Water transportation channel congestion identification method and device based on AIS big data

Abstract

The invention provides a water transportation channel congestion identification method and device based on AIS big data. The method provided by the invention comprises the following steps: acquiring ship AIS data of a target channel; determining the to-be-identified duration of the target channel, segmenting the to-be-identified duration based on the distribution characteristics of the ship AIS data, and determining a data analysis time unit; extracting data fragments of the corresponding ship AIS data based on the data analysis time unit; determining a plurality of types of channel congestion degree evaluation index values corresponding to each time period according to the calculated values of the plurality of types of channel congestion degree evaluation index values, and summarizing to obtain channel traffic characteristics in the to-be-identified duration; and clustering the channel traffic features, and determining the crowding degree of the target channel in the to-be-identified duration according to a clustering result. The method and the device provided by the invention are used for calculating a more accurate water transportation channel congestion evaluation index, reflecting the congestion condition in a water transportation channel passing process in a more detailed manner, and improving the navigation efficiency.

Description

Technical Field

[0001] The present application relates to the field of waterway monitoring technology, and in particular to a method and device for identifying waterway congestion based on AIS big data. Background Art

[0002] In the context of the rapid development of the global shipping industry, the identification of congestion in waterways is particularly important. With the increase in demand for ocean transportation, the frequency of waterway use continues to rise, resulting in increasingly serious congestion in waterways. This not only affects the navigation efficiency of ships, but may also cause safety hazards. Therefore, identifying and analyzing the congestion of waterways can provide a scientific basis for waterway management and scheduling optimization, thereby improving shipping safety and efficiency and ensuring unimpeded ocean transportation.

[0003] However, there are relatively few studies on waterway congestion, and many studies still draw on the analysis methods of road traffic and air traffic. Although some scholars have tried to propose a definition of cross-channel congestion and construct a complex network model, or introduced indicators such as traffic speed index, traffic rate index and stay time index, there are still certain shortcomings. First, existing studies mostly focus on congestion at the macro level, lacking in-depth analysis of specific waterway environment and ship characteristics. Secondly, indicators such as traffic speed index and traffic rate index provide real-time traffic conditions at the micro level, which can help identify congestion locations and times, but they cannot fully reflect the overall congestion level of waterways, especially in complex situations where multiple factors interact.

[0004] Therefore, there is an urgent need for a method that combines AIS data, ship motion characteristics, channel environment and other multi-dimensional factors to calculate more accurate waterway congestion evaluation indicators, reflect the congestion situation during the passage of waterways in more detail, and improve navigation efficiency. Summary of the invention

[0005] In view of this, the present application provides a method and device for identifying waterway congestion based on AIS big data, which combines AIS data, ship motion characteristics, channel environment and other multi-dimensional factors to calculate more accurate waterway congestion evaluation indicators, reflect the congestion situation during the passage of waterways in more detail, and improve navigation efficiency.

[0006] Specifically, the present application is implemented through the following technical solutions: The first aspect of the present application provides a method for identifying waterway congestion based on AIS big data, the method comprising: Acquire ship AIS data of a target waterway; the target waterway is a waterway whose congestion is to be identified, and the ship AIS data records the passage conditions of different ships; Determine the time to be identified of the target waterway, divide the time to be identified based on the distribution characteristics of the ship AIS data, and determine the data analysis time unit; the time to be identified is composed of multiple time periods; Extracting a data segment of the ship AIS data corresponding to each time unit based on the data analysis time unit; Based on the data segments, calculating multiple types of waterway congestion evaluation index values ​​within the data analysis time unit corresponding to the target waterway; Determine the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods based on the values ​​of the multiple types of waterway congestion evaluation index values ​​corresponding to the different data segments in each of the time periods; Summarize the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods to obtain the waterway traffic characteristics within the time period to be identified; The waterway traffic characteristics are clustered, and the congestion degree of the target waterway within the time period to be identified is determined according to the clustering result.

[0007] The second aspect of the present application provides a waterway congestion identification device based on AIS big data, the device comprising an acquisition module, a determination module, an extraction module and a calculation module; wherein: The acquisition module is used to acquire the ship AIS data of the target waterway; the target waterway is a waterway whose congestion is to be identified, and the ship AIS data records the passage conditions of different ships; The determination module is used to determine the time to be identified of the target channel, divide the time to be identified based on the distribution characteristics of the ship AIS data, and determine the data analysis time unit; the time to be identified is composed of multiple time periods; The extraction module is used to extract the data segment of the ship AIS data corresponding to each time unit based on the data analysis time unit; The calculation module is used to calculate the evaluation index values ​​of the congestion degree of multiple types of waterways in the data analysis time unit corresponding to the target waterway based on the data segment; The determination module is further used to determine the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods based on the values ​​of the multiple types of waterway congestion evaluation index values ​​corresponding to the different data segments in each of the time periods; The acquisition module is further used to summarize the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods to obtain the waterway traffic characteristics within the time period to be identified; The determination module is further used to cluster the waterway traffic characteristics and determine the congestion level of the target waterway within the time period to be identified according to the clustering results.

