Multi-means fusion unmanned aerial vehicle countering method and system

Abstract

The invention provides a multi-means fusion unmanned aerial vehicle countering method and system, and the method comprises the steps: obtaining three-dimensional topographic data, atmospheric turbulence features and electromagnetic spectrum features in real time through deploying a distributed sensing array in a target region, constructing a multi-layer virtual signal barrier boundary, and generating a frequency band interference parameter set; when it is detected that the unmanned aerial vehicle passes through the barrier boundary, the cooperative interference module is triggered to generate an initial interference signal weight, polarization direction distribution and power density distribution are dynamically corrected in combination with electromagnetic scattering characteristics fed back by the unmanned aerial vehicle in real time, and the initial interference signal weight serves as flight path modeling input and is used for generating a three-dimensional dynamic flight path model; according to the method, the azimuth angle correction, the elevation angle compensation coefficient and the energy gradient parameter of the ground interception equipment are calculated, and then the dynamic interception strategy is generated through the dynamic strategy fuser, so that the interference and interception efficiency of the unmanned aerial vehicle is improved through fusion and dynamic closed-loop control of excessive means, and the full-link closed loop from environmental perception to interception control is realized.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of drone technology, and more particularly to a drone countermeasure method and system integrating multiple means. Background Art

[0002] With the rapid development of drone technology, its application areas have expanded from entertainment and photography to important areas such as logistics and distribution, agricultural monitoring, disaster response, and military reconnaissance. However, this has also brought new security challenges, especially in protecting critical infrastructure (such as airports, nuclear power plants, government agencies, etc.) from unauthorized or malicious drone intrusions; Current countermeasures against drones mainly include radar detection, radio jamming, physical capture (such as using nets to capture drones), laser destruction, etc. These methods usually work independently and are responsible for different tasks. For example, radar is used for preliminary detection, while radio jamming attempts to cut off the communication link between the drone and its operator. In addition, there are solutions based on visual recognition systems, which try to identify and track drones through image analysis; Although existing drone countermeasures can deal with drone threats to a certain extent, each method has its obvious limitations. Although radar detection can provide basic location information, its ability to identify small drones is limited and it is prone to false alarms. Radio interference lacks specificity and may cause unnecessary interference to surrounding legitimate radio signals. Physical capture and laser destruction are not only costly and complex to deploy, but also have certain limitations in operational flexibility and response speed. Visual recognition systems are limited by weather conditions and light conditions, resulting in low accuracy and reliability. Summary of the invention

[0003] The embodiments of the present application provide a multi-means integrated drone countermeasure method and system to solve the problems in the prior art of limited recognition capability of small drones, easy false alarms, lack of targeted interference measures, possible unnecessary interference to surrounding legitimate radio signals, high cost and complex deployment of physical capture and laser destruction methods, limited operational flexibility and response speed, and low accuracy and reliability of visual recognition systems under different weather conditions and lighting environments.

[0004] In a first aspect, the present application provides a multi-means integrated drone countermeasure method, including: Deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. Construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and associate the abnormal frequency bands extracted from the electromagnetic spectrum characteristics to generate a set of frequency band interference parameters. When a drone is detected crossing the boundary of the virtual signal barrier, the coordinated interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set; Dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as input parameters for track modeling; The input parameters of the track modeling are combined with the real-time collected UAV heading offset and velocity vector changes to generate a three-dimensional dynamic track model, and the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment are calculated based on the three-dimensional dynamic track model; The azimuth correction value, elevation compensation coefficient and energy gradient parameter are input into a preset dynamic strategy fuser to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a transmission timing optimization sequence and a spatial density distribution matrix.

[0005] Optionally, the azimuth correction value, the elevation compensation coefficient and the energy gradient parameter are input into a preset dynamic strategy fusion device to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a transmission timing optimization sequence and a spatial density distribution matrix, including: The azimuth correction value and the elevation compensation coefficient are input into the geometric parameter solving unit, and the azimuth dynamic compensation value and the elevation attenuation factor are generated based on the obstacle shielding effect in the three-dimensional terrain data; The energy gradient parameter, the azimuth dynamic compensation amount and the elevation attenuation factor are input into the nonlinear optimization model, and the launch timing optimization sequence is generated by solving the constraint conditions, wherein the constraint conditions include: the instantaneous acceleration of the UAV motion trajectory, the physical response delay of the ground interception equipment and the upper limit of the dynamic allocation of the energy gradient parameter; Construct a spatial density distribution matrix based on the azimuth dynamic compensation and elevation attenuation factor; The launch timing optimization sequence and the spatial density distribution matrix are input into the preset dynamic strategy fuser, and a dynamic interception strategy is generated through a multi-objective optimization algorithm.

[0006] Optionally, the energy gradient parameter, the azimuth angle dynamic compensation amount, and the elevation angle attenuation factor are input into a nonlinear optimization model, and a transmission timing optimization sequence is generated by solving the constraint conditions, including: The energy gradient parameter is dynamically coupled with the azimuth dynamic compensation to generate the energy azimuth coupling coefficient, and the elevation attenuation factor is convolved with the instantaneous acceleration of the UAV motion trajectory in the time domain to generate the elevation dynamic response parameter; The energy azimuth coupling coefficient and the elevation angle dynamic response parameters are input into a multi-constraint optimization framework, which divides the time window by the three-dimensional curvature characteristics of the UAV motion trajectory and performs the following processing in each time window: Generate the equipment response time baseline based on the physical response delay parameters of the ground interception equipment, and calculate the energy allocation window function in combination with the dynamic allocation upper limit of the energy gradient parameter; A dynamic weight allocation vector is constructed by using the vector modulus of the instantaneous acceleration of the drone and the device response time baseline, wherein the dynamic weight allocation vector is used to adjust the time phase offset of the energy allocation window function; The dynamic weight allocation vector and the elevation angle dynamic response parameter are nonlinearly superimposed to generate the initial timing distribution diagram, and the peak segment exceeding the upper limit of energy allocation in the initial timing distribution diagram is truncated and compensated by the constraint conflict resolution algorithm to generate the emission timing optimization sequence.

[0007] Optionally, the polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics fed back by the UAV in real time to obtain the corrected polarization direction distribution and power density distribution, and the corrected polarization direction distribution and power density distribution are used as track modeling input parameters, including: The electromagnetic scattering characteristics fed back by the UAV in real time are input into the polarization characteristic solving unit to extract the main polarization component and cross-polarization component of the scattered signal and generate the initial solution of polarization direction distribution; Calculating polarization matching based on the initial solution of polarization direction distribution and the initial interference signal weight to generate a polarization mismatch factor, and inputting the polarization mismatch factor into a power density correction model; In the power density correction model, the power density reference value is generated by the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and the power density dynamic correction coefficient is generated by combining the polarization mismatch factor and the instantaneous acceleration of the UAV motion trajectory; The dynamic correction coefficient of power density and the initial solution of polarization direction distribution are jointly optimized to generate the corrected polarization direction distribution and power density distribution, wherein the polarization direction distribution is dynamically compensated by the phase offset of the main polarization component, and the power density distribution is generated by multiplying the power density reference value by the dynamic correction coefficient; The corrected polarization direction distribution and power density distribution are used as input parameters for track modeling, and the polarization mismatch factor and power density dynamic correction coefficient are synchronized to the heading offset solution module of the three-dimensional dynamic track model.

[0008] Optionally, a distributed sensing array is deployed in the target area to obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time, a multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and atmospheric turbulence characteristics, and a frequency band interference parameter set is generated by associating abnormal frequency bands extracted from the electromagnetic spectrum characteristics, including: Deploy a multi-node distributed sensing array outside the target area to collect three-dimensional terrain data in real time through lidar scanning and multi-spectral imaging technology, and obtain atmospheric turbulence characteristics and electromagnetic spectrum characteristics through atmospheric turbulence sensors and spectrum monitoring devices respectively; Input the three-dimensional terrain data into the terrain shielding effect analysis module, extract the terrain elevation gradient and obstacle distribution characteristics, and generate the terrain shielding weight matrix; Based on the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, the atmospheric disturbance compensation parameters are generated, and the atmospheric disturbance compensation parameters are spatially superimposed with the terrain shielding weight matrix to generate the initial distribution map of the multi-layer virtual signal barrier boundary; Input the electromagnetic spectrum features into the abnormal frequency band detection unit, extract the abnormal energy peak and frequency band width features in the electromagnetic spectrum features, and generate an abnormal frequency band fingerprint set; The abnormal frequency band fingerprint set and the initial distribution map of the multi-layer virtual signal barrier boundary are mapped in the frequency band space to generate a frequency band interference parameter set, wherein each frequency band interference parameter in the frequency band interference parameter set includes: the center frequency, bandwidth and power allocation ratio of the interference signal.

