on the parameters.
A specific model example is shown in fig. 2, for example, the minimum point minpts=3 in each domain, and the target point trace number threshold KJK _ptminnum=3 in a spatial block.
And 2, generating a target frequent activity area.
(1) Acquiring historical activity track point data of a ship target;
(2) Counting the number of the stippling in each space block area according to the size of the set space block;
specific target track point positions and statistics examples specific numerical values of the positions and the counted numbers of the target track points in each space block shown in the example are shown in fig. 3.
(3) According to the set threshold value, space blocks with track points smaller than the threshold value are filtered;
and in particular, the space block example shown in fig. 4 is obtained after filtering the space blocks with fewer target track points based on the space blocks with the target track point positions shown in fig. 3.
(4) In the space block, forming a core point in the space block area according to the fact that the Eps neighborhood of the track points at least contains the track points of the minimum number LY_PtMinNum;
a specific core point determination example is shown in fig. 5.
In the figure, 5 blocks are selected, each block selects a point for illustration, and a circle taking each point as a center and Eps as a radius is represented; and calculating the number of the target track points in the circle, wherein the number of the target track points in the circle is greater than or equal to MinPts and is taken as a core point, and the solid point is taken as the core point in FIG. 5.
(5) Forming boundary points in a space block area according to the neighborhood of a certain core point if the track is located in the space block, but not the core point;
a specific boundary point determination example is shown in fig. 6.
A target track point in 1 block is selected for illustration in the figure, and a circle with the center of each point and Eps as a radius is represented; and calculating the number of target track points in the circle, wherein the number of the target track points in the circle is smaller than MinPts and is used as a boundary point, and meanwhile, the number of the target track points (including the track points) in the circle is larger than 1, and the solid points in FIG. 6 are used as boundary points.
(6) Forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
a specific noise point determination example is shown in fig. 7.
Noise points are points in the radius circle area with Eps not in any block, and fig. 7 solid points are noise points.
An example of a spatial block with the noise point remaining specifically removed is shown in fig. 8.
The remaining spatial blocks include only blocks of core points and boundary points.
(7) Judging the density accessibility between the remaining traces, and if one trace chain p1, …, pi, …, pn exists in the remaining traces, satisfying p1=p and pn=q, pi is directly accessible from pi+1 about L0 and LY_PtMinNum densities, then trace p is accessible from trace q about L0 and LY_PtMinNum densities; thereby judging whether the space block has reachable density; if the density is not reachable, deleting the space block;
an example of a specific density unreachable spatial block determination is shown in fig. 9.
Numbering p (i, j) on each remaining target track point, wherein i represents the number of blocks and j represents the number of points; the number of the remaining blocks is n, and the number of target track points in the ith block is m (i). Calculating the distance between p (i, j) and any other point in the block, recording each point with the distance between p (i, j) smaller than Eps and with reachable density, forming m (i) sets, comparing the m (i) sets with elements, combining two sets with the same points, comparing the combined sets with the rest sets again until each set is compared, and if the number of the last sets is larger than 1, the density is unreachable.
(8) Recording all the remaining space blocks to form a ship target frequent activity area;
an example of a specific residual block of space is shown in fig. 10.
(9) The spatial average of the densities of all core points in each spatial block is calculated as the track point pi of the spatial block.
A specific residual space block density center generation example is shown in fig. 11.
The density space average value is the average value of the geographic coordinates of the corresponding track points.
I.e., x= (x1+x2+) +xn)/n;
y＝(y1+y2+…+yn)/n；
wherein the spatial track point geographic coordinates are (xi, yi); i=1, … …, n
Hollow dot density center points in fig. 11.
And step 3, generating a classical track of the ship target.
(1) According to the principle of nearest distance, carrying out connection between track points pi and p (i+1) in a space block, and processing by using a passing point curve;
an example of a specific track point connection is shown in fig. 12.
(2) Setting an entry point and an exit point of a target in a space region;
(3) And loading all target active track point data on the map platform, and simultaneously loading the formed track point curve to perform manual adjustment to form a ship target classical track point.
Specific manual intervention generation target classical track examples are shown in fig. 13.
The invention further provides a ship target classical track generation system based on the density spatial clustering, which specifically comprises an initialization module, an active area generation module and a target classical track generation module.
