Unmanned aerial vehicle countering plan generation method and system based on artificial intelligence technology

Abstract

The invention provides an unmanned aerial vehicle countering plan generation method and system based on an artificial intelligence technology, and the method comprises the steps: analyzing the dynamic monitoring data flow of a target unmanned aerial vehicle, recognizing the flight path characteristics and electromagnetic signal frequency spectrum of the target unmanned aerial vehicle, and constructing a three-dimensional space threat situation model to evaluate the threat level. Decomposing behavior patterns and attack intention parameters of a target unmanned aerial vehicle based on threat levels, generating a dynamic decision factor set in combination with geo-fence constraint conditions and environmental interference factors, generating a plurality of candidate countering plans by matching countering means basic plans in a plan knowledge graph, and constructing a plan logic tree; a multi-dimensional evaluation parameter set is generated based on a three-dimensional space threat situation model, a dynamic decision factor set and a countering efficiency index, collaborative optimization processing is performed on a plan logic tree, an optimal countering plan is generated and converted into an executable instruction set, the executable instruction set is synchronized to a countering device for execution, and the real-time performance and scene adaptability of the unmanned aerial vehicle countering plan are improved.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of drone technology, and in particular to a method and system for generating a drone countermeasure plan based on artificial intelligence technology. Background Art

[0002] With the rapid development of drone technology, drones are increasingly used in civil and military fields. However, illegal or unauthorized drone activities pose a serious threat to public safety, privacy protection and the security of critical infrastructure. Especially in sensitive areas such as large public events, airports and military facilities, how to effectively monitor and counter these potential threats has become an urgent technical need; Existing drone countermeasures mainly rely on a single type of sensor, such as radar and radio frequency detectors, to monitor and identify drones. Once an abnormal flying object is detected, the data obtained is manually analyzed to assess the threat level and formulate corresponding countermeasures. In addition, some advanced systems attempt to combine geo-fencing technology to restrict drones from entering specific airspaces. However, most of these solutions lack the ability to deeply integrate multi-source heterogeneous sensor data, making it difficult to build an accurate three-dimensional spatial threat situation model, and the basic plans for countermeasures are usually static presets that cannot be dynamically adjusted according to real-time changing environmental factors, resulting in limited countermeasure effects. These problems together lead to the slow response of existing countermeasure systems and inaccurate response strategies, which may not be able to effectively prevent potential threats posed by drones. Summary of the invention

[0003] The embodiments of the present application provide a method and system for generating a drone countermeasure plan based on artificial intelligence technology, so as to solve the problem that the prior art lacks the ability to deeply integrate multi-source heterogeneous sensor data and cannot dynamically adjust according to real-time changing environmental factors, resulting in limited countermeasure effects.

[0004] In a first aspect, an embodiment of the present application provides a method for generating a drone countermeasure plan based on artificial intelligence technology, comprising: Analyze the dynamic monitoring data stream obtained by the target UAV, identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and build a three-dimensional space threat situation model in combination with multi-source heterogeneous sensor fusion technology. Use the three-dimensional space threat situation model to evaluate the threat level of the target UAV and generate threat level assessment results; Based on the threat level assessment results, the behavior patterns and attack intention parameters of the target drone are decomposed, and a dynamic decision factor set is generated by combining geographic fence constraints and environmental interference factors. Generate multiple candidate countermeasure plans by matching the basic countermeasure plans preset in the plan knowledge graph through a dynamic decision factor set, and construct a plan logic tree based on the multiple candidate countermeasure plans; Generate a multi-dimensional evaluation parameter set based on the three-dimensional threat situation model, the dynamic decision factor set, and the countermeasure effectiveness indicators preset in the plan knowledge graph; The plan logic tree is collaboratively optimized based on a multi-dimensional evaluation parameter set to generate an optimal countermeasure plan, which is then converted into an executable instruction set and synchronized to the countermeasure device in the target area.

[0005] Optionally, the plan logic tree is collaboratively optimized based on the multi-dimensional evaluation parameter set to generate an optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area, including: Extract trajectory prediction deviation and electromagnetic suppression priority from the three-dimensional space threat situation model, extract geo-fence penetration risk value and environmental interference attenuation coefficient from the dynamic decision factor set, and extract response delay threshold and spectrum interference compatibility from the plan knowledge graph; The trajectory prediction deviation and the response delay threshold are combined through the time series compression algorithm to generate dynamic response margin parameters; The peak value of the cross-correlation function between the electromagnetic suppression priority and the spectrum interference compatibility is calculated through the frequency domain overlapping analysis algorithm. When the peak value exceeds the preset interference threshold, the spectrum avoidance coefficient is generated and the countermeasures of the corresponding frequency band are locked; An attenuation compensation model is constructed using the geo-fence penetration risk value and the environmental interference attenuation coefficient to dynamically correct the transmission power weight factor of the countermeasure device. The dynamic response margin parameters, spectrum avoidance coefficient and transmission power weight factor are input into the node activation function of the plan logic tree, and the plan paths in the plan logic tree are pruned with confidence weighted by the Monte Carlo tree search algorithm. The plan paths with confidence higher than the preset dynamic threshold are used to generate the optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area.

[0006] Optionally, the dynamic response margin parameter, the spectrum avoidance coefficient and the transmission power weight factor are input into the node activation function of the plan logic tree, the plan path in the plan logic tree is pruned with confidence weight by the Monte Carlo tree search algorithm, the plan path with a confidence higher than a preset dynamic threshold is used to generate an optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area, including: Generate a time-sensitive search window based on the dynamic response margin parameter, wherein the width of the time-sensitive search window shrinks exponentially as the trajectory prediction deviation increases, and align the time-sensitive search window with the node execution timestamp of the plan logic tree in time and space to screen out candidate paths within the coverage of the time-sensitive search window; Perform frequency band compliance check on candidate paths according to the spectrum avoidance coefficient. When the transmission frequency band of the countermeasure in the candidate path overlaps with the currently locked conflicting frequency band and the dynamic spectrum migration mechanism is not configured, the candidate path is marked as a disabled candidate path and removed from the plan logic tree; The real-time energy consumption estimation of each candidate path is calculated using the transmit power weight factor, and the dynamic fuse threshold is generated by combining the remaining power of the countermeasure device and the environmental interference attenuation coefficient. When the cumulative energy consumption of the candidate path exceeds the fuse threshold, the backtracking mechanism is triggered and a low-power node is reselected. The electromagnetic suppression priority data of the three-dimensional space threat situation model is integrated to weight the confidence of the candidate paths containing the coordinated countermeasure nodes. The coordinated countermeasure nodes must simultaneously meet the timing linkage conditions of radio frequency interference and navigation deception methods. The pruning threshold is dynamically adjusted according to the level of the geo-fence penetration risk value, and the plan paths with confidence levels higher than the preset dynamic threshold are subjected to conflict detection and redundancy elimination to generate the optimal countermeasure plan.

[0007] Optionally, the dynamic monitoring data stream obtained by the target UAV is parsed to identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and a three-dimensional space threat situation model is constructed in combination with multi-source heterogeneous sensor fusion technology, and the threat level of the target UAV is evaluated using the three-dimensional space threat situation model, and a threat level evaluation result is generated, and the threat level evaluation result is generated, including: Analyze the dynamic monitoring data stream of the target UAV collected synchronously by multi-source sensors, and perform time alignment processing on the position coordinates, velocity vector and electromagnetic spectrum data in the dynamic monitoring data stream to generate the original data set; Based on the position coordinates and velocity vector in the original data set, the heading angle change rate and acceleration fluctuation characteristics of the target UAV are calculated, and the trajectory prediction deviation is generated by combining the preset trajectory prediction model, and the flight trajectory feature vector is constructed; Extract frequency domain features from the electromagnetic spectrum data in the original data set, obtain the main frequency band energy distribution and signal frequency hopping law of the target UAV, and generate the electromagnetic signal spectrum feature vector; The heading angle change rate, acceleration fluctuation characteristics and trajectory prediction deviation in the flight trajectory feature vector are fused with the main frequency band energy distribution and signal frequency hopping law in the electromagnetic signal spectrum feature vector to generate the threat confidence parameters of the target UAV. A three-dimensional threat situation model is constructed based on threat confidence parameters, and the real-time position coordinates of the target UAV are mapped to the three-dimensional grid space under the constraints of geographic fences. The threat level assessment results are generated by combining the trajectory prediction deviation and signal frequency hopping law.