[0008] The present application provides a method and device for identifying waterway congestion based on AIS big data. On the one hand, by dynamically dividing the time to be identified according to the distribution characteristics of the ship's AIS data, it can ensure that the data analysis time unit is closely related to the actual situation of the waterway. This dynamic time unit division can avoid the data loss or redundancy problems caused by fixed time periods, thereby improving the accuracy and flexibility of waterway congestion identification, and can effectively handle complex and frequently changing water transport environments. On this basis, through the comprehensive calculation of multiple types of waterway congestion evaluation index values, the multi-dimensional characteristics of waterway congestion can be fully captured. This method not only focuses on a single flow or density, but also reflects the complex traffic behavior of the waterway, such as ship detention, convergence effect, etc., providing deeper insights for subsequent decision-making. By extracting multiple types of waterway congestion evaluation index values ​​corresponding to each time period within the identification time, it is possible to focus on the most representative information, reduce redundant data, and enhance data processing efficiency. On the second hand, by further clustering analysis to identify time periods with different congestion levels, it can help form clear and operational classification standards. This process combines expert scoring with data learning to ensure a high degree of consistency between the analysis results and actual waterway management needs. The clustering results can not only reflect the current congestion status of the waterway, but also reveal potential trend changes and support the prediction of possible congestion in the future. It is not only suitable for current congestion assessment, but also provides a predictive basis for long-term traffic management and resource scheduling. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A flowchart of Embodiment 1 of the method for identifying waterway congestion based on AIS big data provided by this application; Figure 2 A schematic diagram of the pursuit and convergence degree shown in an exemplary embodiment of the present application; Figure 3 A schematic diagram of cross-convergence degree shown in an exemplary embodiment of the present application; Figure 4 This is a structural schematic diagram of Example 1 of a waterway congestion identification device based on AIS big data provided in this application. DETAILED DESCRIPTION

[0010] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0011] The terms used in this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0012] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0013] Specific embodiments are given below to introduce the technical solution of the present application in detail.

[0014] Figure 1 This is a flowchart of the first embodiment of the waterway congestion identification method based on AIS big data provided by this application. Please refer to Figure 1 , the method provided in this embodiment may include: S101. Acquire ship AIS data of a target waterway; the target waterway is a waterway whose congestion is to be identified, and the ship AIS data records the passage conditions of different ships.

[0015] Specifically, the target waterway is a waterway where congestion is to be identified, and the ship AIS data records the passage of different ships, including the ship's location information, speed, heading, type and destination.

[0016] In specific implementation, the target waterway is determined according to the waterway with congestion to be identified, and the ship AIS data of the target waterway is extracted from the data interface provided by the shore-based AIS receiving station, satellite AIS service or relevant maritime departments at the set time interval and frequency. The extracted ship AIS data is cleaned to remove missing values ​​and outliers.

[0017] S102, determining the time to be identified of the target waterway, dividing the time to be identified based on the distribution characteristics of the ship AIS data, and determining a data analysis time unit; the time to be identified is composed of multiple time periods.

[0018] Specifically, the target channel's identification time is set according to actual needs, and this embodiment is not limited thereto. For example, in one embodiment, the target channel's identification time is 24 hours a day. The identification time is composed of multiple time periods, for example, the identification time is composed of 24 time periods.

[0019] It should be noted that since the ship AIS data of the target channel changes dynamically during the time to be identified, it is necessary to segment the time to be identified and perform congestion identification according to the ship AIS data corresponding to the minimum time unit (data analysis time unit). The data analysis time unit is the basic time unit of the target channel, which is obtained by segmenting the time to be identified of the target channel. The time length of each data analysis time unit is not exactly the same.

[0020] In a specific implementation, the time to be identified is divided based on the distribution characteristics of the ship AIS data to determine the data analysis time unit, including: determining a distribution curve of the number of ships passing through the target waterway within the time to be identified based on the ship AIS data; determining a change trend demarcation point on the ship number distribution curve based on a change trend of the ship number distribution curve; dividing the time to be identified based on the change trend demarcation point to determine the data analysis time unit, and the time length of each data analysis time unit is not exactly the same.

[0021] Specifically, in combination with the above description, the ship AIS data records the passage of different ships, including the number of different ships passing through the target channel at each time point within the time to be identified. By obtaining the ship AIS data of the time to be identified in the target channel, the number of different ships passing through the target channel at each time point is extracted to obtain time series data. Further, the number of ships is counted according to the time window (for example, every minute or every hour) to obtain a ship number distribution curve. The rate of change of the ship number distribution curve is calculated by numerical difference, and the local extreme value point of the rate of change (such as the point where the rate of change changes from positive to negative or from negative to positive) is determined as the change trend demarcation point. If there are too many change trend demarcation points, the minimum time interval or minimum change amplitude can be set to further screen out important demarcation points. Furthermore, the time to be identified is divided into several time periods according to the change trend demarcation point, and each time period represents a data analysis time unit.

[0022] S103: extracting a data segment of the ship AIS data corresponding to each time unit based on the data analysis time unit.

[0023] Specifically, combined with the above description, the ship AIS data records the passage of different ships at different time points, that is, it includes the passage of ships in different data analysis time units. In this step, based on the timestamp corresponding to each data analysis time unit, the ship AIS data within the time range is filtered to form corresponding data segments, and the data segments of the ship AIS data corresponding to each time unit are obtained.

[0024] S104. Based on the data segments, calculate multiple types of waterway congestion evaluation index values ​​within the data analysis time unit corresponding to the target waterway.

[0025] Specifically, the waterway congestion evaluation index value represents the congestion degree of the target waterway within a preset time period. Multiple types of waterway congestion evaluation index values ​​include retention, convergence, equivalent traffic volume and saturation.

[0026] In a specific implementation, the calculation of multiple types of channel congestion evaluation index values ​​within the data analysis time unit corresponding to the target waterway based on the data fragments includes: determining the data analysis time unit to be calculated, extracting the ship AIS data fragments of each ship in the target waterway within the data analysis time unit to be calculated, and obtaining the data fragments; obtaining the real-time positions and trajectories of different ships in the target waterway within the data analysis time unit to be calculated based on the data fragments, and calculating multiple types of channel congestion evaluation index values ​​according to the real-time positions and trajectories, the multiple types including at least detention rate, convergence rate, equivalent traffic volume and saturation.

