Anti-unmanned aerial vehicle learning method and device and computer equipment
The anti-drone learning method addresses systemization and scene-specific training gaps by using user profiling and simulation-based content recommendation, enhancing training precision and adaptability.
Patent Information
- Application Number
- CN202510399207.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-31
- Publication Date
- 2025-07-04
AI Technical Summary
Existing anti-drone technologies lack systemization, scene-specificity, and intelligence in user training, leading to inefficiencies in addressing low-altitude security threats posed by drones.
A personalized anti-drone learning method that utilizes user profiling and scene-specific simulation to recommend content entity courses and learning suggestions, incorporating multiple knowledge domains and employing large models for path planning and content recommendation.
Enhances the precision and systematization of anti-drone training by tailoring content to individual user needs, improving learning accuracy and adaptability across diverse scenarios.
Smart Images
Figure CN120259046A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the technical field of unmanned aerial vehicles, and particularly to an anti-unmanned aerial vehicle learning method, device, and computer device. Background Art
[0002] With the development of unmanned aerial vehicle technology, unmanned aerial vehicles are widely used in various fields. However, in practical applications, improper use of unmanned aerial vehicles can also pose low-altitude safety threats, such as illegal reconnaissance, carrying dangerous items, interfering with aviation order, etc. Among them, anti-unmanned aerial vehicle technology is an important means to ensure low-altitude safety. Therefore, anti-unmanned aerial vehicle learning becomes particularly important.
[0003] However, in the related technology, there are problems of insufficient systematicness, scenario awareness, and intelligence in the process of unmanned aerial vehicle learning. Summary of the Invention
[0004] Based on this, it is necessary to provide an anti-unmanned aerial vehicle learning method, device, and computer device for the above technical problems.
[0005] In a first aspect, an embodiment of the present application provides an anti-unmanned aerial vehicle learning method, including:
[0006] Obtaining content entity course information of anti-unmanned aerial vehicles recommended for the current user; and obtaining learning advice information of the current user for anti-unmanned aerial vehicles; the content entity course information is planned according to the portrait information of the current user and knowledge points of multiple different disciplines, and the learning advice information is determined after performing anti-unmanned aerial vehicle simulation processing according to the demand information of the current user for the disposal scenario of unmanned aerial vehicles;
[0007] Based on the content entity course information and the learning advice information, prompting the current user to perform anti-unmanned aerial vehicle learning.
[0008] In one embodiment, obtaining content entity course information of anti-unmanned aerial vehicles recommended for the current user includes:
[0009] Obtaining the low-altitude knowledge security map of anti-unmanned aerial vehicles and the portrait information of the current user;
[0010] According to the low-altitude knowledge security map of anti-unmanned aerial vehicles and the portrait information of the current user, planning the learning path of the current user to obtain the learning path information of the current user;
[0011] According to the learning path information, the low-altitude knowledge security map, and the security knowledge learning resource library, recommending content entity course information of anti-unmanned aerial vehicles to the current user.
[0012] In one embodiment, according to the low-altitude knowledge security map of anti-unmanned aerial vehicles and the portrait information of the current user, planning the learning path of the current user to obtain the learning path information of the current user includes:
[0013] According to the low-altitude knowledge security map and portrait information, a recommendation algorithm is used to plan the learning path of the current user to obtain learning path information; the recommendation algorithm is constructed based on a low-altitude large model.
[0014] In one embodiment, according to the learning path information, the low-altitude knowledge security map, and the security knowledge learning resource library, content entity course information on anti-drone is recommended to the current user, including:
[0015] Extract anti-drone learning content entity information from the learning path information;
[0016] Adopt a learning path recommendation algorithm to obtain anti-drone recommended content entity information from the low-altitude knowledge security map according to the learning content entity; the learning path recommendation algorithm is constructed based on a low-altitude large model;
[0017] Adopt a collaborative filtering algorithm to determine content entity course information according to the recommended content entity information and the security knowledge learning resource library; the collaborative filtering algorithm is constructed based on a low-altitude large model.
[0018] In one embodiment, the acquisition process of the security knowledge learning resource library includes:
[0019] Obtain the people's air defense knowledge base and the national defense mobilization basic general knowledge base;
[0020] Perform information extraction and processing according to the people's air defense knowledge base and the national defense mobilization basic general knowledge base to generate a security knowledge learning resource library.
[0021] In one embodiment, obtain learning advice information for the current user on anti-drone, including:
[0022] Obtain the disposal evaluation result of the drone;
[0023] Based on the disposal evaluation result of the drone and the information portrait, generate learning advice information for the current user.
[0024] In one embodiment, obtaining the disposal evaluation result of the drone includes:
[0025] Obtain the disposal scenario requirement information of the current user for the drone;
[0026] Adopt an air situation disposal algorithm to determine the corresponding air situation disposal information according to the disposal scenario requirement information; the air situation disposal algorithm is constructed based on a low-altitude large model; the air situation disposal information includes anti-drone disposal response strategies and corresponding operation strategies;
[0027] Based on the air situation disposal information, conduct anti-drone simulation on the drone to determine the disposal result of the drone;
[0028] Evaluate the disposal result to obtain the disposal evaluation result of the UAV.