[0009] Optionally, the track modeling input parameters are combined with the real-time collected heading offset and velocity vector change of the UAV to generate a three-dimensional dynamic track model, and the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment are calculated based on the three-dimensional dynamic track model, including: Input the corrected polarization direction distribution and power density distribution in the track modeling input parameters into the track feature extraction unit to extract the main polarization direction offset and power density change gradient of the UAV motion state; The main polarization direction offset is combined with the real-time collected UAV heading offset to generate a heading integrated offset vector, and the power density change gradient is combined with the velocity vector change to generate a velocity dynamic response parameter. Inputting the heading integrated offset vector and the velocity dynamic response parameters into the three-dimensional track reconstruction module, generating a three-dimensional dynamic track model based on the instantaneous curvature characteristics of the UAV motion track, wherein the three-dimensional dynamic track model includes: track curvature distribution, instantaneous acceleration distribution and energy attenuation characteristics; Inputting the track curvature distribution in the three-dimensional dynamic track model into the azimuth angle solving unit, and generating the azimuth angle correction value in combination with the terrain shielding weight matrix, wherein the azimuth angle correction value includes: terrain shielding compensation value and curvature dynamic offset; Inputting the instantaneous acceleration distribution and the energy attenuation characteristics into the elevation angle compensation solver to generate an elevation angle compensation coefficient, wherein the elevation angle compensation coefficient includes: an acceleration dynamic response factor and an energy attenuation compensation value; The track curvature distribution, instantaneous acceleration distribution and energy attenuation characteristics are input into the energy gradient solution module to generate energy gradient parameters, wherein the energy gradient parameters include: curvature energy distribution weight, acceleration energy compensation factor and attenuation energy correction value.

[0010] Optionally, when a drone is detected crossing the boundary of the virtual signal barrier, the coordinated interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set, including: Obtain the hierarchical index of the virtual signal barrier boundary traversed by the UAV and the associated frequency band interference parameter set, and extract the center frequency and bandwidth parameters from the frequency band interference parameters corresponding to the virtual signal barrier boundary based on the hierarchical index; Input the center frequency and bandwidth parameters into the frequency band matching calculation module to generate a frequency band coverage matching matrix, wherein the frequency band coverage matching matrix includes the weight allocation priority and spectrum overlap coefficient of the interference signal of each frequency band; Generate interference signal baseband parameters based on the spectrum overlap coefficient and the motion speed vector of the UAV when it passes through the barrier, wherein the interference signal baseband parameters include: pulse repetition frequency, modulation depth and phase jump interval; The frequency band coverage matching matrix and the interference signal baseband parameters are input into a multi-objective weight allocation algorithm to generate an initial interference signal weight, wherein the initial interference signal weight includes the power ratio distribution of the interference signal in each frequency band and the time-frequency resource allocation matrix.

[0011] In a second aspect, the embodiment of the present application provides a multi-means integrated drone countermeasure system, including: A deployment module is used to deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. A multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and atmospheric turbulence characteristics, and a frequency band interference parameter set is generated by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics. A trigger module, configured to trigger the coordinated interference module to generate an initial interference signal weight matching the frequency band interference parameter set when a drone is detected crossing the boundary of the virtual signal barrier; A correction module is used to dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as track modeling input parameters; A calculation module is used to combine the input parameters of the track modeling with the heading offset and velocity vector change of the UAV collected in real time to generate a three-dimensional dynamic track model, and calculate the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment based on the three-dimensional dynamic track model; A generation module is used to input the azimuth correction value, elevation compensation coefficient and energy gradient parameter into a preset dynamic strategy fuser to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a launch timing optimization sequence and a spatial density distribution matrix.

[0012] In a third aspect, an embodiment of the present application provides a computing device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a multi-means fusion drone countermeasure method as described in any one of the first aspects.

[0013] In a fourth aspect, an embodiment of the present application provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a multi-means integrated drone countermeasure method as described in any one of the first aspects.

[0014] In an embodiment of the present application, a distributed sensing array is deployed in the target area and three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics are acquired in real time. A multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and the atmospheric turbulence characteristics, and a frequency band interference parameter set is generated by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics. When a drone is detected crossing the virtual signal barrier boundary, a collaborative interference module is triggered to generate an initial interference signal weight that matches the frequency band interference parameter set. The polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics fed back in real time by the drone to obtain a corrected polarization direction distribution. The modified polarization direction distribution and power density distribution are used as input parameters for track modeling; the track modeling input parameters are combined with the heading offset and velocity vector change of the UAV collected in real time to generate a three-dimensional dynamic track model, and the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment are calculated based on the three-dimensional dynamic track model; the azimuth correction, elevation compensation coefficient and energy gradient parameter are input into a preset dynamic strategy fuser to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a launch timing optimization sequence and a spatial density distribution matrix.

[0015] The technical solution of this application has the following beneficial effects: The recognition accuracy and reaction speed of drone targets are improved, and the false alarm rate and environmental impact are effectively reduced, providing strong technical support for ensuring the safety of key areas. At the same time, the system design takes into account the comprehensive processing of multiple factors, including but not limited to terrain shielding effects, atmospheric disturbance compensation, abnormal frequency band detection, etc., thus achieving a more intelligent, accurate and efficient drone countermeasure solution; Furthermore, the azimuth correction and elevation compensation coefficients are input into the geometric parameter solution unit, and the azimuth dynamic compensation and elevation attenuation factor are calculated in combination with the obstacle shielding effect in the three-dimensional terrain data. Then, the nonlinear optimization model is used to generate a launch timing optimization sequence by considering constraints such as the instantaneous acceleration of the UAV's motion trajectory, the physical response delay of the ground interception equipment, and the upper limit of the dynamic allocation of the energy gradient parameters. Then, the spatial density distribution matrix is ​​constructed based on the azimuth dynamic compensation and the elevation attenuation factor. Finally, the launch timing optimization sequence and the spatial density distribution matrix are input into the preset dynamic strategy fusion unit, and an efficient dynamic interception strategy is generated through a multi-objective optimization algorithm, which significantly improves the UAV anti-attack efficiency. The accuracy and response speed of the control system are improved by comprehensively considering factors such as terrain characteristics, atmospheric turbulence, and electromagnetic spectrum characteristics, thereby achieving precise positioning and efficient interference of UAV targets. Specifically, the introduction of azimuth dynamic compensation and elevation attenuation factor effectively overcomes the influence of terrain shielding effect and improves interception accuracy; the application of nonlinear optimization model ensures that the launch timing optimization sequence can maximize the interception efficiency under complex constraints. In addition, the dynamic interception strategy generated based on the multi-objective optimization algorithm not only enhances the adaptability to different types of UAV threats, but also optimizes resource allocation, reduces false alarm rate and environmental impact, thereby providing a more intelligent and effective solution for the security protection of key areas.

[0016] These and other aspects of the present application will become more clearly understood in the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flowchart of a multi-means integrated drone countermeasure method provided in an embodiment of the present application; Figure 2A schematic diagram of the structure of a multi-means integrated drone countermeasure system provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0020] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., do not represent the order of precedence, and do not limit the "first" and "second" to be different types.

