Vehicle-mounted unmanned aerial vehicle defense method, device, equipment and medium

Abstract

This invention relates to the field of artificial intelligence technology, providing a vehicle-mounted drone defense method, device, equipment, and medium. Applied to a vehicle-mounted drone defense system, it performs real-time radio detection within a pre-established defense perimeter to obtain radio data and real-time image data. The radio data and image data are then fused to obtain fused features, achieving complementary advantages between radio and photoelectric technologies to improve the accuracy of drone perception. The fused features are input into a drone detection model to detect the presence of drones within the defense perimeter. The detected target drones are then fused and located, effectively avoiding positioning errors. Further photoelectric detection and tracking of the target drones are performed, and countermeasures are taken based on the tracking results to achieve precise drone defense.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence technology, and in particular to a method, device, equipment and medium for defending against vehicle-mounted drones. Background Technology

[0002] Currently, with the continuous development of drone technology, more and more fields are using drones to perform tasks such as data collection. However, because drones can fly at low altitudes, are small in size, and have high flexibility and maneuverability, they can cause certain safety hazards if used maliciously.

[0003] Furthermore, if the area to be defended by drones is large, insufficient personnel responsible for security will increase security risks. Therefore, there is an urgent need for a method that can accurately defend against drones. Summary of the Invention

[0004] In view of the above, it is necessary to provide a vehicle-mounted drone defense method, device, equipment and medium that can accurately defend against drones.

[0005] A vehicle-mounted drone defense method, applied to a vehicle-mounted drone defense system, the vehicle-mounted drone defense method comprising:

[0006] Radio data is obtained through real-time radio detection within a pre-established defense perimeter, and image data is acquired in real-time.

[0007] The radio data and the image data are fused to obtain fused features;

[0008] The fused features are input into a pre-trained drone detection model, and the presence of drones within the defense perimeter is detected based on the output data of the drone detection model.

[0009] When a drone is detected within the defense perimeter, the detected drone is identified as the target drone, and the target drone is fused and located to obtain the target location of the target drone.

[0010] Based on the target location, the target UAV is subjected to photoelectric detection and tracking to obtain the tracking result;

[0011] Countermeasures are taken against the target drone based on the tracking results.

[0012] According to a preferred embodiment of the present invention, before performing real-time radio detection to obtain radio data and real-time image data acquisition within a pre-established defense perimeter, the method further includes:

[0013] Obtain the defense center;

[0014] With the defense center as the center and the first preset length as the radius, a first three-dimensional enclosing circle is drawn to obtain the core area;

[0015] A second three-dimensional encirclement is defined with the defense center as the center and the second preset length as the radius. The area obtained by removing the first three-dimensional encirclement from the second three-dimensional encirclement is defined as the deportation zone.

[0016] A third three-dimensional encirclement is defined with the defense center as the center and the third preset length as the radius. The area obtained by removing the second three-dimensional encirclement from the third three-dimensional encirclement is defined as the warning zone.

[0017] The core area, the deportation area, and the warning area are combined to form the defense perimeter;

[0018] Wherein, the first preset length is less than the second preset length, and the second preset length is less than the third preset length.

[0019] According to a preferred embodiment of the present invention, the feature fusion of the radio data and the image data to obtain fused features includes:

[0020] The radio data is filtered to obtain filtered features;

[0021] The first feature is obtained by using a convolutional neural network to extract features from the filtered features;

[0022] The image data is preprocessed to obtain an intermediate image;

[0023] The convolutional neural network is used to extract features from the intermediate image to obtain the second feature;

[0024] The first feature and the second feature are fused to obtain the fused feature.

[0025] According to a preferred embodiment of the present invention, before inputting the fused features into the pre-trained UAV detection model, the method further includes:

[0026] Acquire historical radio data and historical image data collected by the vehicle-mounted unmanned aerial vehicle (UAV) defense system;

[0027] Based on the historical radio data and the historical image data, feature fusion is performed to obtain historical fused features;

[0028] The historical fusion features are reduced in dimensionality using the PCA dimensionality reduction algorithm; dimensionality-reduced features;

[0029] Obtain an initial neural network model that performs classification tasks;

[0030] The dimensionality reduction features are used as training samples to train the initial neural network model until the accuracy of the initial neural network model reaches the configured accuracy, at which point training stops, and the UAV detection model is obtained.

[0031] According to a preferred embodiment of the present invention, before performing fusion positioning on the target UAV, the method further includes:

[0032] Determine whether the target drone is continuously detected within a preset time period;

[0033] If the target drone is not continuously detected within the preset time period, continue to detect whether there is a drone within the defense zone; or

[0034] When the target drone is continuously detected within the preset time period, an alarm is issued and the target drone is located using a fusion positioning method.