[0027] Specifically, the ship AIS data of each ship within the time range of the data analysis time unit to be calculated and located within the target waterway is screened, and a data segment of each ship AIS data is extracted. For each data segment, the data segment is traversed to determine the real-time position of different ships in the target waterway within the data analysis time unit to be calculated corresponding to the data segment. Further, based on the change of the real-time position of different ships in the target waterway under the data segment over time, the trajectory of different ships in the target waterway is determined. The waterway congestion evaluation index value of each type is calculated based on the calculation method of the waterway congestion evaluation index value of different types.

[0028] Optionally, when the multiple types of waterway congestion evaluation index values ​​are equivalent traffic volumes, the multiple types of waterway congestion evaluation index values ​​are calculated according to the real-time position and trajectory, including: (1) Traverse the data segments to determine the size and quantity of each ship.

[0029] Specifically, the equivalent traffic volume represents the ship flow that the target waterway can carry in a specific time period. Combined with the above description, the data segment records the ship AIS data corresponding to each data analysis time unit, including the passage of different ships, which includes different types of ships. In this step, each data segment is traversed to determine the size of each ship and the number of each ship contained in each data segment.

[0030] (2) Determining the type of each ship according to the size of each ship, and determining the equivalent coefficient based on the ship type; the equivalent coefficient is positively correlated with the size of the ship.

[0031] Specifically, the type of ship is related to the size of the ship. The equivalent coefficient of a ship represents the relative influence of different types of ships in the use of a waterway, and the equivalent coefficient of a ship is positively correlated with the size of the ship, that is, the larger the size of the ship, the higher the equivalent coefficient of the ship. Combined with the above description, since in this embodiment, the ships are classified according to their sizes, the equivalent coefficients of different types of ships are different.

[0032] In specific implementation, based on the size information of each ship collected, clustering methods (such as K-means, hierarchical clustering and other algorithms) are used to analyze the size information of different ships. In the clustering process, a preset size difference threshold (i.e., preset value, which is set according to the size information with similar size information and a large number in the clustering process) is set, and ships with size differences below the threshold are classified into the same type of ships. After all ships are classified, for each type of ship, the average size of the ship of that type (such as the average length) is calculated, and by comparing the average sizes of ships of different types, ships with larger average sizes are given higher equivalent coefficients.

[0033] For example, in one embodiment, different types of ships determined by the clustering method include small ships, medium ships, relatively large ships, large ships and ultra-large ships, the average size of small ships is less than 25 meters, the average size of medium ships is 25-75 meters, the average size of relatively large ships is 75-125 meters, the average size of large ships is 125-175 meters, and the average size of ultra-large ships is more than 175 meters. The equivalent coefficient of small ships is 0.1, the equivalent coefficient of medium ships is 0.15, the equivalent coefficient of relatively large ships is 0.2, the equivalent coefficient of large ships is 0.25, and the equivalent coefficient of ultra-large ships is 0.3.

[0034] (3) The product of the equivalent coefficient and the number of each ship is determined as the equivalent traffic volume of each ship, and the sum of the equivalent traffic volumes of each ship is determined as the equivalent traffic volume within the data analysis time unit corresponding to the target waterway.

[0035] In specific implementation, for each type of ship, based on the number of ships of this type collected and the equivalent coefficient corresponding to this type of ship, the product of the two is determined as the equivalent traffic volume of this type of ship. Further, the sum of the equivalent traffic volumes of all types of ships is determined as the equivalent traffic volume in the data analysis time unit corresponding to the target waterway, and the equivalent traffic volume in the data analysis time unit corresponding to the target waterway can be calculated according to the following formula: ; Among them, the The equivalent traffic volume in the data analysis time unit corresponding to the target waterway; , , , , is the equivalent coefficient corresponding to different ships; , , , , The number of different ships.

[0036] For example, in one embodiment, combined with the above example, when the equivalent coefficient of a small ship is 0.1, the equivalent coefficient of a medium-sized ship is 0.15, the equivalent coefficient of a larger ship is 0.2, the equivalent coefficient of a large ship is 0.25, and the equivalent coefficient of an ultra-large ship is 0.3, and the number of small ships is 30, the equivalent coefficient of a medium-sized ship is 20, the equivalent coefficient of a larger ship is 10, the equivalent coefficient of a large ship is 10, and the equivalent coefficient of an ultra-large ship is 5, then the equivalent traffic volume in the target channel corresponding to the data analysis time unit is 0.1×30+0.15×20+0.2×10+0.25×10+0.3×5=12.

[0037] The method provided in this embodiment takes into account that the space occupied by ships of different sizes in the waterway and the impact on the waterway load are significantly different. The positive correlation between the size of the ship and the equivalent coefficient can more accurately reflect the different demands of various types of ships for waterway resources. Since small ships and large ships have different impacts on the congestion of the waterway, the calculated equivalent traffic volume can be closer to the actual traffic load through this differentiated equivalent coefficient. Moreover, by weighted summation of the equivalent coefficients of different types of ships, complex ship types can be converted into a single equivalent traffic volume indicator. This quantitative assessment facilitates the establishment of a unified standard, making the traffic volume assessment comparable and operational. Since directly adding up the number of ships may ignore the actual impact of large ships, the equivalent coefficient can ensure that the congestion pressure of large ships on the waterway is fully considered, thereby improving the accuracy of the assessment.

[0038] Optionally, when the multiple types of waterway congestion evaluation index values ​​are saturation, the calculating the multiple types of waterway congestion evaluation index values ​​according to the real-time position and trajectory includes: (1) Determining corresponding ship areas based on the real-time positions and trajectories of the different ships in the target waterway.

[0039] Specifically, saturation represents the ratio of the actual ship flow to the maximum capacity of the target channel. The ship domain refers to the spatial area occupied by ships in the waters. The ship domain is usually an elliptical or rectangular area, and its specific shape is determined by the type of ship and the ship's real-time position and trajectory. The ship domain takes into account the safety buffer zone when the ship is sailing, turning and avoiding other ships, ensuring the safe passage of other ships in the target channel.