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0022] Figure 1 A flowchart of a multi-means integrated drone countermeasure method is provided for an embodiment of the present application, such as Figure 1 As shown, the method includes: Step 101: deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. A multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and the atmospheric turbulence characteristics, and an abnormal frequency band extracted from the electromagnetic spectrum characteristics is associated to generate a frequency band interference parameter set, including: In this step, the distributed sensing array refers to a network composed of multiple sensor nodes arranged around the target area, which is used to monitor multiple parameters in the environment in real time, such as three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics; three-dimensional terrain data refers to the digital information of the surface morphology of the target area obtained by laser radar scanning or satellite remote sensing and other technologies, including elevation, slope and obstacle distribution; atmospheric turbulence characteristics refer to a data set describing the irregular flow characteristics in the air, usually including indicators such as wind speed gradient and turbulence intensity, which affect the propagation of radio signals; electromagnetic spectrum characteristics refer to data reflecting the activities of all electromagnetic waves in a specific area, including frequency, intensity and other information, which are used to identify abnormal signal sources; multi-layer virtual signal barrier boundaries refer to multi-level electronic protection areas built based on terrain and atmospheric conditions, which are designed to detect and respond to unauthorized drone intrusions; frequency band interference parameter set: a parameter set extracted from abnormal frequency bands in the electromagnetic spectrum, including center frequency, bandwidth and power allocation ratio, etc., which are used to generate targeted interference signals; First, a distributed sensing array consisting of lidar, atmospheric turbulence sensors and spectrum monitoring devices is deployed outside the target area to collect three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics in real time. Next, the three-dimensional terrain data is input into the terrain shielding effect analysis module to generate a terrain shielding weight matrix, and the atmospheric disturbance compensation parameters are generated in combination with the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, so as to construct an initial distribution map of the multi-layer virtual signal barrier boundary. At the same time, the abnormal frequency band detection unit is used to extract abnormal energy peaks and frequency band width characteristics from the electromagnetic spectrum characteristics to form an abnormal frequency band fingerprint set. Finally, the abnormal frequency band fingerprint set is associated with the initial distribution map of the virtual signal barrier boundary through the frequency band space mapping technology to generate a frequency band interference parameter set containing the center frequency, bandwidth and power allocation ratio of the interference signal of each frequency band; For example, a distributed sensing array consisting of multiple lidar scanners, atmospheric turbulence sensors, and spectrum monitoring stations is deployed around the airport. These devices collect three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics around the airport in real time. Subsequently, the system establishes a multi-layer virtual signal barrier boundary based on the collected data, paying special attention to areas that may pose a threat to the aircraft's take-off and landing paths. When abnormal electromagnetic activity is found in a certain frequency band, the system automatically extracts the characteristics of the frequency band, generates a corresponding set of frequency band interference parameters, and prepares targeted interference strategies. For example, once an unauthorized drone attempts to cross the barrier, the system immediately activates the collaborative interference module and generates a matching interference signal based on the preset frequency band interference parameter set, effectively preventing the drone from taking further action and ensuring the safe operation of the airport. It not only improves the accuracy and efficiency of drone countermeasures, but also greatly reduces the false alarm rate and the impact on legal radio communications. Step 102, when it is detected that the UAV crosses the boundary of the virtual signal barrier, triggering the coordinated interference module to generate an initial interference signal weight matching the frequency band interference parameter set, including: In this step, the virtual signal barrier boundary is a multi-level electronic protection area built based on three-dimensional terrain data and atmospheric turbulence characteristics, which is used to monitor and respond to unauthorized drone intrusions; the collaborative interference module refers to an integrated system component responsible for generating and executing interference strategies based on detected drone characteristics and environmental parameters to prevent illegal drone activities; the initial interference signal weight refers to the interference signal strength distribution preliminarily set according to the frequency band interference parameter set, which is used to guide the subsequent adjustment and optimization of the interference signal; When the distributed sensing array detects that a drone has crossed the boundary of a pre-built virtual signal barrier, the system will automatically trigger the collaborative interference module to enter the working state. First, the system obtains the specific level index that the drone has crossed and its associated frequency band interference parameter set, and extracts the corresponding center frequency and bandwidth parameters from it. Next, these parameters are input into the frequency band matching calculation module to generate a frequency band coverage matching matrix containing the weight allocation priority of interference signals in each frequency band and the spectrum overlap coefficient. Based on this matrix, and combined with the velocity vector of the drone when it crosses the barrier, the interference signal baseband parameters such as pulse repetition frequency, modulation depth and phase jump interval are further calculated. Finally, through the multi-target weight allocation algorithm, the initial interference signal weight that matches the frequency band interference parameter set is generated to provide basic data support for subsequent dynamic corrections; For example, when the distributed sensing array deployed around the airport detects an unauthorized drone attempting to cross the virtual signal barrier boundary, the system immediately activates the collaborative interference module. The collaborative interference module first determines the specific location and direction of the drone's crossing, and then calculates the optimal interference frequency range for the drone based on the center frequency and bandwidth parameters in the preset frequency band interference parameter set. Next, using the frequency band coverage matching matrix and the drone's velocity vector, the system formulates specific interference signal baseband parameters including pulse repetition frequency and modulation depth. Based on these parameters, the collaborative interference module generates preliminary interference signal weights and begins to send customized interference signals to the drone, effectively interrupting its communication link and forcing it to change course or land, thereby ensuring the safe operation of the airport.

[0023] Step 103, dynamically correcting the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, obtaining the corrected polarization direction distribution and power density distribution, and using the corrected polarization direction distribution and power density distribution as track modeling input parameters, including: Electromagnetic scattering characteristics refer to the characteristics of electromagnetic waves reflected or emitted by drones, including signal strength, frequency, polarization direction, etc., which are used to analyze and identify targets; initial interference signal weight refers to the preliminary interference signal strength distribution set based on the frequency band interference parameter set, which provides a basis for subsequent adjustments; polarization direction distribution refers to the description of the polarization state of the interference signal in different directions, which affects the directionality and pertinence of the interference effect; power density distribution refers to the energy intensity of the interference signal received per unit area, which determines the effective range and intensity of the interference; track modeling input parameters refer to the data set used to generate the three-dimensional dynamic track model of the drone, including information such as the corrected polarization direction distribution and power density distribution; When the system detects that the drone has crossed the boundary of the virtual signal barrier and triggers the cooperative interference module, it begins to receive the electromagnetic scattering characteristics fed back by the drone in real time. First, these electromagnetic scattering characteristics are input into the polarization characteristic solving unit, from which the main polarization component and cross-polarization component of the scattered signal are extracted to generate the initial solution of the polarization direction distribution. Next, the polarization matching degree is calculated based on this initial solution and the initial interference signal weight, and the polarization mismatch factor is generated and input into the power density correction model. In this model, the power density reference value is generated according to the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and the power density dynamic correction coefficient is generated by combining the polarization mismatch factor and the instantaneous acceleration of the drone's motion trajectory. Finally, through the joint optimization process, the corrected polarization direction distribution and power density distribution are generated, and these corrected parameters are used as input parameters for track modeling to further optimize the drone's interception strategy; For example, in the aforementioned airport security protection application example, when the collaborative interference module is activated and sends a customized interference signal to an unauthorized drone, the system continues to receive the real-time feedback of the electromagnetic scattering characteristics of the drone. Through the polarization characteristic solution unit, the system analyzes the main polarization and cross-polarization components of the electromagnetic waves returned by the drone, and determines whether the polarization direction distribution of the current interference signal needs to be adjusted. Based on the calculated polarization mismatch factor, the system finds that the current interference signal fails to effectively cover the communication frequency band of the drone, so it is dynamically adjusted through the power density correction model. Specifically, the system re-evaluates the power density baseline value based on the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and generates a power density dynamic correction coefficient taking into account changes in the drone's flight path (such as acceleration or turning). Finally, the system generates a corrected polarization direction distribution and power density distribution, and uses these data as input parameters for track modeling, further optimizing the three-dimensional dynamic track model of the drone. This enables ground interception equipment to track and respond to the movement of drones more accurately, ensuring effective protection of airport airspace. Step 104, the track modeling input parameters are combined with the real-time collected heading offset and velocity vector change of the UAV to generate a three-dimensional dynamic track model, and the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment are calculated based on the three-dimensional dynamic track model, including: The heading offset refers to the actual deviation angle of the drone relative to its predetermined path, which is used to evaluate the change of the flight trajectory; the velocity vector change refers to the change of the speed and direction of the drone at different time points, reflecting the dynamic characteristics of its motion state; the three-dimensional dynamic track model is a model built based on the real-time data of the drone, which is used to predict the future position, speed, acceleration and other information of the drone; the azimuth correction refers to the adjustment of the horizontal rotation angle of the ground interception equipment according to the terrain shielding effect to ensure accurate aiming at the target; the elevation compensation coefficient refers to the adjustment of the vertical pointing angle of the ground interception equipment considering the influence of factors such as atmospheric turbulence on signal propagation; the energy gradient parameter refers to the gradient of the energy emitted by the ground interception equipment with distance, which affects the effect of interfering with or destroying the drone; First, the corrected polarization direction distribution and power density distribution are used as input parameters for track modeling, and are fused with the real-time collected UAV heading offset and velocity vector changes. Using these data, the system generates a three-dimensional dynamic track model of the UAV through a track feature extraction unit. The model not only includes basic information such as the current position, speed, and acceleration of the UAV, but also covers its future movement trends. Then, based on the generated three-dimensional dynamic track model, the system further calculates the azimuth correction, elevation compensation coefficient, and energy gradient parameter of the ground interception equipment. Specifically, the azimuth correction is adjusted by combining the terrain shielding weight matrix to adjust the horizontal rotation angle of the ground interception equipment, while the elevation compensation coefficient adjusts the vertical pointing angle based on the atmospheric disturbance compensation parameter. Finally, the energy gradient parameter determines the optimal energy allocation scheme based on the instantaneous acceleration and energy attenuation characteristics of the UAV to achieve efficient interception; For example, after successfully generating the corrected polarization direction distribution and power density distribution, the system begins to integrate these track modeling input parameters with the real-time collected drone heading offset and velocity vector changes. As the drone attempts to avoid interference signals and change its flight path, the system quickly updates its three-dimensional dynamic track model and accurately predicts the drone's next action. Based on this model, the system calculates the azimuth correction and elevation compensation coefficient required by the ground interception equipment, ensuring that the interception equipment can accurately track and lock fast-moving targets. At the same time, taking into account the situation of the drone accelerating to escape, the system also adjusts the energy gradient parameters and optimizes the energy output strategy of the ground interception equipment, so that the interference signal can cover the target within the most effective range. In this way, even if the drone attempts to escape through complex flight movements, the system can respond quickly and adjust the interception measures, greatly improving the success rate of countermeasures and effectively ensuring the safety of the airport airspace.