[0029] In a second aspect, an anti-UAV learning device provided by an embodiment of the present application includes:
[0030] An acquisition module, configured to acquire content entity course information of an anti-UAV recommended for the current user; and acquire learning suggestion information of the current user for anti-UAV learning; the content entity course information is planned according to the portrait information of the current user and knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti-UAV simulation processing according to the disposal scenario requirement information of the current user for the UAV;
[0031] An anti-UAV learning module, configured to prompt the current user to perform anti-UAV learning based on the content entity course information and the learning suggestion information.
[0032] In a third aspect, the present application further provides a computer device, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method in the embodiment of the first aspect are implemented.
[0033] In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps of the method in the embodiment of the first aspect are implemented.
[0034] In a fifth aspect, the present application further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the steps of the method in the embodiment of the first aspect are implemented.
[0035] The above anti-drone learning method, device, and computer equipment obtain content entity course information on anti-drones recommended for the current user, and obtain learning advice information of the current user on anti-drones. Based on the content entity course information and learning advice information, the current user is prompted to conduct anti-drone learning. Among them, the content entity course information is planned according to the portrait information of the current user and knowledge points of multiple different disciplines, and the learning advice information is determined after performing anti-drone simulation processing according to the disposal scenario demand information of the current user for drones. By adopting the above method, not only can personalized content entity course information be formulated for different groups of users, improving the intelligence in the process of planning content entity course information, and the content entity course information is planned by integrating knowledge points of multiple different disciplines, with a wide coverage of knowledge, so as to form systematic content entity course information. At the same time, in the process of anti-drone simulation processing, the actual disposal scenario requirements input by the current user are considered, so that the anti-drone simulation processing process is scenario-based, improving the accuracy of the obtained learning advice information of the current user on anti-drones. On this basis, the systematization, scenario-based, and intelligence in the anti-drone learning process can be improved; at the same time, the above method can achieve personalized anti-drone learning for different users, thereby improving the accuracy of users' anti-drone learning; in addition, the above method can achieve personalized anti-drone learning for different groups of users, making the application scenarios of the anti-drone learning method richer, improving the wide applicability of the anti-drone learning method and the low-altitude anti-drone learning efficiency of different groups of users. BRIEF DESCRIPTION OF THE DRAWINGS
[0036] To more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for use in the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can be obtained based on these drawings.
[0037] Figure 1 It is a schematic flowchart of the anti-drone learning method in one embodiment;
[0038] Figure 2 It is a schematic flowchart of the anti-drone learning method in another embodiment;
[0039] Figure 3 It is a schematic flowchart of the anti-drone learning method in another embodiment;
[0040] Figure 4 It is a schematic flowchart of the anti-drone learning method in another embodiment;
[0041] Figure 5Schematic flowchart of the anti - UAV learning method in another embodiment;
[0042] Figure 6 Schematic flowchart of the anti - UAV learning method in another embodiment;
[0043] Figure 7 Block diagram of the structure of the anti - UAV learning device in one embodiment;
[0044] Figure 8 Internal structure diagram of a computer device in one embodiment. Detailed implementation manners
[0045] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to 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.
[0046] The anti - UAV learning method provided in the embodiments of the present application can be applied to a computer device. Among them, the computer device can be a personal computer, a desktop computer, a laptop computer, an ipad, a smart bracelet, a smart watch, a smart TV, a smart vehicle device, a projection device or a server, etc. In the embodiments of the present application, the computer device has a user interaction interface, can receive information input by the user, and can also output corresponding information for the user to view.
[0047] In an exemplary embodiment, as Figure 1 shown, a kind of anti - UAV learning method is provided. Taking the application of this method to a computer device as an example, this method may include:
[0048] S100. Obtain the content entity course information of anti - UAV recommended for the current user; and obtain the learning suggestion information of the current user for anti - UAV. Among them, the content entity course information is planned according to the portrait information of the current user and the knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti - UAV simulation processing according to the demand information of the current user for the UAV disposal scenario.
[0049] Specifically, the computer device can obtain the content entity course information of anti - UAV recommended for the current user and the learning suggestion information of the current user for anti - UAV pre - stored from positions such as the cloud, local, disk, hard disk, etc.
[0050] It should be noted here that the content entity course information on anti-drone recommended for the current user can be obtained through personalized planning based on the portrait information of the current user and knowledge points in multiple different disciplines. This can not only formulate personalized content entity course information for different groups of users, improve the intelligence in the process of planning content entity course information, but also form systematic content entity course information because the content entity course information is planned by integrating knowledge points in multiple different disciplines.
[0051] Optionally, the learning advice information on anti-drone for the current user can be understood as the content of learning advice on anti-drone recommended for the current user; the learning advice information on anti-drone for the current user can be determined after performing anti-drone simulation processing according to the information on the disposal scenario requirements of the current user for drones. In this way, the actual disposal scenario requirements input by the current user are considered in the process of anti-drone simulation processing, making the anti-drone simulation processing process scenario-based. Among them, the above-mentioned information on the disposal scenario requirements of drones can be the information on disposal requirements in complex drone scenarios or the information on disposal requirements in simple drone scenarios, and the embodiments of the present application do not make any limitations in this regard.
[0052] S200. Based on the content entity course information and the learning advice information, prompt the current user to conduct anti-drone learning.