[0008] Optionally, the behavior pattern and attack intention parameters of the target drone are decomposed based on the threat level assessment result, and a dynamic decision factor set is generated by combining the geographic fence constraint and the environmental interference factor, including: Based on the trajectory prediction deviation and signal frequency hopping rule in the threat level assessment result, the behavior mode category of the target UAV is identified, wherein the behavior mode category includes: reconnaissance mode, interference mode and attack mode; Extract the attack intention parameters of the target UAV according to the behavior pattern category, where the reconnaissance mode corresponds to the target area scanning frequency, the interference mode corresponds to the electromagnetic suppression intensity of the countermeasure device, and the attack mode corresponds to the dive angle and speed change rate of the target UAV; Spatially match the attack intention parameters with the geo-fence constraints, calculate the minimum distance between the target drone’s current position and the geo-fence boundary, and generate a spatial threat weight in combination with environmental interference factors; The attack intention parameters are dynamically modified based on the spatial threat weight to obtain the modified attack intention parameters. In the reconnaissance mode, the target area scanning frequency increases as the distance decreases, the electromagnetic suppression intensity in the interference mode decreases as the environmental interference factor increases, and the dive angle and speed change rate in the attack mode increase as the distance decreases. The modified attack intention parameters are combined with the behavior pattern categories to generate a dynamic decision factor set including spatial threat weights, modified attack intention parameters and behavior pattern categories.

[0009] Optionally, it is characterized in that a plurality of candidate countermeasure plans are generated by matching the basic countermeasure plans preset in the plan knowledge graph through a dynamic decision factor set, and a plan logic tree is constructed according to the plurality of candidate countermeasure plans, including: Based on the behavioral pattern categories in the dynamic decision factor set, the matching basic countermeasure plan is extracted from the plan knowledge graph. Among them, the reconnaissance mode corresponds to the spectrum detection and interference plan, the interference mode corresponds to the frequency band suppression and navigation deception plan, and the attack mode corresponds to the physical interception and directional strike plan; According to the modified attack intention parameters in the dynamic decision factor set, the basic countermeasure plan extracted from the plan knowledge graph is adjusted to obtain the basic countermeasure plan after parameter adaptation, in which the scanning frequency of the spectrum detection plan is synchronized with the scanning frequency of the target area, the transmission power of the frequency band suppression plan is matched with the electromagnetic suppression intensity, and the triggering timing of the physical interception plan is related to the dive angle and speed change rate; Based on the spatial threat weights in the dynamic decision factor set, the basic countermeasure plans after parameter adaptation are prioritized to generate a candidate countermeasure plan set including execution timing, intensity parameters and trigger conditions; The plan nodes in the candidate countermeasure plan set are constructed into a plan logic tree according to the execution sequence and spatial position relationship, where the connecting edges between the plan nodes represent the switching conditions of the countermeasure measures, where the switching conditions include: electromagnetic suppression intensity threshold, navigation deception success rate and physical interception feasibility.

[0010] Optionally, based on the three-dimensional space threat situation model, the dynamic decision factor set, and the countermeasure effectiveness indicators preset in the plan knowledge graph, a multi-dimensional evaluation parameter set is generated, including: The real-time position coordinates, trajectory prediction deviation and electromagnetic suppression priority of the target UAV are extracted from the three-dimensional space threat situation model, and the space threat assessment parameters are generated by combining the countermeasure effectiveness indicators in the plan knowledge graph. The space threat assessment parameters include: target distance attenuation coefficient, trajectory coverage completeness and electromagnetic suppression effectiveness value; Calculate the response time parameter and intensity matching parameter of the countermeasure from the modified attack intention parameter in the dynamic decision factor set, where the response time parameter is calculated by the speed change rate of the target UAV and the deployment position of the countermeasure device, and the intensity matching parameter is calculated by the electromagnetic suppression intensity and the transmission power range of the countermeasure; According to the countermeasure effectiveness indicators in the plan knowledge graph, extract the spectrum compatibility parameters and energy efficiency parameters of the countermeasures. The spectrum compatibility parameters are calculated by the overlapping area between the transmission frequency band of the countermeasures and the protection frequency band, and the energy efficiency parameters are calculated by the energy consumption per unit time of the countermeasures and the remaining power of the countermeasure device. The spatial threat assessment parameters, response time parameters, intensity matching parameters, spectrum compatibility parameters and energy efficiency parameters are normalized to generate a multidimensional assessment parameter set containing weight coefficients, where the weight coefficients are dynamically adjusted through the spatial threat weights in the dynamic decision factor set.

[0011] In a second aspect, the embodiment of the present application provides a UAV countermeasure plan generation system based on artificial intelligence technology, including: The parsing module is used to parse the dynamic monitoring data stream obtained by the target UAV, identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and build a three-dimensional space threat situation model in combination with multi-source heterogeneous sensor fusion technology. The three-dimensional space threat situation model is used to evaluate the threat level of the target UAV and generate a threat level evaluation result; A decomposition module is used to decompose the behavior patterns and attack intention parameters of the target UAV based on the threat level assessment results, and generate a dynamic decision factor set in combination with geographic fence constraints and environmental interference factors; A construction module is used to match the basic countermeasure plans preset in the plan knowledge graph through a dynamic decision factor set, generate multiple candidate countermeasure plans, and construct a plan logic tree according to the multiple candidate countermeasure plans; A generation module is used to generate a multi-dimensional evaluation parameter set based on a three-dimensional space threat situation model, a dynamic decision factor set, and a countermeasure effectiveness index preset in a plan knowledge graph; The synchronization module is used to collaboratively optimize the plan logic tree based on a multi-dimensional evaluation parameter set to generate an optimal countermeasure plan, convert the optimal countermeasure plan into an executable instruction set and synchronize it to the countermeasure device in the target area.

[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 method for generating a drone countermeasure plan based on artificial intelligence technology 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 method for generating a drone countermeasure plan based on artificial intelligence technology as described in any one of the first aspects.

[0014] In an embodiment of the present application, a dynamic monitoring data stream obtained by a target UAV is parsed, the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV are identified, and a three-dimensional space threat situation model is constructed in combination with multi-source heterogeneous sensor fusion technology. The threat level of the target UAV is evaluated using the three-dimensional space threat situation model to generate a threat level evaluation result; based on the threat level evaluation result, the behavior pattern and attack intention parameters of the target UAV are decomposed, and a dynamic decision factor set is generated in combination with geographic fence constraints and environmental interference factors; the dynamic decision factor set is matched with the basic countermeasure plan preset in the plan knowledge graph to generate multiple candidate countermeasure plans, and a plan logic tree is constructed according to the multiple candidate countermeasure plans; based on the three-dimensional space threat situation model, the dynamic decision factor set and the countermeasure effectiveness index preset in the plan knowledge graph, a multidimensional evaluation parameter set is generated; based on the multidimensional evaluation parameter set, the plan logic tree is collaboratively optimized to generate an optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area.

[0015] The technical solution of this application has the following beneficial effects: The above method can effectively improve the accuracy of threat assessment, dynamically adjust the countermeasure strategy to adapt to complex and changeable environmental conditions, and generate the optimal countermeasure plan through optimization processing, thereby greatly enhancing the security protection capability of the target area and reducing the risks brought by potential threats. In addition, this method also improves the automation level and response flexibility of the countermeasure system, reduces the need for manual intervention, and makes the entire countermeasure process more intelligent and efficient.

[0016] Furthermore, by extracting key parameters such as trajectory prediction deviation, electromagnetic suppression priority, and geographic fence penetration risk value from the three-dimensional threat situation model, the dynamic decision factor set, and the plan knowledge graph, and generating dynamic response margin parameters through a time series compression algorithm, the peak value of the cross-correlation function is calculated using a frequency domain overlap analysis algorithm to determine whether to generate a countermeasure to lock the corresponding frequency band with a spectrum avoidance coefficient, and at the same time constructing an attenuation compensation model to dynamically correct the transmission power weight factor of the countermeasure device, these parameters are finally input into the node activation function of the plan logic tree, and confidence-weighted pruning is performed through a Monte Carlo tree search algorithm to generate the optimal countermeasure plan; Not only can it dynamically adjust the countermeasure strategy according to real-time changing environmental conditions, but it can also effectively avoid unnecessary spectrum interference and optimize the transmission power of the countermeasure device, thereby ensuring that the optimal countermeasure plan is generated quickly and accurately in complex environments, greatly improving the system's response flexibility and overall effectiveness, and ensuring the safety of the target area.

[0017] 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

[0018] 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.

[0019] Figure 1 A flowchart of a method for generating a UAV countermeasure plan based on artificial intelligence technology provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of a UAV countermeasure plan generation system based on artificial intelligence technology 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

[0020] 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.

[0021] 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.

[0022] 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.