[0024] Step 105, inputting the azimuth correction value, the elevation compensation coefficient and the energy gradient parameter into a preset dynamic strategy fusion device to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a transmission timing optimization sequence and a spatial density distribution matrix, including: The dynamic strategy fuser is an integrated system component responsible for comprehensively analyzing various input parameters and generating dynamic interception strategies; the launch timing optimization sequence refers to the optimal launch order and timing arrangement for different time points to maximize the interception effect; the spatial density distribution matrix refers to the energy density distribution required for launch at each location in a specific area, which is used to guide the effective allocation of resources; First, the calculated azimuth correction, elevation compensation coefficient and energy gradient parameter are input into the preset dynamic strategy fuser. The dynamic strategy fuser first inputs the azimuth correction and elevation compensation coefficient into the geometric parameter solution unit, and generates the azimuth dynamic compensation and elevation attenuation factor based on the obstacle shielding effect in the three-dimensional terrain data. Next, the energy gradient parameter, the azimuth dynamic compensation and the elevation attenuation factor are input into the nonlinear optimization model, and the launch timing optimization sequence is generated by considering the constraints such as the instantaneous acceleration of the UAV motion trajectory, the physical response delay of the ground interception equipment and the upper limit of the dynamic allocation of the energy gradient parameter. In addition, a spatial density distribution matrix is ​​constructed based on the azimuth dynamic compensation and the elevation attenuation factor to determine the optimal energy distribution in each area. Finally, the launch timing optimization sequence and the spatial density distribution matrix are input into the preset dynamic strategy fuser, and a dynamic interception strategy including the launch timing optimization sequence and the spatial density distribution matrix is ​​generated through a multi-objective optimization algorithm to achieve efficient and accurate UAV countermeasures; For example, continuing the previous airport security protection case, after successfully calculating the azimuth correction, elevation compensation coefficient and energy gradient parameters, these parameters are input into the dynamic strategy fusion. The dynamic strategy fusion first uses the geometric parameter solving unit to adjust the azimuth dynamic compensation of the ground interception equipment according to the obstacle shielding effect in the three-dimensional terrain data, and adjusts the elevation attenuation factor based on the atmospheric disturbance compensation parameter. Subsequently, the nonlinear optimization model combines the energy gradient parameter, azimuth dynamic compensation and elevation attenuation factor, taking into account factors such as the instantaneous acceleration of the UAV motion trajectory and the physical response delay of the ground interception equipment, and generates a launch timing optimization sequence. At the same time, the system constructs a spatial density distribution matrix to clarify the optimal energy density distribution at each position in a specific area. Finally, the dynamic strategy fusion generates a dynamic interception strategy containing the launch timing optimization sequence and the spatial density distribution matrix. This strategy not only improves the response speed and accuracy of the ground interception equipment, but also optimizes the energy distribution, so that it can effectively respond to UAV threats even in complex environments and ensure the safe operation of the airport.

[0025] In order to solve the problems of low positioning accuracy, slow response speed and unreasonable energy allocation in traditional drone countermeasure methods, in some embodiments, according to step 105, the azimuth correction value, elevation compensation coefficient and energy gradient parameter are input into a preset dynamic strategy fusion device to generate a dynamic interception strategy. The process includes: First, the azimuth correction and elevation compensation coefficients are input into the geometric parameter solving unit, and the azimuth dynamic compensation and elevation attenuation factors are generated based on the obstacle shielding effect in the three-dimensional terrain data; then, the energy gradient parameters, the azimuth dynamic compensation and the elevation attenuation factors are input into the nonlinear optimization model, and the launch timing optimization sequence is generated by considering the constraints such as the instantaneous acceleration of the UAV motion trajectory, the physical response delay of the ground interception equipment and the upper limit of the dynamic allocation of the energy gradient parameters; next, the spatial density distribution matrix is ​​constructed based on the azimuth dynamic compensation and the elevation attenuation factor; finally, the launch timing optimization sequence and the spatial density distribution matrix are input into the preset dynamic strategy fusion, and the dynamic interception strategy is generated through the multi-objective optimization algorithm; In this embodiment, the azimuth dynamic compensation refers to the horizontal rotation angle of the ground interception equipment adjusted to overcome the terrain shielding effect to ensure accurate aiming at the target; the elevation attenuation factor is the vertical pointing angle adjusted based on the influence of factors such as atmospheric turbulence on signal propagation to improve interception accuracy; the nonlinear optimization model is used to deal with complex constraints, such as the instantaneous acceleration of the UAV, the response delay of the ground equipment, etc., to generate the optimal launch timing; the spatial density distribution matrix describes the optimal energy density distribution at each position in a specific area, guiding the effective allocation of resources; In the embodiment of the present application, the geometric parameter solving unit is first used to analyze the influence of terrain shielding effect and atmospheric disturbance on signal propagation, and the azimuth dynamic compensation and elevation attenuation factor are generated. Then, these parameters are fed into the nonlinear optimization model together with the energy gradient parameters, and the optimal launch timing optimization sequence is calculated in combination with the real-time motion data of the UAV. At the same time, the system constructs a spatial density distribution matrix based on the azimuth dynamic compensation and the elevation attenuation factor to determine the optimal energy density at each position. Finally, all key parameters are integrated into the dynamic strategy fuser, and the final dynamic interception strategy is generated through a multi-objective optimization algorithm to achieve efficient and accurate UAV countermeasures.

