AIS anomaly identification method and device, computer equipment and storage medium

Abstract

The invention relates to an AIS anomaly identification method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring region track data corresponding to a target region; the area track data comprises a moving track of a moving object in the target area; the moving track is obtained by processing image data shot by the unmanned aerial vehicle in the target area; acquiring AIS track data corresponding to the target area, and comparing the area track data with the AIS track data to obtain a comparison result; and if it is determined that the area trajectory data and the AIS trajectory data are different based on the comparison result, outputting an AIS anomaly recognition result for the target area. By adopting the method, whether the AIS signal abnormal condition exists in the target water area or not can be intelligently detected, so that the maritime affair supervision efficiency is improved, the maritime affair ship cruise cost is reduced, and effective support is provided for maritime affair supervision processes such as overwater and underwater operation and activities.

Description

Technical Field

[0001] The present application relates to the technical field of mobile object information processing, and in particular to an AIS anomaly recognition method, device, computer equipment and storage medium. Background Art

[0002] At present, the movement of ships on the water is monitored through AIS (Automatic Identification System) signals. However, the AIS of some ships is shut down, damaged, or used fraudulently, and there is no effective means to identify them. Summary of the invention

[0003] Based on this, it is necessary to provide an AIS anomaly identification method, device, computer equipment and storage medium that can automatically identify abnormal conditions of ship AIS in response to the above technical problems.

[0004] In a first aspect, the present application provides an AIS anomaly identification method, the method comprising:

[0005] Acquire regional trajectory data corresponding to the target area; the regional trajectory data includes the moving trajectory of the moving object in the target area; the moving trajectory is obtained by processing the image data of the target area photographed by the drone;

[0006] Obtain the AIS track data of the ship automatic identification system corresponding to the target area, compare the regional track data with the AIS track data, and obtain the comparison result;

[0007] If it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different, the AIS anomaly recognition result for the target area is output.

[0008] In one embodiment, if it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different, outputting an AIS anomaly recognition result for the target area includes:

[0009] For each mobile object, the moving trajectory of the mobile object is obtained according to the regional trajectory data, and the AIS trajectory of the mobile object is obtained according to the AIS trajectory data;

[0010] When it is determined that the moving trajectory of the moving object is different from the AIS trajectory of the moving object, an AIS anomaly recognition result for the moving object is output.

[0011] In one embodiment, when it is determined that the moving trajectory of the moving object is different from the AIS trajectory of the moving object, outputting an AIS anomaly recognition result for the moving object includes:

[0012] When the moving track indicates that the moving object is in a moored state, and the AIS track indicates that the moving object is in a sailing state, the AIS abnormal recognition result of the moving object is output as follows: the moving object has impersonated another ship's name or the AIS has been impersonated.

[0013] In one embodiment, when it is determined that the moving trajectory of the moving object is different from the AIS trajectory of the moving object, outputting an AIS anomaly recognition result for the moving object includes:

[0014] When the moving track indicates that the moving object is in a navigation state, and the AIS track indicates that the moving object has no real-time signal or is in a moored state, the AIS abnormal recognition result of the moving object is output as the AIS closing behavior of the moving object.

[0015] In one embodiment, when it is determined that the moving trajectory of the moving object is different from the AIS trajectory of the moving object, outputting an AIS anomaly recognition result for the moving object includes:

[0016] When the moving trajectory indicates that the moving object is in a navigation state, the AIS trajectory indicates that the moving object is in a navigation state, and the navigation route indicated by the moving trajectory is different from the navigation route indicated by the AIS trajectory, the AIS abnormal identification result of the mobile object is output as at least one of the following: the mobile object has impersonated another ship's name, the AIS has been impersonated, and the AIS has been turned off.

[0017] In one embodiment, for each mobile object, before obtaining the moving trajectory of the mobile object according to the regional trajectory data and obtaining the AIS trajectory of the mobile object according to the AIS trajectory data, the method further includes:

[0018] Based on the identification information of the mobile object, a moving track of the mobile object and an AIS track of the mobile object are respectively obtained; the identification information includes at least one of a ship name and a ship license plate.

[0019] In a second aspect, the present application also provides an AIS anomaly identification device. The device comprises:

[0020] The regional trajectory data acquisition module is used to acquire regional trajectory data corresponding to the target area; the regional trajectory data includes the moving trajectory of the moving object in the target area; the moving trajectory is obtained by processing the image data obtained by the drone shooting the target area;

[0021] The AIS track data acquisition module is used to obtain the ship automatic identification system AIS track data corresponding to the target area, compare the regional track data with the AIS track data, and obtain the comparison result;

[0022] The abnormality identification result output module is used to output the AIS abnormality identification result for the target area if it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different.

