A method and system for intelligent monitoring of ship trajectories

Abstract

The application discloses a kind of to the method and system of intelligent monitoring of ship track, when needing to carry out track monitoring to target ship, the current position information and identification information of target ship are acquired, and according to the current position information of target ship, the multiple first internet of things nodes in the first area range of target ship are determined, so as to acquire the identification information of each first internet of things node, and then according to the identification information, current position information of target ship and the identification information of each first internet of things node, the monitoring control parameter of the first internet of things node is generated, such as the monitoring identification of target ship, to control the first internet of things node to the navigation track of target ship and execute monitoring operation, so as to improve the monitoring accuracy of target ship track, and then it is favorable to monitor and regulate target ship, and when collision is about to occur, it is favorable to make collision-prevention preparation in advance, reduce personal safety and property loss.

Description

Technical Field

[0001] This invention relates to shipping monitoring technology, and in particular to a method and system for intelligent monitoring of ship trajectories. Background Technology

[0002] With the rapid development of science and technology and the socio-economic landscape, maritime transport volume and capacity have increased dramatically, which in turn has driven social and technological progress. However, because the marine environment is easily affected by weather and climate, there are many factors that can impact ship navigation. Therefore, whether for safety reasons or for management and development needs, it is essential to monitor ship trajectories.

[0003] Currently, the commonly used method for monitoring ship trajectories involves collecting position data from radar on board the vessel and analyzing the collected data to monitor its trajectory. However, practical experience has shown that adverse weather conditions such as rain, fog, and snow severely affect the accuracy and speed of radar propagation, making accurate ship trajectory monitoring impossible. Therefore, there is an urgent need for a technical solution that can improve the accuracy of ship trajectory monitoring. Summary of the Invention

[0004] Purpose of the invention: The purpose of this invention is to provide a method and system for intelligent monitoring of ship trajectories, thereby improving the accuracy of ship trajectory monitoring.

[0005] Technical solution: The present invention provides a method for intelligent monitoring of ship trajectories, comprising the following steps:

[0006] When it is necessary to monitor the trajectory of a target vessel, the current location information and identification information of the target vessel are obtained, and multiple first IoT nodes within the first area of ​​the target vessel are determined based on the current location information of the target vessel.

[0007] Obtain information for each first IoT node, the information of which includes the identification information of the first IoT node;

[0008] Based on the identification information of the target vessel, its current location information, and the identification information of each first IoT node, the monitoring and control parameters of the first IoT node are generated. The monitoring and control parameters of the first IoT node include the monitoring identification of the IoT node on the target vessel.

[0009] Based on the monitoring and control parameters of each first IoT node, the first IoT node is controlled to perform monitoring operations on the navigation trajectory of the target ship.

[0010] The method further includes the following steps:

[0011] When the distance between the current position of the target vessel and the end position of the first area is less than or equal to a preset distance value, the first navigation information of the target vessel is obtained. The first navigation information includes the current navigation channel, the current navigation direction, the vessel type, and the vessel's destination.

[0012] Based on the first navigation information, predict the target channel that the target vessel will travel next, and determine the second Internet of Things node within the second area of ​​the target channel that can monitor the target vessel;

[0013] Obtain the node type of all second IoT nodes and determine the second navigation information of the target vessel, which includes the current navigation speed and the current navigation acceleration;

[0014] Based on the second navigation information of the target vessel and the node type of each second IoT node, the activation monitoring parameters for the target vessel of the second IoT node are determined, and the activation monitoring parameters corresponding to the second IoT node are sent to the second IoT node to trigger the second IoT node to monitor the target vessel according to the activation monitoring parameters. The activation monitoring parameters include the activation monitoring time and the identification information of the target vessel.

[0015] The determination of the activation monitoring parameters for the second IoT node for the target vessel based on the second navigation information of the target vessel and the node type of each second IoT node specifically involves:

[0016] For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a fixed-location IoT node, the activation monitoring parameters of the second IoT node for the target ship are analyzed based on the second navigation information of the target ship, the current location information, and the location of the second IoT node.

[0017] For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a location-movable IoT node, the current information of the second IoT node is determined, and the relative distance between the target ship and the second IoT node is calculated based on the current information of the second IoT node and the second navigation information of the target ship. The current information of the second IoT node includes the current position, moving speed, moving direction and moving acceleration.

[0018] Based on the target vessel's second navigation information, current location information, relative distance between the target vessel and the second IoT node, and the current information of the second IoT node, analyze the activation monitoring parameters of the second IoT node for the target vessel.

[0019] The method further includes the following steps:

[0020] For any second IoT node, the monitoring area of ​​the second IoT node is determined based on the location of the second IoT node and the target area where the target vessel appears on the target waterway.

[0021] The system collects environmental information about the monitoring area and analyzes the impact of the environment on the monitoring results obtained by the second IoT node monitoring the target ship based on the environmental information. The higher the impact value, the greater the impact on the monitoring results. The environmental information includes fog information and wave information, and the wave information includes wave height and wave fluctuation.

[0022] Based on the degree of impact and the monitoring performance of the second IoT node, the working parameters required for the second IoT node to monitor the target vessel are determined, and the working parameters and the start monitoring parameters are correlated to obtain the correlation information of the second IoT node.

[0023] The association information of each second IoT node is updated to the startup monitoring parameters of that second IoT node. Then, the updated startup monitoring parameters of the second IoT node are sent to the second IoT node to trigger the second IoT node to monitor the target ship according to the updated startup monitoring parameters.

[0024] The analysis of the impact of the environmental information of the monitoring area on the monitoring results obtained by the second IoT node monitoring the target ship is as follows:

[0025] Based on the fog information, the visibility of the monitoring area is determined, and based on the water wave height and wave fluctuation, the swaying of the second IoT node is estimated.

[0026] By analyzing the visibility of the monitoring area and the swaying of the second IoT node, the degree of influence of the environment of the monitoring area on the monitoring results obtained by the second IoT node on the target ship is obtained.

[0027] The method further includes the following steps:

[0028] The monitoring data of each IoT node on the target vessel during its navigation process are acquired sequentially. The monitoring data corresponding to each IoT node includes the trajectory monitoring data of the target vessel, the monitoring time of the trajectory monitoring data, and the identification information of the corresponding monitoring area.