[0053] In practical applications, the computer device can output both the content entity course information on anti-drone recommended for the current user and the learning advice information on anti-drone for the current user, and prompt the current user to view the content entity course information and the learning advice information to complete the anti-drone learning of the current user.
[0054] In the technical solution of the embodiment of the present application, content entity course information on anti-drone recommended for the current user is obtained, and learning suggestion information of the current user on anti-drone is obtained. Based on the content entity course information and the learning suggestion information, the current user is prompted to conduct anti-drone learning. Among them, the content entity course information is planned according to the portrait information of the current user and knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti-drone simulation processing according to the disposal scenario requirement information of the current user for drones. The above method can not only formulate personalized content entity course information for different groups of users, improve the intelligence in the process of planning content entity course information, and the content entity course information is planned by integrating knowledge points of multiple different disciplines, with a wide range of knowledge coverage, so as to form systematic content entity course information. At the same time, in the process of anti-drone simulation processing, the actual disposal scenario requirements input by the current user are considered, so that the anti-drone simulation processing process is scenario-based, improving the accuracy of the obtained learning suggestion information of the current user on anti-drone. On this basis, the anti-drone learning process is made more systematic, scenario-based and intelligent. At the same time, the above method can achieve personalized anti-drone learning for different users, thereby improving the accuracy of users' anti-drone learning. In addition, the above method can achieve personalized anti-drone learning for different groups of users, making the application scenario of the anti-drone learning method richer, improving the wide applicability of the anti-drone learning method and the low-altitude anti-drone learning efficiency of different groups of users.
[0055] The process of obtaining the content entity course information on anti-drone recommended for the current user will be described below. In one embodiment, as Figure 2 shown, the step of obtaining the content entity course information on anti-drone recommended for the current user in S100 above can be implemented in the following manner:
[0056] S110. Obtain the low-altitude knowledge security map of anti-drone and the portrait information of the current user.
[0057] In practical applications, the computer device can obtain the low-altitude knowledge security map of anti-drone and the portrait information of the current user from locations such as the cloud, local, disk, and hard disk.
[0058] In addition, the computer device can obtain the portrait information of the current user input by the current user in real time.
[0059] Optionally, the above anti-drone low-altitude knowledge security map may include multiple links such as low-altitude drone threat types, low-altitude drone detection, identification, and countermeasures. In the embodiments of the present application, the above low-altitude knowledge security map may include course contents of knowledge points in multiple different disciplines, such as anti-drone theory learning, anti-drone skill training, anti-drone actual combat simulation and other course contents; specifically, the low-altitude knowledge security map includes an anti-drone course knowledge map, an original text content ontology of anti-drone textbooks, and a course content ontology of anti-drone textbooks. At the same time, the portrait information of the current user may include the basic information of the current user, knowledge mastery information, learning goal setting information, learning style information, etc.; the above basic information may include personal background, occupation type, work experience, etc., the above knowledge mastery information may include anti-drone theoretical knowledge, technical background, work experience, etc., the above learning goal setting information may include improving technology, passing certification, etc., and the above learning style information may include video learning, graphic learning, simulation training, etc.
[0060] S120. Plan the learning path of the current user according to the anti-drone low-altitude knowledge security map and the portrait information of the current user to obtain the learning path information of the current user.
[0061] Specifically, the computer device may plan the learning path of the current user according to the anti-drone low-altitude knowledge security map and the portrait information of the current user according to a preset learning path planning strategy to obtain the learning path information of the current user.
[0062] In addition, the computer device may pre-train a learning path planning model, and then input both the anti-drone low-altitude knowledge security map and the portrait information of the current user into the learning path planning model. After the learning path planning model plans the learning path of the current user, it outputs the learning path information of the current user.
[0063] Optionally, the above learning path planning model may be implemented by at least one of a fully connected neural network model, a long short-term memory neural network model, a recurrent neural network model, a convolutional neural network model, a residual neural network model, etc.
[0064] In one embodiment, the steps in S120 above may include: planning the learning path of the current user according to the low-altitude knowledge security map and the portrait information by using a recommendation algorithm to obtain the learning path information. Among them, the recommendation algorithm is constructed based on a low-altitude large model.
[0065] It should be noted here that the above learning path information may include information such as the adjusted learning path and phased goal setting. Among them, the adjusted learning path may be the result of dynamically adjusting the learning path for the current user to learn anti-drone technology generated at the beginning, and this learning path may include at least one stage of learning anti-drone technology. Optionally, the above phased goal setting may adjust the comprehensiveness (i.e., breadth) and depth of each stage in the adjusted learning path.
[0066] In the embodiments of the present application, the learning path information of the current user can be stored in local caches, the cloud, hard disks, etc. for subsequent use.
[0067] S130. Recommend content entity course information on anti-drones to the current user according to the learning path information, the low-altitude knowledge security map, and the security knowledge learning resource library.
[0068] In practical applications, the computer device can look up the identifiers of the learning path information, the low-altitude knowledge security map, and the security knowledge learning resource library in the mapping relationship table, and then determine the content entity course information that matches the identifiers of the learning path information, the low-altitude knowledge security map, and the security knowledge learning resource library in the mapping relationship table as the content entity course information on anti-drones recommended to the current user. Optionally, the mapping relationship table may include the corresponding relationships between the identifiers of different learning path information, different low-altitude knowledge security maps, and different security knowledge learning resource libraries.
