Complexity-based ship dynamic clustering method

Abstract

The invention discloses a ship dynamic clustering method based on complexity, and relates to the technical field of ship traffic management and safety, and the method comprises the steps: obtaining ship AIS data in a research region and a time period, and carrying out the abnormal data preprocessing of the AIS data; dividing the ship AIS data into a plurality of time periods according to a time sequence at the same time interval, and obtaining the ship AIS data of different time periods; calculating the traffic complexity between the ships based on the ship AIS data in different time periods; constructing ship topology network diagrams in different time periods based on the traffic complexity between the ships; constructing a Louvain model based on the ship topology network diagrams in different time periods, dynamically clustering the ships, and identifying partitions to which the ships belong; and carrying out effect detection on the identified partitions to which the ships belong by adopting modularity, and completing ship dynamic clustering based on complexity. The problem that an existing clustering method does not consider interaction characteristics between ships is solved.

Description

Technical Field

[0001] The invention relates to the technical field of ship traffic management and safety, and in particular to a complexity-based ship dynamic clustering method. Background Art

[0002] Ship traffic safety has always been a concern in the shipping industry, especially when the ship traffic volume is large, which increases the possibility of maritime traffic accidents, accompanied by casualties, environmental pollution and economic losses, etc. Therefore, strengthening ship traffic safety management, optimizing navigation routes, and improving monitoring and early warning capabilities have become important tasks to ensure the safety and sustainable development of maritime transportation.

[0003] Currently, a variety of methods and models have been proposed in the field of collision risk estimation and control to reduce risks and improve efficiency in maritime transportation. In particular, the widespread deployment of automatic identification systems (AIS) can not only provide advance collision warnings and assist anti-collision decisions to enhance safety management, but also provide rich AIS trajectory data, which promotes the accuracy and effectiveness of trajectory behavior analysis and modeling, and promotes the development of this field. However, there are still many challenges to be solved, such as the dynamic interaction characteristics of ships in complex navigation environments and the identification of different ship behavior patterns. In waters with high ship density, ship traffic usually shows clustering characteristics, with high spatiotemporal interaction between ships in the same group, while the interaction between different groups is relatively sparse. The most commonly used clustering method is the density-based DBSCAN algorithm. However, this method only considers the spatial distance between ships, and fails to consider the interaction characteristics between ships, such as the influence of the navigation angle, speed and time dimension between ships. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a complexity-based dynamic clustering method for ships, which solves the problem that the existing clustering methods fail to consider the interaction characteristics between ships.

[0005] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: a ship dynamic clustering method based on complexity, comprising the following steps:

[0006] S1: Obtain ship AIS data within the study area and time period, and perform abnormal data preprocessing on the ship AIS data;

[0007] S2: Based on the preprocessing results, the ship AIS data is divided into multiple time periods in chronological order at the same time interval to obtain the ship AIS data of different time periods;

[0008] S3: Calculate the traffic complexity between ships based on the ship AIS data in different time periods;

[0009] S4: Based on the traffic complexity between ships, construct the ship topology network diagram in different time periods;

[0010] S5: Based on the ship topology network diagram in different time periods, the Louvain model is constructed to dynamically cluster ships and identify the partition to which each ship belongs;

[0011] S6: Modularity is used to detect the effectiveness of the partitions to which each identified ship belongs, and dynamic clustering of ships based on complexity is completed.

[0012] Furthermore, the traffic complexity between ships in S3 includes traffic density complexity and traffic conflict complexity. The traffic density complexity Den ij for:

[0013]

[0014] Among them, λ and α are correction parameters that depend on the navigation environment, both of which are positive numbers. is the relative distance vector of the ship, SD ij is the minimum safe navigation distance between ship i and ship j;

[0015] The traffic conflict complexity conf ij for:

[0016] conf ij =angle ij ·conv ij

[0017]

[0018]

[0019] Among them, angle ij is the complexity of the angle between the two ships, conv ij is the convergence complexity between the two ships, f(·) is the function between the navigation angle complexity and the angle, θ is the navigation angle, L low is the minimum threshold of the distance between two ships, L up is the maximum threshold of the distance between two ships, is the relative velocity vector.