[0023] Figure 1 A flowchart of a method for generating a drone countermeasure plan based on artificial intelligence technology is provided for an embodiment of the present application. Figure 1 As shown, the method includes: Step 101, parse the dynamic monitoring data stream obtained by the target UAV, identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and build a three-dimensional space threat situation model in combination with multi-source heterogeneous sensor fusion technology, and use the three-dimensional space threat situation model to evaluate the threat level of the target UAV, and generate a threat level evaluation result, including: In this step, dynamic monitoring of data streams refers to the use of various sensors and technical means to collect real-time data generated by drones during flight, including but not limited to flight trajectory and electromagnetic signal spectrum; flight trajectory characteristics refer to the movement path of the drone in space and the characteristics of the drone; electromagnetic signal spectrum refers to the frequency range of radio signals used for communication between the drone and the drone control terminal; multi-source heterogeneous sensor fusion technology is the comprehensive processing of data from different types of sensors (such as radar, camera, radio receiver, etc.); In this step, a series of multi-source heterogeneous sensor networks are first deployed, including but not limited to radar systems, optical cameras, radio spectrum analyzers, etc., to capture the flight trajectory and electromagnetic signal information of the drone. Then, a special data processing algorithm is used to clean and pre-process these raw data to extract the flight trajectory characteristics and electromagnetic signal spectrum of the drone. Then, advanced sensor fusion technology is used to integrate the above information to build an accurate three-dimensional space threat situation model. Finally, based on the three-dimensional space threat situation model, a threat assessment algorithm is used to determine the threat level of the drone and generate a threat level assessment result. For example, in an actual case of security monitoring around an airport, in order to prevent illegal drone intrusions from affecting the normal operation of flights, a multi-source heterogeneous sensor network including high-precision radars, infrared cameras, and radio spectrum monitoring equipment was deployed. When a drone is detected entering the monitoring area, the system immediately begins to collect its flight trajectory and electromagnetic signal data, and processes it through a powerful backend data analysis platform. The machine learning algorithm is used to identify the behavior pattern of the drone, and a three-dimensional spatial threat situation model is built in combination with the geographic information system (GIS) to accurately assess the threat level of the drone. Based on the assessment results, the airport security department can quickly activate the corresponding early warning mechanism and take effective countermeasures to ensure the safety and stability of the airspace, and effectively improve the monitoring and response capabilities to drone threats.

[0024] Step 102, based on the threat level assessment result, the behavior pattern and attack intention parameters of the target drone are decomposed, and a dynamic decision factor set is generated in combination with the geographic fence constraint and the environmental interference factor, including: In this step, geo-fence constraints refer to pre-set electronic boundaries that restrict drones from entering specific areas; environmental interference factors include the impact of external factors such as weather conditions and radio interference on drone operations; and the dynamic decision factor set includes key data extracted from information such as the target drone's behavior pattern, attack intent parameters, geo-fence constraints, and environmental interference factors. These data are used to assess the specific threat situation of drones and provide a basis for formulating corresponding countermeasure plans; In this step, firstly, according to the threat level assessment results provided by the three-dimensional space threat situation model, the behavior analysis algorithm is used to analyze the behavior pattern of the target UAV, such as whether it is in a hovering state, whether there is an abnormal flight path, etc., and the potential attack intention of the target UAV is inferred by combining historical data. Then, the geographic fence constraints (such as airport no-fly zones) and real-time monitored environmental interference factors (such as strong winds or electromagnetic interference) are taken into consideration. By integrating this information, a dynamic decision factor set is established. This set not only includes the behavior pattern and attack intention of the UAV, but also integrates the specific impact of geographic fences and environmental interference, providing a detailed reference for the countermeasures in the subsequent matching plan knowledge graph; For example, when an illegal drone approaching an airport and its flight trajectory characteristics and electromagnetic signal spectrum have been identified, the threat level of the illegal drone is assessed through the three-dimensional spatial threat situation model. Next, the drone's behavior pattern is deeply analyzed based on the threat level assessment results, and it is found that the drone is trying to cross the no-fly zone near the airport, and there are signs that the drone may be carrying some kind of jamming equipment. At the same time, strong radio interference and unfavorable weather conditions are detected. Based on this information, a dynamic decision factor set with a high geographic fence penetration risk value and a large environmental interference attenuation coefficient is generated, and the decision factor set is immediately input into the plan logic tree to quickly match the countermeasure plan that best suits the current situation, ensuring that this potential threat can be responded to in a timely and effective manner, protecting the safety of the airport airspace, and improving the airport's response efficiency to illegal drone activities.

[0025] Step 103, matching the basic countermeasure plans preset in the plan knowledge graph through the dynamic decision factor set, generating multiple candidate countermeasure plans, and constructing a plan logic tree according to the multiple candidate countermeasure plans, including: In this step, the plan knowledge graph is a knowledge base containing a variety of basic plans for preset countermeasures, which stores response strategies designed according to different types of threat scenarios. Each basic plan in the map describes in detail the specific countermeasures and their applicable conditions, aiming to match a specific set of dynamic decision factors to generate an effective countermeasure plan; the basic countermeasure plan refers to a standard response plan pre-set for different types of drone threats. Each basic plan contains detailed execution steps and required resources, such as using radio jammers to suppress signals, deploying physical interception devices, or activating electromagnetic pulse devices, to ensure that potential threats can be effectively responded to; candidate countermeasure plans are multiple potentially applicable countermeasure plans generated after matching the dynamic decision factor set with the plan knowledge map. These plans are preliminarily screened according to their adaptability to the current threat situation to form a selection set for further evaluation; the plan logic tree is a logical structure used to show the relationship and priority between candidate countermeasure plans. By constructing a plan logic tree, the multi-dimensional evaluation parameters under different plan paths can be systematically analyzed to determine the optimal countermeasure plan. This tree structure helps optimize the decision-making process and ensure that the most effective countermeasures are quickly found and implemented in a complex environment; In this step, the dynamic decision factor set is first input into the plan knowledge graph system. The plan knowledge graph system contains a series of basic countermeasure plans preset for different threat scenarios, and automatically selects the most relevant basic plans as candidate countermeasure plans according to the specific parameters in the dynamic decision factor set (such as the geographic fence penetration risk value, environmental interference attenuation coefficient, etc.). Then, according to the logical relationship and priority between the candidate plans, a plan logic tree is constructed. The plan logic tree not only shows the hierarchical relationship between the candidate plans, but also provides a framework for the subsequent multi-dimensional evaluation of each plan path. Finally, through the analysis of the plan logic tree, the most suitable countermeasure plan for the current situation can be determined more accurately; For example, in the previous step, an illegal drone that attempted to cross the airport's no-fly zone and may be carrying jamming equipment has been identified, and a set of dynamic decision factors including a high geographic fence penetration risk value and a large-environment interference attenuation coefficient has been generated. Next, this set is applied to the plan knowledge graph to match multiple basic countermeasure plans suitable for the current scenario, such as using a radio jammer to suppress its signal, deploying a capture net to intercept, or activating an electromagnetic pulse device to disable it. Based on these candidate plans, a plan logic tree is constructed, which lists in detail the execution order and conditions of each plan. For example, when the radio interference effect is not good, the physical capture plan is given priority, ensuring that even in a complex and changeable actual environment, the most effective countermeasures can be quickly found and implemented to maximize the safe operation of the airport and enhance the ability to respond quickly to drone threats.

[0026] Step 104, based on the three-dimensional threat situation model, the dynamic decision factor set and the countermeasure effectiveness index preset in the plan knowledge graph, generates a multi-dimensional evaluation parameter set, including: In this step, the countermeasure effectiveness index is the evaluation standard preset in the plan knowledge graph, covering the effect evaluation data of various countermeasure measures, such as response speed, success rate, resource consumption, etc., which is used to evaluate the actual execution effect of different countermeasure plans; the multidimensional evaluation parameter set is a data set generated based on the three-dimensional space threat situation model and the dynamic decision factor set, including but not limited to trajectory prediction deviation, electromagnetic suppression priority, geographic fence penetration risk value, etc., which aims to comprehensively evaluate the effectiveness and applicability of each candidate countermeasure plan; In this step, the latest data provided by the three-dimensional threat situation model is first combined with the key evaluation data in the dynamic decision factor set. Then, the preset countermeasure effectiveness indicators are extracted from the plan knowledge graph to ensure that all relevant evaluation criteria are covered. Then, through comprehensive analysis of this information, a multidimensional evaluation parameter set is generated. This multidimensional evaluation parameter set not only includes various parameters that affect the effectiveness of countermeasures, but also considers various limiting factors in actual operations. Finally, the multidimensional evaluation parameter set is used to comprehensively evaluate each candidate countermeasure plan to determine the optimal countermeasure plan. For example, in the previous step, multiple candidate countermeasure plans for illegal drones have been matched according to the dynamic decision factor set, and a plan logic tree has been constructed. Next, the real-time flight trajectory data and electromagnetic signal information in the three-dimensional space threat situation model are integrated, combined with the geographic fence penetration risk value and environmental interference attenuation coefficient in the dynamic decision factor set, and the preset countermeasure effectiveness indicators in the plan knowledge graph, such as response speed and success rate, to generate a detailed multi-dimensional evaluation parameter set; for example, for the plan of using radio jammers to suppress drone signals, the system evaluates the response time, success rate and possible spectrum interference of the plan. At the same time, for the physical interception plan, the deployment time and the probability of successful capture are analyzed in detail. Based on these detailed evaluation results, the most effective countermeasure plan can be selected more accurately to ensure a quick and accurate response to the threat of illegal drones and to protect the safety of sensitive areas.