[0040] In specific implementation, by obtaining the real-time position and trajectory of different ships in the target channel, the real-time position and trajectory are analyzed using an appropriate fitting algorithm (such as polynomial regression or curve fitting), and the paths and dynamic changes of different ships during navigation are obtained by fitting, and the nonlinear motion trajectories of different ships are determined. For the nonlinear motion trajectory obtained by fitting, a detailed analysis is performed to observe the trajectory changes of the ship at different headings and speeds during navigation, especially the performance when sailing at high speed or turning. According to the change pattern of the motion trajectory, the behavioral characteristics of the ship under different navigation conditions can be identified, and the ship domain is determined as an elliptical area based on the analysis results (because the ellipse can more accurately reflect the space occupation of the ship when sailing, especially the dynamic characteristics when turning and changing the heading). Further, the major axis and minor axis of the ship domain are determined based on the motion range of the ship under different navigation states. When the ship is sailing at high speed or turning, the major axis of the ship domain can be determined based on the maximum speed and acceleration of the ship, which is usually the farthest moving range of the ship when sailing in a straight line. The minor axis of the ship domain is determined based on the minimum turning radius of the ship when turning. The turning radius is usually related to the size and navigation speed of the ship, and is obtained by analyzing the specific trajectory data of different ships when turning.

[0041] (2) Based on the lengths of different ships in the data segments and the corresponding ship territories, the ship territories areas of different ships are calculated.

[0042] In specific implementation, the product of the length of different ships and the first preset coefficient is determined as the long axis of different ships, and the product of the length of different ships and the second preset coefficient is determined as the short axis of different ships. Wherein, the first preset coefficient is greater than the second preset coefficient, and the product of the long axis of different ships, the short axis of different ships and pi is determined as the ship area of ​​different ships.

[0043] Specifically, the first preset coefficient and the second preset coefficient are set according to actual needs, and are not limited in this embodiment. It should be noted that the first preset coefficient is greater than the second preset coefficient. For example, in one embodiment, the first preset coefficient is 8 and the second preset coefficient is 3.2. In specific implementation, the ship area of ​​different ships can be calculated according to the following formula: ; Among them, the For ships The area of ​​the ship's territory; For ships Captain.

[0044] (3) The quotient of the sum of the ship area areas of different ships and the actual navigable water area of ​​the target waterway is determined as the saturation of the target waterway within the data analysis time unit.

[0045] In specific implementation, the ship areas of different ships are added together, and the quotient of the sum of the ship areas of different ships and the actual navigable water area of ​​the target channel is determined as the saturation within the data analysis time unit corresponding to the target channel, that is, the saturation is calculated according to the following formula: ; ; Among them, the The saturation of the target channel corresponding to the data analysis time unit; is the sum of the ship areas of all ships; is the actual navigable water area of ​​the target waterway; For ships The area of ​​the ship's territory; For ships Captain.

[0046] The method provided in this embodiment, on the one hand, takes into account that the movement characteristics of the ship directly affect its demand and impact on the surrounding waters. By defining the occupancy range of the ship field, the saturation assessment can dynamically reflect the actual congestion level of the waterway when the ship is sailing in real time. Since ships require more space when they are at high speed or turning, the ship field is defined based on these movement characteristics to ensure that the saturation calculation not only considers the number, but also the changes in space occupancy caused by the navigation state, thereby improving the calculation accuracy. In addition, by calculating the saturation by summing the field area of ​​each ship and comparing it with the total water area of ​​the waterway, the actual demand for space of the ship in the waterway can be quantified, making the evaluation value more representative, especially in the case of mixed navigation of ships of different sizes. On the second hand, compared with previous methods that simply determine saturation by the number or size of ships, the area of ​​the ship field includes the space demand of each ship in motion, which is more in line with the actual situation, making the saturation assessment more reasonable and comprehensive. When calculating saturation, not only the number of ships but also their territory area is taken into account. This can avoid misjudgment of congestion caused by the increase in the number of ships, especially when there are a large number of ships but a small territory area gathering, it can accurately identify them. Since some specific types of ships (such as large transport ships or faster ships) have more obvious effects in different environments, calculating saturation based on the ship's territory area can better cope with the impact of these special ships in the waterway and effectively prevent congestion.

[0047] Further, in combination with the above description, both the equivalent traffic volume and the saturation are channel congestion evaluation index values ​​for evaluating the congestion of the target channel. Optionally, the channel congestion evaluation index value also includes detention, which is the quotient of the difference between the number of ships entering the target channel and the number of ships leaving the target channel in the corresponding data analysis time unit and the number of ships leaving the target channel. The detention reflects the mobility of the target channel in the corresponding data analysis time unit; the higher the detention, the more congested the target channel.

[0048] In specific implementation, the detention degree of the target channel can be calculated according to the following formula: ; Among them, the is the retention rate of the target channel in the corresponding data analysis time unit; is the number of ships entering the target channel; is the number of ships leaving the target channel.

[0049] Optionally, the waterway congestion evaluation index value also includes convergence degree, which characterizes the convergence trend between different ships; the convergence degree includes overtaking convergence degree and crossing convergence degree, the overtaking convergence degree characterizes the convergence of different ships when sailing in the same direction, and the crossing convergence degree characterizes the convergence of different ships when they are in different directions or crossing directions.

[0050] Specifically, Figure 2 This is a schematic diagram of the tracking convergence degree shown in an exemplary embodiment of the present application. Figure 2 , the pursuit convergence degree includes two convergence characteristics: static and dynamic. The pursuit convergence degree can be calculated based on the following formula: ; Among them, the is the degree of pursuit and convergence between different ships; It is the static part of the chasing and convergence between different ships; It is the dynamic part of the chasing and convergence degree between different ships; , , is the adjustment parameter; For ships , Ships The distance between For ships , Ships The minimum distance between For ships , Ships The maximum distance between , Ships , Ships speed; is the pursuit convergence function.