[0020] Furthermore, the traffic complexity between ships in S3 is:

[0021]

[0022] Among them, complex ij (·) is the traffic complexity function between ships, θ ijis the navigation angle between ship i and ship j, Den ij (·) is the traffic density complexity function, conf ij (·) is the traffic conflict complexity function.

[0023] Furthermore, in S4, the traffic complexity between ships is used as the weight of the edge, and the ships are used as vertices to construct the ship topology network diagram in different time periods. The formula is:

[0024] G Ti =(V Ti ,E Ti ,w Ti )

[0025] Among them, G Ti is the ship topology network, V Ti is the vertex set in the topological network in Ti period, E Ti is the set of edges in the topological network in time period Ti, w Ti is the weight set in the topological network in the Ti period.

[0026] Furthermore, the S5 comprises the following sub-steps:

[0027] S51: Ship topology network G in time period Ti Ti Assign unique partition numbers to ships in The set of partition numbers to which the ship initially belongs in the Ti time period;

[0028] S52: The ship topology network G in the Ti time period Ti and partition number Input the Louvain model and output the ship topology network G Ti The ship belongs to the zone C Ti =(V Ti ,c ti ), c ti The set of partition numbers to which the ship belongs in the Ti time period;

[0029] S53: Extract the ship topology network G in time period Ti Ti The ship set and the ship topology network G in the Ti+1 time period Ti+1 The same ship in the ship set V Ti ∩V Ti+1 , V Ti+1 is the vertex set in the topological network in the Ti+1 period;

[0030] S54: V Ti ∩V Ti+1 The ships in the set are assigned the zone number of the ship in the Ti time period, which is V Ti+1Each of the remaining ships in the Ti+1 time period is assigned a unique partition number, and the initial partition to which the ship belongs is obtained. The set of initial partition numbers of the ship in time period Ti+1;

[0031] S55: The ship topology network G in the Ti+1 time period Ti+1 The initial zone to which the ship belongs during the Ti+1 period Input the Louvain model and output the ship topology network G Ti+1 The ship belongs to the zone C Ti+1 =(V Ti+1 ,c ti+1 ), c ti+1 The set of partition numbers to which the ships belong in the Ti+1 time period;

[0032] S56: Repeat the above steps S51-S55 until the AIS data of all time periods are analyzed and the partition to which each ship belongs is identified.

[0033] Furthermore, the modularity Q in S6 is:

[0034]

[0035] Where m is the total weight of all edges, w ij is the weight of the edge between ship i and ship j, k i is the number of edges connected to ship i, k j is the number of edges connected to ship j, δ(·) is the Creel function, c i is the zone to which ship i belongs, c j is the zone to which ship j belongs.

[0036] The beneficial effects of the present invention are as follows: the present invention can more effectively capture and represent the interactive characteristics of the influence of the navigation angle, speed and time dimensions between ships, and then cluster the ships, identify highly interactive ship sets, and help quickly establish dynamic constraint boundaries, that is, identify neighboring ships that need to be considered in conflict avoidance strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a flow chart of a complexity-based ship dynamic clustering method.

[0038] Figure 2 The following are the results of ship partitioning in different time periods. DETAILED DESCRIPTION

[0039] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0040] like Figure 1As shown, a complexity-based ship dynamic clustering method comprises the following steps:

[0041] S1: Obtain ship AIS data within the study area and time period, and perform abnormal data preprocessing on the ship AIS data;

[0042] S2: Based on the preprocessing results, the ship AIS data is divided into multiple time periods in chronological order at the same time interval to obtain the ship AIS data of different time periods;

[0043] S3: Calculate the traffic complexity between ships based on the ship AIS data in different time periods;

[0044] S4: Based on the traffic complexity between ships, construct the ship topology network diagram in different time periods;

[0045] S5: Based on the ship topology network diagram in different time periods, the Louvain model is constructed to dynamically cluster ships and identify the partition to which each ship belongs;

[0046] S6: Modularity is used to detect the effectiveness of the partitions to which each identified ship belongs, and dynamic clustering of ships based on complexity is completed.