[0027] Step 105, based on the multi-dimensional evaluation parameter set, the plan logic tree is collaboratively optimized to generate an optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area, including: In this step, collaborative optimization processing refers to the process of analyzing and optimizing the plan logic tree using algorithms (such as the Monte Carlo tree search algorithm), and selecting the plan path with the highest confidence as the optimal countermeasure plan through weighted pruning and other technologies; executable instruction set refers to converting the optimal countermeasure plan into a specific action guide or operation command, including but not limited to starting specific equipment, adjusting setting parameters, etc., so as to be directly applied to the countermeasure device; countermeasure device refers to multiple countermeasure devices deployed in the target area, which can receive and execute instruction sets from the system to implement specific countermeasures against illegal drones; In this step, first, each plan path in the plan logic tree is evaluated based on a multi-dimensional evaluation parameter set, and the plan path is confidence-weighted pruned through a collaborative optimization algorithm (such as a Monte Carlo tree search algorithm). Then, based on the optimization results, one or more plan paths with the highest confidence are selected as the optimal countermeasure plan. Next, this optimal countermeasure plan is converted into a detailed executable instruction set to ensure that each step is clear and specific. Finally, these instruction sets are synchronized to the distributed countermeasure devices in the target area through the communication network, so that they can immediately execute the corresponding countermeasures; For example, in the previous step, a multi-dimensional evaluation parameter set has been generated and the plan logic tree has been preliminarily screened, and several possible countermeasure plans have been identified. Now, these countermeasure plan paths are further analyzed using collaborative optimization processing technology, and finally a plan with the highest confidence is determined as the optimal countermeasure plan. For example, when facing illegal drones attempting to cross the no-fly zone, a comprehensive solution of using radio interference combined with a physical interception network is chosen as the optimal countermeasure plan. This plan is then converted into a series of executable instruction sets, including starting radio jammers in specific frequency bands, adjusting the interference power, and the specific location and time of deploying the physical interception network. These executable instruction sets are quickly synchronized to the countermeasure devices deployed around the airport to ensure that they can respond immediately and effectively to the threat of illegal drones and ensure the safety and stability of the airspace.

[0028] In order to solve the complexity and uncertainty problems faced in the drone threat assessment process and significantly improve the accuracy and response efficiency of countermeasures, in some embodiments, according to step 105, the plan logic tree is collaboratively optimized based on the multi-dimensional evaluation parameter set to generate an optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area, specifically including: Extract trajectory prediction deviation and electromagnetic suppression priority from the three-dimensional space threat situation model, extract geo-fence penetration risk value and environmental interference attenuation coefficient from the dynamic decision factor set, and extract response delay threshold and spectrum interference compatibility from the plan knowledge graph; generate dynamic response margin parameters from trajectory prediction deviation and response delay threshold through time series compression algorithm; calculate the peak value of the cross-correlation function of electromagnetic suppression priority and spectrum interference compatibility through frequency domain overlap analysis algorithm, generate spectrum avoidance coefficient when the peak value exceeds the preset interference threshold and lock the countermeasure of the corresponding frequency band; construct an attenuation compensation model using geo-fence penetration risk value and environmental interference attenuation coefficient, and dynamically correct the transmission power weight factor of the countermeasure device; input dynamic response margin parameters, spectrum avoidance coefficient and transmission power weight factor into the node activation function of the plan logic tree, perform confidence-weighted pruning on the plan path in the plan logic tree through the Monte Carlo tree search algorithm, generate the optimal countermeasure plan from the plan path with confidence higher than the preset dynamic threshold, convert the optimal countermeasure plan into an executable instruction set and synchronize it to the countermeasure device in the target area; In this embodiment, the dynamic response margin parameter is a comprehensive indicator calculated by combining the trajectory prediction deviation and the response delay threshold using a time series compression algorithm, which is used to measure the real-time response capability of the system when facing drone threats; the spectrum avoidance coefficient is a parameter generated by calculating the peak value of the cross-correlation function between the electromagnetic suppression priority and the spectrum interference compatibility when the peak value exceeds the preset interference threshold, which is used to guide the selection of a suitable frequency band to avoid unnecessary spectrum interference; the attenuation compensation model is constructed based on the geographic fence penetration risk value and the environmental interference attenuation coefficient, which is used to dynamically adjust the transmission power weight factor of the countermeasure device to ensure that it can play an effective role under various environmental conditions; In an embodiment of the present application, necessary parameters are first extracted from the three-dimensional threat situation model, the dynamic decision factor set and the plan knowledge graph, such as trajectory prediction deviation, electromagnetic suppression priority, etc. Next, the dynamic response margin parameters are generated by the time series compression algorithm, and the spectrum avoidance coefficient is calculated by the frequency domain overlap analysis algorithm to lock the frequency band that may be interfered with. Then, an attenuation compensation model is constructed based on the geographic fence penetration risk value and the environmental interference attenuation coefficient, and the transmission power weight factor of the countermeasure device is dynamically corrected. Finally, the dynamic response margin parameters, spectrum avoidance coefficient and transmission power weight factor generated above are input into the node activation function of the plan logic tree, and the Monte Carlo tree search algorithm is used to perform confidence-weighted pruning on the plan path, and the plan with the highest confidence is selected as the optimal countermeasure plan, and it is converted into a specific executable instruction set and synchronized to the countermeasure device; For example, in a security monitoring scenario around an airport, the system detected an illegal drone attempting to cross a no-fly zone and engaging in potential radio interference. It first extracted relevant parameters such as trajectory prediction deviation and electromagnetic suppression priority, determined the best response strategy after analysis, and used a time series compression algorithm to calculate the dynamic response margin parameters to ensure a rapid response. At the same time, it used a frequency domain overlap analysis algorithm to identify the frequency bands that needed to be avoided and adjusted the corresponding countermeasures. In addition, considering the impact of current meteorological conditions (such as strong winds) on drone operations, the system also constructed an attenuation compensation model to optimize the transmission power of the countermeasure device. Finally, it optimized the plan logic tree through a Monte Carlo tree search algorithm, selected the most effective countermeasure plan, and quickly synchronized the instructions to the countermeasure devices around the airport, successfully preventing the intrusion of illegal drones and ensuring airspace safety.