[0023] In a third aspect, the present application further provides a computer device, wherein the computer device comprises a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of the above-mentioned AIS anomaly identification method are implemented.

[0024] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned AIS anomaly identification method are implemented.

[0025] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps of the above-mentioned AIS anomaly identification method are implemented.

[0026] The above-mentioned AIS abnormality identification method, device, computer equipment and storage medium obtain regional trajectory data corresponding to the target area; the regional trajectory data includes the movement trajectory of the mobile object in the target area; the movement trajectory is obtained by processing the image data obtained by the drone shooting of the target area; the ship automatic identification system AIS trajectory data corresponding to the target area is obtained, and the regional trajectory data and the AIS trajectory data are compared to obtain a comparison result; if it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different, the AIS abnormality identification result for the target area is output, and the target water area is intelligently detected whether there is a trajectory abnormality, that is, an AIS signal abnormality, so as to improve the efficiency of maritime supervision, reduce the cost of maritime vessel cruising, and provide effective support for maritime supervision processes such as water and underwater operations and activities. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] FIG1 is an application environment diagram of an AIS anomaly identification method in one embodiment;

[0028] Figure 2 Schematic diagram of a flow chart of an AIS abnormality identification method in one embodiment;

[0029] 3 is a flowchart of a step of outputting an AIS anomaly recognition result for a target area if it is determined based on a comparison result that the regional trajectory data and the AIS trajectory data are different, in one embodiment;

[0030] FIG4 is a schematic flow chart of an AIS abnormality identification method in another embodiment;

[0031] FIG5 is a block diagram of an AIS abnormality identification device in one embodiment;

[0032] FIG. 6 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0033] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0035] It is to be understood that terms such as “first”, “second”, etc. in the present application are only used to distinguish similar objects, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features.

[0036] It can be understood that the “connection” in the following embodiments should be understood as “electrical connection”, “communication connection”, etc. if the connected circuits, modules, units, etc. have electrical signals or data transmission between each other.

[0037] It will be understood that “at least one” means one or more, and “plurality” means two or more.

[0038] When used herein, the singular forms "a", "an", and "said / the" may also include plural forms, unless the context clearly indicates otherwise. It should also be understood that the terms "include / comprise" or "have" and the like specify the presence of stated features, wholes, steps, operations, components, parts, or combinations thereof, but do not exclude the possibility of the presence or addition of one or more other features, wholes, steps, operations, components, parts, or combinations thereof. At the same time, the term "and / or" used in this specification includes any and all combinations of the relevant listed items.

[0039] The AIS anomaly identification method provided in the embodiment of the present application can be applied to the application environment shown in Figure 1. Among them, the maritime department smart control center 101 communicates with the drone flight mission and route planning software 102 through the network, the drone flight mission and route planning software 102 communicates with the maritime unmanned aerial vehicle platform 103 through the network, the maritime unmanned aerial vehicle platform 103 is connected to the ship dynamic detection and identification device 104 through the network, the ship dynamic detection and identification device 104 is connected to the ship AIS analysis and comparison big data platform 105 through the network, and the ship AIS analysis and comparison big data platform 105 is connected to the maritime department smart control center 101 and the drone flight mission and route planning software 102 through the network.

[0040] Exemplarily, the maritime department smart control center 101 receives the supervision requirements of the maritime department and the cruise plan formulated according to the supervision requirements, and publishes them to the drone flight mission and route planning software 102. The drone flight mission and route planning software 102 sets the target waters according to the maritime supervision requirements issued by the maritime department smart control center 101, and realizes the daily flight mission management and route planning of the drone. The maritime unmanned aerial vehicle platform 103 uses drones to monitor ships in the target waters according to the flight missions issued by the drone flight mission and route planning software 102, identifies the monitoring data through the ship dynamic detection and identification device 104, and sends the identification results to the ship AIS analysis and comparison big data platform 105 in real time. The ship dynamic detection and identification device 104 forms the target ship dynamic trajectory data according to the drone aerial video and the built-in GPS device. The ship AIS analysis and comparison big data platform 105 realizes that the target ship dynamic trajectory data formed by the ship dynamic detection and identification device is compared with the ship AIS trajectory in the maritime supervision command system, and alarms for abnormal situations, and sends the abnormal situation alarm information to the maritime department smart control center 101.

[0041] It is understandable that the maritime department's smart control center 101, the drone flight mission and route planning software 102, the maritime unmanned aerial vehicle platform 103, the ship dynamic detection and identification device 104, and the ship AIS analysis and comparison big data platform 105 can be program software, or a terminal or server running the program software, or a system including a terminal and a server, and the functions of the program software are realized through the interaction between the terminal and the server. Further, the maritime department's smart control center 101, the drone flight mission and route planning software 102, the maritime unmanned aerial vehicle platform 103, the ship dynamic detection and identification device 104, and the ship AIS analysis and comparison big data platform 105 can be 5 independent program softwares respectively, and run on 5 independent terminals, servers or systems, or can be program softwares integrating multiple functions, or multiple program softwares running on the same terminal, server or system.