[0047] The present invention is based on the historical trajectory data of the ship automatic identification system (AIS), combined with the speed difference, heading angle, distance, etc. between ships to calculate the traffic complexity between ships (including traffic density complexity and traffic conflict complexity). A topological network is constructed based on the complexity between ships, that is, the ships are vertices, the complexity between ships is greater than 0, there are edges between ships, and the complexity is the weight of the edge. Based on the constructed topological network, the dynamic Louvain algorithm is used to cluster the ships. The Louvain algorithm is very efficient in processing complex graphs, and the time complexity is O (nlogn).

[0048] The traffic complexity between ships in S3 includes traffic density complexity and traffic conflict complexity. Traffic density complexity refers to the traffic complexity caused by ship density. The traffic density complexity Den between ships is defined as ij is a function of the distance between ships. The traffic density complexity Den ij for:

[0049]

[0050] Among them, λ and α are correction parameters that depend on the navigation environment, both of which are positive numbers. is the relative distance vector of the ship, SD ij is the minimum safe sailing distance between ship i and ship j, SD ij The value of depends on the type of ship and the navigation environment;

[0051] Traffic conflict complexity refers to the traffic complexity caused by navigation conflicts between ships. Factors affecting conflicts include navigation angle and whether they converge.

[0052] When the navigation angle θ between the two ships is small, such as less than 20 degrees, the two ships are almost parallel, and their minimum collision distance is short, so the ships have more time to take evasive measures, so this situation is relatively safe. When θ is 180 degrees (under encounter conditions), the two ships will be in a head-on encounter. Although the relative speed of the ships is the largest, according to the International Regulations for Preventing Collisions at Sea (COLREGs, 1972), the responsibilities of both parties are clear, so this situation is not the most dangerous. When θ is about 120 degrees, its minimum collision distance is the longest, and the ships need to take action in advance. When θ is about 120 degrees and the two ships meet, the relative speed of the ships is high, and the actions become more complicated. This situation is the most dangerous. The functional relationship between the complexity of the navigation angle and the angle is defined as:

[0053]

[0054] Consider that when the distance between the two ships is smaller, the complexity between the ships is higher and the complexity value reaches the maximum value. This means that when other ships approach your ship, the potential risk of collision increases. However, when the distance between the two ships is very small, the navigation tracks and angles of each other become highly correlated, and there is almost no room for any changes or adjustments. Therefore, the impact of the change in angle on the risk of collision is reduced and becomes less significant. When the distance between the ships is greater than a certain threshold, there is no potential risk between the ships. Therefore, the complexity of the angle between the two ships angle ij Only exists when the distance between the two ships is less than a certain threshold L up , and is greater than a certain threshold L low , so the complexity of the angle between the two ships is defined as angle ij for:

[0055]

[0056] Assuming that two ships are sailing at a certain angle, when they are far away from each other, the collision risk and traffic complexity will be reduced. When the two ships gradually approach and meet, the collision risk and navigation complexity will increase. The traffic complexity caused by the convergence of two ships is the convergence complexity. The convergence complexity between two ships is:

[0057]

[0058] when It means that the two ships are approaching each other and will meet, otherwise, the two ships are gradually moving away from each other. Then the traffic conflict complexity between the two ships is:

[0059] conf ij =angle ij ·conv ij

[0060] Among them, angle ij is the complexity of the angle between the two ships, conv ij is the convergence complexity between the two ships, f(·) is the function between the navigation angle complexity and the angle, θ is the navigation angle, L low is the minimum threshold of the distance between two ships, L up is the maximum threshold of the distance between two ships, is the relative velocity vector.

[0061] The traffic complexity between ships in S3 is:

[0062]

[0063] Among them, complex ij (·) is the traffic complexity function between ships, θ ij is the navigation angle between ship i and ship j, Den ij (·) is the traffic density complexity function, conf ij (·) is the traffic conflict complexity function.