[0029] In order to further improve the adaptability and reliability of the countermeasures, according to the previous embodiment, the dynamic response margin parameter, the spectrum avoidance coefficient and the transmit power weight factor are input into the node activation function of the plan logic tree, and the plan path in the plan logic tree is pruned by confidence weighting through the Monte Carlo tree search algorithm, and the plan path with a confidence higher than the preset dynamic threshold is used to generate the optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area, specifically including: A time-sensitive search window is generated based on the dynamic response margin parameter, wherein the width of the time-sensitive search window shrinks exponentially with the increase of the trajectory prediction deviation, and the time-sensitive search window is spatially and temporally aligned with the node execution timestamp of the plan logic tree to screen out candidate paths within the coverage of the time-sensitive search window; the frequency band compliance of the candidate paths is checked according to the spectrum avoidance coefficient, and when the transmission frequency band of the countermeasure in the candidate path overlaps with the currently locked conflicting frequency band and the dynamic spectrum migration mechanism is not configured, the candidate path is marked as a disabled candidate path and removed from the plan logic tree; the transmission power weight factor is used to calculate the compliance of each candidate The real-time energy consumption estimation of the path is combined with the remaining power of the countermeasure device and the environmental interference attenuation coefficient to generate a dynamic fuse threshold. When the cumulative energy consumption of the candidate path exceeds the fuse threshold, the backtracking mechanism is triggered and the low-power node is reselected; the electromagnetic suppression priority data of the three-dimensional space threat situation model is integrated to weight the confidence of the candidate path containing the coordinated countermeasure node. Among them, the coordinated countermeasure node must simultaneously meet the timing linkage conditions of radio frequency interference and navigation deception; the pruning threshold is dynamically adjusted according to the level of the geographic fence penetration risk value, and the plan path with a confidence higher than the preset dynamic threshold is subjected to conflict detection and redundancy elimination to generate the optimal countermeasure plan; In this embodiment, the time-sensitive search window refers to a time range generated based on the dynamic response margin parameter, and its width shrinks exponentially with the increase of the trajectory prediction deviation, and is used to align the node execution timestamps in the plan logic tree in time and space, so as to screen out feasible candidate paths within a specific time. Frequency band compliance verification is a process that uses the spectrum avoidance coefficient to check whether the transmission frequency band of the countermeasures in the candidate path overlaps with the currently locked conflicting frequency band to ensure that no unnecessary spectrum interference is generated. The dynamic fuse threshold is a threshold calculated based on the transmission power weight factor, the remaining power and the environmental interference attenuation coefficient, which is used to trigger the backtracking mechanism to avoid equipment failure due to excessive energy consumption. A collaborative countermeasure node refers to a node that simultaneously meets the timing linkage conditions of radio frequency interference and navigation deception means, which can effectively enhance the countermeasure effect; In an embodiment of the present application, a time-sensitive search window is first generated based on a dynamic response margin parameter, and it is aligned in time and space with the node execution timestamp of the plan logic tree to screen out candidate paths within the coverage of the time-sensitive search window. Then, the frequency band compliance of these candidate paths is checked according to the spectrum avoidance coefficient, and paths that do not meet the conditions are removed. Then, the real-time energy consumption estimate of each candidate path is calculated using the transmission power weight factor, and a dynamic fuse threshold is generated in combination with the remaining power of the countermeasure device and the environmental interference attenuation coefficient. When the cumulative energy consumption of the candidate path exceeds this threshold, the backtracking mechanism is triggered to reselect a low-power node. In addition, the electromagnetic suppression priority data in the three-dimensional space threat situation model is integrated to weight the confidence of the path containing the collaborative countermeasure node to ensure efficient countermeasure. Finally, the pruning threshold is dynamically adjusted according to the geographic fence penetration risk value, and conflict detection and redundancy elimination are performed on the candidate paths, and the path with a confidence higher than the preset dynamic threshold is screened out as the optimal countermeasure plan. For example, in a security monitoring scenario around an airport, an illegal drone was detected attempting to cross a no-fly zone and there was potential radio interference. First, a time-sensitive search window was generated based on the dynamic response margin parameter to ensure that only the plan paths that can be executed within a reasonable time were considered. Then, the frequency band compliance of the candidate paths was checked through the spectrum avoidance coefficient to exclude those paths that may cause spectrum conflicts. Considering that the current environmental conditions (such as strong winds) may affect the energy consumption of the equipment, the system used the transmission power weight factor to calculate the real-time energy consumption estimate and set a dynamic fuse threshold to prevent equipment failure due to excessive energy consumption. For those paths that contain collaborative countermeasure nodes (such as the combined use of RF interference and navigation deception), the system performed a confidence weighting enhancement to ensure that these efficient paths were given priority. Finally, the pruning threshold was dynamically adjusted according to the geographic fence penetration risk value, the optimal countermeasure plan was screened out, and the instructions were quickly synchronized to the distributed countermeasure devices around the airport, successfully preventing the intrusion of illegal drones, ensuring airspace safety, and improving the effectiveness and adaptability of countermeasures.

[0030] In order to further improve the monitoring accuracy and response speed of illegal or abnormal drone activities, as another embodiment, according to step 101, the dynamic monitoring data stream obtained by the target drone is parsed, the flight trajectory characteristics and electromagnetic signal spectrum of the target drone are identified, and a three-dimensional space threat situation model is constructed in combination with multi-source heterogeneous sensor fusion technology. The model is used to evaluate the threat level of the target drone and generate a threat level evaluation result, which specifically includes: Analyze the dynamic monitoring data stream of the target UAV collected synchronously by multi-source sensors, and perform time alignment processing on the position coordinates, velocity vectors and electromagnetic spectrum data in the dynamic monitoring data stream to generate an original data set; calculate the heading angle change rate and acceleration fluctuation characteristics of the target UAV based on the position coordinates and velocity vectors in the original data set, generate the trajectory prediction deviation degree in combination with the preset trajectory prediction model, and construct the flight trajectory feature vector; extract the frequency domain features of the electromagnetic spectrum data in the original data set, obtain the main frequency band energy distribution and signal frequency hopping law of the target UAV, and generate the electromagnetic signal spectrum feature vector; perform multi-source data fusion on the heading angle change rate, acceleration fluctuation characteristics and trajectory prediction deviation degree in the flight trajectory feature vector and the main frequency band energy distribution and signal frequency hopping law in the electromagnetic signal spectrum feature vector to generate the threat confidence parameters of the target UAV; construct a three-dimensional space threat situation model based on the threat confidence parameters, map the real-time position coordinates of the target UAV to the three-dimensional grid space under the constraints of the geographic fence, and generate the threat level assessment results in combination with the trajectory prediction deviation degree and the signal frequency hopping law; In this embodiment, the original data set includes dynamic monitoring data of the target UAV collected synchronously from multi-source sensors, such as position coordinates, velocity vectors and electromagnetic spectrum data, which are formed after time alignment processing to form a basic data set for subsequent analysis and modeling; the flight trajectory feature vector is composed of the heading angle change rate, acceleration fluctuation characteristics and trajectory prediction deviation, which is used to describe the flight behavior characteristics of the target UAV; the electromagnetic signal spectrum feature vector includes the main frequency band energy distribution and signal frequency hopping law, reflecting the main characteristics of the UAV communication signal and its dynamic changes; the threat confidence parameter is an evaluation index obtained by comprehensively analyzing the flight trajectory feature vector and the electromagnetic signal spectrum feature vector, which is used to quantify the potential threat level of the target UAV; In the embodiment of the present application, firstly, the dynamic monitoring data stream of the target UAV is synchronously collected by multi-source sensors (such as radar, optical camera, radio receiver, etc.) deployed in the monitoring area, and the data are time-aligned to ensure that the data from different sources can be analyzed on the same time basis. Then, the heading angle change rate and acceleration fluctuation characteristics of the target UAV are calculated based on the position coordinates and velocity vector in the original data set, and the trajectory prediction deviation is generated in combination with the preset trajectory prediction model, and the flight trajectory feature vector is constructed. At the same time, the electromagnetic spectrum data in the original data set is subjected to frequency domain feature extraction to obtain the main frequency band energy distribution and signal frequency hopping law of the target UAV, and the electromagnetic signal spectrum feature vector is generated. Then, the flight trajectory feature vector and the electromagnetic signal spectrum feature vector are subjected to multi-source data fusion to generate the threat confidence parameter of the target UAV. Finally, a three-dimensional space threat situation model is constructed based on this parameter, and the real-time position coordinates of the target UAV are mapped to the three-dimensional grid space under the constraint of the geographic fence. The threat level assessment result is generated in combination with the trajectory prediction deviation and the signal frequency hopping law. For example, when a drone suspected of illegal intrusion is detected, the drone's position coordinates, velocity vector and electromagnetic spectrum data are synchronously collected through various sensors deployed around the airport, and time alignment is performed. The system then calculates the drone's heading angle change rate and acceleration fluctuation characteristics, and finds that its flight path has abnormal fluctuations, which may be intended to evade monitoring. At the same time, through the analysis of the electromagnetic spectrum data, it is identified that the drone uses a specific frequency band for communication and has a certain frequency hopping pattern. This information is fused through multi-source data to generate threat confidence parameters, and a detailed three-dimensional spatial threat situation model is constructed based on this. The model not only displays the real-time location of the drone, but also predicts its possible flight trajectory, and uses this model to evaluate the drone's threat level. According to the threat level assessment results, the corresponding early warning mechanism is quickly activated, and necessary countermeasures are prepared to effectively ensure the safety of the airspace.