[0029] For any of the monitoring areas, based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node, the trajectory monitoring data corresponding to all IoT nodes are analyzed to obtain the trajectory monitoring results of the target ship in the monitoring area. The trajectory monitoring results include the monitoring trajectory and the monitoring time corresponding to the monitoring trajectory.

[0030] Based on the monitoring time corresponding to all the monitoring tracks, the monitoring tracks are spliced ​​together from morning to night to obtain the monitoring track of the target vessel.

[0031] The method further includes the following steps:

[0032] For any of the monitoring areas, determine the number of IoT nodes monitored within the monitoring area;

[0033] When the number of monitoring is greater than 1, determine the monitoring azimuth angle of each IoT node relative to the target ship, and based on the monitoring azimuth angle corresponding to each IoT node, determine whether there are at least two target IoT nodes in the same monitoring azimuth angle whose difference in monitoring azimuth angle is less than or equal to the preset monitoring azimuth angle.

[0034] When the judgment result is negative, the operation is to analyze the trajectory monitoring data corresponding to all IoT nodes based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node, and obtain the trajectory monitoring result of the target ship in the monitoring area.

[0035] When the judgment result is yes, an integration operation is performed on the trajectory monitoring data corresponding to all target IoT nodes in each monitoring direction to obtain integrated trajectory monitoring data. Based on the identification information of the monitoring area, the trajectory monitoring data corresponding to each monitoring direction is analyzed to obtain the trajectory monitoring result of the target ship in the monitoring area.

[0036] A system for intelligent monitoring of ship trajectories, employing the aforementioned method for intelligent monitoring of ship trajectories, includes the following modules:

[0037] Acquisition module: used to acquire the current location information and identification information of the target vessel when trajectory monitoring of the target vessel is required; also used to acquire information of each first IoT node, the information of the first IoT node including the identification information of the first IoT node;

[0038] Determination module: used to determine multiple first IoT nodes within a first area of ​​the target vessel based on the target vessel's current location information;

[0039] The generation module is used to generate the monitoring and control parameters of the first IoT node based on the identification information of the target vessel, the current location information, and the identification information of each first IoT node. The monitoring and control parameters of the first IoT node include the monitoring identification of the IoT node on the target vessel.

[0040] Control module: Used to control the first IoT node to perform monitoring operations on the navigation trajectory of the target ship according to the monitoring and control parameters of each first IoT node.

[0041] Preferably, the acquisition module is further configured to acquire first navigation information of the target vessel when the distance between the current position of the target vessel and the end position of the first area is less than or equal to a preset distance value. The first navigation information includes the current navigation channel, the current navigation direction, the vessel type, and the vessel's destination.

[0042] Preferably, the system further includes the following modules:

[0043] Prediction module: used to predict the target channel that the target vessel will travel next based on the initial navigation information;

[0044] The determining module is also used to determine a second Internet of Things node within the second area of ​​the target waterway that can monitor the target vessel;

[0045] The acquisition module is also used to acquire the node type of all second IoT nodes;

[0046] The determining module is also used to determine the second navigation information of the target vessel, the second navigation information including the current navigation speed and the current navigation acceleration;

[0047] The determining module is also used to determine the activation monitoring parameters of the second Internet of Things node for the target ship based on the second navigation information of the target ship and the node type of each second Internet of Things node;

[0048] Communication module: Used to send the start monitoring parameters corresponding to the second IoT node to the second IoT node, so as to trigger the second IoT node to monitor the target ship according to the start monitoring parameters. The start monitoring parameters corresponding to the second IoT node include the start monitoring time and the identification information of the target ship.

[0049] Preferably, the determining module determines the specific method by which the second IoT node determines the activation monitoring parameters for the target vessel based on the second navigation information of the target vessel and the node type of each second IoT node:

[0050] For any of the second IoT nodes, when the node type of the second IoT node is used to indicate that the second IoT node is a fixed-location IoT node, the activation monitoring parameters of the second IoT node for the target ship are analyzed based on the second navigation information of the target ship, the current location information, and the location of the second IoT node.

[0051] For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a location-movable IoT node, the current information of the second IoT node is determined, and the relative distance between the target ship and the second IoT node is calculated based on the current information of the second IoT node and the second navigation information of the target ship. The current information of the second IoT node includes the current position, moving speed, moving direction and moving acceleration.

[0052] Based on the target vessel's second navigation information, current location information, relative distance, and the current information of the second IoT node, the activation monitoring parameters of the second IoT node for the target vessel are analyzed.

[0053] Preferably, the determining module is further configured to, for any second IoT node, determine the monitoring area of ​​the second IoT node based on the location of the second IoT node and the target area where the target vessel appears on the target waterway;

[0054] Preferably, the system further includes the following modules:

[0055] Data acquisition module: used to collect environmental information of the monitoring area; the environmental information includes fog information and water wave information, the water wave information includes water wave height and water wave fluctuation;

[0056] The first analysis module is used to analyze the degree of influence of the environment in the monitoring area on the monitoring results obtained by the second Internet of Things node on the target ship, based on environmental information. The higher the degree of influence value, the greater the impact on the monitoring results.

[0057] The determining module is also used to determine the working parameters required for the second IoT node to monitor the target ship based on the degree of impact and the monitoring performance of the second IoT node.

[0058] Association module: Used to associate the working parameters and start monitoring parameters required for the second IoT node to monitor the target ship, and obtain association information;

[0059] Update module: Used to update the association information of each second IoT node to the start monitoring parameters of the second IoT node, and trigger the communication module to send the start monitoring parameters corresponding to the second IoT node to the second IoT node, so as to trigger the second IoT node to monitor the target ship according to the start monitoring parameters.

[0060] The first analysis module analyzes the impact of the environment of the monitoring area on the monitoring results obtained by the second IoT node on the target ship based on environmental information in the following specific way:

[0061] Based on the fog information, the visibility of the monitoring area is determined, and based on the water wave height and the water wave fluctuation, the swaying of the second IoT node is estimated.

[0062] By analyzing the visibility of the monitoring area and the swaying of the second IoT node, the degree of influence of the environment of the monitoring area on the monitoring results obtained by the second IoT node on the target ship is obtained.