[0035] According to a preferred embodiment of the present invention, the step of performing fusion positioning on the target UAV to obtain the target UAV's target position includes:

[0036] The coordinates of each base station used for positioning assistance are obtained as each first coordinate, and the number of base stations used for positioning assistance is obtained as the target number.

[0037] The TDOA algorithm is used to locate the target UAV and obtain the second coordinates;

[0038] The target UAV is located using the AOA algorithm to obtain the third coordinates;

[0039] The distance from the target drone to each base station is calculated as each first distance;

[0040] Calculate the distance between the second coordinate and each first coordinate to obtain each second distance;

[0041] Calculate the square of the difference between each first distance and each corresponding second distance to obtain each first squared value corresponding to each base station;

[0042] Calculate the sum of each first squared value as the first cumulative sum;

[0043] Calculate the quotient of the first accumulated sum and the target quantity to obtain the first coefficient corresponding to the TDOA algorithm;

[0044] Each third distance is obtained by calculating the distance between the third coordinate and each first coordinate;

[0045] Calculate the square of the difference between each first distance and each corresponding third distance to obtain each second square value corresponding to each base station;

[0046] Calculate the sum of each second squared value as the second cumulative sum;

[0047] Calculate the quotient of the second cumulative sum and the target quantity to obtain the second coefficient corresponding to the AOA algorithm;

[0048] The quotient of the second coordinate and the first coefficient is calculated as the first positioning result, and the quotient of the third coordinate and the second coefficient is calculated as the second positioning result;

[0049] Calculate the sum of the first positioning result and the second positioning result to obtain the current positioning;

[0050] The first weight is obtained by calculating the reciprocal of the first coefficient, and the second weight is obtained by calculating the reciprocal of the second coefficient;

[0051] Calculate the sum of the first weight and the second weight to obtain the target weight;

[0052] The target location is obtained by calculating the quotient of the current location and the target weight.

[0053] According to a preferred embodiment of the present invention, the step of countering the target drone based on the tracking result includes:

[0054] When the tracking results show that the target drone is in the drive-off zone or the core zone, the system detects whether anyone is on duty within the operating area of ​​the vehicle-mounted drone defense system.

[0055] When an unattended drone is detected, a radio jamming signal of the configured duration is transmitted to the target drone; or when a manned drone is detected, a countermeasure prompt message is issued, and when a countermeasure command is received, a radio jamming signal of the configured duration is transmitted to the target drone.

[0056] Continue to perform photoelectric detection and tracking on the target drone;

[0057] When the target drone is detected returning to its home location, the countermeasures are deemed effective.

[0058] When the target drone is found to have failed to return, and the countermeasure is deemed ineffective, a secondary countermeasure prompt message is issued.

[0059] If the countermeasure is deemed ineffective after a second countermeasure, the vehicle of the vehicle-mounted drone defense system is controlled to follow the flight trajectory of the target drone and obtain the real-time distance between the target drone and the vehicle of the vehicle-mounted drone defense system. When the real-time distance reaches the firing range, a radio suppression signal of the configured duration is transmitted to the target drone.

[0060] A vehicle-mounted drone defense device, operating within a vehicle-mounted drone defense system, the vehicle-mounted drone defense device comprising:

[0061] The detection unit is used to perform real-time radio detection within a pre-established defense perimeter to obtain radio data and to acquire image data in real time.

[0062] A fusion unit is used to perform feature fusion on the radio data and the image data to obtain fused features;

[0063] The detection unit is used to input the fused features into a pre-trained drone detection model and detect whether there are drones within the defense zone based on the output data of the drone detection model.

[0064] The positioning unit is used to identify the detected drone as a target drone when a drone is detected within the defense perimeter, and to perform fusion positioning on the target drone to obtain the target location of the target drone.

[0065] A tracking unit is used to perform photoelectric detection and tracking of the target UAV based on the target location, and obtain tracking results;

[0066] The countermeasure unit is used to counter the target drone based on the tracking results.

[0067] A computer device, the computer device comprising:

[0068] Memory, storing at least one instruction; and

[0069] The processor executes the instructions stored in the memory to implement the vehicle-mounted drone defense method.

[0070] A computer-readable storage medium storing at least one instruction, which is executed by a processor in a computer device to implement the vehicle-mounted unmanned aerial vehicle (UAV) defense method.