[0031] In order to solve the complexity and uncertainty in drone threat identification and further improve targeted countermeasures against drones with different behavior patterns, as another embodiment, according to step 102, the behavior pattern and attack intention parameters of the target drone are decomposed based on the threat level assessment result, and a dynamic decision factor set is generated in combination with the geographic fence constraint and the environmental interference factor, specifically including: Based on the trajectory prediction deviation and signal frequency hopping law in the threat level assessment result, the behavior pattern category of the target UAV is identified, wherein the behavior pattern category includes: reconnaissance mode, interference mode and attack mode; the attack intention parameters of the target UAV are extracted according to the behavior pattern category, wherein the reconnaissance mode corresponds to the target area scanning frequency, the interference mode corresponds to the electromagnetic suppression intensity of the countermeasure device, and the attack mode corresponds to the dive angle and speed change rate of the target UAV; the attack intention parameters are spatially matched with the geographic fence constraint conditions, the minimum distance between the current position of the target UAV and the boundary of the geographic fence is calculated, and the spatial threat weight is generated in combination with the environmental interference factor; the attack intention parameters are dynamically corrected based on the spatial threat weight to obtain the corrected attack intention parameters, wherein the target area scanning frequency in the reconnaissance mode increases as the distance decreases, the electromagnetic suppression intensity in the interference mode decays as the environmental interference factor increases, and the dive angle and speed change rate in the attack mode increase as the distance decreases; the corrected attack intention parameters are combined with the behavior pattern category to generate a dynamic decision factor set including the spatial threat weight, the corrected attack intention parameters and the behavior pattern category; In this embodiment, the behavior pattern category refers to different behavior types divided according to the flight characteristics of the drone (such as trajectory prediction deviation and signal frequency hopping law), mainly including reconnaissance mode, interference mode and attack mode; the attack intention parameter is a specific parameter extracted according to different behavior pattern categories, such as the scanning frequency in the reconnaissance mode, the electromagnetic suppression intensity in the interference mode, the dive angle and speed change rate in the attack mode; the spatial threat weight is a weight value that comprehensively considers the minimum distance between the current position of the drone and the boundary of the geographic fence and the environmental interference factor, and is used to quantify the actual threat level of the drone to a specific area; In the embodiment of the present application, firstly, based on the trajectory prediction deviation and signal frequency hopping law in the threat level assessment result, the behavior pattern category of the target UAV is identified, then the corresponding attack intention parameters are extracted according to the identified behavior pattern category, and then these attack intention parameters are spatially matched with the geographic fence constraint conditions, the minimum distance between the current position of the target UAV and the boundary of the geographic fence is calculated, and the spatial threat weight is generated in combination with the environmental interference factor. Based on this weight, the attack intention parameter is dynamically corrected, so that the target area scanning frequency in the reconnaissance mode increases with the decrease of distance, the electromagnetic suppression intensity in the interference mode is attenuated with the increase of the environmental interference factor, and the dive angle and speed change rate in the attack mode increase with the decrease of distance. Finally, the corrected attack intention parameters are combined with the behavior pattern category to generate a dynamic decision factor set including the spatial threat weight, the corrected attack intention parameters and the behavior pattern category; For example, when the system detects a drone suspected of illegal intrusion, it identifies that the drone is in reconnaissance mode based on the trajectory prediction deviation and signal frequency hopping pattern of the drone. The system extracts the target area scanning frequency of the drone as the attack intention parameter, and spatially matches it with the geographic fence constraint condition to calculate the minimum distance between the current position of the drone and the boundary of the airport's no-fly zone. Taking into account the existence of certain radio interference in the current environment, the system generates a spatial threat weight in combination with the environmental interference factor. Based on this weight, the system dynamically corrects the scanning frequency of the drone so that it gradually increases as it approaches the boundary of the no-fly zone. In addition, the system also considers other possible behavior modes, such as interference mode or attack mode, and calculates the corresponding corrected attack intention parameters through similar methods. Finally, a detailed set of dynamic decision factors is generated, which not only includes the current behavior mode of the drone and the corrected attack intention parameters, but also reflects its actual threat level to airport security, thereby providing key information for formulating accurate and effective countermeasure plans.

[0032] In order to solve the problem of accuracy and pertinence of plan matching in the process of drone countermeasures, and to further improve the ability to respond quickly to drones with different behavior patterns, as another embodiment, according to step 103, a dynamic decision factor set is matched with the basic countermeasure plan preset in the plan knowledge graph to generate multiple candidate countermeasure plans, and a plan logic tree is constructed based on these plans. Specifically, it includes: Based on the behavior pattern categories in the dynamic decision factor set, the matching basic countermeasure plan is extracted from the plan knowledge graph, where the reconnaissance mode corresponds to the spectrum detection and interference plan, the interference mode corresponds to the frequency band suppression and navigation deception plan, and the attack mode corresponds to the physical interception and directional strike plan; according to the modified attack intention parameters in the dynamic decision factor set, the basic countermeasure plan extracted from the plan knowledge graph is adjusted by parameter adaptation to obtain the basic countermeasure plan after parameter adaptation, where the scanning frequency of the spectrum detection plan is synchronized with the scanning frequency of the target area, and the transmission power of the frequency band suppression plan is synchronized with the power of the electric field. Matching the intensity of magnetic suppression, the triggering time of the physical interception plan is associated with the dive angle and the speed change rate; based on the spatial threat weight in the dynamic decision factor set, the basic countermeasure plan after parameter adaptation is prioritized to generate a candidate countermeasure plan set containing execution timing, intensity parameters and triggering conditions; the plan nodes in the candidate countermeasure plan set are constructed into a plan logic tree according to the execution timing and spatial position relationship, where the connecting edges between the plan nodes represent the switching conditions of the countermeasures, where the switching conditions include: electromagnetic suppression intensity threshold, navigation deception success rate and physical interception feasibility; In this embodiment, the basic plan of countermeasures after parameter adaptation refers to a specific plan obtained by adjusting the basic plan extracted from the plan knowledge graph according to the modified attack intention parameters (such as scanning frequency, electromagnetic suppression intensity, etc.) in the dynamic decision factor set; priority sorting is a process of sorting the adapted basic plan of countermeasures based on the spatial threat weight to determine the priority order of each plan in actual operation; the plan logic tree is a structured tree model, which contains multiple plan nodes and the connecting edges between them, which is used to show the execution order of different countermeasures and their switching conditions; In an embodiment of the present application, firstly, based on the behavior mode category (reconnaissance mode, interference mode or attack mode) in the dynamic decision factor set, the corresponding basic plan of countermeasures is extracted from the plan knowledge graph. For example, for the reconnaissance mode, spectrum detection and interference plans are extracted; for the interference mode, frequency band suppression and navigation deception plans are extracted; for the attack mode, physical interception and directional strike plans are extracted, and then the parameters of the extracted basic plans are adapted and adjusted according to the modified attack intention parameters in the dynamic decision factor set to ensure that the scanning frequency of the spectrum detection plan is synchronized with the scanning frequency of the target area, the transmission power of the frequency band suppression plan matches the electromagnetic suppression intensity, and the triggering timing of the physical interception plan is associated with the dive angle and speed change rate. Then, based on the spatial threat weight in the dynamic decision factor set, the basic countermeasure plans after parameter adaptation are prioritized to generate a set of candidate countermeasure plans containing execution timing, intensity parameters and triggering conditions. Finally, these candidate countermeasure plans are constructed into a plan logic tree according to the execution timing and spatial position relationship, where the connecting edges between the plan nodes represent the switching conditions of the countermeasures, such as the electromagnetic suppression intensity threshold, the navigation deception success rate and the feasibility of physical interception. For example, in a security monitoring system around an airport, the system identified a drone in reconnaissance mode and extracted a spectrum detection and interference plan as the basic plan. The system adjusted the scanning frequency of the spectrum detection plan based on the modified attack intention parameters in the dynamic decision factor set to keep it consistent with the scanning frequency of the drone's target area. At the same time, considering the radio interference in the current environment, the system also adjusted the transmission power of the frequency band suppression plan to match the current electromagnetic suppression intensity requirements. Next, based on the spatial threat weight, the system prioritized the adjusted plans to ensure that the most effective countermeasures can be taken quickly in high-threat situations. Finally, these adjusted plans were used to construct a detailed plan logic tree according to the execution sequence and spatial position relationship, clarifying the switching conditions between different countermeasures. For example, when the electromagnetic suppression intensity reaches a certain threshold, the system will automatically switch to the navigation deception plan; if the navigation deception fails, the physical interception plan will be further initiated.

[0033] In order to solve the accuracy and comprehensiveness of multi-dimensional evaluation parameters in the drone countermeasure process, and to further improve the adaptability and response efficiency to different threat scenarios, as another embodiment, according to step 104, based on the three-dimensional space threat situation model, the dynamic decision factor set and the countermeasure effectiveness index preset in the plan knowledge graph, a multi-dimensional evaluation parameter set is generated, specifically including: The real-time position coordinates, trajectory prediction deviation and electromagnetic suppression priority of the target UAV are extracted from the three-dimensional space threat situation model, and the space threat assessment parameters are generated by combining the countermeasure effectiveness indicators in the plan knowledge graph. The space threat assessment parameters include: target distance attenuation coefficient, trajectory coverage completeness and electromagnetic suppression effectiveness value; the response time parameter and intensity matching parameter of the countermeasure are calculated from the modified attack intention parameters based on the dynamic decision factor set. The response time parameter is calculated through the speed change rate of the target UAV and the deployment position of the countermeasure device, and the intensity matching parameter is calculated through the electromagnetic suppression intensity and the countermeasure. Calculation of the transmission power range; extracting the spectrum compatibility parameters and energy efficiency parameters of the countermeasures according to the countermeasure effectiveness indicators in the plan knowledge graph, wherein the spectrum compatibility parameters are calculated by the overlapping area between the transmission frequency band of the countermeasures and the protection frequency band, and the energy efficiency parameters are calculated by the energy consumption per unit time of the countermeasures and the remaining power of the countermeasures; normalizing the spatial threat assessment parameters, response time parameters, intensity matching parameters, spectrum compatibility parameters and energy efficiency parameters to generate a multidimensional assessment parameter set containing weight coefficients, wherein the weight coefficients are dynamically adjusted through the spatial threat weights in the dynamic decision factor set.