[0069] In addition, the computer device can pre-train an algorithm model, and then input the learning path information, the low-altitude knowledge security map, and the security knowledge learning resource library into the algorithm model, and the algorithm model outputs the content entity course information on anti-drones recommended to the current user.
[0070] The technical solution in the embodiments of the present application obtains the low-altitude knowledge security map of anti-drones and the portrait information of the current user, plans the learning path of the current user according to the low-altitude knowledge security map of anti-drones and the portrait information of the current user to obtain the learning path information of the current user, and recommends content entity course information on anti-drones to the current user according to the learning path information, the low-altitude knowledge security map, and the security knowledge learning resource library; the above method can not only formulate personalized, accurate, and most suitable content entity course information for different groups of users, improve the intelligence in the process of planning content entity course information, but also the content entity course information is planned by integrating knowledge points from multiple different disciplines, with a wide coverage of knowledge, so as to form systematic content entity course information.
[0071] The process of recommending content entity course information for anti-drone to the current user based on the learning path information, low-altitude knowledge security graph, and security knowledge learning resource library is described below. In one embodiment, as Figure 3 shown, the steps in S130 above can be implemented in the following manner:
[0072] S131. Extract the learning content entity information for anti-drone from the learning path information.
[0073] In practical applications, the computer device can use an information extraction algorithm to extract the learning content entity information for anti-drone from the learning path information. Optionally, the above information extraction algorithm can be a rule-based extraction algorithm, a deep learning-based extraction algorithm, a machine learning-based extraction algorithm, etc.
[0074] In addition, the computer device can compare the strings of the learning path information and the standard learning content entity information, and determine the strings with consistent comparison as the learning content entity information for anti-drone in the learning path information, so as to complete the extraction of the learning content entity information for anti-drone from the learning path information.
[0075] Optionally, the above learning content entity information for anti-drone can include the learning objectives at each stage statistically in the learning path information.
[0076] S132. Use the learning path recommendation algorithm to obtain the recommended content entity information for anti-drone from the low-altitude knowledge security graph according to the learning content entity. Among them, the learning path recommendation algorithm is constructed based on the low-altitude large model.
[0077] In the embodiment of the present application, the computer device can use the learning path recommendation algorithm constructed based on the low-altitude large model to obtain the recommended content entity information for anti-drone from the low-altitude knowledge security graph according to the learning content entity. Optionally, the recommended content entity information for anti-drone can include the content entity information for anti-drone.
[0078] At the same time, the computer device can output the recommended content entity information for anti-drone for the user to view through the user interaction interface.
[0079] S133. Use the collaborative filtering algorithm to determine the content entity course information according to the recommended content entity information and the security knowledge learning resource library. Among them, the collaborative filtering algorithm is constructed based on the low-altitude large model.
[0080] Specifically, the computer device can use the collaborative filtering algorithm to determine the content entity course information according to the recommended content entity information and the security knowledge learning resource library.
[0081] In the technical solution of the embodiment of the present application, the learning content entity information of anti-drone is extracted from the learning path information, and the learning path recommendation algorithm is adopted. According to the learning content entity, the recommended content entity information of anti-drone is obtained from the low-altitude knowledge security graph, and the collaborative filtering algorithm is adopted. According to the recommended content entity information and the security knowledge learning resource library, the content entity course information is determined; the above method can adopt the learning path recommendation algorithm and collaborative filtering algorithm based on the low-altitude large model to obtain the content entity course information of anti-drone recommended to the current user, so that the accuracy and comprehensiveness of the finally obtained content entity course information are higher. At the same time, it can meet the timeliness requirement of information acquisition, and on this basis, it can improve the comprehensiveness of subsequent anti-drone learning and improve the anti-drone combat ability of users in actual combat.
[0082] The acquisition process of the above security knowledge learning resource library will be described below. In one embodiment, before performing the steps in S133 above, as Figure 4 shown, the above method may further include:
[0083] S134. Obtain the people's air defense knowledge base and the general knowledge base of national defense mobilization foundation.
[0084] In practical applications, the computer device can obtain the people's air defense knowledge base and the general knowledge base of national defense mobilization foundation from locations such as the cloud, local, disk, and hard disk.
[0085] In addition, the computer device can also read the people's air defense knowledge base and the general knowledge base of national defense mobilization foundation from the database.
[0086] Among them, the above people's air defense knowledge base may include information such as anti-drone theory knowledge, anti-drone equipment, and people's air defense disposal cases; the above general knowledge base of national defense mobilization foundation may include information such as basic concepts of national defense mobilization, national defense mobilization deeds, and national defense mobilization rules and regulations.
[0087] S135. Perform information extraction processing according to the people's air defense knowledge base and the general knowledge base of national defense mobilization foundation to generate a security knowledge learning resource library.
[0088] In one embodiment, the computer device can screen out the security knowledge information from the people's air defense knowledge base and the general knowledge base of national defense mobilization foundation to complete the information extraction processing, and generate a security knowledge learning resource library according to the screened security knowledge information.
[0089] In another embodiment, the computer device can adopt an information extraction algorithm to perform information extraction processing on the people's air defense knowledge base and the general knowledge base of national defense mobilization foundation to generate a security knowledge learning resource library.