[0071] As can be seen from the above technical solution, this invention is applied to a vehicle-mounted drone defense system. Within a pre-established defense perimeter, radio data and image data are obtained through real-time radio detection. Feature fusion is performed on the radio data and image data to obtain fused features, achieving complementary advantages between radio and photoelectric technologies to improve the accuracy of drone perception. The fused features are input into a drone detection model to detect the presence of drones within the defense perimeter. The detected target drones are then located using fused positioning, effectively avoiding positioning errors. Furthermore, photoelectric detection and tracking of the target drones are performed, and countermeasures are taken based on the tracking results to achieve precise drone defense. Attached Figure Description

[0072] Figure 1 This is a flowchart of a preferred embodiment of the vehicle-mounted drone defense method of the present invention.

[0073] Figure 2 This is a functional block diagram of a preferred embodiment of the vehicle-mounted drone defense device of the present invention.

[0074] Figure 3 This is a schematic diagram of the structure of a computer device that implements a preferred embodiment of the vehicle-mounted drone defense method of the present invention. Detailed Implementation

[0075] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0076] like Figure 1 The diagram shown is a flowchart of a preferred embodiment of the vehicle-mounted drone defense method of the present invention. The order of the steps in this flowchart can be changed, and some steps can be omitted, depending on different requirements.

[0077] The vehicle-mounted drone defense method is applied to one or more computer devices. The computer device is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions. Its hardware includes, but is not limited to, microprocessors, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0078] The computer device can be any electronic product that can interact with the user, such as a personal computer, tablet computer, smartphone, personal digital assistant (PDA), game console, interactive network television (IPTV), smart wearable device, etc.

[0079] The computer equipment may also include network equipment and / or user equipment. The network equipment includes, but is not limited to, a single network server, a server group consisting of multiple network servers, or a cloud based on cloud computing consisting of a large number of hosts or network servers.

[0080] The server can be a standalone server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms.

[0081] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0082] Foundational technologies for artificial intelligence generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing, operating / interactive systems, and mechatronics. AI software technologies mainly encompass computer vision, robotics, biometrics, speech processing, natural language processing, and machine learning / deep learning.

[0083] The network in which the computer device is located includes, but is not limited to, the Internet, wide area network, metropolitan area network, local area network, and virtual private network (VPN).

[0084] The vehicle-mounted drone defense method described in this embodiment is applied to a vehicle-mounted drone defense system, and the vehicle-mounted drone defense method includes:

[0085] S10 performs real-time radio detection within a pre-established defense perimeter to obtain radio data and acquire image data in real time.

[0086] In this embodiment, the vehicle-mounted drone defense system is a mobile vehicle-mounted system used for monitoring, interfering with, and intercepting drones.

[0087] In this embodiment, before performing real-time radio detection to obtain radio data and real-time image data acquisition within the pre-established defense perimeter, the method further includes:

[0088] Obtain the defense center;

[0089] With the defense center as the center and the first preset length as the radius, a first three-dimensional enclosing circle is drawn to obtain the core area;

[0090] A second three-dimensional encirclement is defined with the defense center as the center and the second preset length as the radius. The area obtained by removing the first three-dimensional encirclement from the second three-dimensional encirclement is defined as the deportation zone.

[0091] A third three-dimensional encirclement is defined with the defense center as the center and the third preset length as the radius. The area obtained by removing the second three-dimensional encirclement from the third three-dimensional encirclement is defined as the warning zone.

[0092] The core area, the deportation area, and the warning area are combined to form the defense perimeter;

[0093] Wherein, the first preset length is less than the second preset length, and the second preset length is less than the third preset length. The first preset length, the second preset length, and the third preset length can be configured according to the actual protection needs of the defense center.

[0094] The defense center can be the location coordinates of the object that needs to be protected.

[0095] Through the above embodiments, a multi-layered three-dimensional protection circle can be delineated based on the defense center, forming a three-dimensional three-level defense circle in the airspace dimension. Subsequently, targeted actions can be taken based on the area to which the drone intrudes, that is, the distance from the drone to the defense center is used as the basis for judging the threat level of the drone and determining response measures.

[0096] In this embodiment, the radio data can be obtained in real time by using a radio device (such as a radio detector) in the vehicle-mounted drone defense system to perform radio detection within the defense zone, and the image data can be obtained in real time by using an image acquisition device (such as a high-definition camera) in the vehicle-mounted drone defense system.

[0097] S11, perform feature fusion on the radio data and the image data to obtain fused features.

[0098] In this embodiment, the feature fusion of the radio data and the image data to obtain fused features includes:

[0099] The radio data is filtered to obtain filtered features;

[0100] The first feature is obtained by using a convolutional neural network to extract features from the filtered features;

[0101] The image data is preprocessed to obtain an intermediate image;

[0102] The convolutional neural network is used to extract features from the intermediate image to obtain the second feature;

[0103] The first feature and the second feature are fused to obtain the fused feature.