[0026] For example, in an actual application of airport security protection, when the distributed sensing array detects an unauthorized drone crossing a virtual barrier, the system immediately starts the collaborative interference module and generates a preliminary interference signal weight. As the drone continues to change its flight path, the system continues to receive its electromagnetic scattering characteristics and dynamically adjusts the polarization direction distribution and power density distribution of the interference signal. Based on the updated track modeling input parameters and the change in the drone's velocity vector, the system generates an accurate three-dimensional dynamic track model and calculates the azimuth correction, elevation compensation coefficient and energy gradient parameters. Subsequently, these parameters are input into the dynamic strategy fusion, and after detailed geometric parameter solution and nonlinear optimization processing, a dynamic interception strategy containing the launch timing optimization sequence and the spatial density distribution matrix is ​​generated. This strategy not only improves the response speed and accuracy of the ground interception equipment, but also optimizes the energy distribution, so that even in complex environments, it can effectively respond to drone threats and ensure the safe operation of the airport.

[0027] In order to further improve the accuracy and response speed of drone countermeasures, according to the previous embodiment, the energy gradient parameter, the azimuth dynamic compensation amount and the elevation attenuation factor are input into the nonlinear optimization model, and the emission timing optimization sequence is generated by solving the constraint conditions. The process includes: Firstly, the energy gradient parameter and the azimuth dynamic compensation are dynamically coupled to generate the energy azimuth coupling coefficient, and the elevation attenuation factor is convolved with the instantaneous acceleration of the UAV motion trajectory in the time domain to generate the elevation dynamic response parameter; then, the energy azimuth coupling coefficient and the elevation dynamic response parameter are input into the multi-constraint optimization framework, and the equipment response time baseline is generated based on the physical response delay parameter of the ground interception equipment in each time window, and the energy allocation window function is calculated in combination with the dynamic allocation upper limit of the energy gradient parameter; the dynamic weight allocation vector is constructed by the vector modulus of the instantaneous acceleration of the UAV and the equipment response time baseline, which is used to adjust the time phase offset of the energy allocation window function; finally, the dynamic weight allocation vector and the elevation dynamic response parameter are nonlinearly superimposed to generate the initial timing distribution diagram, and the peak segment exceeding the energy allocation upper limit is truncated and compensated by the constraint conflict resolution algorithm to generate the final launch timing optimization sequence; In this embodiment, the energy azimuth coupling coefficient is a comprehensive index obtained by coupling the energy gradient parameter with the azimuth dynamic compensation amount, which is used to describe the relationship between the energy distribution of the interference signal and the target direction; the elevation dynamic response parameter is generated by performing a time-domain convolution operation on the elevation attenuation factor and the time-varying instantaneous acceleration of the UAV, reflecting the dynamic changes in the vertical direction during the flight of the UAV; the multi-constraint optimization framework is a mathematical model that can handle multiple complex constraints (such as the three-dimensional curvature characteristics of the UAV motion trajectory, the response delay of the ground equipment, etc.), and is used to generate the optimal launch timing; the energy allocation window function represents the energy range that the ground interception equipment should allocate within a specific time to ensure the effective use of resources; the dynamic weight allocation vector is used to adjust the time phase offset of the energy allocation window function to adapt to the real-time motion state of the UAV; In the embodiment of the present application, the energy gradient parameter and the azimuth dynamic compensation are first coupled to generate an energy azimuth coupling coefficient, and the elevation attenuation factor is convolved with the instantaneous acceleration of the UAV in the time domain to generate the elevation dynamic response parameter. Then, these parameters are sent to the multi-constraint optimization framework, which divides the time window according to the three-dimensional curvature characteristics of the UAV's motion trajectory, and performs detailed optimization calculations in each window. Specifically, the system generates a device response time baseline based on the physical response delay parameters of the ground interception equipment, and calculates the energy allocation window function in combination with the dynamic allocation upper limit of the energy gradient parameter. The dynamic weight allocation vector is constructed by the vector modulus of the instantaneous acceleration of the UAV and the device response time baseline, and the time phase offset of the energy allocation window function is adjusted. Subsequently, the system performs a nonlinear superposition operation on the dynamic weight allocation vector and the elevation dynamic response parameter to generate an initial timing distribution diagram, and truncates and compensates the peak segment that exceeds the energy allocation upper limit through the constraint conflict resolution algorithm, and finally generates a launch timing optimization sequence; For example, in the actual application of airport security protection, when an unauthorized drone is detected crossing a virtual barrier, the system starts the cooperative interference module and generates a preliminary interference signal weight. As the drone continuously changes its flight path, the system continuously receives its electromagnetic scattering characteristics and dynamically corrects the polarization direction distribution and power density distribution of the interference signal. Based on the updated track modeling input parameters and the change of the drone's velocity vector, the system generates an accurate three-dimensional dynamic track model and calculates the azimuth correction, elevation compensation coefficient and energy gradient parameter. Next, the system couples the energy gradient parameter with the azimuth dynamic compensation to generate the energy azimuth coupling coefficient, and convolves the elevation attenuation factor with the drone's instantaneous acceleration in the time domain to generate the elevation dynamic response parameter. These parameters are fed into the multi-constraint optimization framework. The system divides the time window according to the three-dimensional curvature characteristics of the drone, and calculates the device response time baseline and energy allocation window function in detail in each window. The time phase offset of the energy allocation window function is adjusted by the dynamic weight allocation vector, and the initial timing distribution diagram is generated. Finally, the system uses the constraint conflict resolution algorithm to truncate and compensate the peak segment that exceeds the upper limit of the energy allocation to generate the final emission timing optimization sequence. This strategy not only improves the response speed and accuracy of ground interception equipment, but also optimizes energy distribution, making it possible to effectively respond to drone threats even in complex environments and ensure the safe operation of the airport.

[0028] In order to further improve the interference effect and accuracy of the UAV, as another embodiment, according to step 103, the polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics of the UAV real-time feedback to obtain the corrected polarization direction distribution and power density distribution, and the corrected polarization direction distribution and power density distribution are used as track modeling input parameters. The specific steps include: First, the electromagnetic scattering characteristics fed back by the UAV in real time are input into the polarization characteristic solution unit, from which the main polarization component and cross-polarization component of the scattered signal are extracted, and the initial solution of the polarization direction distribution is generated; then, the polarization matching degree is calculated based on the initial solution and the initial interference signal weight to generate the polarization mismatch factor, and it is input into the power density correction model; in the power density correction model, the power density baseline value is determined by analyzing the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and the power density dynamic correction coefficient is generated by combining the polarization mismatch factor and the instantaneous acceleration of the UAV motion trajectory; next, the power density dynamic correction coefficient is jointly optimized with the initial solution of the polarization direction distribution to generate the final corrected polarization direction distribution and power density distribution; finally, these correction results are used as track modeling input parameters and synchronized to the heading offset solution module of the three-dimensional dynamic track model; In this embodiment, the polarization characteristic solving unit is used to separate the main polarization component and the cross-polarization component from the received electromagnetic scattering signal to provide accurate polarization information; the polarization mismatch factor is a quantitative indicator to measure the difference between the expected polarization direction and the actually received electromagnetic scattering signal, which helps to evaluate the effectiveness of the current interference strategy; the power density correction model uses the abnormal frequency band intensity distribution, polarization mismatch factor and the motion state of the drone in the electromagnetic spectrum characteristics to adjust the power density of the interference signal to ensure the maximization of the interference efficiency; the power density dynamic correction coefficient is a key parameter output by the model, indicating how to adjust the power level of the interference signal according to the current environmental conditions; In the embodiment of the present application, the electromagnetic scattering characteristics of the UAV real-time feedback are first analyzed by the polarization characteristic solving unit, and the main polarization component and the cross polarization component of the scattered signal are extracted to form a preliminary estimate of the polarization direction distribution. Then, the polarization matching degree is calculated using this preliminary estimate and the initially set interference signal weight, so as to determine the polarization mismatch factor and send it to the power density correction model. In this model, the basic reference value of the power density is determined by checking the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and the influence of the polarization mismatch factor and the instantaneous acceleration of the UAV is considered to calculate the power density dynamic correction coefficient. Subsequently, the power density dynamic correction coefficient is combined with the initial solution of the polarization direction distribution by a joint optimization method to generate a corrected polarization direction distribution and power density distribution. Specifically, the polarization direction distribution will be adjusted according to the phase offset of the main polarization component, and the power density distribution is obtained by multiplying the power density reference value and the dynamic correction coefficient. Finally, these corrected parameters are applied to the track modeling process, and the relevant parameters in the three-dimensional dynamic track model, such as the data of the heading offset solving module, are synchronously updated.