[0042] In one embodiment, Figure 2 As shown, a method for identifying an AIS anomaly is provided, and the method is applied to the ship AIS analysis and comparison big data platform 105 in FIG. 1 as an example for explanation, including the following steps:

[0043] Step 202, obtaining regional trajectory data corresponding to the target area; the regional trajectory data includes the moving trajectory of the moving object in the target area; the moving trajectory is obtained by processing the image data of the target area photographed by the drone.

[0044] The target area is the target area that needs to be cruised and supervised as determined by the drone flight mission and route planning software. The mobile object is a movable vehicle or other object that needs to be supervised. For example, the target area can be a target water area, and the mobile object can be a ship in the target water area, which can also be called a target ship.

[0045] Furthermore, the moving trajectory of the mobile object can be obtained by processing the image data of the mobile object captured by the drone. Optionally, the drone performs a cruise mission in the target waters, uses an onboard gimbal camera to capture ships in the target waters, and uses a ship dynamic detection and recognition device to identify the target waters ships in the captured images or videos, to form the dynamic trajectory data of the target ship, and transmit the ship dynamic trajectory data back to the ship AIS analysis and comparison big data platform through a wireless data transmission link. Exemplarily, the ship dynamic detection and recognition device processes and recognizes the received video and image data captured by the maritime drone, monitors the number and dynamics of target ships in the target waters, and forms the dynamic trajectory of the target ship through the built-in remote sensing detector and GPS positioning device, and uploads it to the ship AIS analysis and comparison big data platform in real time.

[0046] For example, the regional trajectory data may include the moving trajectories formed by all moving objects in the target area (also referred to as the dynamic trajectory of the target ship). There may be several moving objects in the moored state and several moving objects in the sailing state in the target area, and the regional trajectory data may be several moving trajectories and several stationary points. In some examples, there may be no moving objects in the target area, that is, the image captured by the drone includes 0 target ships.

[0047] Step 204, obtaining AIS trajectory data corresponding to the target area, comparing the area trajectory data with the AIS trajectory data, and obtaining a comparison result.

[0048] Specifically, the ship AIS analysis and comparison big data platform obtains the ship automatic identification system AIS trajectory data corresponding to the target area, and compares the regional trajectory data with the AIS trajectory data to obtain the comparison results.

[0049] Among them, the AIS trajectory of the mobile object can be obtained based on the AIS signal sent by the mobile object (such as a ship). The AIS trajectory data of the target area includes the AIS trajectories of all mobile objects in the target area. Exemplarily, the ship AIS analysis and comparison big data platform is integrated with the electronic chart and ship database module of the maritime supervision and command system, and the AIS trajectory data of the target area can be obtained through the electronic chart and ship database of the maritime supervision and command system.

[0050] For example, the AIS track data may include the AIS tracks formed by all mobile objects in the target area. There may be several mobile objects in the moored state and several mobile objects in the sailing state in the target area, and the AIS track data may be several AIS tracks and several stationary points. In some examples, there may be no mobile objects in the target area, that is, the AIS track data indicates that there are 0 target ships in the area.

[0051] Step 206: If it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different, an AIS anomaly recognition result for the target area is output.

[0052] The AIS abnormality identification result is the AIS abnormality identification result for the target area. Specifically, if it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different, the ship AIS analysis and comparison big data platform outputs the AIS abnormality identification result for the target area. Exemplarily, the AIS abnormality identification result can represent at least one of the abnormal results such as the difference in the number of regional trajectory data and AIS trajectory data of the mobile object in the target area, the difference in trajectory route, etc.

[0053] Illustratively, the ship AIS analysis and comparison big data platform performs algorithm analysis on the ship dynamic data uploaded by the ship dynamic detection and identification device, compares it with the ship AIS trajectory of the maritime regulatory command system, and outputs AIS anomaly identification results for the target area.

[0054] In the above-mentioned AIS anomaly identification method, the regional trajectory data in the target area obtained by the drone and the AIS trajectory data generated by the AIS signal in the AIS system are obtained and compared to obtain the AIS anomaly identification result for the target area. The advantages of drones such as fast response speed, wide radiation area, short cruise interval time, and high economy can effectively make up for the shortcomings of traditional maritime ship cruises. The drone uses a high-definition camera to patrol the navigation environment and transmits the on-site image to the control platform terminal in real time, which is conducive to the control of the water traffic situation in the jurisdiction. If illegal ship behavior is found during the drone cruise, the shouting system can also be used to intervene and correct it in time. If necessary, a patrol ship will be assigned to the scene for processing, which can greatly improve the efficiency of maritime supervision.