[0104] The preprocessing may include, but is not limited to, one or a combination of the following processes: image enhancement, edge detection, noise reduction, etc.

[0105] In the above embodiments, the fusion of radio characteristics and photoelectric characteristics can achieve complementary advantages of radio and photoelectric technologies, thereby improving the accuracy of UAV perception.

[0106] S12, the fused features are input into a pre-trained drone detection model, and the presence of drones within the defense perimeter is detected based on the output data of the drone detection model.

[0107] In this embodiment, before inputting the fused features into the pre-trained UAV detection model, the method further includes:

[0108] Acquire historical radio data and historical image data collected by the vehicle-mounted unmanned aerial vehicle (UAV) defense system;

[0109] Based on the historical radio data and the historical image data, feature fusion is performed to obtain historical fused features;

[0110] The historical fusion features were dimensionality reduced using the PCA (Principal Components Analysis) dimensionality reduction algorithm, resulting in dimensionality-reduced features.

[0111] Obtain an initial neural network model that performs classification tasks;

[0112] The dimensionality reduction features are used as training samples to train the initial neural network model until the accuracy of the initial neural network model reaches the configured accuracy, at which point training stops, and the UAV detection model is obtained.

[0113] For example, the initial neural network model can be a binary classification network model, so that the trained drone detection model can output yes or no to determine whether a drone has been detected.

[0114] The accuracy rate can be customized, such as 95%.

[0115] In the above embodiment, historical fusion features are first generated. Furthermore, since the historical fusion features have too high a dimension, which can easily cause the curse of dimensionality, PCA is used for dimensionality reduction. Finally, the dimensionality-reduced features are used as training samples to train the initial neural network to obtain the UAV detection model.

[0116] S13, when a drone is detected within the defense perimeter, the detected drone is identified as a target drone, and the target drone is fused and located to obtain the target location of the target drone.

[0117] In this embodiment, before performing fusion positioning on the target UAV, the method further includes:

[0118] Determine whether the target drone is continuously detected within a preset time period;

[0119] If the target drone is not continuously detected within the preset time period, continue to detect whether there is a drone within the defense zone; or

[0120] When the target drone is continuously detected within the preset time period, an alarm is issued and the target drone is located using a fusion positioning method.

[0121] The preset duration can be configured to be 10 seconds, etc.

[0122] For example, if the target drone is not continuously detected within 10 seconds, it indicates a possible misjudgment (e.g., the drone only briefly stopped or mistakenly entered the area, in which case the stop time is usually short). In this case, the detection of drones within the defense zone continues. If the target drone is continuously detected within 10 seconds, an alarm can be directly issued to alert the drone operator and security personnel of the drone intrusion, allowing for timely countermeasures. The target drone can also be recorded to preserve evidence of the intrusion. Furthermore, the target drone's location can be fused for real-time tracking.

[0123] The above embodiments can be used to reconfirm the test results and avoid misjudgment.

[0124] In this embodiment, the step of performing fusion positioning on the target UAV to obtain the target location of the target UAV includes:

[0125] The coordinates of each base station used for positioning assistance are obtained as each first coordinate, and the number of base stations used for positioning assistance is obtained as the target number.

[0126] The target UAV is located using the TDOA (Time Difference of Arrival) algorithm to obtain the second coordinates;

[0127] The target UAV is located using the AOA (Angle of Arrival) algorithm to obtain the third coordinates;

[0128] The distance from the target drone to each base station is calculated as each first distance;

[0129] Calculate the distance between the second coordinate and each first coordinate to obtain each second distance;

[0130] Calculate the square of the difference between each first distance and each corresponding second distance to obtain each first squared value corresponding to each base station;

[0131] Calculate the sum of each first squared value as the first cumulative sum;

[0132] Calculate the quotient of the first accumulated sum and the target quantity to obtain the first coefficient corresponding to the TDOA algorithm;

[0133] Each third distance is obtained by calculating the distance between the third coordinate and each first coordinate;

[0134] Calculate the square of the difference between each first distance and each corresponding third distance to obtain each second square value corresponding to each base station;

[0135] Calculate the sum of each second squared value as the second cumulative sum;

[0136] Calculate the quotient of the second cumulative sum and the target quantity to obtain the second coefficient corresponding to the AOA algorithm;

[0137] The quotient of the second coordinate and the first coefficient is calculated as the first positioning result, and the quotient of the third coordinate and the second coefficient is calculated as the second positioning result;

[0138] Calculate the sum of the first positioning result and the second positioning result to obtain the current positioning;

[0139] The first weight is obtained by calculating the reciprocal of the first coefficient, and the second weight is obtained by calculating the reciprocal of the second coefficient;

[0140] Calculate the sum of the first weight and the second weight to obtain the target weight;

[0141] The target location is obtained by calculating the quotient of the current location and the target weight.