[0090] In the technical solution of the embodiment of the present application, a people's air defense knowledge base and a general knowledge base for national defense mobilization foundation are obtained, and information extraction and processing are performed according to the people's air defense knowledge base and the general knowledge base for national defense mobilization foundation to generate a safety knowledge learning resource library; the above method can extract key information from the original knowledge base to generate a small-scale learning resource library for subsequent use in the application process, which can reduce the complexity of the subsequent processing process and speed up the processing speed of the subsequent process.
[0091] The process of obtaining the learning suggestion information of the current user for anti-drone will be described below. In one embodiment, as Figure 5 shown, the step of obtaining the learning suggestion information of the current user for anti-drone in S100 above may include:
[0092] S140. Obtain the disposal evaluation result of the drone.
[0093] In one embodiment, the computer device can obtain the pre-stored disposal evaluation result of the drone from locations such as the cloud, local, disk, hard disk, etc.
[0094] In another embodiment, the computer device can send a command to obtain the disposal evaluation result of the drone to a third-party device, instructing the third-party device to send the disposal evaluation result of the drone to the computer device. Correspondingly, the computer device can receive the disposal evaluation result of the drone sent by the third-party device.
[0095] S150. Generate the learning suggestion information of the current user based on the disposal evaluation result of the drone and the information portrait.
[0096] Among them, the computer device can pre-train a learning suggestion model, and then input both the disposal evaluation result of the drone and the information portrait into the learning suggestion model, and the learning suggestion model outputs the learning suggestion information of the current user. Optionally, the above learning suggestion model can be implemented by at least one of a convolutional neural network model, a fully connected neural network model, a long short-term memory neural network model, a residual neural network model, etc.
[0097] In addition, the computer device can call a learning suggestion function to generate the learning suggestion information of the current user based on the disposal evaluation result of the drone and the information portrait.
[0098] Optionally, the above disposal evaluation result of the drone may include the learning behavior data analysis result, and the learning behavior data analysis result may include information such as the disposal correct rate and disposal completion time of the drone.
[0099] In the embodiment of the present application, the computer device can also generate an anti-drone learning report of the current user based on the disposal evaluation result of the drone and the information portrait.
[0100] In the technical solution of the embodiment of the present application, the disposal evaluation result of the unmanned aerial vehicle can be obtained, and the learning suggestion information of the current user can be generated based on the disposal evaluation result of the unmanned aerial vehicle and the information portrait, so that the obtained learning suggestion information can be more comprehensive.
[0101] The process of obtaining the disposal evaluation result of the unmanned aerial vehicle will be described below. In one embodiment, as Figure 6 shown, the steps in S140 above can be implemented in the following manner:
[0102] S141. Obtain the disposal scenario requirement information of the current user for the unmanned aerial vehicle.
[0103] In practical applications, the computer device can obtain the disposal scenario requirement information of the current user input by the current user. Optionally, the input method of the disposal scenario requirement information can be voice, gesture, keyboard, button, etc.
[0104] Optionally, the above disposal scenario requirement information may include an unmanned aerial vehicle intrusion scenario and a mission type. The unmanned aerial vehicle intrusion scenario may include scenarios such as around airports, in the city center, and in mountainous areas. The mission type may include types such as detecting intrusion, performing interference, and simulating capture.
[0105] S142. Adopt an air situation disposal algorithm to determine the corresponding air situation disposal information according to the disposal scenario requirement information. Among them, the air situation disposal algorithm is constructed based on a low-altitude large model; the air situation disposal information includes the disposal response strategy against the unmanned aerial vehicle and the corresponding operation strategy.
[0106] Specifically, the computer device can adopt an air situation simulation algorithm constructed based on a low-altitude large model to determine the corresponding air situation information according to the disposal scenario requirement information, and then adopt an air situation disposal algorithm constructed based on a low-altitude large model to determine the corresponding air situation disposal information according to the air situation information.
[0107] Optionally, the above air situation information may include UAV identification information, UAV positioning information, UAV airspace information, UAV flight dynamic information, etc.; the above UAV identification information may include flight target characteristics, flight behavior analysis information, flight trajectory, etc.; the above UAV positioning information may include information such as the longitude, latitude, altitude, and movement trajectory of the UAV; the above UAV airspace information may include information such as the speed, flight attitude, and dynamic information of the nearby airspace of the UAV; the above UAV flight dynamic information may include information such as the flight area, acceleration, and flight attitude of the UAV. At the same time, the above air situation disposal information includes anti-UAV disposal response strategies and corresponding operation strategies; among them, the above disposal response strategies may include information such as the interference frequency band and capture path for the UAV during the anti-UAV process, and the above operation strategies may include information such as the anti-UAV equipment selected in the disposal response strategy, operation steps, operation angles, and execution times.
[0108] S143. Based on the air situation disposal information, conduct anti-UAV simulation on the UAV to determine the disposal result of the UAV.
[0109] In practical applications, the computer device may, based on the air situation disposal information, call an anti-UAV actual combat simulation tool to conduct anti-UAV simulation on the UAV to obtain the disposal result of the UAV.