[0063] Preferably, the acquisition module is further configured to sequentially acquire monitoring data of the target vessel from each Internet of Things node during the navigation process of the target vessel, wherein the monitoring data corresponding to the Internet of Things node includes the trajectory monitoring data of the target vessel, the monitoring time of the trajectory monitoring data, and the identification information of the corresponding monitoring area;

[0064] Preferably, the system further includes the following modules:

[0065] The second analysis module is used to analyze the trajectory monitoring data of all IoT nodes for any of the monitoring areas based on the identification information of the monitoring areas and the monitoring time corresponding to each IoT node, and obtain the trajectory monitoring results of the target ship in the monitoring area. The trajectory monitoring results include the monitoring trajectory and the monitoring time corresponding to the monitoring trajectory.

[0066] The stitching module is used to stitch together all the monitoring tracks from morning to night according to the monitoring time corresponding to all the monitoring tracks, so as to obtain the monitoring track of the target ship.

[0067] Preferably, the determining module is further configured to determine the number of IoT nodes monitored in any monitoring area;

[0068] The determining module is also used to determine the monitoring azimuth angle of each IoT node relative to the target ship when the number of monitoring is greater than 1.

[0069] Preferably, the system further includes the following modules:

[0070] Judgment module: used to determine whether there are at least two target IoT nodes at the same monitoring azimuth angle with a difference of less than or equal to a preset monitoring azimuth angle, based on the monitoring azimuth angle corresponding to each IoT node; when the judgment result is negative, the second analysis module analyzes the trajectory monitoring data corresponding to all IoT nodes based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node, and obtains the trajectory monitoring result of the target ship in the monitoring area.

[0071] Integration module: When the judgment result is yes, it performs an integration operation on the trajectory monitoring data corresponding to all target IoT nodes in the same monitoring direction to obtain integrated trajectory monitoring data;

[0072] The second analysis module is also used to analyze the trajectory monitoring data corresponding to each monitoring direction based on the identification information of the monitoring area, and obtain the trajectory monitoring results of the target ship in the monitoring area.

[0073] A computer storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for intelligent monitoring of ship trajectories.

[0074] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for intelligent monitoring of ship trajectories.

[0075] Beneficial effects: Compared with the prior art, the present invention has the following advantages: When it is necessary to monitor the trajectory of a target vessel, the current location information and identification information of the target vessel are obtained, and multiple first IoT nodes within the first area of ​​the target vessel are determined based on the current location information of the target vessel, thereby obtaining the identification information of each first IoT node. Then, based on the identification information of the target vessel, the current location information, and the identification information of each first IoT node, monitoring and control parameters of the first IoT node are generated, such as the monitoring identification of the target vessel, so as to control the first IoT node to perform monitoring operations on the navigation trajectory of the target vessel, thereby improving the accuracy of target vessel trajectory monitoring, which is conducive to the supervision of the target vessel and, when a collision is about to occur, facilitates the preparation for collision avoidance in advance, reducing personal safety and property loss. Attached Figure Description

[0076] Figure 1 This is a flowchart of the steps of a method for intelligent monitoring of ship trajectories disclosed in Example 1;

[0077] Figure 2 This is a flowchart of another method for intelligent monitoring of ship trajectories disclosed in Example 2;

[0078] Figure 3 This is a schematic diagram of the principle of a system for intelligent monitoring of ship trajectories disclosed in Example 3;

[0079] Figure 4 This is a schematic diagram of another intelligent monitoring system for ship trajectories disclosed in Example 4. Detailed Implementation

[0080] The technical solution of the present invention will be further described below with reference to the accompanying drawings.

[0081] Example 1

[0082] like Figure 1 The diagram shown is a flowchart illustrating the steps of a method for intelligent monitoring of ship trajectories disclosed in Embodiment 1. Wherein, Figure 1 The described method can be applied to a device for intelligent monitoring of ship trajectories. This device may include one of the following: ship monitoring equipment capable of monitoring ship trajectories, a ship monitoring server (such as a local server or a cloud server), and a ship monitoring platform. The method includes the following steps:

[0083] 101. When it is necessary to monitor the trajectory of a target vessel, obtain the current location information and identification information of the target vessel, and determine multiple first IoT nodes within the first area of ​​the target vessel based on the current location information of the target vessel.

[0084] In this embodiment of the invention, the target vessel can be located via satellite to obtain its current position, or it can be located by receiving the current position transmitted by the positioning device installed on the target vessel. The identification information of the target vessel includes one or more of the following: number, model, shape, etc.

[0085] In this embodiment of the invention, the first IoT node includes, but is not limited to, one or more of other ships, buoy devices, and other devices capable of communication within the first area.

[0086] 102. Obtain information for each first IoT node, including the identification information of that first IoT node.

[0087] In this embodiment of the invention, devices can be searched and located within a first area using satellites to obtain information about the first IoT node. The identification information of the first IoT node includes one or more of the following: number, model, color, and shape. Furthermore, the information of the first IoT node also includes its type, which may include a ship type, a buoy device type, or other device types, such as a flight device type.

[0088] 103. Based on the identification information of the target vessel, its current location information, and the identification information of each first IoT node, generate the monitoring and control parameters of the first IoT node. The monitoring and control parameters of each first IoT node include the monitoring identification of the target vessel by the IoT node.

[0089] In this embodiment of the invention, the monitoring identifier corresponding to each IoT node is used to monitor the target vessel; that is, the IoT node monitoring the target vessel can be distinguished based on the monitoring identifier. The monitoring identifier type may include one or more of the following: numeric, color-coded, letter-based, and pattern-based.

[0090] 104. Based on the monitoring and control parameters of each first IoT node, control the first IoT node to perform monitoring operations on the navigation trajectory of the target ship.

[0091] In this embodiment of the invention, the monitoring and control parameters of each first IoT node are used to control the first IoT node to perform a monitoring operation on the navigation trajectory of the target ship. Specifically, the monitoring and control parameters of each first IoT node are sent to the corresponding first IoT node to trigger the IoT node to perform a monitoring operation on the navigation trajectory of the target ship.