[0051] Furthermore, Figure 3 This is a schematic diagram of the cross-convergence degree shown in an exemplary embodiment of the present application. Similar to the pursuit convergence degree, the calculation of the cross-convergence degree is also divided into static and dynamic. The static convergence situation is similar to the pursuit convergence degree. It should be noted that the dynamic convergence situation, when considering the dynamic convergence characteristics of the cross-convergence degree, it is not enough to only use the relative distance divided by the relative time to indicate that the two ships have reached the minimum safe distance. It is necessary to establish a coordinate system, based on the coordinates of the two ships on the coordinate system at the corresponding time, calculate the relative distance, and solve the minimum time taken for the two ships to reach the minimum safe distance. . Solution When, let , i.e. ship , Ships exist The distance at the moment is equal to the ship , Ships The minimum safe distance between the two. Calculate according to the following formula : ; Among them, the For ships , Ships The minimum distance between , Ships , Ships speed; is the current moment; For ships , Ships The distance in the x direction at the initial moment; For ships , Ships The distance in the y direction at the initial moment; For ships , Ships The distance at the initial moment; For ships , Ships The angle between.

[0052] Furthermore, the cross-convergence degree can be calculated based on the following formula: ; Among them, the is the cross-convergence degree between different ships; is the static part of the cross-convergence between different ships; is the dynamic part of the cross-convergence between different ships; , , is the adjustment parameter; For ships , Ships The distance between The time taken for the two ships to reach the minimum safe distance; For ships , Ships The minimum distance between For ships , Ships The maximum distance between .

[0053] Furthermore, the cross-convergence is equal to the sum of the pursuit convergence and the cross-convergence, that is, , wherein the is the cross-convergence degree between different ships; is the degree of pursuit and convergence between different ships; is the cross-convergence degree between different ships.

[0054] S105. Determine the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods based on the values ​​of the multiple types of waterway congestion evaluation index values ​​corresponding to different data segments in each of the time periods.

[0055] Specifically, combined with the above description, each time period contains multiple data segments. In this step, it is necessary to compare the values ​​of the waterway congestion evaluation index corresponding to the multiple data segments in each time period, and finally obtain the multiple types of waterway congestion evaluation index values ​​corresponding to each time period (detention, convergence, equivalent traffic volume and saturation). In summary, multiple types of waterway congestion evaluation index values ​​corresponding to 24 time periods are finally obtained.

[0056] In specific implementation, the multiple types of channel congestion evaluation index values ​​corresponding to each time period are determined based on the numerical values ​​of the multiple types of channel congestion evaluation index values ​​corresponding to different data segments in each time period, including: for each time period within the time length to be identified, determining the multiple types of channel congestion evaluation index values ​​corresponding to each data segment in the time period; for the same type of channel congestion evaluation index values, comparing the channel congestion evaluation index values ​​corresponding to each data segment, and determining the maximum channel congestion evaluation index value as the channel congestion evaluation index value of the type in the time period.

[0057] Specifically, in combination with the above description, in this step, for each time period within the time length to be identified, multiple types of waterway congestion evaluation index values ​​corresponding to each data segment contained in each time period are determined (since they have been calculated in the above steps, only the time period needs to be filtered in this step). Further, for each time period, the waterway congestion evaluation index values ​​of the same type corresponding to all data segments contained in the time period are compared (for example, the convergence corresponding to all data segments in the same time period is compared), and the waterway congestion evaluation index value with the largest value is determined as the waterway congestion evaluation index value of the type in the time period (for example, the convergence corresponding to all data segments in the same time period is determined as the convergence in the time period).

[0058] For example, in combination with the above description, the time period to be identified is 24 hours, and the time period corresponding to 12 noon is used as an example. There are 6 moments under 12 noon, namely moment 1, moment 2, moment 3, moment 4, moment 5, and moment 6. The retention rates corresponding to these 6 moments are compared in turn, and the target retention rate with the largest retention rate is determined as the retention rate corresponding to 12 noon. Similarly, the convergence rates corresponding to these 6 moments are compared in turn, and the target convergence rate with the largest convergence rate is determined as the convergence rate corresponding to 12 noon. The equivalent traffic volumes corresponding to these 6 moments are compared in turn, and the target equivalent traffic volume with the largest equivalent traffic volume is determined as the equivalent traffic volume corresponding to 12 noon. The saturations corresponding to these 6 moments are compared in turn, and the target saturation with the largest saturation is determined as the saturation corresponding to 12 noon.

[0059] S106, summarizing the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods to obtain waterway traffic characteristics within the time period to be identified.

[0060] Specifically, combined with the above description, each time period corresponds to multiple types of channel congestion evaluation index values, and the time to be identified is composed of multiple time periods. In this step, the multiple types of channel congestion evaluation index values ​​corresponding to all time periods need to be combined in chronological order to obtain the channel traffic characteristics of the target channel within the entire time to be identified.

[0061] In a specific implementation, the channel congestion evaluation index values ​​of multiple types corresponding to each of the time periods are summarized to obtain the channel traffic characteristics within the time period to be identified, including: using the time unit corresponding to each time period as the time identifier of each group of channel congestion evaluation index values; each group of channel congestion evaluation index values ​​contains multiple types; and arranging each group of channel congestion evaluation index values ​​according to the sequence of the time identifiers to obtain the channel traffic characteristics.