[0110] In addition, the computer device may send the air situation disposal information to a third-party device, instructing the third-party device to conduct anti-UAV simulation on the UAV based on the air situation disposal information and obtain the disposal result of the UAV. Optionally, the above UAV simulation may be understood as UAV interference scenario simulation.
[0111] S144. Evaluate the disposal result to obtain the disposal evaluation result of the UAV.
[0112] Among them, the computer device may use an evaluation algorithm to evaluate the disposal result of the UAV to obtain the disposal evaluation result of the UAV. In addition, the computer device may also perform operations such as analysis processing and comparison processing on the disposal result to achieve the evaluation and obtain the disposal evaluation result of the UAV.
[0113] Furthermore, the computer device simulates the dynamic emergency support drill of the UAV according to the disposal evaluation result of the UAV.
[0114] In the technical solution of the embodiment of the present application, the demand information of the current user for the disposal scenario of the unmanned aerial vehicle (UAV) is obtained, and the corresponding air situation disposal information is determined according to the demand information of the disposal scenario by using the air situation disposal algorithm. Based on the air situation disposal information, anti-UAV simulation is carried out on the UAV to determine the disposal result of the UAV, and the disposal evaluation result of the UAV is obtained by evaluating the disposal result. Among them, the air situation disposal algorithm is constructed based on the low-altitude large model. The above method determines the air situation disposal information based on the air situation disposal algorithm constructed based on the low-altitude large model, which can make the obtained air situation disposal information more comprehensive. On this basis, subsequent processing can be carried out based on the air situation disposal information, which can improve the accuracy of the subsequent obtained disposal evaluation result of the UAV.
[0115] In one embodiment, the embodiment of the present application also provides an anti-UAV learning method, which is applied to a computer device. The method includes the following processes:
[0116] S10. Obtain the low-altitude knowledge security map of anti-UAV and the portrait information of the current user;
[0117] S11. According to the low-altitude knowledge security map and the portrait information, use the recommendation algorithm to plan the learning path of the current user to obtain the learning path information. The recommendation algorithm is constructed based on the low-altitude large model;
[0118] S12. Extract the learning content entity information of anti-UAV from the learning path information;
[0119] S13. Use the learning path recommendation algorithm to obtain the recommended content entity information of anti-UAV from the low-altitude knowledge security map according to the learning content entity. The learning path recommendation algorithm is constructed based on the low-altitude large model;
[0120] S14. Use the collaborative filtering algorithm to determine the content entity course information according to the recommended content entity information and the security knowledge learning resource library. The collaborative filtering algorithm is constructed based on the low-altitude large model. The content entity course information is planned according to the portrait information of the current user and the knowledge points of multiple different disciplines; and,
[0121] S15. Obtain the demand information of the current user for the disposal scenario of the UAV;
[0122] S16. Use the air situation disposal algorithm to determine the corresponding air situation disposal information according to the demand information of the disposal scenario. The air situation disposal algorithm is constructed based on the low-altitude large model. The air situation disposal information includes the disposal response strategy of anti-UAV and the corresponding operation strategy;
[0123] S17. Based on the air situation disposal information, carry out anti-UAV simulation on the UAV to determine the disposal result of the UAV;
[0124] S18. Evaluate the disposal result to obtain the disposal evaluation result of the UAV.
[0125] S19. Generate learning suggestion information for the current user based on the disposal evaluation result and information portrait of the UAV; the learning suggestion information is determined after performing anti-UAV simulation processing according to the disposal scenario requirement information of the current user for the UAV.
[0126] S20. Prompt the current user to conduct anti-UAV learning based on the content entity course information and learning suggestion information.
[0127] Among them, the acquisition process of the safety knowledge learning resource library includes:
[0128] S21. Obtain the people's air defense knowledge library and the basic general knowledge library of national defense mobilization.
[0129] S22. Perform information extraction processing according to the people's air defense knowledge library and the basic general knowledge library of national defense mobilization to generate a safety knowledge learning resource library.
[0130] For the execution processes of S10 to S22 above, reference can specifically be made to the descriptions of the above embodiments. Their implementation principles and technical effects are similar, and will not be elaborated here.
[0131] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are shown in sequence according to the arrows, these steps do not necessarily need to be executed in the order indicated by the arrows. Unless there is a clear indication in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least some of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages do not necessarily need to be executed at the same moment, but can be executed at different moments. The execution order of these steps or stages does not necessarily need to be sequential, but can be executed alternately or in turn with at least some of the steps or stages in other steps or other steps.
[0132] Based on the same inventive concept, the embodiments of the present application also provide an anti-UAV learning device for implementing the above-mentioned anti-UAV learning method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions described in the above method. Therefore, the specific limitations in one or more embodiments of the anti-UAV learning device provided below can refer to the limitations on the anti-UAV learning method in the above text, and will not be elaborated here.
[0133] In an exemplary embodiment, as Figure 7 shown, an anti-UAV learning device is provided, including: an acquisition module 11 and an anti-UAV learning module 12, where:
[0134] An acquisition module 11, configured to acquire content entity course information on anti-drone recommended for the current user; and acquire learning suggestion information of the current user regarding anti-drone; the content entity course information is planned based on the portrait information of the current user and knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti-drone simulation processing according to the demand information of the current user for the disposal scenario of drones;
[0135] An anti-drone learning module 12, configured to prompt the current user to conduct anti-drone learning based on the content entity course information and the learning suggestion information.