[0092] It is evident that implementation Figure 1 The described method for intelligent monitoring of ship trajectories involves acquiring the current location and identification information of a target ship when trajectory monitoring is required. Based on the target ship's current location information, multiple first IoT nodes within a first area of ​​the target ship are identified, and the identification information of each first IoT node is acquired. Then, based on the target ship's identification information, current location information, and the identification information of each first IoT node, monitoring and control parameters for that first IoT node are generated, such as the monitoring identifier of the target ship. This controls the first IoT node to perform monitoring operations on the target ship's navigation trajectory, thereby improving the accuracy of target ship trajectory monitoring. This facilitates the supervision of the target ship and, in the event of an impending collision, allows for advance collision avoidance preparations, reducing personal safety and property damage.

[0093] In an optional embodiment, the method may further include the following steps:

[0094] When the distance between the current position of the target vessel and the end position of the first area is less than or equal to a preset distance value, the first navigation information of the target vessel is obtained. The first navigation information includes the current navigation channel, the current navigation direction, the vessel type, and the vessel's destination.

[0095] Based on the first navigation information, predict the target channel that the target vessel will travel next, and determine the second Internet of Things node within the second area of ​​the target channel that can monitor the target vessel;

[0096] Obtain the node type of all second IoT nodes and determine the second navigation information of the target vessel, including the current navigation speed and current navigation acceleration;

[0097] Based on the second navigation information of the target vessel and the node type of each second IoT node, the activation monitoring parameters for the target vessel of the second IoT node are determined, and the activation monitoring parameters corresponding to the second IoT node are sent to the second IoT node to trigger the second IoT node to monitor the target vessel according to the activation monitoring parameters. The activation monitoring parameters corresponding to each second IoT node include the activation monitoring time and the identification information of the target vessel.

[0098] In this optional embodiment, the end position is the position corresponding to the side of the first area furthest from the target vessel. Vessel type includes passenger-carrying and / or cargo-carrying types.

[0099] In this optional embodiment, the second IoT node capable of monitoring the target vessel can be understood as all devices located within the second area, all devices currently in operation within the second area, or devices within the second area that are not monitoring other vessels; no limitation is made here.

[0100] In this optional embodiment, the node type includes a fixed-location type or a movable-location type.

[0101] As can be seen, when the target vessel is about to finish its navigation in the current area, this optional embodiment continues to select suitable IoT nodes based on its navigation information, and combines its navigation speed and acceleration with the node type of the IoT node to generate corresponding start monitoring parameters, and continues to monitor it. This enables comprehensive monitoring of the entire navigation trajectory of the target vessel, further improving the accuracy and reliability of the target vessel monitoring.

[0102] In this optional embodiment, based on the second navigation information of the target vessel and the node type of each second IoT node, the activation monitoring parameters for the second IoT node for the target vessel are determined, including:

[0103] For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a fixed-location IoT node (such as a fixed-location buoy device), the activation monitoring parameters of the second IoT node for the target ship are analyzed based on the second navigation information of the target ship, the current location information, and the location of the second IoT node.

[0104] For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a location-movable IoT node, the current information of the second IoT node is determined, and the relative distance between the target ship and the second IoT node is calculated based on the current information of the second IoT node and the second navigation information of the target ship. The current information of the second IoT node includes the current position, moving speed, moving direction and moving acceleration.

[0105] Based on the target vessel's second navigation information, current location information, relative distance, and the current information of the second IoT node, analyze the activation monitoring parameters of the second IoT node for the target vessel.

[0106] In this optional embodiment, the second navigation information, current position information, relative distance of the target vessel, and current information of the second IoT node are input into a pre-trained startup monitoring parameter analysis model for analysis, thereby obtaining the startup monitoring parameters of the second IoT node for the target vessel. The startup monitoring parameter analysis model is obtained by training a basic multilayer perceptron and convolutional neural network model based on sample navigation information, sample position information, sample relative distance of the sample vessel, sample current position of the sample IoT node, sample moving speed, sample moving direction, and sample moving acceleration.

[0107] As can be seen, this optional embodiment can also determine the corresponding information for analyzing the start monitoring parameters according to the different node types of IoT nodes, which improves the accuracy of analysis while ensuring the efficiency of start monitoring parameter analysis, so as to accurately monitor the trajectory of the target ship.

[0108] In yet another optional embodiment, the method may further include the following steps:

[0109] For any second IoT node, the monitoring area of ​​the second IoT node is determined based on the location of the second IoT node and the target area where the target ship appears on the target channel.

[0110] Collect environmental information of the monitoring area, and analyze the degree of influence of the environment of the monitoring area on the monitoring results obtained by the second Internet of Things node on the target ship based on the environmental information. The higher the degree of influence value, the greater the impact on the monitoring results.

[0111] Based on the degree of impact and the monitoring performance of the second IoT node, the operating parameters required for the second IoT node to monitor the target vessel are determined, and the operating parameters and the start monitoring parameters are correlated to obtain correlation information.

[0112] The association information of each second IoT node is updated to the startup monitoring parameters of that second IoT node. Then, the updated startup monitoring parameters of the second IoT node are sent to the second IoT node to trigger the second IoT node to monitor the target ship according to the updated startup monitoring parameters.

[0113] In this optional embodiment, the target area is located within the second area, and the monitoring area corresponding to each second IoT node is determined by the target area and its location.

[0114] In this optional embodiment, the monitoring performance of each second IoT node represents its ability to monitor objects, wherein stronger monitoring capabilities equate to stronger monitoring performance. Furthermore, the operating parameters of each IoT node include one or more of the following: monitoring field of view, monitoring direction, monitoring rotation angle, monitoring signal transmission strength, and monitoring signal transmission frequency.

[0115] In this optional embodiment, the association information corresponding to each second IoT node is used to represent the correlation between its operating parameters and its startup monitoring parameters. That is, when its operating parameters are known, the corresponding startup monitoring parameters can be known through the corresponding association information, or when its startup monitoring parameters are known, the corresponding operating parameters can be known through the corresponding association information.

[0116] As can be seen, this optional embodiment improves the accuracy of target vessel trajectory monitoring by comprehensively analyzing the impact of the environment of the monitoring area of ​​the IoT node on the target vessel and its monitoring performance, and then combining the working parameters required for the IoT node to monitor the target vessel with its start monitoring parameters.