[0029] In order to further improve the monitoring accuracy and response speed of drone threats, as another embodiment, according to step 101, a distributed sensing array is deployed in the target area and three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics are acquired in real time. A multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and atmospheric turbulence characteristics, and a frequency band interference parameter set is generated by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics, specifically including: A multi-node distributed sensing array is deployed on the periphery of the target area, and three-dimensional terrain data is collected in real time through lidar scanning and multi-spectral imaging technology, and atmospheric turbulence characteristics and electromagnetic spectrum characteristics are obtained respectively through atmospheric turbulence sensors and spectrum monitoring devices; the three-dimensional terrain data is input into the terrain shielding effect analysis module, and the terrain elevation gradient and obstacle distribution characteristics are extracted to generate a terrain shielding weight matrix; atmospheric disturbance compensation parameters are generated based on the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, and the atmospheric disturbance compensation parameters are spatially superimposed with the terrain shielding weight matrix to generate an initial distribution map of the multi-layer virtual signal barrier boundary; the electromagnetic spectrum characteristics are input into the abnormal frequency band detection unit, and the abnormal energy peak and frequency band width characteristics in the electromagnetic spectrum characteristics are extracted to generate an abnormal frequency band fingerprint set; finally, the abnormal frequency band fingerprint set and the initial distribution map of the multi-layer virtual signal barrier boundary are frequency band space mapped to generate a frequency band interference parameter set, where each frequency band interference parameter includes the center frequency, bandwidth and power allocation ratio of the interference signal; In this embodiment, the multi-node distributed sensing array is a network composed of multiple sensor nodes, which is used to monitor multiple parameters in the environment in real time, such as three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics; the terrain shielding weight matrix is ​​a data set describing the terrain elevation gradient and obstacle distribution characteristics, which is used to evaluate the impact of terrain on signal propagation; the atmospheric disturbance compensation parameter is a set of parameters generated based on atmospheric turbulence characteristics, which is used to compensate for the impact of atmospheric disturbance on signal propagation; the abnormal frequency band fingerprint set is a set of abnormal energy peak and frequency band width characteristics extracted from the electromagnetic spectrum characteristics, which is used to identify potential drone communication frequency bands; the frequency band interference parameter set contains detailed information for each abnormal frequency band, such as center frequency, bandwidth and power allocation ratio, which is used to formulate targeted interference strategies; In the embodiment of the present application, a distributed sensing array consisting of a laser radar, an atmospheric turbulence sensor and a spectrum monitoring station is first deployed outside the target area. These devices collect three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics of the target area in real time. The system inputs the three-dimensional terrain data into the terrain shielding effect analysis module, extracts the terrain elevation gradient and obstacle distribution characteristics from it, and generates a terrain shielding weight matrix. At the same time, the atmospheric disturbance compensation parameters are generated based on the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, and the parameters are spatially superimposed with the terrain shielding weight matrix to generate an initial distribution map of the multi-layer virtual signal barrier boundary. Next, the system inputs the electromagnetic spectrum characteristics into the abnormal frequency band detection unit, extracts the abnormal energy peak and frequency band width characteristics from it, and generates an abnormal frequency band fingerprint set. Finally, through the frequency band space mapping technology, the system associates the abnormal frequency band fingerprint set with the initial distribution map of the multi-layer virtual signal barrier boundary, and generates a frequency band interference parameter set containing the center frequency, bandwidth and power allocation ratio of the interference signal of each frequency band.

[0030] In order to further improve the accurate prediction of the UAV motion trajectory and the response efficiency of the ground interception equipment, as another embodiment, according to step 104, the track modeling input parameters are combined with the real-time collected UAV heading offset and velocity vector change to generate a three-dimensional dynamic track model, and the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment are calculated based on the three-dimensional dynamic track model, specifically including: The corrected polarization direction distribution and power density distribution in the track modeling input parameters are input into the track feature extraction unit to extract the main polarization direction offset and power density change gradient of the UAV motion state; the main polarization direction offset and the real-time collected UAV heading offset are subjected to space vector superposition operation to generate a heading integrated offset vector, and the power density change gradient and the velocity vector change are subjected to time domain convolution operation to generate velocity dynamic response parameters; the heading integrated offset vector and velocity dynamic response parameters are input into the three-dimensional track reconstruction module to generate a three-dimensional dynamic track model based on the instantaneous curvature characteristics of the UAV motion trajectory, which includes the track curvature distribution, instantaneous acceleration distribution, and the velocity dynamic response parameters. The track curvature distribution in the three-dimensional dynamic track model is input into the azimuth angle solving unit, and the azimuth angle correction is generated in combination with the terrain shielding weight matrix, wherein the azimuth angle correction includes the terrain shielding compensation value and the curvature dynamic offset; the instantaneous acceleration distribution and the energy attenuation characteristics are input into the elevation angle compensation solver to generate the elevation angle compensation coefficient, wherein the elevation angle compensation coefficient includes the acceleration dynamic response factor and the energy attenuation compensation value; finally, the track curvature distribution, the instantaneous acceleration distribution and the energy attenuation characteristics are input into the energy gradient solving module to generate the energy gradient parameter, which includes the curvature energy distribution weight, the acceleration energy compensation factor and the attenuation energy correction value; In this embodiment, the track feature extraction unit is an algorithm or device for extracting the UAV motion state information from the corrected polarization direction distribution and power density distribution, and can identify the main polarization direction offset and the power density change gradient; the heading integrated offset vector is the result obtained by spatial vector superposition of the actual heading offset of the UAV and the main polarization direction offset, and is used to describe the actual flight path of the UAV; the speed dynamic response parameter is data generated by performing a time domain convolution operation on the power density change gradient and the time-varying speed vector change, reflecting the speed change trend of the UAV; the three-dimensional track reconstruction module is a mathematical model, which uses the heading integrated offset vector and speed dynamic response parameters of the UAV to generate a three-dimensional dynamic track model; the azimuth angle solver is used to calculate the optimal horizontal rotation angle of the ground interception equipment in combination with the terrain shielding weight matrix; the elevation angle compensation solver is used to adjust the vertical pointing angle according to the instantaneous acceleration distribution and energy attenuation characteristics; the energy gradient solver is responsible for determining the optimal energy allocation scheme according to the track curvature distribution, instantaneous acceleration distribution and energy attenuation characteristics; In the embodiment of the present application, the corrected polarization direction distribution and power density distribution are first input into the track feature extraction unit, from which the main polarization direction offset and power density change gradient of the UAV motion state are extracted. Then, the main polarization direction offset is subjected to a spatial vector superposition operation with the real-time collected UAV heading offset to generate a heading comprehensive offset vector, and the power density change gradient is subjected to a time domain convolution operation with the velocity vector change to generate a velocity dynamic response parameter. Then, these data are input into a three-dimensional track reconstruction module, and a three-dimensional dynamic track model is generated based on the instantaneous curvature characteristics of the UAV motion trajectory. The model describes in detail the track curvature distribution, instantaneous acceleration distribution, and energy attenuation characteristics of the UAV. Next, the system inputs the track curvature distribution in the three-dimensional dynamic track model into the azimuth solution unit, and generates an azimuth correction value in combination with the terrain shielding weight matrix to ensure that the ground interception equipment can accurately aim at the target. At the same time, the instantaneous acceleration distribution and energy attenuation characteristics are input into the elevation compensation solver to generate an elevation compensation coefficient to optimize the vertical pointing angle. Finally, the track curvature distribution, instantaneous acceleration distribution and energy attenuation characteristics are input into the energy gradient solution module to generate energy gradient parameters, providing the best energy distribution solution for ground interception equipment.