[0055] In one embodiment, as shown in FIG3 , if it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different, outputting the AIS anomaly recognition result for the target area may include the following steps:

[0056] Step 302: for each mobile object, obtain the moving trajectory of the mobile object according to the regional trajectory data, and obtain the AIS trajectory of the mobile object according to the AIS trajectory data.

[0057] Exemplarily, the ship AIS analysis and comparison big data platform obtains the moving trajectory of the mobile object through the ship dynamic detection and identification device, obtains the AIS trajectory of the mobile object through the electronic nautical chart of the maritime supervision and command system and the ship database, and analyzes and compares the moving trajectory of the mobile object and the AIS trajectory of the mobile object to obtain a comparison result.

[0058] Among them, the moving trajectory of the mobile object can be obtained by processing the image data taken by the drone; the AIS trajectory of the mobile object can be obtained by processing the AIS signal sent by the mobile object (such as a ship). Optionally, the drone performs a cruise mission in the target waters, uses an onboard pan-tilt camera to shoot the target waters ship, and uses the ship dynamic detection and recognition device to identify the target waters ship in the captured image or video to form the dynamic trajectory data of the target ship. Specifically, an object tracking algorithm can be used to generate a dynamic trajectory by identifying and tracking specific objects in a video sequence. Exemplarily, the object tracking algorithm can be implemented by using various visual features (such as color, texture, shape, etc.) or a deep learning model. Optionally, a deep learning model, such as a recurrent neural network, a long short-term memory network, etc., can also be used to analyze the video sequence and generate a dynamic trajectory.

[0059] Step 304: When it is determined that the moving trajectory of the moving object is different from the AIS trajectory of the moving object, an AIS anomaly recognition result for the moving object is output.

[0060] Specifically, for each moving object, when it is determined that the moving trajectory of the moving object and the AIS trajectory of the moving object are different, the ship AIS analysis and comparison big data platform outputs the AIS anomaly recognition result for the moving object.

[0061] Optionally, the ship AIS analysis and comparison big data platform screens out ships with abnormal AIS and triggers an AIS abnormality alarm, and displays detailed information on the abnormal ship position on the ship AIS abnormality identification system terminal (i.e., a terminal equipped with the ship AIS analysis and comparison big data platform).

[0062] In the above-mentioned AIS anomaly identification method, by obtaining the moving trajectory and AIS trajectory of the target ship and comparing them, the video images of the target ship in the waters within the specified range can be quickly compared and identified, and problems such as ship AIS anomalies can be effectively discovered.

[0063] In one embodiment, when it is determined that the moving trajectory of the moving object is different from the AIS trajectory of the moving object, outputting an AIS anomaly recognition result for the moving object includes:

[0064] When the moving track indicates that the moving object is in a moored state, and the AIS track indicates that the moving object is in a sailing state, the AIS abnormal recognition result of the moving object is output as follows: the moving object has impersonated another ship's name or the AIS has been impersonated.

[0065] Specifically, the mooring state may indicate that the position change of the mobile object within a preset time period is less than a preset threshold value, and optionally, the mooring state may indicate that the position change of the mobile object within a preset time period is 0. The navigation state may indicate that the position change parameter or speed parameter of the mobile object within a preset time period is greater than a preset threshold value, or that the mobile trajectory obtained by processing the ship dynamic detection and identification device or the AIS trajectory extracted from the electronic chart of the maritime supervision and command system is a continuous linear trajectory.

[0066] For example, when the moving track indicates that the moving object is in a moored state, and the AIS track indicates that the moving object is in a sailing state, the moving object indicated by the AIS track is the target ship, indicating that the target ship has misused the AIS signal. In a specific application scenario, for example, the moving track obtained by the drone shows that ship A is in a moored state, and the AIS track indicates that ship A is in a sailing state, indicating that another ship B has misused the AIS signal of ship A, and further boarding inspections can be performed on the sailing ship B (locating its position information based on the AIS signal of ship A).

[0067] In one embodiment, when it is determined that the moving trajectory of the moving object is different from the AIS trajectory of the moving object, outputting an AIS anomaly recognition result for the moving object includes:

[0068] When the moving track indicates that the moving object is in a navigation state, and the AIS track indicates that the moving object has no real-time signal or is in a moored state, the AIS abnormal recognition result of the moving object is output as the AIS closing behavior of the moving object.

[0069] For example, when the moving track indicates that the moving object is in a moored state, and the AIS track indicates that the moving object is in a sailing state, the moving object indicated by the AIS track is the target ship, indicating that the target ship has AIS turned off. In a specific application scenario, for example, the moving track obtained by the drone shows that ship A is in a sailing state, and the AIS track indicates that ship A is in a moored state, indicating that ship A did not turn on and send AIS signals as required during the voyage, and further boarding inspections can be performed on the sailing ship A.