[0117] In this optional embodiment, the environmental information includes fog information and water wave information, and the water wave information includes water wave height and water wave fluctuation.

[0118] Among these, based on environmental information, the degree of influence of the environment in the monitoring area on the monitoring results obtained by the second IoT node monitoring the target ship is analyzed, including:

[0119] Based on fog information, the visibility of the monitoring area is determined, and based on the height and fluctuation of the water waves, the swaying of the second IoT node is estimated.

[0120] By analyzing the visibility of the monitoring area and the swaying of the second IoT node, the degree of influence of the environment of the monitoring area on the monitoring results obtained by the second IoT node on the target ship is obtained.

[0121] In this optional embodiment, the fog information includes fog size and fog concentration.

[0122] As can be seen, this optional embodiment can also specifically combine the information on water waves and fog in the monitoring area to predict the swaying of the IoT node, and analyze its swaying and the visibility of the monitoring area together, thereby achieving a precise analysis of the degree of influence of the environment on the target ship, thereby improving the accuracy of determining the working parameters of the IoT node, and thus helping to further improve the accuracy of monitoring the trajectory of the target ship.

[0123] Example 2

[0124] like Figure 2The diagram shown is a flowchart illustrating the steps of a method for intelligent monitoring of ship trajectories disclosed in Embodiment 2. Wherein, Figure 2 The described method can be applied to a device for intelligent monitoring of ship trajectories. This device may include one of the following: ship monitoring equipment capable of monitoring ship trajectories, a ship monitoring server (such as a local server or a cloud server), and a ship monitoring platform. The method may further include the following steps:

[0125] 201. When it is necessary to monitor the trajectory of a target vessel, obtain the current location information and identification information of the target vessel, and determine multiple first IoT nodes within the first area of ​​the target vessel based on the current location information of the target vessel.

[0126] 202. Obtain information for each first IoT node, including the identification information of the first IoT node.

[0127] 203. Based on the identification information of the target vessel, its current location information, and the identification information of each first IoT node, generate the monitoring and control parameters of the first IoT node. The monitoring and control parameters of each first IoT node include the monitoring identification of the IoT node for the target vessel.

[0128] 204. Based on the monitoring and control parameters of each first IoT node, control the first IoT node to perform monitoring operations on the navigation trajectory of the target ship.

[0129] In this embodiment of the invention, for the description of steps 201-204, please refer to the detailed description of steps 101-104 in Embodiment 1, which will not be repeated here.

[0130] 205. Sequentially acquire the monitoring data of each IoT node on the target vessel during its navigation process. The monitoring data corresponding to each IoT node includes the trajectory monitoring data of the target vessel, the monitoring time of the trajectory monitoring data, and the identification information of the corresponding monitoring area.

[0131] In this embodiment of the invention, each IoT node is the first IoT node described above and the second IoT node in Embodiment 1.

[0132] In this embodiment of the invention, optionally, monitoring data of the target ship by each IoT node can be obtained by communicating with IoT nodes.

[0133] 206. For any monitoring area, based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node, analyze the trajectory monitoring data corresponding to all IoT nodes to obtain the trajectory monitoring results of the target ship in the monitoring area. The trajectory monitoring results include the monitoring trajectory and the monitoring time corresponding to the monitoring trajectory.

[0134] In this embodiment of the invention, the navigation trajectory of each IoT node for the target ship is obtained based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node. The navigation trajectories corresponding to all IoT nodes are analyzed, and the median trajectory of all navigation trajectories and one of the target monitoring times among all monitoring times corresponding to the monitoring area are taken as the trajectory monitoring results. The median trajectory is the monitoring trajectory, and the target monitoring time is the monitoring time corresponding to the monitoring trajectory.

[0135] 207. Based on the monitoring time corresponding to all monitoring tracks, perform a splicing operation on all monitoring tracks from morning to night to obtain the monitoring track of the target ship.

[0136] It is evident that implementation Figure 2 The described method for intelligent monitoring of ship trajectories involves acquiring the current location and identification information of a target ship when trajectory monitoring is required. Based on the target ship's current location, multiple first IoT nodes within a first area of ​​the target ship are identified, and the identification information of each first IoT node is obtained. Then, based on the target ship's identification information, the current location information, and the identification information of each first IoT node, monitoring and control parameters for that first IoT node are generated, such as the monitoring identifier for the target ship. This controls the first IoT node to perform monitoring operations on the target ship's navigation trajectory, thereby improving the accuracy of target ship trajectory monitoring. This facilitates the supervision of the target ship and allows for advance collision avoidance preparations in case of impending collisions, reducing personal safety and property damage. Furthermore, by analyzing the trajectory monitoring data corresponding to each IoT node in each monitoring area separately, corresponding trajectory monitoring results are obtained. Finally, by comprehensively analyzing the trajectory monitoring results corresponding to all IoT nodes, a precise and reliable monitoring trajectory of the target ship is obtained.

[0137] In an optional embodiment, the method may further include the following steps:

[0138] For any given monitoring area, determine the number of IoT nodes monitored within that area;

[0139] When the number of monitoring is greater than 1, determine the monitoring azimuth angle of each IoT node relative to the target ship, and based on the monitoring azimuth angle corresponding to each IoT node, determine whether there are at least two target IoT nodes in the same monitoring azimuth angle whose difference in monitoring azimuth angle is less than or equal to the preset monitoring azimuth angle.

[0140] If the judgment result is negative, perform the above operation of analyzing the trajectory monitoring data of all IoT nodes based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node to obtain the trajectory monitoring result of the target ship in the monitoring area.

[0141] When the judgment result is yes, an integration operation is performed on the trajectory monitoring data corresponding to all target IoT nodes in the same monitoring direction to obtain the integrated trajectory monitoring data. Based on the identification information of the monitoring area, the trajectory monitoring data corresponding to each monitoring direction is analyzed to obtain the trajectory monitoring result of the target ship in the monitoring area. Then, the above-mentioned operation of splicing all monitoring trajectories according to the monitoring time corresponding to all monitoring trajectories from morning to night is triggered to obtain the monitoring trajectory of the target ship.