[0062] Specifically, in combination with the above description, each time period corresponds to multiple types of waterway congestion evaluation index values. In this step, the timestamp recorded in the time unit corresponding to each time period is used as the time identifier, and each group of waterway congestion evaluation index values ​​(multiple types of waterway congestion evaluation index values) corresponding to each time period are arranged in the order of the time identifiers, and the group of waterway congestion evaluation index values ​​corresponding to the time period with the earliest time identifier is placed at the beginning, and the group of waterway congestion evaluation index values ​​corresponding to the time period with the latest time identifier is placed at the end, so as to obtain the waterway traffic characteristics within the time length to be identified that includes the waterway congestion evaluation index values ​​of all time periods.

[0063] S107, clustering the waterway traffic characteristics, and determining the congestion degree of the target waterway within the time period to be identified according to the clustering result.

[0064] In a specific implementation, the waterway traffic characteristics are clustered, and the congestion degree of the target waterway within the time period to be identified is determined based on the clustering results, including: obtaining navigation information of the target waterway with different congestion degrees; scoring the navigation information using expert scoring; learning the scoring results to obtain the characteristics of navigation information at different waterway congestion degrees; calculating the standard waterway congestion degree evaluation index values ​​of each type at different waterway congestion degrees based on the navigation information characteristics; and clustering the waterway traffic characteristics using the standard waterway congestion degree evaluation index values ​​of each type at each waterway congestion degree as clustering centers.

[0065] Specifically, the navigation information of the target waterway with different congestion levels may be image information or the number of ships.

[0066] In the specific implementation, the navigation information of different numbers of ships in the target waterway is obtained to obtain the navigation information of different congestion levels. Experts score the navigation information of different congestion levels of the target waterway and mark them into 4 categories: unobstructed, relatively unobstructed, slightly congested, and severely congested (each category contains several data fragments). For each category, the evaluation value index of different types of waterway congestion in the data fragments corresponding to these categories is extracted respectively. For each category of data fragments, the average or median value of each type of waterway congestion evaluation value index in all data fragments is calculated as the standard waterway congestion evaluation index value under the category of waterway congestion. Further, the cluster center is initialized as the standard waterway congestion evaluation index value of each type, and the Euclidean distance between each data fragment and the cluster center is calculated. According to the distance, the data fragment is assigned to the closest cluster center, and the cluster center is updated until convergence. According to the clustering results, the waterway congestion corresponding to the cluster containing the largest number of data fragments is determined as the congestion level of the target waterway within the time to be identified.

[0067] Optionally, the channel traffic characteristics are clustered, and the congestion degree of the target channel within the time period to be identified is determined based on the clustering results, including: calculating the distance between each type of channel congestion degree evaluation index value in the channel traffic characteristics and multiple cluster centers, each cluster center representing a channel congestion degree; determining the subclass to which each channel congestion degree evaluation index value belongs based on the distance; determining a representative subclass based on the number of each channel congestion degree evaluation index value in each subclass, and using the channel congestion degree corresponding to the representative subclass as the congestion degree of the target channel within the time period to be identified.

[0068] Specifically, the cluster center is the standard waterway congestion evaluation index value of each type under different waterway congestion conditions. In this step, the distance between each type of waterway congestion evaluation index value and multiple cluster centers in each time period in the waterway traffic characteristics is calculated based on the Euclidean distance. For each type of waterway congestion evaluation index value, the cluster with the nearest cluster center is determined as the subclass to which the waterway congestion evaluation index value belongs, and the waterway congestion evaluation index value is assigned to the subclass. After all the waterway congestion evaluation index values ​​are assigned, the subclass containing the largest number of waterway congestion evaluation index values ​​is determined as the representative subclass based on the number of waterway congestion evaluation index values ​​contained in each subclass, and the waterway congestion level corresponding to the representative subclass is determined as the congestion level of the target waterway during the time to be identified.

[0069] For example, in one embodiment, subclass 1, subclass 2, subclass 3, and subclass 4 correspond to unimpeded, relatively unimpeded, slightly congested, and severely congested, respectively. Subclass 1 contains 10 waterway congestion evaluation index values, subclass 2 contains 20 waterway congestion evaluation index values, subclass 3 contains 15 waterway congestion evaluation index values, and subclass 4 contains 30 waterway congestion evaluation index values. Therefore, subclass 4 is a representative subclass, subclass 4 corresponds to severe congestion, and the congestion level of the target waterway during the time to be identified is severe congestion.

[0070] It should be noted that when there are multiple representative subcategories, when determining the degree of waterway congestion corresponding to the representative subcategories, it is necessary to combine the time unit corresponding to the data segment where the representative subcategories are located. If the data segment corresponds to the same time unit, it is necessary to consider the comprehensive impact of the congestion levels of these representative subcategories on the data segment, and calculate the average value between the scores corresponding to these representative subcategories (for example, if multiple representative subcategories correspond to mild congestion and severe congestion, the average value of the scores corresponding to mild congestion and severe congestion is determined as the degree of congestion). If the data segment corresponds to different time units, for example, the representative subcategory in the previous time unit corresponds to mild congestion, and the representative subcategory in the next time unit corresponds to severe congestion, then the degree of congestion is determined to have changed from mild congestion to severe congestion.