The initialization module is used for initializing the cluster analysis of the ship target activity tracks.
The method comprises the following steps: the initialization module sets the size of a target trace neighborhood radius Eps of the active area, and initially sets L0 sea;
setting the minimum point number MinPts included in each field, and initially setting MinPts as LY_PtMinNum;
setting the size of a space block divided by a target active region as w0 x h0;
the target trace number threshold in the spatial block is set to KJK _PtMinNum.
The active region generation module is used for generating a target frequent active region.
The method comprises the following steps: the activity area generating module acquires historical activity track point data of a ship target;
then, counting the number of the stippling in each space block area according to the set space block size;
the active region generation module filters space blocks with track points smaller than a threshold according to the set threshold;
in the space block, forming a core point in the space block area according to the fact that the Eps neighborhood of the track points at least contains the track points of the minimum number LY_PtMinNum;
in the space block, the active region generation module forms boundary points in the space block region according to the neighborhood of a certain core point if the track is in the neighborhood of the core point, but not the core point;
then, forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
the active region generating module judges the density accessibility between the remaining traces, and according to the condition that if one trace chain p1, …, pi, …, pn exists in the remaining traces, p1=p and pn=q are satisfied, pi is directly density accessibility from pi+1 about L0 and LY_PtMinNum, the trace p is density accessibility from the trace q about L0 and LY_PtMinNum; thereby judging whether the space block has reachable density; if the density is not reachable, deleting the space block;
simultaneously, the activity area generating module records all the remaining space blocks to form a ship target frequent activity area;
and calculates the spatial average of the densities of all core points in each spatial block as the track points pi of the spatial block.
The target classical track generation module is used for generating a ship target classical track.
The method comprises the following steps: the target classical track generation module is arranged in a space area, and an entry point and an exit point of a target are arranged in the space area;
then, according to the principle of distance nearest, carrying out the connection of track points pi and p (i+1) in the space block, and processing by using a passing point curve;
and the target classical track generation module loads all target active track point data on the map platform, and simultaneously loads the formed track point curve to perform manual adjustment to form the ship target classical track point.
In the description provided herein, numerous specific details are set forth. However, it is understood that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim.
Claims (2)
1. A ship target classical track generation method based on density space clustering specifically comprises the following steps:
step 1, initializing ship target activity track cluster analysis;
step 2, generating a target frequent activity area;
forming a core point in the space block region and a boundary point in the space block region in the space block;
forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
then deleting the corresponding space blocks by judging the density accessibility between the residual traces; forming a frequent activity area of a ship target and generating a track point pi of a space block;
step 3, generating a classical track of the ship target;
the step 1 specifically comprises the following steps:
(1) Setting the size of a target point trace neighborhood radius Eps of an active area, and initially setting L0 sea;
(2) Setting the minimum point number MinPts included in each field, and initially setting MinPts as LY_PtMinNum;
(3) Setting the size of a space block divided by a target active region as w0 x h0;
(4) Setting a target trace number threshold value in a space block as KJK _PtMinNum;
the step 2 specifically comprises the following steps:
(1) Acquiring historical activity track point data of a ship target;
(2) Counting the number of the stippling in each space block area according to the size of the set space block;
(3) According to the set threshold value, space blocks with track points smaller than the threshold value are filtered;
(4) In the space block, forming a core point in the space block area according to the fact that the Eps neighborhood of the track points at least contains the track points of the minimum number LY_PtMinNum;
(5) Forming boundary points in a space block area according to the neighborhood of a certain core point if the track is located in the space block, but not the core point;
(6) Forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
(7) Judging the density accessibility between the remaining traces, and if there is one trace chain p1, …, pi,..pn, p1 = p and p n = q, pi is directly density accessible from pi+1 about L0 and LY_PtMinNum, then trace p is density accessible from trace q about L0 and LY_PtMinNum; thereby judging whether the space block has reachable density; if the density is not reachable, deleting the space block;
(8) Recording all the remaining space blocks to form a ship target frequent activity area;
(9) Calculating the density space average value of all core points in each space block to be used as track points pi of the space block;
the step 3 specifically comprises the following steps:
(1) According to the principle of nearest distance, carrying out connection between track points pi and p (i+1) in a space block, and processing by using a passing point curve;
(2) Setting an entry point and an exit point of a target in a space region;
(3) And loading all target active track point data on the map platform, and simultaneously loading the formed track point curve to perform manual adjustment to form a ship target classical track point.