[0064] In S4, the traffic complexity between ships is used as the edge weight, and the ships are used as vertices to construct the ship topology network diagram in different time periods. The formula is:

[0065] G Ti =(V Ti ,E Ti ,w Ti )

[0066] Among them, G Ti is the ship topology network, V Ti is the vertex set in the topological network in Ti period, E Ti is the set of edges in the topological network in time period Ti, w Ti is the weight set in the topological network in the Ti period.

[0067] The S5 comprises the following sub-steps:

[0068] S51: Ship topology network G in time period Ti Ti Assign unique partition numbers to ships in The set of partition numbers to which the ship initially belongs in the Ti time period;

[0069] S52: The ship topology network G in the Ti time period Ti and partition number Input the Louvain model and output the ship topology network G Ti The ship belongs to the zone C Ti =(V Ti ,c ti ), c ti The set of partition numbers to which the ship belongs in the Ti time period;

[0070] S53: Extract the ship topology network G in time period Ti Ti The ship set and the ship topology network G in the Ti+1 time period Ti+1 The same ship in the ship set V Ti ∩V Ti+1 , V Ti+1 is the vertex set in the topological network in the Ti+1 period;

[0071] S54: V Ti ∩V Ti+1 The ships in the set are assigned the zone number of the ship in the Ti time period, which is V Ti+1 Each of the remaining ships in the Ti+1 time period is assigned a unique partition number, and the initial partition to which the ship belongs is obtained. The set of initial partition numbers of the ship in time period Ti+1;

[0072] S55: The ship topology network G in the Ti+1 time period Ti+1 The initial zone to which the ship belongs during the Ti+1 period Input the Louvain model and output the ship topology network G Ti+1 The ship belongs to the zone C Ti+1 =(V Ti+1 ,c ti+1 ), c ti+1 The set of partition numbers to which the ships belong in the Ti+1 time period;

[0073] S56: Repeat the above steps S51-S55 until the AIS data of all time periods are analyzed and the partition to which each ship belongs is identified.

[0074] The modularity Q in S6 is:

[0075]

[0076] Where m is the total weight of all edges, w ij is the weight of the edge between ship i and ship j, that is, the traffic complexity between ships. ij , k i is the degree of ship i, that is, the number of edges connected to ship i, k jis the degree of ship j, i.e., the number of edges connected to ship j, δ(·) is the Kronecker delta function, when c i =c j The value is 1 when the i is the zone to which ship i belongs, c j is the zone to which ship j belongs.

[0077] The modularity value range is usually between -1 and 1, and the higher the value, the more obvious the partition structure. When the Q value is greater than 0.3, the partition structure is usually considered to be obvious, and the value between 0.3 and 0.7 usually indicates a relatively good community structure. When Q is close to 0 or negative, it means that the partition structure is not obvious.

[0078] In one embodiment of the present invention, the AIS data of ships in the Qiongzhou Strait area from 09:00:00 to 09:09:00 on October 9, 2023 is selected. Through steps S1 and S2, the AIS data is preprocessed and divided into three time periods every 3 minutes, with time labels T0, T1 and T2; the traffic complexity between ships in different time periods is calculated through step S3; through step S4, a topological network diagram of ship traffic complexity in different time periods is constructed; through step S5, the ship partition division results of the three time periods are obtained. Figure 2 As shown, the results of ship partitioning in different time periods (the circled numbers are the partition numbers). The results show that the ship partitioning structure is significant (the modularity values ​​are 0.706, 0.643 and 0.626, respectively), the ships within the partitions interact with each other at a high density, while the interactions between partitions are relatively sparse.