[0136] The anti-drone learning device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the anti-drone learning method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.
[0137] In one embodiment, the acquisition module 11 includes: a first acquisition unit, a planning unit, and a recommendation unit, where:
[0138] The first acquisition unit is configured to acquire the low-altitude knowledge security map of anti-drone and the portrait information of the current user;
[0139] The planning unit is configured to plan the learning path of the current user according to the low-altitude knowledge security map of anti-drone and the portrait information of the current user as an education trainee, and obtain the learning path information of the current user;
[0140] The recommendation unit is configured to recommend content entity course information on anti-drone to the current user according to the learning path information, the low-altitude knowledge security map, and the security knowledge learning resource library.
[0141] The anti-drone learning device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the anti-drone learning method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.
[0142] In one embodiment, the planning unit is specifically configured to:
[0143] Plan the learning path of the current user according to the low-altitude knowledge security map and the portrait information by using a recommendation algorithm, and obtain the learning path information; the recommendation algorithm is constructed based on a low-altitude large model.
[0144] The anti-drone learning device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned embodiments of the anti-drone learning method of the present application. The implementation principles and technical effects are similar and will not be elaborated here.
[0145] In one embodiment, the recommendation unit includes: an extraction subunit, a first acquisition subunit, and a first determination subunit, where:
[0146] An extraction subunit, configured to extract anti-drone learning content entity information from learning path information;
[0147] A first acquisition subunit, configured to use a learning path recommendation algorithm to acquire anti-drone recommended content entity information from a low-altitude knowledge security graph according to the learning content entity; the learning path recommendation algorithm is constructed based on a low-altitude large model;
[0148] A first determination subunit, configured to use a collaborative filtering algorithm to determine content entity course information according to the recommended content entity information and a security knowledge learning resource library; the collaborative filtering algorithm is constructed based on a low-altitude large model.
[0149] The anti-drone learning device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned anti-drone learning method embodiments of the present application. The implementation principles and technical effects are similar and will not be elaborated here.
[0150] In one embodiment, the recommendation unit further includes: a resource library construction subunit, where:
[0151] The resource library construction subunit is specifically configured to:
[0152] Acquire a people's air defense knowledge library and a national defense mobilization basic general knowledge library;
[0153] Perform information extraction processing according to the people's air defense knowledge library and the national defense mobilization basic general knowledge library to generate a security knowledge learning resource library.
[0154] The anti-drone learning device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned anti-drone learning method embodiments of the present application. The implementation principles and technical effects are similar and will not be elaborated here.
[0155] In one embodiment, the acquisition module 11 includes: a second acquisition unit and an information generation unit, where:
[0156] The second acquisition unit is configured to acquire the disposal evaluation result of the drone;
[0157] The information generation unit is configured to generate learning recommendation information for the current user based on the disposal evaluation result of the drone and the information portrait.
[0158] The anti-drone learning device provided by the embodiments of the present application can be used to execute the technical solutions in the above-mentioned anti-drone learning method embodiments of the present application. The implementation principles and technical effects are similar and will not be elaborated here.
[0159] In one embodiment, the second acquisition unit includes: a second acquisition subunit, a second determination subunit, a simulation processing subunit, and an evaluation subunit, where:
[0160] A second acquisition subunit, configured to acquire the disposal scenario requirement information of the current user for the unmanned aerial vehicle;
[0161] A second determination subunit, configured to use an air situation disposal algorithm to determine corresponding air situation disposal information according to the disposal scenario requirement information; the air situation disposal algorithm is constructed based on a low-altitude large model; the air situation disposal information includes anti-unmanned aerial vehicle disposal response strategies and corresponding operation strategies;
[0162] A simulation processing subunit, configured to perform anti-unmanned aerial vehicle simulation on the unmanned aerial vehicle based on the air situation disposal information to determine the disposal result of the unmanned aerial vehicle;
[0163] An evaluation subunit, configured to evaluate the disposal result to obtain the disposal evaluation result of the unmanned aerial vehicle.
[0164] The anti-unmanned aerial vehicle learning device provided in the embodiments of the present application can be used to execute the technical solutions in the above-mentioned anti-unmanned aerial vehicle learning method embodiments of the present application. The implementation principles and technical effects are similar and will not be elaborated here.
[0165] Each module in the above anti-unmanned aerial vehicle learning device can be implemented in whole or in part by software, hardware, and their combination. The above-mentioned modules can be embedded in the processor of the computer device in the form of hardware or be independent of it, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to the above-mentioned modules.
[0166] In an exemplary embodiment, a computer device is provided. The computer device can be a server, and its internal structure diagram can be as Figure 8 shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, abbreviated as I / O), and a communication interface. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. Among them, 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, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store the content entity course information for recommending anti-unmanned aerial vehicles to the current user and the learning suggestion information of the current user for anti-unmanned aerial vehicles. The input / output interface of the computer device is used to exchange information between the processor and external devices. The communication interface of the computer device is used to communicate with external terminals through a network connection. When the computer program is executed by the processor, it implements an anti-unmanned aerial vehicle learning method.
[0167] Those skilled in the art can understand, Figure 8The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.