2. The system specifically comprises an initialization module, an active area generation module and a target classical track generation module;
the initialization module is used for initializing the cluster analysis of the ship target activity track;
the active region generation module is configured to generate a target frequent active region,
the active region generation module forms a core point in the space block region and a boundary point in the space block region in the space block;
forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
then the active region generation module deletes the corresponding space blocks by judging the density accessibility between the residual traces; forming a frequent activity area of a ship target and generating a track point pi of a space block;
the target classical track generation module is used for generating a ship target classical track;
the initialization module is specifically configured to:
(1) Setting the size of a target point trace neighborhood radius Eps of an active area, and initially setting L0 sea;
(2) Setting the minimum point number MinPts included in each field, and initially setting MinPts as LY_PtMinNum;
(3) Setting the size of a space block divided by a target active region as w0 x h0;
(4) Setting a target trace number threshold value in a space block as KJK _PtMinNum;
the active region generation module is specifically configured to:
(1) Acquiring historical activity track point data of a ship target;
(2) Counting the number of the stippling in each space block area according to the size of the set space block;
(3) According to the set threshold value, space blocks with track points smaller than the threshold value are filtered;
(4) In the space block, forming a core point in the space block area according to the fact that the Eps neighborhood of the track points at least contains the track points of the minimum number LY_PtMinNum;
(5) Forming boundary points in a space block area according to the neighborhood of a certain core point if the track is located in the space block, but not the core point;
(6) Forming noise points according to the core points and the boundary points, and deleting the noise points of the space block area;
(7) Judging the density accessibility between the remaining traces, and if there is one trace chain p1, …, pi,..pn, p1 = p and p n = q, pi is directly density accessible from pi+1 about L0 and LY_PtMinNum, then trace p is density accessible from trace q about L0 and LY_PtMinNum; thereby judging whether the space block has reachable density; if the density is not reachable, deleting the space block;
(8) Recording all the remaining space blocks to form a ship target frequent activity area;
(9) Calculating the density space average value of all core points in each space block to be used as track points pi of the space block;
the target classical track generation module is specifically configured to:
(1) According to the principle of nearest distance, carrying out connection between track points pi and p (i+1) in a space block, and processing by using a passing point curve;
(2) Setting an entry point and an exit point of a target in a space region;
(3) And loading all target active track point data on the map platform, and simultaneously loading the formed track point curve to perform manual adjustment to form a ship target classical track point.
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Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3739295B1 (en) * | 2019-05-13 | 2021-11-10 | S.A.T.E. - Systems and Advanced Technologies Engineering S.R.L. | Method for determining an optimal naval navigation routes from historical gnss data of naval trajectories |
| CN110309383B (en) * | 2019-06-17 | 2021-07-13 | 武汉科技大学 | Ship track clustering analysis method based on improved DBSCAN algorithm |
| CN111582380B (en) * | 2020-05-09 | 2024-05-24 | 中国人民解放军92493部队试验训练总体研究所 | Ship track density clustering method and device based on space-time characteristics |
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| US11468999B2 (en) * | 2020-07-31 | 2022-10-11 | Accenture Global Solutions Limited | Systems and methods for implementing density variation (DENSVAR) clustering algorithms |
| CN113032378B (en) * | 2021-03-05 | 2024-07-19 | 北京工业大学 | Ship behavior pattern mining method based on clustering algorithm and pattern mining |
| CN113298195A (en) * | 2021-07-27 | 2021-08-24 | 中国电子科技集团公司第十五研究所 | Method and device for generating classical trajectory of offshore target and storage medium |
| CN113887590B (en) * | 2021-09-22 | 2023-06-09 | 中国电子科技集团公司第二十九研究所 | Target typical track and area analysis method |
| CN114091578A (en) * | 2021-11-03 | 2022-02-25 | 浙江海洋大学 | Ship track clustering method based on curve length distance |
| CN114564545A (en) * | 2022-01-13 | 2022-05-31 | 武汉理工大学 | System and method for extracting ship experience course based on AIS historical data |
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