[0142] The above embodiments can effectively solve the problem of UAV positioning deviation by combining time and frequency domains.

[0143] S14, perform photoelectric detection and tracking on the target UAV based on the target location to obtain the tracking result.

[0144] For example, starting from the target location, the target drone can be continuously tracked using the radio device in the vehicle-mounted drone defense system. This way, evidence of drone intrusion can be obtained while preventing the target from being lost.

[0145] S15, Countermeasures are taken against the target drone based on the tracking results.

[0146] In this embodiment, countering the target drone based on the tracking result includes:

[0147] When the tracking results show that the target drone is in the drive-off zone or the core zone, the system detects whether anyone is on duty within the operating area of ​​the vehicle-mounted drone defense system.

[0148] When an unattended drone is detected, a radio jamming signal of the configured duration is transmitted to the target drone; or when a manned drone is detected, a countermeasure prompt message is issued, and when a countermeasure command is received, a radio jamming signal of the configured duration is transmitted to the target drone.

[0149] Continue to perform photoelectric detection and tracking on the target drone;

[0150] When the target drone is detected returning to its home location, the countermeasures are deemed effective.

[0151] When the target drone is found to have failed to return, and the countermeasure is deemed ineffective, a secondary countermeasure prompt message is issued.

[0152] If the countermeasure is deemed ineffective after a second countermeasure, the vehicle of the vehicle-mounted drone defense system is controlled to follow the flight trajectory of the target drone and obtain the real-time distance between the target drone and the vehicle of the vehicle-mounted drone defense system. When the real-time distance reaches the firing range, a radio suppression signal of the configured duration is transmitted to the target drone.

[0153] The configuration duration can be 30 seconds.

[0154] The countermeasure prompt information can be sent to the terminal device of the on-duty personnel, or it can be displayed on the screen of the vehicle-mounted drone defense system, while simultaneously issuing a warning sound.

[0155] The countermeasure command can be triggered by the on-duty personnel. For example, when a preset button (which can be a virtual button or a physical button) in the vehicle-mounted drone defense system is detected to be triggered, it is determined that the countermeasure command has been received; or, when a preset voice command (such as "transmit radio suppression signal") is detected, it is determined that the countermeasure command has been received.

[0156] If the secondary countermeasure is still ineffective, it indicates that the target drone may have exceeded its effective range. In this case, the vehicle controlling the vehicle-mounted drone defense system follows the flight path of the target drone. Once the target drone is detected to be within its effective range, the vehicle immediately transmits the configured duration of the radio suppression signal to the target drone to achieve effective countermeasure.

[0157] As can be seen from the above technical solution, this invention is applied to a vehicle-mounted drone defense system. Within a pre-established defense perimeter, radio data and image data are obtained through real-time radio detection. Feature fusion is performed on the radio data and image data to obtain fused features, achieving complementary advantages between radio and photoelectric technologies to improve the accuracy of drone perception. The fused features are input into a drone detection model to detect the presence of drones within the defense perimeter. The detected target drones are then located using fused positioning, effectively avoiding positioning errors. Furthermore, photoelectric detection and tracking of the target drones are performed, and countermeasures are taken based on the tracking results to achieve precise drone defense.

[0158] like Figure 2 The diagram shown is a functional block diagram of a preferred embodiment of the vehicle-mounted drone defense device of the present invention. The vehicle-mounted drone defense device 11 includes a detection unit 110, a fusion unit 111, a detection unit 112, a positioning unit 113, a tracking unit 114, and a countermeasure unit 115. The module / unit referred to in this invention is a series of computer program segments that can be executed by a processor and perform a fixed function, stored in memory. In this embodiment, the functions of each module / unit will be described in detail in subsequent embodiments.

[0159] The vehicle-mounted drone defense device 11 described in this embodiment operates within a vehicle-mounted drone defense system and specifically includes:

[0160] The detection unit 110 is used to perform radio detection in real time within a pre-established defense perimeter to obtain radio data, and to collect image data in real time.

[0161] The fusion unit 111 is used to perform feature fusion on the radio data and the image data to obtain fused features;

[0162] The detection unit 112 is used to input the fused features into a pre-trained drone detection model, and to detect whether there are drones within the defense zone based on the output data of the drone detection model.

[0163] The positioning unit 113 is used to identify the detected drone as a target drone when a drone is detected within the defense perimeter, and to perform fusion positioning on the target drone to obtain the target location of the target drone.