[0142] In this optional embodiment, when the number of monitoring is equal to 1, the above-mentioned operation of analyzing the trajectory monitoring data corresponding to all IoT nodes based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node is performed to obtain the trajectory monitoring results of the target ship in the monitoring area.

[0143] In this optional embodiment, an integration operation is performed on the trajectory monitoring data corresponding to all target IoT nodes at each monitoring location to obtain integrated trajectory monitoring data. Specifically, the median data or mean data operation is performed on the trajectory monitoring data corresponding to all target IoT nodes at each monitoring location to obtain integrated trajectory monitoring data.

[0144] As can be seen, this optional embodiment first analyzes the monitoring azimuth angles of any two IoT nodes at the same monitoring direction in all monitoring areas. If the difference in monitoring azimuth angles is large, the trajectory monitoring data of all IoT nodes are directly analyzed. If the difference is small, the trajectory monitoring data in that monitoring direction is integrated before analyzing the trajectory of the target ship. This can improve the analysis efficiency while ensuring the accuracy of the trajectory monitoring results analysis of the target ship.

[0145] Example 3

[0146] like Figure 3 The diagram shown is a schematic representation of a system for intelligent monitoring of ship trajectories disclosed in Embodiment 3. Wherein, Figure 3The described apparatus may include one of the following: ship monitoring equipment capable of monitoring ship trajectories, ship monitoring server (such as a local server or a cloud server), and ship monitoring platform. The system includes the following modules:

[0147] Acquisition module 301: used to acquire the current location information and identification information of the target vessel when it is necessary to monitor the trajectory of the target vessel.

[0148] Determining module 302: used to determine multiple first IoT nodes within a first area of ​​the target vessel based on the target vessel's current location information.

[0149] Acquisition module 301: is also used to acquire information of each first IoT node, the information of each first IoT node including the identification information of the first IoT node.

[0150] Generation module 303: is used to generate monitoring and control parameters for the first IoT node based on the identification information of the target vessel, the current location information, and the identification information of each first IoT node. The monitoring and control parameters of each first IoT node include the monitoring identification of the IoT node for the target vessel.

[0151] Control module 304: Used to control the first IoT node to perform monitoring operations on the navigation trajectory of the target ship according to the monitoring and control parameters of each first IoT node.

[0152] Figure 3 The described intelligent monitoring device for ship trajectories acquires the current location and identification information of the target ship when trajectory monitoring is required. Based on the current location information, it identifies multiple first IoT nodes within a first area of ​​the target ship, thereby acquiring the identification information of each first IoT node. Then, based on the identification information of the target ship, the current location information, and the identification information of each first IoT node, it generates monitoring and control parameters for that first IoT node, such as the monitoring identifier of the target ship, to control the first IoT node to perform monitoring operations on the target ship's navigation trajectory. This improves the accuracy of target ship trajectory monitoring, which is beneficial for monitoring the target ship and for making collision avoidance preparations in advance when a collision is imminent, thereby reducing personal safety and property losses.

[0153] In an optional embodiment, such as Figure 3 As shown, the acquisition module 301 is also used to acquire the first navigation information of the target vessel when the distance between the current position of the target vessel and the end position of the first area is less than or equal to a preset distance value. The first navigation information includes the current navigation channel, the current navigation direction, the vessel type, and the vessel's destination.

[0154] like Figure 4 As shown, the system may further include the following modules:

[0155] Prediction module 305: Used to predict the target channel that the target vessel will travel next based on the first navigation information.

[0156] Module 302: A second Internet of Things node capable of monitoring the target vessel within a second area of ​​the target waterway.

[0157] Acquisition module 301: is also used to acquire the node type of all second IoT nodes.

[0158] Module 302 is further used to determine the second navigation information of the target vessel, which includes the current navigation speed and the current navigation acceleration.

[0159] Module 202 is further configured to determine the activation monitoring parameters of the second IoT node for the target vessel based on the second navigation information of the target vessel and the node type of each second IoT node.

[0160] Communication module 306: Used to send the start monitoring parameters corresponding to the second IoT node to the second IoT node, so as to trigger the second IoT node to monitor the target ship according to the start monitoring parameters. The start monitoring parameters corresponding to each second IoT node include the start monitoring time and the identification information of the target ship.

[0161] Figure 4 The device described for intelligent monitoring of ship trajectories can also continue to select suitable Internet of Things (IoT) nodes based on the navigation information when the target ship is about to finish its navigation in the current area. It combines the target ship's navigation speed and acceleration with the node type of the IoT node to generate corresponding start monitoring parameters and continue monitoring the target ship. This enables comprehensive monitoring of the entire navigation trajectory of the target ship, further improving the accuracy and reliability of the target ship monitoring.

[0162] In another alternative embodiment, such as Figure 4 As shown, the determining module 302 determines the specific method by which the second IoT node determines the activation monitoring parameters for the target vessel based on the second navigation information of the target vessel and the node type of each second IoT node, including:

[0163] For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a fixed-location IoT node (such as a fixed-location buoy device), the activation monitoring parameters of the second IoT node for the target ship are analyzed based on the second navigation information of the target ship, the current location information, and the location of the second IoT node.

[0164] For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a location-movable IoT node, the current information of the second IoT node is determined, and the relative distance between the target ship and the second IoT node is calculated based on the current information of the second IoT node and the second navigation information of the target ship. The current information of the second IoT node includes the current position, moving speed, moving direction and moving acceleration.

[0165] Based on the target vessel's second navigation information, current location information, relative distance, and the current information of the second IoT node, analyze the activation monitoring parameters of the second IoT node for the target vessel.

[0166] Figure 4 The system described for intelligent monitoring of ship trajectories can also determine the corresponding information for analyzing and initiating monitoring parameters based on the different node types of IoT nodes. This improves the accuracy of the analysis while ensuring the efficiency of the initiation monitoring parameter analysis, so as to accurately monitor the trajectory of the target ship.

[0167] In another optional embodiment, the determining module 302 is further configured to, for any second IoT node, determine the monitoring area of ​​the second IoT node based on the location of the second IoT node and the target area where the target vessel appears on the target waterway.

[0168] And such as Figure 4 As shown, the module may further include the following modules:

[0169] Data Acquisition Module 307: Used to collect environmental information of the monitoring area.