[0070] In one embodiment, when it is determined that the moving trajectory of the moving object is different from the AIS trajectory of the moving object, outputting an AIS anomaly recognition result for the moving object includes:

[0071] When the moving trajectory indicates that the moving object is in a navigation state, the AIS trajectory indicates that the moving object is in a navigation state, and the navigation route indicated by the moving trajectory is different from the navigation route indicated by the AIS trajectory, the AIS abnormal identification result of the mobile object is output as at least one of the following: the mobile object has impersonated another ship's name, the AIS has been impersonated, and the AIS has been turned off.

[0072] Exemplarily, when the moving trajectory indicates that the moving object is in a navigation state, the AIS trajectory indicates that the moving object is in a navigation state, and the navigation route indicated by the moving trajectory is different from the navigation route indicated by the AIS trajectory, taking the moving object indicated by the AIS trajectory as a target ship as an example, it can be determined that the target ship has one of the following behaviors: the mobile object has impersonated another ship's name, the AIS has been impersonated, and the AIS has been turned off. It can also be determined that the target ship has multiple codes.

[0073] In a specific application scenario, for example, the moving track captured by the drone shows that ship A is in a sailing state, while the AIS track indicates that ship A is in a sailing state of another route track, indicating that there is another ship B that has sent out the AIS signal of ship A. Taking ship B as the target ship, there is a false AIS track of ship B on the electronic chart of the maritime regulatory command system, indicating that the AIS signal of ship B is not turned on as required, or ship B has used the AIS signal of ship A, or ship B has multiple codes (ship B is equipped with two AIS systems named ship A and ship B). Therefore, further boarding inspections can be carried out on the sailing ship B (locating its position information based on the AIS signal of ship A).

[0074] In the AIS anomaly identification method provided in the embodiment of the present application, by analyzing and comparing the movement trajectory obtained by the drone and the AIS trajectory in the electronic nautical chart of the maritime supervision and command system, the video images of target ships in the waters within the specified range can be quickly compared and identified, effectively solving the difficulties in illegal evidence collection such as malicious shutdown of the ship's automatic identification system, one ship with multiple codes, etc. to evade maritime supervision.

[0075] In one embodiment, for each mobile object, before obtaining the moving trajectory of the mobile object according to the regional trajectory data and obtaining the AIS trajectory of the mobile object according to the AIS trajectory data, the method further includes:

[0076] Based on the identification information of the mobile object, a moving track of the mobile object and an AIS track of the mobile object are respectively obtained; the identification information includes at least one of a ship name and a ship license plate.

[0077] Specifically, for moving objects captured by drones, the identification information of the moving objects can be obtained by processing and identifying the video and image data captured by the received maritime drones. Furthermore, the drone can be manually controlled through the drone platform ground control station to track abnormal AIS ships, and the ship name, ship license plate and other ship-specific information (i.e. identification information) can be captured through the airborne pan-tilt camera. If it is in insufficient light conditions such as at night, the infrared night vision and airborne searchlight functions can be turned on to capture the abnormal AIS ships for evidence.

[0078] For the AIS track in the electronic nautical chart of the maritime supervision and command system, generally speaking, the AIS signal includes fields such as information, user identification code, navigation status, longitude, latitude, etc., and the database of the maritime supervision and command system stores identification information such as the ship name and license plate corresponding to the information such as the information identification code and user identification code. Therefore, the identification information of the mobile object corresponding to the AIS track can be directly obtained through the maritime supervision and command system.

[0079] For example, after identifying the identification information of the mobile object in the moving trajectory photographed by the drone, the identification information can be used to search for the corresponding AIS trajectory in the electronic nautical chart of the maritime supervision and command system; or after obtaining the identification information of the mobile object using the AIS trajectory, the identification information can be used to search for the moving trajectory that matches the corresponding mobile object in the moving trajectory photographed by the drone.

[0080] Optionally, based on the supervision needs in actual work, the identification information of the target ship to be supervised can be obtained, and then the movement trajectory and AIS trajectory of the target ship can be searched in the regional trajectory data and the AIS trajectory data respectively.

[0081] In the AIS anomaly identification method provided in the embodiment of the present application, the moving trajectory of the mobile object and the AIS trajectory are matched by using specific identification information such as the ship name and ship license plate, and then compared. If the comparison is consistent, it indicates that the ship's AIS situation is normal; if the comparison is inconsistent, it indicates that the ship's AIS situation is abnormal, and an abnormal alarm will be issued. The video images of target ships in the waters within the specified range can be quickly compared and identified, effectively solving the difficulties in illegal evidence collection such as malicious shutdown of the ship's automatic identification system, one ship with multiple codes, etc. to evade maritime supervision.