[0164] The tracking unit 114 is used to perform photoelectric detection and tracking on the target UAV based on the target position to obtain a tracking result;

[0165] The countermeasure unit 115 is used to counter the target drone based on the tracking result.

[0166] As can be seen from the above technical solution, this invention is applied to a vehicle-mounted drone defense system. Within a pre-established defense perimeter, radio data and image data are obtained through real-time radio detection. Feature fusion is performed on the radio data and image data to obtain fused features, achieving complementary advantages between radio and photoelectric technologies to improve the accuracy of drone perception. The fused features are input into a drone detection model to detect the presence of drones within the defense perimeter. The detected target drones are then located using fused positioning, effectively avoiding positioning errors. Furthermore, photoelectric detection and tracking of the target drones are performed, and countermeasures are taken based on the tracking results to achieve precise drone defense.

[0167] like Figure 3 The diagram shown is a schematic representation of the computer device used to implement the vehicle-mounted drone defense method of the present invention.

[0168] The computer device 1 may include a memory 12, a processor 13 and a bus, and may also include a computer program stored in the memory 12 and executable on the processor 13, such as a vehicle-mounted drone defense program.

[0169] Those skilled in the art will understand that the schematic diagram is merely an example of computer device 1 and does not constitute a limitation on computer device 1. Computer device 1 can be either a bus topology or a star topology. Computer device 1 may also include more or fewer other hardware or software than shown in the diagram, or different component arrangements. For example, computer device 1 may also include input / output devices, network access devices, etc.

[0170] It should be noted that the computer device 1 described is merely an example. Other existing or future electronic products that are adaptable to this invention should also be included within the scope of protection of this invention and are incorporated herein by reference.

[0171] The memory 12 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 12 can be an internal storage unit of the computer device 1, such as a portable hard drive of the computer device 1. In other embodiments, the memory 12 can be an external storage device of the computer device 1, such as a plug-in portable hard drive, smart media card (SMC), secure digital card (SD), flash card, etc., equipped on the computer device 1. Furthermore, the memory 12 can include both internal and external storage units of the computer device 1. The memory 12 can be used not only to store application software and various types of data installed on the computer device 1, such as the code of a vehicle-mounted drone defense program, but also to temporarily store data that has been output or will be output.

[0172] In some embodiments, the processor 13 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits packaged with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 13 is the control unit of the computer device 1, connecting various components of the computer device 1 via various interfaces and lines. It executes programs or modules stored in the memory 12 (e.g., executing vehicle-mounted drone defense programs) and calls data stored in the memory 12 to perform various functions of the computer device 1 and process data.

[0173] The processor 13 executes the operating system of the computer device 1 and various installed applications. The processor 13 executes these applications to implement the steps in the various embodiments of the vehicle-mounted drone defense method described above, for example... Figure 1 The steps are shown.

[0174] For example, the computer program may be divided into one or more modules / units, which are stored in the memory 12 and executed by the processor 13 to complete the present invention. The one or more modules / units may be a series of computer-readable instruction segments capable of performing specific functions, which describe the execution process of the computer program in the computer device 1. For example, the computer program may be divided into a detection unit 110, a fusion unit 111, a detection unit 112, a positioning unit 113, a tracking unit 114, and a countermeasure unit 115.

[0175] The integrated unit implemented as a software functional module described above can be stored in a computer-readable storage medium. This software functional module, stored in a storage medium, includes several instructions to cause a computer device (which may be a personal computer, computer equipment, or network device, etc.) or processor to execute portions of the vehicle-mounted drone defense method described in the various embodiments of this invention.

[0176] If the modules / units integrated in the computer device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments of the present invention can also be implemented by a computer program instructing related hardware devices. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above.

[0177] The computer program includes computer program code, which may be in the form of source code, object code, executable file, or some intermediate form. The computer-readable medium may include any entity or device capable of carrying the computer program code, recording media, USB flash drive, portable hard drive, magnetic disk, optical disk, computer memory, read-only memory (ROM), random access memory, etc.

[0178] Furthermore, the computer-readable storage medium may primarily include a stored program area and a stored data area, wherein the stored program area may store the operating system, an application program required for at least one function, etc.; and the stored data area may store data created based on the use of blockchain nodes, etc.

[0179] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0180] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, in... Figure 3 The bus is represented by only one straight line, but this does not mean that there is only one bus or one type of bus. The bus is configured to enable communication between the memory 12 and at least one processor 13, etc.

[0181] Although not shown, the computer device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 13 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The computer device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0182] Furthermore, the computer device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish a communication connection between the computer device 1 and other computer devices.