[0034] In this embodiment, the space threat assessment parameter is a set of parameters obtained by combining the real-time position coordinates of the target UAV in the three-dimensional space threat situation model, the trajectory prediction deviation, and the electromagnetic suppression priority and the countermeasure effectiveness index in the plan knowledge graph, which is used to quantify the actual threat level of the UAV to a specific area; the response time parameter refers to the time delay calculated based on the speed change rate of the target UAV and the deployment position of the countermeasure device, reflecting the timeliness of the initiation of the countermeasure; the intensity matching parameter is a parameter obtained by comparing the electromagnetic suppression intensity with the transmission power range of the countermeasure to ensure the effectiveness of the countermeasure; the spectrum compatibility parameter is to evaluate whether the countermeasure will interfere with the key communication frequency band by calculating the overlapping area between the transmission frequency band of the countermeasure and the protection frequency band; the energy efficiency parameter is to evaluate the sustainable operation capability of the countermeasure through the relationship between the energy consumption per unit time and the remaining power of the countermeasure device; In the embodiment of the present application, the real-time position coordinates, trajectory prediction deviation and electromagnetic suppression priority of the target UAV are first extracted from the three-dimensional space threat situation model, and the space threat assessment parameters such as the target distance attenuation coefficient, trajectory coverage completeness and electromagnetic suppression effectiveness value are generated in combination with the countermeasure effectiveness index in the plan knowledge graph. Then, based on the modified attack intention parameters in the dynamic decision factor set, the response time parameter and the intensity matching parameter of the countermeasure are calculated. For example, the response time parameter is determined by the speed change rate of the target UAV and the deployment position of the countermeasure device, and the intensity matching parameter is calculated by the electromagnetic suppression intensity and the transmission power range of the countermeasure. Then, according to the countermeasure effectiveness index in the plan knowledge graph, the spectrum compatibility parameter and energy efficiency parameter of the countermeasure are extracted to ensure that the countermeasure will not interfere with the key communication frequency band and has good energy utilization efficiency. Finally, the space threat assessment parameters, response time parameters, intensity matching parameters, spectrum compatibility parameters and energy efficiency parameters are normalized to generate a multidimensional evaluation parameter set including weight coefficients, wherein the weight coefficient is dynamically adjusted according to the space threat weight in the dynamic decision factor set to ensure the accuracy and adaptability of the evaluation result. For example, in a security monitoring system around an airport, the system identified a suspected illegal intrusion drone and extracted its real-time position coordinates, trajectory prediction deviation and electromagnetic suppression priority from the three-dimensional space threat situation model. The system combined the countermeasure effectiveness indicators in the plan knowledge graph to generate spatial threat assessment parameters, such as target distance attenuation coefficient and electromagnetic suppression effectiveness value. Then the system calculated the response time parameters of the countermeasures, taking into account the speed change rate of the drone and the deployment position of the countermeasures to ensure rapid response. At the same time, the system also calculated the intensity matching parameters to ensure the effectiveness of the countermeasures by comparing the electromagnetic suppression intensity with the transmission power range of the countermeasures. In addition, the system extracted the spectrum compatibility parameters and energy efficiency parameters of the countermeasures to ensure that there would be no interference with the airport's key communication frequency bands and to ensure the energy efficiency of the countermeasures. Finally, the system normalized these parameters and generated a multidimensional evaluation parameter set containing weight coefficients, and dynamically adjusted the weight coefficients according to the spatial threat weights in the dynamic decision factor set.

[0035] Figure 2 A schematic diagram of a UAV countermeasure plan generation system based on artificial intelligence technology is provided for an embodiment of the present application. Figure 2 As shown, the system includes: The parsing module 21 is used to parse the dynamic monitoring data stream obtained by the target UAV, identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and build a three-dimensional space threat situation model in combination with multi-source heterogeneous sensor fusion technology, and use the three-dimensional space threat situation model to evaluate the threat level of the target UAV and generate a threat level evaluation result; A decomposition module 22, for decomposing the behavior pattern and attack intention parameters of the target UAV based on the threat level assessment result, and generating a dynamic decision factor set in combination with the geographic fence constraint condition and the environmental interference factor; A construction module 23 is used to match the basic countermeasure plans preset in the plan knowledge graph through a dynamic decision factor set, generate multiple candidate countermeasure plans, and construct a plan logic tree according to the multiple candidate countermeasure plans; A generation module 24, used to generate a multi-dimensional evaluation parameter set based on the three-dimensional space threat situation model, the dynamic decision factor set and the countermeasure effectiveness index preset in the plan knowledge graph; The synchronization module 25 is used to perform collaborative optimization processing on the plan logic tree based on the multi-dimensional evaluation parameter set to generate an optimal countermeasure plan, convert the optimal countermeasure plan into an executable instruction set and synchronize it to the countermeasure device in the target area.

[0036] Figure 2 The UAV countermeasure plan generation system based on artificial intelligence technology can execute Figure 1 The implementation principle and technical effect of the method for generating a UAV countermeasure plan based on artificial intelligence technology described in the embodiment shown will not be repeated. The specific way in which each module and unit performs operations in the UAV countermeasure plan generation system based on artificial intelligence technology in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0037] In one possible design, Figure 2 The UAV countermeasure plan generation system based on artificial intelligence technology in 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 .

[0038] The processing component 32 is used to parse the dynamic monitoring data stream obtained by the target UAV, identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and build a three-dimensional space threat situation model in combination with multi-source heterogeneous sensor fusion technology, and use the three-dimensional space threat situation model to evaluate the threat level of the target UAV and generate a threat level evaluation result; based on the threat level evaluation result, decompose the behavior pattern and attack intention parameters of the target UAV, and generate a dynamic decision factor set in combination with geographic fence constraints and environmental interference factors; match the basic countermeasure plan preset in the plan knowledge graph through the dynamic decision factor set to generate multiple candidate countermeasure plans, and build a plan logic tree according to the multiple candidate countermeasure plans; based on the three-dimensional space threat situation model, the dynamic decision factor set and the countermeasure effectiveness index preset in the plan knowledge graph, generate a multidimensional evaluation parameter set; based on the multidimensional evaluation parameter set, perform collaborative optimization processing on the plan logic tree to generate an optimal countermeasure plan, convert the optimal countermeasure plan into an executable instruction set and synchronize it to the countermeasure device in the target area.

[0039] 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.

[0040] 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.

[0041] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0042] 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.

[0043] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0044] 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.

[0045] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for generating a UAV countermeasure plan based on artificial intelligence technology.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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 method for generating a UAV countermeasure plan based on artificial intelligence technology, characterized in that: include: Analyze the dynamic monitoring data stream obtained by the target UAV, identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and build a three-dimensional space threat situation model in combination with multi-source heterogeneous sensor fusion technology. Use the three-dimensional space threat situation model to evaluate the threat level of the target UAV and generate threat level assessment results; Based on the threat level assessment results, the behavior patterns and attack intention parameters of the target drone are decomposed, and a dynamic decision factor set is generated by combining geographic fence constraints and environmental interference factors. Generate multiple candidate countermeasure plans by matching the basic countermeasure plans preset in the plan knowledge graph through a dynamic decision factor set, and construct a plan logic tree based on the multiple candidate countermeasure plans; Generate a multi-dimensional evaluation parameter set based on the three-dimensional threat situation model, the dynamic decision factor set, and the countermeasure effectiveness indicators preset in the plan knowledge graph; The plan logic tree is collaboratively optimized based on a multi-dimensional evaluation parameter set to generate an optimal countermeasure plan, which is then converted into an executable instruction set and synchronized to the countermeasure device in the target area.

2. The method according to claim 1, characterized in that Based on the multi-dimensional evaluation parameter set, the plan logic tree is collaboratively optimized to generate the optimal countermeasure plan, which is converted into an executable instruction set and synchronized to the countermeasure device in the target area, including: Extract trajectory prediction deviation and electromagnetic suppression priority from the three-dimensional space threat situation model, extract geo-fence penetration risk value and environmental interference attenuation coefficient from the dynamic decision factor set, and extract response delay threshold and spectrum interference compatibility from the plan knowledge graph; The trajectory prediction deviation and the response delay threshold are combined through the time series compression algorithm to generate dynamic response margin parameters; The peak value of the cross-correlation function between the electromagnetic suppression priority and the spectrum interference compatibility is calculated through the frequency domain overlapping analysis algorithm. When the peak value exceeds the preset interference threshold, the spectrum avoidance coefficient is generated and the countermeasures of the corresponding frequency band are locked; An attenuation compensation model is constructed using the geo-fence penetration risk value and the environmental interference attenuation coefficient to dynamically correct the transmission power weight factor of the countermeasure device. The dynamic response margin parameters, spectrum avoidance coefficient and transmission power weight factor are input into the node activation function of the plan logic tree, and the plan paths in the plan logic tree are pruned with confidence weighted by the Monte Carlo tree search algorithm. The plan paths with confidence higher than the preset dynamic threshold are used to generate the optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area.