[0168] In an exemplary embodiment, a computer device is further provided, including a memory and a processor. A computer program is stored in the memory. When the processor executes the computer program, the following steps are implemented:
[0169] Obtain the content entity course information of anti-drone recommended for the current user; and obtain the learning suggestion information of the current user for anti-drone; the content entity course information is planned according to the portrait information of the current user and the knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti-drone simulation processing according to the disposal scenario requirement information of the current user for drones;
[0170] Based on the content entity course information and the learning suggestion information, prompt the current user to conduct anti-drone learning.
[0171] In an embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:
[0172] Obtain the content entity course information of anti-drone recommended for the current user; and obtain the learning suggestion information of the current user for anti-drone; the content entity course information is planned according to the portrait information of the current user and the knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti-drone simulation processing according to the disposal scenario requirement information of the current user for drones;
[0173] Based on the content entity course information and the learning suggestion information, prompt the current user to conduct anti-drone learning.
[0174] In an embodiment, a computer program product is further provided, including a computer program. When the computer program is executed by a processor, the following steps are implemented:
[0175] Obtain the content entity course information of anti-drone recommended for the current user; and obtain the learning suggestion information of the current user for anti-drone; the content entity course information is planned according to the portrait information of the current user and the knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti-drone simulation processing according to the disposal scenario requirement information of the current user for drones;
[0176] Based on the content entity course information and the learning suggestion information, prompt the current user to conduct anti-drone learning.
[0177] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. 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 methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile memory 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. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in the present application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in the present application can be general-purpose processors, central processors, graphics processors, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.
[0178] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, 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, it should be considered to be within the scope recorded in the present application.
[0179] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation on the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims
1. An anti-drone learning method, characterized in that, The method includes: Obtaining content entity course information on anti-drone recommended for the current user; and obtaining learning suggestion information of the current user for anti-drone; the content entity course information is planned according to the portrait information of the current user and knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti-drone simulation processing according to the disposal scenario requirement information of the current user for drones; Based on the content entity course information and the learning suggestion information, prompt the current user to conduct anti-drone learning.
2. The method according to claim 1, wherein The obtaining of the content entity course information on anti-drone recommended for the current user includes: Obtaining the low-altitude knowledge security map of anti-drone and the portrait information of the current user; According to the low-altitude knowledge security map of anti-drone and the portrait information of the current user as an educational trainee, plan the learning path of the current user to obtain the learning path information of the current user; According to the learning path information, the low-altitude knowledge security map and the security knowledge learning resource library, recommend the content entity course information on anti-drone to the current user.
3. The method according to claim 2, wherein The planning of the learning path of the current user to obtain the learning path information of the current user according to the low-altitude knowledge security map of anti-drone and the portrait information of the current user includes: According to the low-altitude knowledge security map and the portrait information, use a recommendation algorithm to plan the learning path of the current user to obtain the learning path information; the recommendation algorithm is constructed based on a low-altitude large model.
4. The method according to claim 2 or 3, characterized in that, The recommending of the content entity course information on anti-drone to the current user according to the learning path information, the low-altitude knowledge security map and the security knowledge learning resource library includes: Extract the learning content entity information on anti-drone from the learning path information; Use a learning path recommendation algorithm to obtain the recommended content entity information on anti-drone from the low-altitude knowledge security map according to the learning content entity; the learning path recommendation algorithm is constructed based on the low-altitude large model; Use a collaborative filtering algorithm to determine the content entity course information according to the recommended content entity information and the security knowledge learning resource library; the collaborative filtering algorithm is constructed based on the low-altitude large model.
5. The method according to claim 2 or 3, characterized in that The obtaining process of the security knowledge learning resource library includes: Obtain the people's air defense knowledge base and the general knowledge base for national defense mobilization; Perform information extraction processing according to the people's air defense knowledge base and the general knowledge base for national defense mobilization to generate the security knowledge learning resource library.
6. The method according to any one of claims 1-3, characterized in that, The obtaining of the learning suggestion information of the current user for anti-drone includes: Obtain the disposal evaluation result of the drone; Based on the disposal evaluation result of the drone and the information portrait, generate the learning suggestion information of the current user.
7. The method according to claim 6, wherein The obtaining of the disposal evaluation result of the drone includes: Obtain the disposal scenario requirement information of the current user for drones; Adopt a situation handling algorithm to determine corresponding situation handling information according to the requirement information of the handling scenario; the situation handling algorithm is constructed based on a low-altitude large model; the situation handling information includes the handling response strategy against the drone and the corresponding operation strategy; Based on the situation handling information, conduct anti-drone simulation on the drone to determine the handling result of the drone; Evaluate the handling result to obtain the handling evaluation result of the drone.
8. An anti-drone learning device, characterized in that, The device includes: An acquisition module, configured to acquire content entity course information on anti-drone recommended for the current user; and acquire learning suggestion information of the current user for anti-drone; the content entity course information is planned according to the portrait information of the current user and knowledge points of multiple different disciplines, and the learning suggestion information is determined after performing anti-drone simulation processing according to the requirement information of the handling scenario of the current user for the drone; An anti-drone learning module, configured to prompt the current user to conduct anti-drone learning based on the content entity course information and the learning suggestion information.
9. A computer device, comprising a memory and a processor, the memory storing a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the method according to any one of claims 1 to 7.