[0183] Optionally, the computer device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), and optionally, a standard wired interface or a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the computer device 1 and to display a visual user interface.

[0184] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0185] Figure 3 Only computer device 1 with components 12-13 is shown; those skilled in the art will understand that... Figure 3 The structure shown does not constitute a limitation on the computer device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0186] Combination Figure 1 The memory 12 in the computer device 1 stores multiple instructions to implement a vehicle-mounted drone defense method, and the processor 13 can execute the multiple instructions to achieve the following:

[0187] Radio data is obtained through real-time radio detection within a pre-established defense perimeter, and image data is acquired in real-time.

[0188] The radio data and the image data are fused to obtain fused features;

[0189] The fused features are input into a pre-trained drone detection model, and the presence of drones within the defense perimeter is detected based on the output data of the drone detection model.

[0190] When a drone is detected within the defense perimeter, the detected drone is identified as the target drone, and the target drone is fused and located to obtain the target location of the target drone.

[0191] Based on the target location, the target UAV is subjected to photoelectric detection and tracking to obtain the tracking result;

[0192] Countermeasures are taken against the target drone based on the tracking results.

[0193] Specifically, the processor 13's implementation method for the above instructions can be found in [reference needed]. Figure 1The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0194] It should be noted that all the data involved in this case was legally obtained.

[0195] In the several embodiments provided by this invention, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0196] This invention can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This invention can be described in the general context of computer-executable instructions, such as program modules, that are executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This invention can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0197] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0198] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0199] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0200] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0201] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in this invention can also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0202] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for defending against vehicle-mounted unmanned aerial vehicles, characterized in that, The vehicle-mounted drone defense system is applied to a vehicle-mounted drone defense system, and the vehicle-mounted drone defense method includes: Radio data is obtained through real-time radio detection within a pre-established defense perimeter, and image data is acquired in real-time. The radio data and the image data are fused to obtain fused features; The fused features are input into a pre-trained drone detection model, and the presence of drones within the defense perimeter is detected based on the output data of the drone detection model. When a drone is detected within the defense perimeter, the detected drone is identified as the target drone, and the target drone is fused and located to obtain the target location of the target drone. Based on the target location, the target UAV is subjected to photoelectric detection and tracking to obtain the tracking result; Countermeasures are taken against the target drone based on the tracking results; The process involves: inputting the fused features into a pre-trained UAV detection model; acquiring historical radio data and historical image data collected by the vehicle-mounted UAV defense system; performing feature fusion based on the historical radio data and historical image data to obtain historical fused features; using the PCA dimensionality reduction algorithm to reduce the dimensionality of the historical fused features to obtain dimensionality-reduced features; obtaining an initial neural network model with classification capabilities; and using the dimensionality-reduced features as training samples to train the initial neural network model until the accuracy of the initial neural network model reaches the configured accuracy, at which point training stops, and the UAV detection model is obtained.

2. The vehicle-mounted unmanned aerial vehicle (UAV) defense method as described in claim 1, characterized in that, Before obtaining radio data and acquiring image data in real time within the pre-established defense perimeter, the method further includes: Obtain the defense center; With the defense center as the center and the first preset length as the radius, a first three-dimensional enclosing circle is drawn to obtain the core area; A second three-dimensional encirclement is defined with the defense center as the center and the second preset length as the radius. The area obtained by removing the first three-dimensional encirclement from the second three-dimensional encirclement is defined as the deportation zone. A third three-dimensional encirclement is defined with the defense center as the center and the third preset length as the radius. The area obtained by removing the second three-dimensional encirclement from the third three-dimensional encirclement is defined as the warning zone. The core area, the deportation area, and the warning area are combined to form the defense perimeter; Wherein, the first preset length is less than the second preset length, and the second preset length is less than the third preset length.

3. The vehicle-mounted unmanned aerial vehicle (UAV) defense method as described in claim 1, characterized in that, The feature fusion of the radio data and the image data to obtain the fused features includes: The radio data is filtered to obtain filtered features; The first feature is obtained by using a convolutional neural network to extract features from the filtered features; The image data is preprocessed to obtain an intermediate image; The convolutional neural network is used to extract features from the intermediate image to obtain a second feature; The first feature and the second feature are fused to obtain the fused feature.

4. The vehicle-mounted unmanned aerial vehicle (UAV) defense method as described in claim 1, characterized in that, Before performing fusion positioning on the target UAV, the method further includes: Determine whether the target drone is continuously detected within a preset time period; If the target drone is not continuously detected within the preset time period, continue to detect whether there is a drone within the defense zone; or When the target drone is continuously detected within the preset time period, an alarm is issued and the target drone is located using a fusion positioning method.