[0031] In order to further improve the response speed and interference accuracy to the UAV crossing the virtual signal barrier boundary, as another embodiment, according to step 102, when the UAV is detected crossing the virtual signal barrier boundary, the coordinated interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set. Specifically, it includes: First, the hierarchical index of the virtual signal barrier boundary crossed by the UAV and the associated frequency band interference parameter set are obtained, and the center frequency and bandwidth parameters in the frequency band interference parameters corresponding to the virtual signal barrier boundary are extracted based on the hierarchical index; then the center frequency and bandwidth parameters are input into the frequency band matching calculation module to generate a frequency band coverage matching matrix including the weight allocation priority and spectrum overlap coefficient of the interference signals of each frequency band; then, the interference signal baseband parameters are generated based on the spectrum overlap coefficient and the motion speed vector of the UAV when crossing the barrier, where the interference signal baseband parameters include pulse repetition frequency, modulation depth and phase jump interval; finally, the frequency band coverage matching matrix and the interference signal baseband parameters are input into the multi-objective weight allocation algorithm to generate the initial interference signal weight including the power ratio distribution of the interference signals of each frequency band and the time-frequency resource allocation matrix; In this embodiment, the hierarchical index refers to the identifier of the specific virtual signal barrier boundary that the drone crosses, which is used to determine the corresponding frequency band interference parameter set; the frequency band interference parameter set contains detailed information for each abnormal frequency band, such as center frequency, bandwidth and power allocation ratio, which is used to formulate targeted interference strategies; the frequency band coverage matching matrix is ​​a data structure that describes the weight allocation priority and spectrum overlap between interference signals in different frequency bands, helping to optimize the selection of interference signals; the interference signal baseband parameters are a set of key parameters, including pulse repetition frequency, modulation depth and phase jump interval, which determine the basic characteristics of the interference signal; the multi-objective weight allocation algorithm is a mathematical model that comprehensively considers multiple factors (such as spectrum overlap coefficient, drone speed, etc.) to generate the optimal interference signal weight; In the embodiment of the present application, the system first detects that the drone crosses the boundary of the virtual signal barrier, and obtains the hierarchical index of the drone and the corresponding set of frequency band interference parameters. Based on the hierarchical index, the system extracts the center frequency and bandwidth parameters in the relevant frequency band interference parameters. These parameters are then sent to the frequency band matching calculation module to generate a frequency band coverage matching matrix, which contains the weight allocation priority and spectrum overlap coefficient of the interference signals in each frequency band. Next, the system uses the spectrum overlap coefficient and the motion speed vector of the drone when crossing the barrier to generate the interference signal baseband parameters, including pulse repetition frequency, modulation depth, and phase jump interval. These baseband parameters reflect the basic characteristics and adaptability of the interference signal. Finally, the frequency band coverage matching matrix and the interference signal baseband parameters are input into the multi-objective weight allocation algorithm to generate the initial interference signal weight containing the power ratio distribution of the interference signals in each frequency band and the time-frequency resource allocation matrix. It ensures that the interference signal can accurately match the communication band of the drone and maximize the interference effect.

[0032] Figure 2 A schematic diagram of the structure of a multi-means integrated drone countermeasure system is provided for an embodiment of the present application, such as Figure 2 As shown, the system includes: A deployment module 21 is used to deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics in real time, build a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and associate the abnormal frequency bands extracted from the electromagnetic spectrum characteristics to generate a frequency band interference parameter set; A trigger module 22, configured to trigger the coordinated interference module to generate an initial interference signal weight matching the frequency band interference parameter set when a drone is detected crossing the boundary of the virtual signal barrier; A correction module 23 is used to dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as track modeling input parameters; The calculation module 24 is used to combine the input parameters of the track modeling with the heading offset and velocity vector change of the UAV collected in real time to generate a three-dimensional dynamic track model, and calculate the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment based on the three-dimensional dynamic track model; The generation module 25 is used to input the azimuth correction value, the elevation compensation coefficient and the energy gradient parameter into a preset dynamic strategy fuser to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a transmission timing optimization sequence and a spatial density distribution matrix.

[0033] Figure 2 The multi-means integrated drone countermeasure system can be implemented Figure 1 The implementation principle and technical effect of the multi-means integrated drone countermeasure method described in the illustrated embodiment will not be repeated. The specific manner in which each module and unit performs operations in the multi-means integrated drone countermeasure system in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0034] In one possible design, Figure 2 A multi-means fusion drone countermeasure system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0035] The processing component 32 is used to deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics in real time, construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and generate a frequency band interference parameter set by associating abnormal frequency bands extracted from the electromagnetic spectrum characteristics; when it is detected that the drone crosses the virtual signal barrier boundary, the collaborative interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set; the polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics fed back by the drone in real time to obtain the corrected polarization direction distribution and power density distribution, and the corrected polarization direction distribution and power density distribution are used as track modeling input parameters; the track modeling input parameters are combined with the real-time collected heading offset and velocity vector change of the drone to generate a three-dimensional dynamic track model, and the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment are calculated based on the three-dimensional dynamic track model; the azimuth correction, elevation compensation coefficient and energy gradient parameter are input into a preset dynamic strategy fusion to generate a dynamic interception strategy.

[0036] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0037] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0038] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0039] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0040] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0041] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0042] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 A multi-means integrated drone countermeasure method in the illustrated embodiment.

[0043] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0044] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0045] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0046] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A multi-means integrated drone countermeasure method, characterized in that: include: Deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. Construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and associate the abnormal frequency bands extracted from the electromagnetic spectrum characteristics to generate a set of frequency band interference parameters. When a drone is detected crossing the boundary of the virtual signal barrier, the coordinated interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set; Dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as input parameters for track modeling; The input parameters of the track modeling are combined with the real-time collected UAV heading offset and velocity vector changes to generate a three-dimensional dynamic track model, and the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment are calculated based on the three-dimensional dynamic track model; The azimuth correction value, elevation compensation coefficient and energy gradient parameter are input into a preset dynamic strategy fuser to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a transmission timing optimization sequence and a spatial density distribution matrix.

2. The method according to claim 1, characterized in that: The azimuth correction, elevation compensation coefficient and energy gradient parameters are input into the preset dynamic strategy fusion to generate a dynamic interception strategy, including: The azimuth correction value and the elevation compensation coefficient are input into the geometric parameter solving unit, and the azimuth dynamic compensation value and the elevation attenuation factor are generated based on the obstacle shielding effect in the three-dimensional terrain data; The energy gradient parameter, the azimuth dynamic compensation amount and the elevation attenuation factor are input into the nonlinear optimization model, and the launch timing optimization sequence is generated by solving the constraint conditions, wherein the constraint conditions include: the instantaneous acceleration of the UAV motion trajectory, the physical response delay of the ground interception equipment and the upper limit of the dynamic allocation of the energy gradient parameter; Construct a spatial density distribution matrix based on the azimuth dynamic compensation and elevation attenuation factor; The launch timing optimization sequence and the spatial density distribution matrix are input into the preset dynamic strategy fuser, and a dynamic interception strategy is generated through a multi-objective optimization algorithm.

3. The method according to claim 2, characterized in that The energy gradient parameters, azimuth dynamic compensation and elevation attenuation factor are input into the nonlinear optimization model, and the emission timing optimization sequence is generated by solving the constraint conditions, including: The energy gradient parameter is dynamically coupled with the azimuth dynamic compensation to generate the energy azimuth coupling coefficient, and the elevation attenuation factor is convolved with the instantaneous acceleration of the UAV motion trajectory in the time domain to generate the elevation dynamic response parameter; The energy azimuth coupling coefficient and the elevation angle dynamic response parameters are input into a multi-constraint optimization framework, which divides the time window by the three-dimensional curvature characteristics of the UAV motion trajectory and performs the following processing in each time window: Generate a device response time baseline based on the physical response delay parameters of the ground interception equipment, and calculate the energy allocation window function in combination with the dynamic allocation upper limit of the energy gradient parameter; A dynamic weight allocation vector is constructed by using the vector modulus of the instantaneous acceleration of the drone and the device response time baseline, wherein the dynamic weight allocation vector is used to adjust the time phase offset of the energy allocation window function; The dynamic weight allocation vector and the elevation angle dynamic response parameter are nonlinearly superimposed to generate the initial timing distribution diagram, and the peak segment exceeding the upper limit of energy allocation in the initial timing distribution diagram is truncated and compensated by the constraint conflict resolution algorithm to generate the emission timing optimization sequence.

