CN114492571A - Ship track classification method based on similarity distance - Google Patents
Ship track classification method based on similarity distance Download PDFInfo
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Abstract
The invention discloses a ship track classification method based on similarity distance, which comprises the steps of firstly, recording track point data of a ship, and selecting a reference ship; calculating the position similarity distance and the speed similarity distance between the reference ship and other ships, and synthesizing the two to calculate the final similarity distance; replacing the similarity distance in the KNN with the final similarity distance for training to obtain an optimal hyper-parameter; sequencing all the final similarity distances according to the numerical value, and counting the times of generating the MMSI values of the ships corresponding to the ship track points of the first K final similarity distance values; the vessel with the highest MMSI value is of the same type as the reference vessel. The classification method mainly aims at the problem that the traditional Euclidean distance is not suitable for marine ship track classification, and improves the accuracy and the rationality of ship track classification.
Description
Technical Field
The invention belongs to the technical field of ships, and particularly relates to a ship track classification method.
Background
To assist marine regulators in tracking ships and ensuring safe voyage, the International Maritime Organization (IMO) requires ships with a total tonnage of over 300, ships carrying cargo with a capacity of over 500 total tons, and passenger ships not in international waters during international voyage, which must be equipped with an Automatic Identification System (AIS). However, the ship trajectory received from the AIS is low in real-time and its time intervals are also irregular. Meanwhile, AIS data is occasionally lost due to communication reliability, which may cause vessel trajectory update to be suspended. For the seafarer, the better the integrity of the vessel data, the more adequate the response space and time to avoid the accident. Therefore, it is necessary to classify the movement locus of the ship to enhance the management of the ship. In recent years, data analysis and predictive modeling have become an emerging research topic.
However, the similarity distance designed in the conventional classification method, KNN (k-nearest neighbors) algorithm, is the euclidean distance, and this similarity distance calculation method is not suitable for the clustering between ship tracks. The reason is that the ship trajectory point data contains many influence factors and cannot be directly regarded as the distance between coordinate points. This results in poor performance of the KNN algorithm in classification of the vessel trajectory.
In conclusion, how to improve the actual effect of the ship track classification method has important research significance.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a ship track classification method based on similarity distance, which comprises the steps of firstly, recording track point data of a ship, and selecting a reference ship; calculating the position similarity distance and the speed similarity distance between the reference ship and other ships, and synthesizing the two to calculate the final similarity distance; replacing the similarity distance in the KNN with the final similarity distance for training to obtain an optimal hyper-parameter; sequencing all the final similarity distances according to the numerical value, and counting the times of generating the MMSI values of the ships corresponding to the ship track points of the first K final similarity distance values; the vessel with the highest MMSI value is of the same type as the reference vessel. The classification method mainly aims at the problem that the traditional Euclidean distance is not suitable for marine ship track classification, and improves the accuracy and the rationality of ship track classification.
The technical scheme adopted by the invention for solving the technical problem comprises the following steps:
step 1: recording track point data of all ships within a delta t time period, preprocessing the track point data, and deleting outliers and null values; selecting a ship as a reference ship;
step 2: selecting track points of a reference ship at the t moment in a delta t time period as reference track points; calculating the position similarity distance between the reference track point and all other ship track points in the delta t time period by adopting the formula (1):
Distance1＝R*arccos[sin(λ1)sin(λ2)+cos(λ1)cos(λ2)cos(l1-l2)] (1)
wherein R is the mean radius of the earth, l1、l2Longitude, λ, of two points of the track respectively1、λ2Respectively the latitude of the two track points;
and step 3: selecting a ship SOG value at a time period t, and calculating the speed similarity distance between the reference track point and all other ship track points in the time period delta t by adopting a formula (2):
Distance2＝v1-v2 (2)
wherein v is1、v2Respectively the navigational speeds of the two track points;
and 4, step 4: step 2 and step 3, obtaining position similarity distances and speed similarity distances of all ship track points to form a data set;
and 5: defining the final similarity distance:
D＝a*Distance1+(1-a)*Distance2 (3)
wherein a is weight, and a belongs to [0,1 ];
step 6: determining the optimal hyper-parameter by using a leave-one method;
selecting 1 piece in the data set as a test set, and using the rest as a training set; replacing the similarity distance in the KNN with D for training to obtain optimal hyper-parameters K and a;
and 7: the value a is taken into the formula (3), the final similarity distance between the reference track point and all other ship track points in the delta t time period is calculated, and all the final similarity distances are sorted according to the numerical value;
and 8: selecting the first K values of the sorted final similarity distance list, and counting the MMSI value times of the ship corresponding to the ship track points generating the first K final similarity distance values;
and step 9: and returning the MMSI value of the ship with the most occurrence times, wherein the ship is the same as the reference ship.
The invention has the following beneficial effects:
the classification method mainly aims at the problem that the traditional Euclidean distance is not suitable for marine ship track classification, and improves the accuracy and the rationality of ship track classification.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 shows the classification accuracy of the method under each parameter according to the embodiment of the present invention.
Fig. 3 is a graph illustrating the comparison between classification accuracy and effect provided by the embodiment of the present invention.
Detailed Description
The invention is further illustrated with reference to the following figures and examples.
According to the actual environment and ship track data, ships in a near port sea area are high in density, due to the fact that mutual influence factors exist among the ships, a classification algorithm based on similarity distance is suitable for the scenes, but similarity distances related to a traditional classification method, such as Euclidean distance and Manhattan distance, are not suitable for classification of ship track points on the sea, and similarity among the track points cannot be measured well, so that the method is based on a KNN method, improves the similarity distance, and classifies the ship track points.