[0170] The first analysis module 308 is used to analyze the degree of influence of the environment in the monitoring area on the monitoring results obtained by the second Internet of Things node on the target ship based on environmental information. The higher the degree of influence value, the greater the impact on the monitoring results.

[0171] Module 302 is further configured to determine the operating parameters required for the second IoT node to monitor the target vessel based on the degree of impact and the monitoring performance of the second IoT node.

[0172] Association module 309: Used to associate working parameters and startup monitoring parameters to obtain association information.

[0173] Update module 310: Used to update the association information of each second IoT node to the start monitoring parameters of the second IoT node, and trigger the communication module to send the start monitoring parameters corresponding to the second IoT node to the second IoT node, so as to trigger the second IoT node to monitor the target ship according to the start monitoring parameters.

[0174] Figure 4 The described device for intelligent monitoring of ship trajectories can also comprehensively analyze the impact of the environment of the monitoring area of ​​the IoT node on the target ship and its monitoring performance, and then combine this with the starting monitoring parameters to monitor the target ship, further improving the accuracy of the target ship's trajectory monitoring. In another optional embodiment, the aforementioned environmental information includes fog information and wave information, and the wave information includes wave height and wave fluctuation.

[0175] The first analysis module 308 analyzes, based on environmental information, the degree of influence of the environment of the monitoring area on the monitoring results obtained by the second IoT node monitoring the target ship. Specific methods include:

[0176] Based on fog information, the visibility of the monitoring area is determined, and based on the height and fluctuation of the water waves, the swaying of the second IoT node is estimated.

[0177] By analyzing the visibility of the monitoring area and the swaying of the second IoT node, the degree of influence of the environment of the monitoring area on the monitoring results obtained by the second IoT node on the target ship is obtained.

[0178] Figure 4 The device described for intelligent monitoring of ship trajectories can also specifically combine information on water waves and fog in the monitoring area to predict the swaying of IoT nodes, and analyze the swaying and visibility of the monitoring area together, thereby achieving a precise analysis of the degree of environmental impact on the target ship, thus improving the accuracy of determining the working parameters of the IoT nodes, and further improving the accuracy of monitoring the trajectory of the target ship.

[0179] In yet another alternative embodiment, such as Figure 4 As shown, the system may further include the following modules:

[0180] The acquisition module 301 is also used to sequentially acquire the monitoring data of each IoT node on the target ship during the navigation process of the target ship. The monitoring data corresponding to each IoT node includes the trajectory monitoring data of the target ship, the monitoring time of the trajectory monitoring data and the identification information of the corresponding monitoring area.

[0181] And such as Figure 4 As shown, the system may further include the following modules:

[0182] The second analysis module 311 is used to analyze the trajectory monitoring data of all IoT nodes for any monitoring area based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node, and obtain the trajectory monitoring result of the target ship in the monitoring area. The trajectory monitoring result includes the monitoring trajectory and the monitoring time corresponding to the monitoring trajectory.

[0183] The splicing module 312 is used to perform splicing operations on all monitoring tracks from morning to night according to the monitoring time corresponding to all monitoring tracks, so as to obtain the monitoring track of the target ship.

[0184] Figure 4 The device described for intelligent monitoring of ship trajectories can also analyze the trajectory monitoring data corresponding to each IoT node in each monitoring area to obtain the corresponding trajectory monitoring results, and then comprehensively analyze the trajectory monitoring results corresponding to all IoT nodes to obtain accurate and reliable monitoring trajectories of the target ship.

[0185] In yet another alternative embodiment, such as Figure 4 As shown, the determining module 302 is also used to determine the number of IoT nodes monitored in any given monitoring area.

[0186] The determination module 302 is also used to determine the monitoring azimuth angle of each IoT node relative to the target ship when the number of monitoring is greater than 1, and to determine the monitoring azimuth angle corresponding to each IoT node.

[0187] And such as Figure 4 As shown, the system also includes the following modules:

[0188] Judgment module 313: used to determine whether there are at least two target IoT nodes at the same monitoring location whose difference in monitoring azimuth angle is less than or equal to the preset monitoring azimuth angle; when the judgment result is negative, the second analysis module 311 is triggered to perform the above-mentioned operation of analyzing the trajectory monitoring data corresponding to all IoT nodes based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node, and obtaining the trajectory monitoring result of the target ship in the monitoring area.

[0189] Integration module 314: When the judgment result is yes, it performs an integration operation on the trajectory monitoring data corresponding to all target IoT nodes at the same monitoring location to obtain the integrated trajectory monitoring data.

[0190] The second analysis module 311 is also used to analyze the trajectory monitoring data corresponding to each monitoring direction based on the identification information of the monitoring area, and obtain the trajectory monitoring results of the target ship in the monitoring area.

[0191] Figure 4The device described for intelligent monitoring of ship trajectories can also analyze the monitoring azimuth angles of any two IoT nodes at the same monitoring direction in all monitoring areas. If the difference in monitoring azimuth angles is large, the trajectory monitoring data of all IoT nodes are analyzed directly. If the difference is small, the trajectory monitoring data in that monitoring direction are integrated before analyzing the trajectory of the target ship. This can improve the analysis efficiency while ensuring the accuracy of the trajectory monitoring results analysis of the target ship.

[0192] A computer storage medium storing a computer program that, when executed by a processor, implements the aforementioned method for intelligent monitoring of ship trajectories.

[0193] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-described method for intelligent monitoring of ship trajectories.