[0082] In order to further explain the scheme of the embodiment of the present application, a specific example is used to illustrate it. For example, in the embodiment of the present application, a ship AIS anomaly identification system based on a drone is constructed, including a maritime unmanned aerial vehicle platform, a drone flight mission and route planning software, a ship dynamic detection and identification device, and a ship AIS analysis and comparison big data platform. The maritime unmanned aerial vehicle platform uses a drone to monitor ships in the target waters according to the flight missions issued by the drone flight mission and route planning software, identifies the monitoring data through the ship dynamic detection and identification device, and sends the identification results to the ship AIS analysis and comparison big data platform in real time; the drone flight mission and route planning software sets the target waters according to the maritime supervision requirements, and realizes the daily flight mission management and route planning of the drone; the ship dynamic detection and identification device forms the target ship dynamic trajectory data according to the drone aerial video and the built-in GPS device; the ship AIS analysis and comparison big data platform realizes that the ship dynamic detection and identification device forms the target ship dynamic trajectory data and compares it with the ship AIS trajectory in the maritime supervision command system, and alarms for abnormal situations.

[0083] Furthermore, the maritime unmanned aerial vehicle platform includes a maritime unmanned aerial vehicle, an unmanned aerial vehicle airport, an airborne infrared pan-tilt camera, an airborne searchlight, and a ground control station. The unmanned aerial vehicle airport realizes the functions of automatic take-off and landing, automatic charging, data relay transmission, etc. of the maritime unmanned aerial vehicle according to the set timing, and is deployed on the shore near the target waters according to the needs of maritime supervision. The maritime unmanned aerial vehicle is equipped with an infrared pan-tilt camera to conduct a scanning aerial patrol inspection of the target waters, and the aerial video of the ships in the target waters is transmitted back in real time. The real-time position trajectory and flight dynamic data of the maritime unmanned aerial vehicle are displayed on the ground control station and the ship AIS abnormal identification system terminal, and the dynamics of the ships in the target waters are automatically monitored. If an abnormality is found in the ship AIS signal, an alarm is automatically triggered on the ship AIS analysis and comparison big data platform, and video evidence and time and location information are saved. The target ship is tracked under manual intervention, and the ship name, ship license plate and other ship-specific identification information are photographed.

[0084] Optionally, all payloads of the maritime unmanned aerial vehicle platform are waterproof, which can meet the needs of maritime cruising in rainy and foggy weather and can successfully complete all-weather maritime cruising missions.

[0085] Optionally, the maritime unmanned aerial vehicle platform's onboard gimbal camera has multiple zoom and infrared night vision functions, and can be linked to control the onboard searchlight, to meet the needs of backlight conditions and night cruise shooting of specific ship appearance identification information such as ship names.

[0086] Furthermore, the UAV flight mission and route planning software integrates the electronic nautical chart and ship database module of the maritime regulatory command system, the UAV aerial video live broadcast module, the flight mission release management module, and the UAV flight route editing module, and displays the UAV location information, flight dynamic data, and real-time aerial video of the UAV on the ship AIS anomaly identification system terminal, manages flight tracks and aerial videos, generates cruise reports with one click, queries and replays historical mission data, and performs statistical analysis on long-term flight missions.

[0087] The ship dynamic detection and identification device processes and identifies the received video and image data taken by the maritime drone, monitors the number and dynamics of target ships in the target waters, and forms the dynamic trajectory of the target ship through the built-in remote sensing detector and GPS positioning device, and uploads it to the ship AIS analysis and comparison big data platform in real time.

[0088] The ship AIS analysis and comparison big data platform performs algorithm analysis on the ship dynamic data uploaded by the ship dynamic detection and identification device, compares it with the ship AIS trajectory of the maritime regulatory command system, screens out ships with abnormal AIS, triggers AIS abnormality alarms, displays detailed information on the abnormal ship locations on the ship AIS abnormality identification system terminal, and then commands the maritime drone platform to perform qualitative detection on suspicious ships and photograph ship-specific identification information such as ship names and license plates.

[0089] By using the drone-based ship AIS anomaly recognition system, as shown in Figure 4, the following workflow can be achieved:

[0090] Based on the regulatory needs of the jurisdiction, the maritime department's smart control center defines the scope of the target waters for supervision in the drone flight mission and route planning software, formulates drone cruise routes and cruise frequencies, issues drone cruise missions, and deploys the drone airport to a suitable shore-based location near the target waters for supervision. Then, the drone flight mission and route planning software sends a cruise mission command to the drone airport, requiring the drone to perform a cruise mission in the target waters.