As shown in fig. 1, a ship trajectory classification method based on similarity distance includes the following steps:
step 1: recording track point data of all ships within a delta t time period, preprocessing the track point data, and deleting outliers and null values; selecting a ship as a reference ship;
step 2: selecting track points of a reference ship at the t moment in a delta t time period as reference track points; calculating the position similarity distance between the reference track point and all other ship track points in the delta t time period by adopting the formula (1):
Distance1＝R*arccos[sin(λ1)sin(λ2)+cos(λ1)cos(λ2)cos(l1-l2)] (1)
wherein R is the mean radius of the earth, l1、l2Longitude, λ, of two points of the track respectively1、λ2Respectively the latitude of the two track points;
and step 3: selecting a ship SOG value at a time period t, and calculating the speed similarity distance between the reference track point and all other ship track points in the time period delta t by adopting a formula (2):
Distance2＝v1-v2 (2)
wherein v is1、v2Respectively the navigational speeds of the two track points;
and 4, step 4: step 2 and step 3, obtaining position similarity distances and speed similarity distances of all ship track points to form a data set;
and 5: defining the final similarity distance:
D＝a*Distance1+(1-a)*Distance2 (3)
wherein a is weight, and a belongs to [0,1 ];
step 6: determining the optimal hyper-parameter by using a leave-one method;
selecting 1 piece in the data set as a test set, and using the rest as a training set; replacing the similarity distance in the KNN with D for training to obtain optimal hyper-parameters K and a;
and 7: the value a is taken into the formula (3), the final similarity distance between the reference track point and all other ship track points in the delta t time period is calculated, and all the final similarity distances are sorted according to the numerical value;
and 8: selecting the front K values of the sorted final similarity distance list, and counting the times of MMSI values of the ship corresponding to the ship track points for generating the front K final similarity distance values;
and step 9: and returning the MMSI of the ship with the most occurrence times, wherein the ship is the same as the reference ship.
FIG. 2 is a graph of classification accuracy of the method of the present invention using different values.
FIG. 3 is a graph showing the comparison between classification accuracy of the method of the present invention and that of the conventional KNN method.
As can be seen from the figure, the method of the invention achieves better effect.
Claims (1)
1. A ship track classification method based on similarity distance is characterized by comprising the following steps:
step 1: recording track point data of all ships within a delta t time period, preprocessing the track point data, and deleting outliers and null values; selecting a ship as a reference ship;
step 2: selecting track points of a reference ship at the t moment in a delta t time period as reference track points; calculating the position similarity distance between the reference track point and all other ship track points in the delta t time period by adopting the formula (1):
Distance1＝R*arccos[sin(λ1)sin(λ2)+cos(λ1)cos(λ2)cos(l1-l2)] (1)
wherein R is the mean radius of the earth, l1、l2Longitude, λ, of two points of the track respectively1、λ2Respectively the latitude of the two track points;
and step 3: selecting a ship SOG value at a time period t, and calculating the speed similarity distance between the reference track point and all other ship track points in the time period delta t by adopting a formula (2):
Distance2＝v1-v2 (2)
wherein v is1、v2Respectively the navigational speeds of the two track points;
and 4, step 4: step 2 and step 3, obtaining position similarity distances and speed similarity distances of all ship track points to form a data set;
and 5: defining the final similarity distance:
D＝a*Distance1+(1-a)*Distance2 (3)
wherein a is weight, and a belongs to [0,1 ];
step 6: determining the optimal hyper-parameter by using a leave-one method;
selecting 1 piece in the data set as a test set, and using the rest as a training set; replacing the similarity distance in the KNN with D for training to obtain optimal hyper-parameters K and a;
and 7: the value a is taken into the formula (3), the final similarity distance between the reference track point and all other ship track points in the delta t time period is calculated, and all the final similarity distances are sorted according to the numerical value;
and 8: selecting the first K values of the sorted final similarity distance list, and counting the MMSI value times of the ship corresponding to the ship track points generating the first K final similarity distance values;
and step 9: and returning the MMSI value of the ship with the most occurrence times, wherein the ship is the same as the reference ship.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021017577A1 (en) * | 2019-07-29 | 2021-02-04 | 南京莱斯网信技术研究院有限公司 | Ship-type-spoofing detection method employing ensemble learning |
| CN113537386A (en) * | 2021-08-01 | 2021-10-22 | 大连海事大学 | Ship typical motion track self-adaptive mining method based on improved K-Medoids clustering |
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021017577A1 (en) * | 2019-07-29 | 2021-02-04 | 南京莱斯网信技术研究院有限公司 | Ship-type-spoofing detection method employing ensemble learning |
| CN113537386A (en) * | 2021-08-01 | 2021-10-22 | 大连海事大学 | Ship typical motion track self-adaptive mining method based on improved K-Medoids clustering |
Non-Patent Citations (2)
| Title |
|---|
| 刘磊;初秀民;蒋仲廉;钟诚;张代勇;: "基于KNN的船舶轨迹分类算法", 大连海事大学学报, no. 03, 15 August 2018 (2018-08-15) * |
| 魏照坤;周康;魏明;史国友;: "基于AIS数据的船舶运动模式识别与应用", 上海海事大学学报, no. 02, 30 June 2016 (2016-06-30) * |
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