[0071] The method provided in this embodiment, on the one hand, can ensure that the data analysis time unit is closely related to the actual situation of the waterway by dynamically dividing the time to be identified according to the distribution characteristics of the ship AIS data. This dynamic time unit division can avoid the data loss or redundancy problems caused by fixed time periods, thereby improving the accuracy and flexibility of waterway congestion identification, and can effectively handle complex and frequently changing water transport environments. On this basis, through the comprehensive calculation of multiple types of waterway congestion evaluation index values, the multi-dimensional characteristics of waterway congestion can be fully captured. This method not only focuses on a single flow or density, but also reflects the complex traffic behavior of the waterway, such as ship detention, convergence effect, etc., providing deeper insights for subsequent decision-making. By extracting multiple types of waterway congestion evaluation index values ​​corresponding to each time period within the identification time, it is possible to focus on the most representative information, reduce redundant data, and enhance data processing efficiency. On the second hand, by further clustering analysis to identify time periods with different congestion levels, it can help form clear and operational classification standards. This process combines expert scoring with data learning to ensure a high degree of consistency between the analysis results and actual waterway management needs. The clustering results can not only reflect the current congestion status of the waterway, but also reveal potential trend changes, support the prediction of possible congestion in the future, and are not only applicable to current congestion assessments, but also provide a prediction basis for long-term traffic management and resource scheduling. Thirdly, this embodiment takes into account that the space occupied by ships of different sizes in the waterway and the impact on the waterway load are significantly different. The size of the ship is positively correlated with the equivalent coefficient, which can more accurately reflect the different needs of various types of ships for waterway resources. Since small ships and large ships have different effects on the degree of congestion of the waterway, through this differentiated equivalent coefficient, the calculated equivalent traffic volume can be closer to the actual traffic load. In addition, by weighted summation of the equivalent coefficients of different types of ships, complex ship types can be converted into a single equivalent traffic volume indicator. This quantitative assessment facilitates the establishment of a unified standard, making the traffic volume assessment comparable and operational. Since directly adding up the number of ships may ignore the actual impact of large ships, the equivalent coefficient can ensure that the congestion pressure of large ships on the waterway is fully considered, thereby improving the accuracy of the assessment. Fourthly, considering that the movement characteristics of ships directly affect their demand and impact on the surrounding waters, the saturation assessment can dynamically reflect the actual congestion of the waterway under the real-time navigation of ships by defining the occupancy range of the ship domain. Since ships require more space when they are at high speed or turning, the ship domain is defined based on these movement characteristics to ensure that the saturation calculation takes into account not only the quantity, but also the changes in space occupancy caused by the navigation status, thereby improving the accuracy of the calculation. In addition, by calculating the saturation by summing the domain area of ​​each ship and comparing it with the total water area of ​​the waterway, the actual demand for space of ships in the waterway can be quantified, making the assessment value more representative, especially in the case of mixed navigation of ships of different sizes.Fifthly, compared with previous methods that simply determine saturation by the number or size of ships, the area of ​​the ship territory includes the space requirements of each ship in motion, which is more in line with the actual situation, making the saturation assessment more reasonable and comprehensive. When calculating saturation, not only the number of ships but also the area of ​​their territory is considered, which can avoid misjudgment of congestion caused by the increase in the number of ships, especially when there are many ships but a small area of ​​ships gathering. Since certain types of ships (such as large transport ships or faster ships) have more obvious effects in different environments, calculating saturation based on the area of ​​the ship territory can better deal with the impact of these special ships in the waterway and effectively prevent congestion.

[0072] Corresponding to the aforementioned embodiment of a method for identifying congestion in a waterway based on AIS big data, the present application also provides an embodiment of a device for identifying congestion in a waterway based on AIS big data.

[0073] Figure 4 This is a schematic diagram of the structure of the first embodiment of the waterway congestion identification device based on AIS big data provided by this application. Figure 4 The device provided in this embodiment includes an acquisition module 410, a determination module 420, an extraction module 430 and a calculation module 440; wherein, The acquisition module 410 is used to acquire the ship AIS data of the target waterway; the target waterway is a waterway whose congestion is to be identified, and the ship AIS data records the passage conditions of different ships; The determination module 420 is used to determine the time to be identified of the target channel, divide the time to be identified based on the distribution characteristics of the ship AIS data, and determine the data analysis time unit; the time to be identified is composed of multiple time periods; The extraction module 430 is used to extract the data segment of the ship AIS data corresponding to each time unit based on the data analysis time unit; The calculation module 440 is used to calculate the evaluation index values ​​of the congestion degree of multiple types of waterways in the data analysis time unit corresponding to the target waterway based on the data segment; The determination module 420 is further configured to determine the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods based on the values ​​of the multiple types of waterway congestion evaluation index values ​​corresponding to the different data segments in each of the time periods; The acquisition module 410 is further used to summarize the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods to obtain the waterway traffic characteristics within the time period to be identified; The determination module 420 is further configured to cluster the waterway traffic characteristics and determine the congestion level of the target waterway within the time period to be identified according to the clustering result.

[0074] The device of this embodiment can be used to perform Figure 1 The steps, specific implementation principles and implementation processes of the method embodiment shown are similar and will not be repeated here.

[0075] The implementation process of the functions and effects of each unit in the above-mentioned device is specifically described in the implementation process of the corresponding steps in the above-mentioned method, and will not be repeated here.

[0076] For the device embodiment, since it basically corresponds to the method embodiment, the relevant parts can refer to the partial description of the method embodiment. The device embodiment described above is only schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the present application scheme. A person of ordinary skill in the art can understand and implement it without paying any creative work.

[0077] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for identifying waterway congestion based on AIS big data, characterized in that: The method comprises: Acquire ship AIS data of a target waterway; the target waterway is a waterway whose congestion is to be identified, and the ship AIS data records the passage conditions of different ships; Determine the time to be identified of the target channel, divide the time to be identified based on the distribution characteristics of the ship AIS data, and determine the data analysis time unit; the time to be identified is composed of multiple time periods; Extracting a data segment of the ship AIS data corresponding to each time unit based on the data analysis time unit; Based on the data segments, calculating multiple types of waterway congestion evaluation index values ​​within the data analysis time unit corresponding to the target waterway; Determine the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods based on the values ​​of the multiple types of waterway congestion evaluation index values ​​corresponding to the different data segments in each of the time periods; Summarize the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods to obtain the waterway traffic characteristics within the time period to be identified; The waterway traffic characteristics are clustered, and the congestion degree of the target waterway within the time period to be identified is determined according to the clustering result.