[0091] According to the cruise mission command, the drone airport autonomously detects the maritime drone's power, ambient temperature, wind speed, whether there is precipitation, etc., and evaluates whether it can cruise according to the cruise command. If the evaluation is qualified, the airport door will be opened, the lifting platform will rise to the top, and the maritime drone will automatically fly out to cruise the target waters according to the planned route; if the evaluation is unqualified, an alarm feedback will be given to the drone flight mission and route planning software, and manual intervention will be carried out.

[0092] Maritime drones take off from drone airports according to planned routes, fly to target waters to perform cruise missions, use airborne gimbal cameras to photograph ships in target waters, identify ships in target waters through ship dynamic detection and identification devices, form target ship dynamic trajectory data, and transmit the ship dynamic trajectory data back to the ship AIS analysis and comparison big data platform through a wireless data transmission link for analysis and comparison.

[0093] The ship AIS analysis and comparison big data platform will compare the received target ship dynamic trajectory data with the ship AIS data in the maritime supervision and command system. If the comparison is consistent, it means that the ship AIS is normal; if the comparison is inconsistent, it means that the ship AIS is abnormal and an abnormal alarm will be issued.

[0094] After receiving the alarm, the maritime department's smart control center manually controls the drone through the drone platform ground control station, tracks the AIS abnormal ship, and uses the onboard gimbal camera to capture ship-specific information such as the ship name and license plate. If it is at night or other low-light conditions, the infrared night vision and airborne searchlight functions are turned on to capture and collect evidence of the AIS abnormal ship. The ship AIS analysis and comparison big data platform further analyzes the AIS abnormal alarm into the following situations: If the target ship has dynamic trajectory data, but the ship does not have a real-time AIS signal in the maritime supervision and command system, it indicates that the ship is suspected of not turning on the ship AIS as required; if the target ship's real-time AIS signal is found in the maritime supervision and command system in the target waters, but there is no dynamic trajectory data of the ship, or the dynamic trajectory data of other ships is displayed, it indicates that the ship has multiple codes, or is impersonating the AIS of other ships.

[0095] After completing the cruise mission, the drone returns according to the mission planning route and lands at the drone airport. The drone airport recovers the drone, performs status detection and charges the drone. The drone uploads the cruise data and waits for the next flight according to the drone flight mission and route planning software cruise mission.

[0096] Optionally, a patrol work report can be generated with one click on the ship AIS anomaly identification system terminal. The report includes the patrol track, patrol video, number and name of patrol ships, ship AIS anomaly conditions and handling suggestions. The maritime department will further handle the AIS abnormal ship based on the patrol work report.

[0097] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0098] Based on the same inventive concept, the embodiment of the present application also provides an AIS anomaly identification device for implementing the AIS anomaly identification method involved above. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more AIS anomaly identification device embodiments provided below can refer to the limitations of the AIS anomaly identification method above, and will not be repeated here.

[0099] In one embodiment, as shown in FIG5 , an AIS abnormality identification device 500 is provided, comprising:

[0100] The regional trajectory data acquisition module 501 is used to acquire regional trajectory data corresponding to the target area; the regional trajectory data includes the moving trajectory of the moving object in the target area; the moving trajectory is obtained by processing the image data obtained by the drone shooting the target area;

[0101] AIS track data acquisition module 502, used to acquire AIS track data corresponding to the target area, compare the area track data with the AIS track data, and obtain a comparison result;

[0102] The abnormality identification result output module 503 is used to output the AIS abnormality identification result for the target area if it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different.

[0103] In one embodiment, the abnormality identification result output module includes:

[0104] A mobile object trajectory comparison unit, for each mobile object, obtaining a moving trajectory of the mobile object according to the regional trajectory data, and obtaining an AIS trajectory of the mobile object according to the AIS trajectory data;

[0105] The mobile object abnormal result output unit is used to determine that the moving trajectory of the mobile object and the AIS trajectory data of the mobile object are different based on the comparison result, and output the AIS abnormality recognition result for the mobile object.

[0106] In one of the embodiments, the mobile object abnormal result output unit is used to output the AIS abnormal identification result of the mobile object, that is, the mobile object has impersonated another ship's name or the AIS has been impersonated, when the moving trajectory indicates that the mobile object is in a moored state and the AIS trajectory indicates that the mobile object is in a sailing state.

[0107] In one of the embodiments, the mobile object abnormal result output unit is used to output the AIS abnormal recognition result of the mobile object as the mobile object having AIS shutdown behavior when the mobile trajectory indicates that the mobile object is in a navigation state and the AIS trajectory indicates that the mobile object has no real-time signal or is in a moored state.