[0079] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A ship dynamic clustering method based on complexity, characterized in that: The following steps are involved: S1: Obtain ship AIS data within the study area and time period, and perform abnormal data preprocessing on the ship AIS data; S2: Based on the preprocessing results, the ship AIS data is divided into multiple time periods in chronological order at the same time interval to obtain the ship AIS data of different time periods; S3: Calculate the traffic complexity between ships based on the ship AIS data in different time periods; S4: Based on the traffic complexity between ships, construct the ship topology network diagram in different time periods; S5: Based on the ship topology network diagram in different time periods, the Louvain model is constructed to dynamically cluster ships and identify the partition to which each ship belongs; S6: Modularity is used to detect the effectiveness of the partitions to which each identified ship belongs, and dynamic clustering of ships based on complexity is completed.

2. A complexity-based ship dynamic clustering method according to claim 1, characterized in that: The traffic complexity between ships in S3 includes traffic density complexity and traffic conflict complexity. ij for: Among them, λ and α are correction parameters that depend on the navigation environment, both of which are positive numbers. is the relative distance vector of the ship, SD ij is the minimum safe navigation distance between ship i and ship j; The traffic conflict complexity conf ij for: conf ij =angle ij ·conv ij Among them, angle ij is the complexity of the angle between the two ships, conv ij is the convergence complexity between the two ships, f(·) is the function between the navigation angle complexity and the angle, θ is the navigation angle, L low is the minimum threshold of the distance between two ships, L up is the maximum threshold of the distance between two ships, is the relative velocity vector.

3. A complexity-based ship dynamic clustering method according to claim 2, characterized in that: The traffic complexity between ships in S3 is: Among them, complex ij (·) is the traffic complexity function between ships, θ ij is the navigation angle between ship i and ship j, Den ij (·) is the traffic density complexity function, conf ij (·) is the traffic conflict complexity function.

4. A complexity-based ship dynamic clustering method according to claim 1, characterized in that: In S4, the traffic complexity between ships is used as the edge weight, and the ships are used as vertices to construct the ship topology network diagram in different time periods. The formula is: G Ti =(V Ti ,E Ti ,w Ti ) Among them, G Ti is the ship topology network, V Ti is the vertex set in the topological network in Ti period, E Ti is the set of edges in the topological network in time period Ti, w Ti is the weight set in the topological network in the Ti period.

5. A complexity-based ship dynamic clustering method according to claim 4, characterized in that: The S5 comprises the following sub-steps: S51: Ship topology network G in time period Ti Ti Assign unique partition numbers to ships in The set of partition numbers to which the ship initially belongs in the Ti time period; S52: The ship topology network G in the Ti time period Ti and partition number Input the Louvain model and output the ship topology network G Ti The ship belongs to the zone C Ti =(V Ti ,c ti ), c ti The set of partition numbers to which the ship belongs in the Ti time period; S53: Extract the ship topology network G in the Ti time period Ti The ship set and the ship topology network G in the Ti+1 time period Ti+1 The same ship in the ship set V Ti ∩V Ti+1 , V Ti+1 is the vertex set in the topological network in the Ti+1 period; S54: V Ti ∩V Ti+1 The ships in the set are assigned the zone number of the ship in the Ti time period, which is V Ti+1 Each of the remaining ships in the Ti+1 time period is assigned a unique partition number, and the initial partition to which the ship belongs is obtained. The set of initial partition numbers of the ship in time period Ti+1; S55: The ship topology network G in the Ti+1 time period Ti+1 The initial zone to which the ship belongs during the Ti+1 period Input the Louvain model and output the ship topology network G Ti+1 The ship belongs to the zone C Ti+1 =(V Ti+1 ,c ti+1 ), c ti+1 The set of partition numbers to which the ships belong in the Ti+1 time period; S56: Repeat the above steps S51-S55 until the AIS data of all time periods are analyzed and the partition to which each ship belongs is identified.

6. A complexity-based ship dynamic clustering method according to claim 1, characterized in that: The modularity Q in S6 is: Where m is the total weight of all edges, w ij is the weight of the edge between ship i and ship j, k i is the number of edges connected to ship i, k j is the number of edges connected to ship j, δ(·) is the Creel function, c i is the zone to which ship i belongs, c j is the zone to which ship j belongs.

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