2. The method according to claim 1, characterized in that The step of dividing the time to be identified based on the distribution characteristics of the ship AIS data and determining the data analysis time unit includes: Determine, based on the ship AIS data, a distribution curve of the number of ships passing through the target waterway within the time to be identified; Based on the change trend of the ship quantity distribution curve, determining a change trend demarcation point on the ship quantity distribution curve; The time length to be identified is divided based on the change trend dividing point to determine the data analysis time unit, and the time length of each data analysis time unit is not exactly the same.

3. The method according to claim 1, characterized in that The calculating, based on the data segments, multiple types of waterway congestion evaluation index values ​​within the data analysis time unit corresponding to the target waterway includes: Determine a time unit for data analysis to be calculated, extract ship AIS data segments of each ship in the target waterway within the time unit for data analysis to be calculated, and obtain the data segments; Based on the data segments, the real-time positions and trajectories of different ships in the target waterway within the data analysis time unit to be calculated are obtained, and multiple types of waterway congestion evaluation index values ​​are calculated according to the real-time positions and trajectories, wherein the multiple types include at least detention rate, convergence rate, equivalent traffic volume and saturation.

4. The method according to claim 1, characterized in that: The determining of the multiple types of waterway congestion evaluation index values ​​corresponding to each time period based on the values ​​of the multiple types of waterway congestion evaluation index values ​​corresponding to different data segments in each time period includes: For each time period within the time length to be identified, determining multiple types of waterway congestion evaluation index values ​​corresponding to each data segment in the time period; For the same type of waterway congestion evaluation index values, the waterway congestion evaluation index values ​​corresponding to each data segment are compared, and the maximum waterway congestion evaluation index value is determined as the waterway congestion evaluation index value of the type in the time period.

5. The method according to claim 1, characterized in that The summarizing of the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods to obtain the waterway traffic characteristics within the time period to be identified includes: The time unit corresponding to each time period is used as the time mark of each group of waterway congestion evaluation index values; each group of waterway congestion evaluation index values ​​includes multiple types; The waterway congestion evaluation index values ​​of each group are arranged according to the sequence of the time marks to obtain the waterway traffic characteristics.

6. The method according to claim 1, characterized in that The clustering of the waterway traffic characteristics and determining the congestion degree of the target waterway within the time period to be identified according to the clustering result includes: Obtaining navigation information of the target waterway at different congestion levels; Scoring the navigation information using expert scoring; The results of learning and scoring are used to obtain the characteristics of navigation information under different degrees of channel congestion; Calculate the evaluation index value of the congestion degree of each type of standard waterway under different waterway congestion degrees according to the characteristics of the navigation information; The waterway traffic characteristics are clustered using the evaluation index values ​​of each type of standard waterway congestion degree at each waterway congestion degree as cluster centers.

7. The method according to claim 1, characterized in that The clustering of the waterway traffic characteristics and determining the congestion degree of the target waterway within the time period to be identified according to the clustering result includes: Calculating the distance between each type of waterway congestion evaluation index value in the waterway traffic characteristics and a plurality of cluster centers, each cluster center representing a waterway congestion degree; Determine the subclass to which each waterway congestion evaluation index value belongs according to the distance; A representative subclass is determined according to the number of the waterway congestion evaluation index values ​​in each of the subclasses, and the waterway congestion degree corresponding to the representative subclass is used as the congestion degree of the target waterway within the time period to be identified.

8. The method according to claim 3, characterized in that When the multiple types of waterway congestion evaluation index values ​​are equivalent traffic volumes, the multiple types of waterway congestion evaluation index values ​​are calculated according to the real-time position and trajectory, including: Traversing the data segments to determine the size and quantity of each ship; Determining the type of each ship according to the size of each ship, and determining an equivalent coefficient based on the ship type; the equivalent coefficient is positively correlated with the size of the ship; The product of the equivalent coefficient and the number corresponding to each ship is determined as the equivalent traffic volume of each ship, and the sum of the equivalent traffic volumes of each ship is determined as the equivalent traffic volume within the data analysis time unit corresponding to the target waterway.

9. The method according to claim 3, characterized in that: When the multiple types of waterway congestion evaluation index values ​​are saturation, the multiple types of waterway congestion evaluation index values ​​are calculated according to the real-time position and trajectory, including: Determining corresponding ship areas based on the real-time positions and trajectories of the different ships in the target waterway; Calculating the ship territory areas of different ships based on the lengths of different ships in the data segments and the corresponding ship territories; The quotient of the sum of the ship domain areas of different ships and the actual navigable water area of ​​the target waterway is determined as the saturation of the target waterway within the data analysis time unit corresponding to the target waterway.

10. A waterway congestion identification device based on AIS big data, characterized in that: The device includes an acquisition module, a determination module, an extraction module and a calculation module; wherein, The acquisition module is used to acquire the ship AIS data of the target waterway; the target waterway is a waterway whose congestion is to be identified, and the ship AIS data records the passage conditions of different ships; The determination module is used to determine the time to be identified of the target channel, divide the time to be identified based on the distribution characteristics of the ship AIS data, and determine the data analysis time unit; the time to be identified is composed of multiple time periods; The extraction module is used to extract the data segment of the ship AIS data corresponding to each time unit based on the data analysis time unit; The calculation module is used to calculate the evaluation index values ​​of the congestion degree of multiple types of waterways in the data analysis time unit corresponding to the target waterway based on the data segment; The determination module is further used to determine the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods based on the values ​​of the multiple types of waterway congestion evaluation index values ​​corresponding to the different data segments in each of the time periods; The acquisition module is further used to summarize the multiple types of waterway congestion evaluation index values ​​corresponding to each of the time periods to obtain the waterway traffic characteristics within the time period to be identified; The determination module is further used to cluster the waterway traffic characteristics and determine the congestion level of the target waterway within the time period to be identified according to the clustering results.

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