[0108] In one of the embodiments, the mobile object abnormal result output unit is used to output the AIS abnormal identification result of the mobile object as at least one of the following: the mobile object has impersonated another ship's name, the AIS has been impersonated, and the AIS has been shut down, when the mobile trajectory indicates that the mobile object is in a navigation state, the AIS trajectory indicates that the mobile object is in a navigation state, and the navigation route indicated by the mobile trajectory is different from the navigation route indicated by the AIS trajectory.

[0109] In one embodiment, the abnormality identification result output module further includes:

[0110] The mobile object trajectory acquisition unit is used to respectively acquire the moving trajectory of the mobile object and the AIS trajectory of the mobile object based on the identification information of the mobile object; the identification information includes at least one of a ship name and a ship license plate.

[0111] Each module in the above-mentioned AIS abnormality identification device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory in the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0112] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be shown in FIG6. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. The processor, the memory, and the input / output interface are connected via a system bus, and the communication interface, the display unit, and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner may be implemented through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, an AIS abnormality identification method is implemented. The display unit of the computer device is used to form a visually visible picture, which may be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0113] Those skilled in the art will understand that the structure shown in FIG. 6 is merely a block diagram of a partial structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0114] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps of the above-mentioned AIS anomaly identification method when executing the computer program.

[0115] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned AIS anomaly identification method are implemented.

[0116] In one embodiment, a computer program product is provided, including a computer program, which implements the steps of the above-mentioned AIS anomaly identification method when executed by a processor.

[0117] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0118] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0119] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the present application. It should be noted that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for identifying anomalies of AIS, characterized in that: The method comprises: Acquire regional trajectory data corresponding to the target area; the regional trajectory data includes the moving trajectory of the moving object in the target area; the moving trajectory is obtained by processing the image data of the target area photographed by the drone; Acquire AIS trajectory data corresponding to the target area, compare the area trajectory data with the AIS trajectory data, and obtain a comparison result; If it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different, an AIS anomaly recognition result for the target area is output.

2. The method according to claim 1, characterized in that: If it is determined based on the comparison result that the regional trajectory data and the AIS trajectory data are different, outputting an AIS anomaly recognition result for the target area includes: For each of the mobile objects, obtaining a moving trajectory of the mobile object according to the regional trajectory data, and obtaining an AIS trajectory of the mobile object according to the AIS trajectory data; When it is determined that the moving trajectory of the mobile object is different from the AIS trajectory of the mobile object, an AIS anomaly recognition result for the mobile object is output.

3. The method according to claim 2, characterized in that When it is determined that the moving trajectory of the mobile object is different from the AIS trajectory of the mobile object, outputting an AIS anomaly recognition result for the mobile object includes: When the moving trajectory indicates that the moving object is in a moored state, and the AIS trajectory indicates that the moving object is in a sailing state, the AIS abnormal identification result of the moving object is outputted as that the moving object has impersonated another ship's name or the AIS has been impersonated.

4. The method according to claim 2, characterized in that: When it is determined that the moving trajectory of the mobile object is different from the AIS trajectory of the mobile object, outputting an AIS anomaly recognition result for the mobile object includes: When the moving trajectory indicates that the moving object is in a navigation state, and the AIS trajectory indicates that the moving object has no real-time signal or is in a moored state, the AIS abnormality recognition result of the moving object is outputted as that the moving object has an AIS shutdown behavior.

5. The method according to claim 2, characterized in that: When it is determined that the moving trajectory of the mobile object is different from the AIS trajectory of the mobile object, outputting an AIS anomaly recognition result for the mobile object includes: When the moving trajectory indicates that the moving object is in a sailing state, the AIS trajectory indicates that the moving object is in a sailing state, and the sailing route indicated by the moving trajectory is different from the sailing route indicated by the AIS trajectory, the AIS abnormality identification result of the mobile object is output as at least one of the following behaviors: the mobile object has impersonated another ship's name, the AIS has been impersonated, and the AIS has been turned off.

6. The method according to any one of claims 2 to 5, characterized in that: Before comparing the moving track of each of the moving objects with the AIS track of the moving object to obtain a comparison result, the method further includes: Based on the identification information of the mobile object, a moving track of the mobile object and an AIS track of the mobile object are respectively acquired; the identification information includes at least one of a ship name and a ship license plate.

7. An AIS anomaly identification device, characterized in that: The device comprises: A regional trajectory data acquisition module is used to acquire regional trajectory data corresponding to a target region; the regional trajectory data includes a moving trajectory of a moving object in the target region; the moving trajectory is obtained by processing image data of the target region captured by a drone; An AIS track data acquisition module is used to acquire the ship automatic identification system AIS track data corresponding to the target area, compare the area track data with the AIS track data, and obtain a comparison result; The abnormality identification result output module is used to output the AIS abnormality identification result for the target area if it is determined based on the comparison result that the area trajectory data and the AIS trajectory data are different.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

Images