4. The method according to claim 1, characterized in that: The polarization direction distribution and power density distribution of the initial interference signal weight are dynamically corrected in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, and the corrected polarization direction distribution and power density distribution are obtained. The corrected polarization direction distribution and power density distribution are used as input parameters for track modeling, including: The electromagnetic scattering characteristics fed back by the UAV in real time are input into the polarization characteristic solving unit to extract the main polarization component and cross-polarization component of the scattered signal and generate the initial solution of polarization direction distribution; Calculating polarization matching based on the initial solution of polarization direction distribution and the initial interference signal weight to generate a polarization mismatch factor, and inputting the polarization mismatch factor into a power density correction model; In the power density correction model, the power density reference value is generated by the abnormal frequency band intensity distribution in the electromagnetic spectrum characteristics, and the power density dynamic correction coefficient is generated by combining the polarization mismatch factor and the instantaneous acceleration of the UAV motion trajectory; The dynamic correction coefficient of power density and the initial solution of polarization direction distribution are jointly optimized to generate the corrected polarization direction distribution and power density distribution, wherein the polarization direction distribution is dynamically compensated by the phase offset of the main polarization component, and the power density distribution is generated by multiplying the power density reference value by the dynamic correction coefficient; The corrected polarization direction distribution and power density distribution are used as input parameters for track modeling, and the polarization mismatch factor and power density dynamic correction coefficient are synchronized to the heading offset solution module of the three-dimensional dynamic track model.

5. The method according to claim 1, characterized in that Deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics and electromagnetic spectrum characteristics in real time. Construct a multi-layer virtual signal barrier boundary based on the three-dimensional terrain data and atmospheric turbulence characteristics, and associate the abnormal frequency bands extracted from the electromagnetic spectrum characteristics to generate a set of frequency band interference parameters, including: Deploy a multi-node distributed sensing array outside the target area to collect three-dimensional terrain data in real time through lidar scanning and multi-spectral imaging technology, and obtain atmospheric turbulence characteristics and electromagnetic spectrum characteristics through atmospheric turbulence sensors and spectrum monitoring devices respectively; Input the three-dimensional terrain data into the terrain shielding effect analysis module, extract the terrain elevation gradient and obstacle distribution characteristics, and generate the terrain shielding weight matrix; Based on the turbulence intensity distribution and wind speed gradient in the atmospheric turbulence characteristics, the atmospheric disturbance compensation parameters are generated, and the atmospheric disturbance compensation parameters are spatially superimposed with the terrain shielding weight matrix to generate the initial distribution map of the multi-layer virtual signal barrier boundary; Input the electromagnetic spectrum features into the abnormal frequency band detection unit, extract the abnormal energy peak and frequency band width features in the electromagnetic spectrum features, and generate an abnormal frequency band fingerprint set; The abnormal frequency band fingerprint set and the initial distribution map of the multi-layer virtual signal barrier boundary are mapped in the frequency band space to generate a frequency band interference parameter set, wherein each frequency band interference parameter in the frequency band interference parameter set includes: the center frequency, bandwidth and power allocation ratio of the interference signal.

6. The method according to claim 1, characterized in that The input parameters of the track modeling are combined with the real-time collected heading offset and velocity vector changes of the UAV to generate a three-dimensional dynamic track model. Based on the three-dimensional dynamic track model, the azimuth correction, elevation compensation coefficient and energy gradient parameters of the ground interception equipment are calculated, including: Input the corrected polarization direction distribution and power density distribution in the track modeling input parameters into the track feature extraction unit to extract the main polarization direction offset and power density change gradient of the UAV motion state; The main polarization direction offset is combined with the real-time collected UAV heading offset to generate a heading integrated offset vector, and the power density change gradient is combined with the velocity vector change to generate a velocity dynamic response parameter. Inputting the heading integrated offset vector and the velocity dynamic response parameters into the three-dimensional track reconstruction module, generating a three-dimensional dynamic track model based on the instantaneous curvature characteristics of the UAV motion track, wherein the three-dimensional dynamic track model includes: track curvature distribution, instantaneous acceleration distribution and energy attenuation characteristics; Inputting the track curvature distribution in the three-dimensional dynamic track model into the azimuth angle solving unit, and generating the azimuth angle correction value in combination with the terrain shielding weight matrix, wherein the azimuth angle correction value includes: terrain shielding compensation value and curvature dynamic offset; Inputting the instantaneous acceleration distribution and the energy attenuation characteristics into the elevation angle compensation solver to generate an elevation angle compensation coefficient, wherein the elevation angle compensation coefficient includes: an acceleration dynamic response factor and an energy attenuation compensation value; The track curvature distribution, instantaneous acceleration distribution and energy attenuation characteristics are input into the energy gradient solution module to generate energy gradient parameters, wherein the energy gradient parameters include: curvature energy distribution weight, acceleration energy compensation factor and attenuation energy correction value.

7. The method according to claim 1, characterized in that When a drone is detected crossing the boundary of the virtual signal barrier, the coordinated interference module is triggered to generate an initial interference signal weight matching the frequency band interference parameter set, including: Obtain the hierarchical index of the virtual signal barrier boundary traversed by the UAV and the associated frequency band interference parameter set, and extract the center frequency and bandwidth parameters from the frequency band interference parameters corresponding to the virtual signal barrier boundary based on the hierarchical index; Input the center frequency and bandwidth parameters into the frequency band matching calculation module to generate a frequency band coverage matching matrix, wherein the frequency band coverage matching matrix includes the weight allocation priority and spectrum overlap coefficient of the interference signal of each frequency band; Generate interference signal baseband parameters based on the spectrum overlap coefficient and the motion speed vector of the UAV when it passes through the barrier, wherein the interference signal baseband parameters include: pulse repetition frequency, modulation depth and phase jump interval; The frequency band coverage matching matrix and the interference signal baseband parameters are input into a multi-objective weight allocation algorithm to generate an initial interference signal weight, wherein the initial interference signal weight includes the power ratio distribution of the interference signal in each frequency band and the time-frequency resource allocation matrix.

8. A multi-means integrated drone countermeasure system, characterized in that: include: A deployment module is used to deploy a distributed sensing array in the target area and obtain three-dimensional terrain data, atmospheric turbulence characteristics, and electromagnetic spectrum characteristics in real time. A multi-layer virtual signal barrier boundary is constructed based on the three-dimensional terrain data and atmospheric turbulence characteristics, and a frequency band interference parameter set is generated by associating the abnormal frequency bands extracted from the electromagnetic spectrum characteristics. A trigger module, configured to trigger the coordinated interference module to generate an initial interference signal weight matching the frequency band interference parameter set when a drone is detected crossing the boundary of the virtual signal barrier; A correction module is used to dynamically correct the polarization direction distribution and power density distribution of the initial interference signal weight in combination with the electromagnetic scattering characteristics fed back by the UAV in real time, obtain the corrected polarization direction distribution and power density distribution, and use the corrected polarization direction distribution and power density distribution as track modeling input parameters; A calculation module is used to combine the input parameters of the track modeling with the heading offset and velocity vector change of the UAV collected in real time to generate a three-dimensional dynamic track model, and calculate the azimuth correction, elevation compensation coefficient and energy gradient parameter of the ground interception equipment based on the three-dimensional dynamic track model; A generation module is used to input the azimuth correction value, elevation compensation coefficient and energy gradient parameter into a preset dynamic strategy fuser to generate a dynamic interception strategy, wherein the dynamic interception strategy includes: a launch timing optimization sequence and a spatial density distribution matrix.

9. A computing device, characterized in that It comprises a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a multi-means integrated drone countermeasure method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a multi-means integrated drone countermeasure method as described in any one of claims 1 to 7 is implemented.

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