5. The vehicle-mounted unmanned aerial vehicle (UAV) defense method as described in claim 1, characterized in that, The step of performing fusion positioning on the target UAV to obtain the target UAV's target location includes: The coordinates of each base station used for positioning assistance are obtained as each first coordinate, and the number of base stations used for positioning assistance is obtained as the target number. The TDOA algorithm is used to locate the target UAV and obtain the second coordinates; The target UAV is located using the AOA algorithm to obtain the third coordinates; The distance from the target drone to each base station is calculated as each first distance; Calculate the distance between the second coordinate and each first coordinate to obtain each second distance; Calculate the square of the difference between each first distance and each corresponding second distance to obtain each first squared value corresponding to each base station; Calculate the sum of each first squared value as the first cumulative sum; Calculate the quotient of the first accumulated sum and the target quantity to obtain the first coefficient corresponding to the TDOA algorithm; Each third distance is obtained by calculating the distance between the third coordinate and each first coordinate; Calculate the square of the difference between each first distance and each corresponding third distance to obtain each second square value corresponding to each base station; Calculate the sum of each second squared value as the second sum; Calculate the quotient of the second cumulative sum and the target quantity to obtain the second coefficient corresponding to the AOA algorithm; The quotient of the second coordinate and the first coefficient is calculated as the first positioning result, and the quotient of the third coordinate and the second coefficient is calculated as the second positioning result; Calculate the sum of the first positioning result and the second positioning result to obtain the current positioning; The first weight is obtained by calculating the reciprocal of the first coefficient, and the second weight is obtained by calculating the reciprocal of the second coefficient; Calculate the sum of the first weight and the second weight to obtain the target weight; The target location is obtained by calculating the quotient of the current location and the target weight.

6. The vehicle-mounted unmanned aerial vehicle (UAV) defense method as described in claim 2, characterized in that, The countermeasure against the target drone based on the tracking results includes: When the tracking results show that the target drone is in the drive-off zone or the core zone, the system detects whether anyone is on duty within the operating area of ​​the vehicle-mounted drone defense system. When an unattended drone is detected, a radio jamming signal of the configured duration is transmitted to the target drone; or when a manned drone is detected, a countermeasure prompt message is issued, and when a countermeasure command is received, a radio jamming signal of the configured duration is transmitted to the target drone. Continue to perform photoelectric detection and tracking on the target drone; When the target drone is detected returning to its home location, the countermeasures are deemed effective. When the target drone is found to have failed to return, and the countermeasure is deemed ineffective, a secondary countermeasure prompt message is issued. If the countermeasure is deemed ineffective after a second countermeasure, the vehicle of the vehicle-mounted drone defense system is controlled to follow the flight trajectory of the target drone and obtain the real-time distance between the target drone and the vehicle of the vehicle-mounted drone defense system. When the real-time distance reaches the firing range, a radio suppression signal of the configured duration is transmitted to the target drone.

7. A vehicle-mounted unmanned aerial vehicle (UAV) defense device, characterized in that, Operating within a vehicle-mounted drone defense system, the vehicle-mounted drone defense device includes: The detection unit is used to conduct real-time radio detection within a pre-established defense perimeter to obtain radio data and to acquire image data in real time. A fusion unit is used to perform feature fusion on the radio data and the image data to obtain fused features; The detection unit is used to input the fused features into a pre-trained drone detection model and detect whether there are drones within the defense zone based on the output data of the drone detection model. The positioning unit is used to identify the detected drone as a target drone when a drone is detected within the defense perimeter, and to perform fusion positioning on the target drone to obtain the target location of the target drone. The tracking unit is used to perform photoelectric detection and tracking of the target UAV based on the target position, and obtain the tracking result; A countermeasure unit is used to counter the target drone based on the tracking results; The process involves: inputting the fused features into a pre-trained UAV detection model; acquiring historical radio data and historical image data collected by the vehicle-mounted UAV defense system; performing feature fusion based on the historical radio data and historical image data to obtain historical fused features; using the PCA dimensionality reduction algorithm to reduce the dimensionality of the historical fused features to obtain dimensionality-reduced features; obtaining an initial neural network model with classification capabilities; and using the dimensionality-reduced features as training samples to train the initial neural network model until the accuracy of the initial neural network model reaches the configured accuracy, at which point training stops, and the UAV detection model is obtained.

8. A computer device, characterized in that, The computer device includes: Memory, storing at least one instruction; and The processor executes instructions stored in the memory to implement the vehicle-mounted drone defense method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, which is executed by a processor in a computer device to implement the vehicle-mounted unmanned aerial vehicle defense method as described in any one of claims 1 to 6.

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