Claims

1. A method for intelligent monitoring of ship trajectories, characterized in that, Includes the following steps: When it is necessary to monitor the trajectory of a target vessel, the current location information and identification information of the target vessel are obtained, and multiple first IoT nodes within the first area of ​​the target vessel are determined based on the current location information of the target vessel. Obtain information for each first IoT node, the information of which includes the identification information of the first IoT node; Based on the identification information of the target vessel, its current location information, and the identification information of each first IoT node, the monitoring and control parameters of the first IoT node are generated. The monitoring and control parameters of the first IoT node include the monitoring identification of the IoT node on the target vessel. Based on the monitoring and control parameters of each first IoT node, control the first IoT node to perform monitoring operations on the navigation trajectory of the target ship; When the distance between the current position of the target vessel and the end position of the first area is less than or equal to a preset distance value, the first navigation information of the target vessel is obtained. The first navigation information includes the current navigation channel, the current navigation direction, the vessel type, and the vessel's destination. Based on the first navigation information, predict the target channel that the target vessel will travel next, and determine the second Internet of Things node within the second area of ​​the target channel that can monitor the target vessel; Obtain the node type of all second IoT nodes and determine the second navigation information of the target vessel, which includes the current navigation speed and the current navigation acceleration; Based on the second navigation information of the target vessel and the node type of each second IoT node, the activation monitoring parameters of the second IoT node for the target vessel are determined, and the activation monitoring parameters corresponding to the second IoT node are sent to the second IoT node to trigger the second IoT node to monitor the target vessel according to the activation monitoring parameters. The activation monitoring parameters include the activation monitoring time and the identification information of the target vessel. The determination of the activation monitoring parameters for the second IoT node for the target vessel based on the second navigation information of the target vessel and the node type of each second IoT node specifically involves: For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a fixed-location IoT node, the activation monitoring parameters of the second IoT node for the target ship are analyzed based on the second navigation information of the target ship, the current location information, and the location of the second IoT node. For any second IoT node, when the node type of the second IoT node is used to indicate that the second IoT node is a location-movable IoT node, the current information of the second IoT node is determined, and the relative distance between the target ship and the second IoT node is calculated based on the current information of the second IoT node and the second navigation information of the target ship. The current information of the second IoT node includes the current position, moving speed, moving direction and moving acceleration. Based on the target vessel's second navigation information, current location information, relative distance between the target vessel and the second IoT node, and the current information of the second IoT node, analyze the activation monitoring parameters of the second IoT node for the target vessel.

2. The method for intelligent monitoring of ship trajectories according to claim 1, characterized in that, It also includes the following steps: For any second IoT node, the monitoring area of ​​the second IoT node is determined based on the location of the second IoT node and the target area where the target vessel appears on the target waterway. Collect environmental information of the monitoring area, and analyze the degree of influence of the environment of the monitoring area on the monitoring results obtained by the second Internet of Things node on the target ship based on the environmental information. The higher the degree of influence value, the greater the impact on the monitoring results. Based on the degree of impact and the monitoring performance of the second IoT node, the working parameters required for the second IoT node to monitor the target vessel are determined, and the working parameters and the start monitoring parameters are correlated to obtain the correlation information of the second IoT node. The association information of each second IoT node is updated to the startup monitoring parameters of that second IoT node. Then, the updated startup monitoring parameters of the second IoT node are sent to the second IoT node to trigger the second IoT node to monitor the target ship according to the updated startup monitoring parameters.

3. The method for intelligent monitoring of ship trajectories according to claim 2, characterized in that, The environmental information includes fog information and water wave information, and the water wave information includes water wave height and water wave fluctuation. The analysis of the impact of the environmental information of the monitoring area on the monitoring results obtained by the second IoT node monitoring the target ship is as follows: Based on the fog information, the visibility of the monitoring area is determined, and based on the water wave height and wave fluctuation, the swaying of the second IoT node is estimated. By analyzing the visibility of the monitoring area and the swaying of the second IoT node, the degree of influence of the environment of the monitoring area on the monitoring results obtained by the second IoT node on the target ship is obtained.

4. A method for intelligent monitoring of ship trajectories according to any one of claims 1-3, characterized in that, It also includes the following steps: The monitoring data of each IoT node on the target vessel during its navigation process are acquired sequentially. The monitoring data corresponding to each IoT node includes the trajectory monitoring data of the target vessel, the monitoring time of the trajectory monitoring data, and the identification information of the corresponding monitoring area. For any of the monitoring areas, based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node, the trajectory monitoring data corresponding to all IoT nodes are analyzed to obtain the trajectory monitoring results of the target ship in the monitoring area. The trajectory monitoring results include the monitoring trajectory and the monitoring time corresponding to the monitoring trajectory. Based on the monitoring time corresponding to all the monitoring tracks, the monitoring tracks are spliced ​​together from morning to night to obtain the monitoring track of the target vessel.

5. The method for intelligent monitoring of ship trajectories according to claim 4, characterized in that, It also includes the following steps: For any of the monitoring areas, determine the number of IoT nodes monitored within the monitoring area; When the number of monitoring is greater than 1, determine the monitoring azimuth angle of each IoT node relative to the target ship, and based on the monitoring azimuth angle corresponding to each IoT node, determine whether there are at least two target IoT nodes in the same monitoring azimuth angle whose difference in monitoring azimuth angle is less than or equal to the preset monitoring azimuth angle. When the judgment result is negative, the operation is to analyze the trajectory monitoring data corresponding to all IoT nodes based on the identification information of the monitoring area and the monitoring time corresponding to each IoT node, and obtain the trajectory monitoring result of the target ship in the monitoring area. When the judgment result is yes, an integration operation is performed on the trajectory monitoring data corresponding to all target IoT nodes in each monitoring direction to obtain integrated trajectory monitoring data. Based on the identification information of the monitoring area, the trajectory monitoring data corresponding to each monitoring direction is analyzed to obtain the trajectory monitoring result of the target ship in the monitoring area.

6. A system for intelligent monitoring of ship trajectories, employing a method for intelligent monitoring of ship trajectories as described in any one of claims 1-5, characterized in that, Includes the following modules: Acquisition module: used to acquire the current location information and identification information of the target vessel when trajectory monitoring of the target vessel is required; also used to acquire information of each first IoT node, the information of the first IoT node including the identification information of the first IoT node; Determination module: used to determine multiple first IoT nodes within a first area of ​​the target vessel based on the target vessel's current location information; The generation module is used to generate the monitoring and control parameters of the first IoT node based on the identification information of the target vessel, the current location information, and the identification information of each first IoT node. The monitoring and control parameters of the first IoT node include the monitoring identification of the IoT node on the target vessel. Control module: Used to control the first IoT node to perform monitoring operations on the navigation trajectory of the target ship according to the monitoring and control parameters of each first IoT node.

7. A computer storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the computer program implements a method for intelligent monitoring of ship trajectories as described in any one of claims 1-5.

8. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements a method for intelligent monitoring of ship trajectories as described in any one of claims 1-5.

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