3. The method according to claim 2, characterized in that The dynamic response margin parameter, spectrum avoidance coefficient and transmission power weight factor are input into the node activation function of the plan logic tree, and the plan path in the plan logic tree is pruned with confidence weight by using the Monte Carlo tree search algorithm. The plan path with confidence higher than the preset dynamic threshold is used to generate the optimal countermeasure plan, and the optimal countermeasure plan is converted into an executable instruction set and synchronized to the countermeasure device in the target area, including: Generate a time-sensitive search window based on the dynamic response margin parameter, wherein the width of the time-sensitive search window shrinks exponentially as the trajectory prediction deviation increases, and align the time-sensitive search window with the node execution timestamp of the plan logic tree in time and space to screen out candidate paths within the coverage of the time-sensitive search window; Perform frequency band compliance check on candidate paths according to the spectrum avoidance coefficient. When the transmission frequency band of the countermeasure in the candidate path overlaps with the currently locked conflicting frequency band and the dynamic spectrum migration mechanism is not configured, the candidate path is marked as a disabled candidate path and removed from the plan logic tree; The real-time energy consumption estimation of each candidate path is calculated using the transmit power weight factor, and the dynamic fuse threshold is generated by combining the remaining power of the countermeasure device and the environmental interference attenuation coefficient. When the cumulative energy consumption of the candidate path exceeds the fuse threshold, the backtracking mechanism is triggered and a low-power node is reselected. The electromagnetic suppression priority data of the three-dimensional space threat situation model is integrated to weight the confidence of the candidate paths containing the coordinated countermeasure nodes. The coordinated countermeasure nodes must simultaneously meet the timing linkage conditions of radio frequency interference and navigation deception methods. The pruning threshold is dynamically adjusted according to the level of the geo-fence penetration risk value, and the plan paths with confidence levels higher than the preset dynamic threshold are subjected to conflict detection and redundancy elimination to generate the optimal countermeasure plan.

4. The method according to claim 1, characterized in that: Parse the dynamic monitoring data stream obtained by the target UAV, identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and build a three-dimensional space threat situation model in combination with multi-source heterogeneous sensor fusion technology. Use the three-dimensional space threat situation model to evaluate the threat level of the target UAV and generate threat level assessment results, including: Analyze the dynamic monitoring data stream of the target UAV collected synchronously by multi-source sensors, and perform time alignment processing on the position coordinates, velocity vector and electromagnetic spectrum data in the dynamic monitoring data stream to generate the original data set; Based on the position coordinates and velocity vector in the original data set, the heading angle change rate and acceleration fluctuation characteristics of the target UAV are calculated, and the trajectory prediction deviation is generated by combining the preset trajectory prediction model, and the flight trajectory feature vector is constructed; Extract frequency domain features from the electromagnetic spectrum data in the original data set, obtain the main frequency band energy distribution and signal frequency hopping law of the target UAV, and generate the electromagnetic signal spectrum feature vector; The heading angle change rate, acceleration fluctuation characteristics and trajectory prediction deviation in the flight trajectory feature vector are fused with the main frequency band energy distribution and signal frequency hopping law in the electromagnetic signal spectrum feature vector to generate the threat confidence parameters of the target UAV. A three-dimensional threat situation model is constructed based on threat confidence parameters, and the real-time position coordinates of the target UAV are mapped to the three-dimensional grid space under the constraints of geographic fences. The threat level assessment results are generated by combining the trajectory prediction deviation and signal frequency hopping law.

5. The method according to claim 1, characterized in that Based on the threat level assessment results, the behavior patterns and attack intention parameters of the target drone are decomposed, and a dynamic decision factor set is generated by combining geographic fence constraints and environmental interference factors, including: Based on the trajectory prediction deviation and signal frequency hopping rule in the threat level assessment result, the behavior mode category of the target UAV is identified, wherein the behavior mode category includes: reconnaissance mode, interference mode and attack mode; Extract the attack intention parameters of the target UAV according to the behavior pattern category, where the reconnaissance mode corresponds to the target area scanning frequency, the interference mode corresponds to the electromagnetic suppression intensity of the countermeasure device, and the attack mode corresponds to the dive angle and speed change rate of the target UAV; Spatially match the attack intention parameters with the geo-fence constraints, calculate the minimum distance between the target drone’s current position and the geo-fence boundary, and generate a spatial threat weight in combination with environmental interference factors; The attack intention parameters are dynamically modified based on the spatial threat weight to obtain the modified attack intention parameters. In the reconnaissance mode, the target area scanning frequency increases as the distance decreases, the electromagnetic suppression intensity in the interference mode decreases as the environmental interference factor increases, and the dive angle and speed change rate in the attack mode increase as the distance decreases. The modified attack intention parameters are combined with the behavior pattern categories to generate a dynamic decision factor set including spatial threat weights, modified attack intention parameters and behavior pattern categories.

6. The method according to claim 1, characterized in that By matching the basic countermeasure plan preset in the plan knowledge graph through the dynamic decision factor set, multiple candidate countermeasure plans are generated, and a plan logic tree is constructed according to the multiple candidate countermeasure plans, including: Based on the behavioral pattern categories in the dynamic decision factor set, the matching basic countermeasure plan is extracted from the plan knowledge graph. Among them, the reconnaissance mode corresponds to the spectrum detection and interference plan, the interference mode corresponds to the frequency band suppression and navigation deception plan, and the attack mode corresponds to the physical interception and directional strike plan; According to the modified attack intention parameters in the dynamic decision factor set, the basic countermeasure plan extracted from the plan knowledge graph is adjusted to obtain the basic countermeasure plan after parameter adaptation, in which the scanning frequency of the spectrum detection plan is synchronized with the scanning frequency of the target area, the transmission power of the frequency band suppression plan is matched with the electromagnetic suppression intensity, and the triggering timing of the physical interception plan is related to the dive angle and speed change rate; Based on the spatial threat weights in the dynamic decision factor set, the basic countermeasure plans after parameter adaptation are prioritized to generate a candidate countermeasure plan set including execution timing, intensity parameters and trigger conditions; The plan nodes in the candidate countermeasure plan set are constructed into a plan logic tree according to the execution sequence and spatial position relationship, where the connecting edges between the plan nodes represent the switching conditions of the countermeasure measures, where the switching conditions include: electromagnetic suppression intensity threshold, navigation deception success rate and physical interception feasibility.

7. The method according to claim 1, characterized in that Based on the three-dimensional threat situation model, the dynamic decision factor set, and the preset countermeasure effectiveness indicators in the plan knowledge graph, a multi-dimensional evaluation parameter set is generated, including: The real-time position coordinates, trajectory prediction deviation and electromagnetic suppression priority of the target UAV are extracted from the three-dimensional space threat situation model, and the space threat assessment parameters are generated by combining the countermeasure effectiveness indicators in the plan knowledge graph. The space threat assessment parameters include: target distance attenuation coefficient, trajectory coverage completeness and electromagnetic suppression effectiveness value; Calculate the response time parameter and intensity matching parameter of the countermeasure from the modified attack intention parameter in the dynamic decision factor set, where the response time parameter is calculated by the speed change rate of the target UAV and the deployment position of the countermeasure device, and the intensity matching parameter is calculated by the electromagnetic suppression intensity and the transmission power range of the countermeasure; According to the countermeasure effectiveness indicators in the plan knowledge graph, extract the spectrum compatibility parameters and energy efficiency parameters of the countermeasures. The spectrum compatibility parameters are calculated by the overlapping area between the transmission frequency band of the countermeasures and the protection frequency band, and the energy efficiency parameters are calculated by the energy consumption per unit time of the countermeasures and the remaining power of the countermeasure device. The spatial threat assessment parameters, response time parameters, intensity matching parameters, spectrum compatibility parameters and energy efficiency parameters are normalized to generate a multidimensional assessment parameter set containing weight coefficients, where the weight coefficients are dynamically adjusted through the spatial threat weights in the dynamic decision factor set.

8. A UAV countermeasure plan generation system based on artificial intelligence technology, characterized in that: include: The parsing module is used to parse the dynamic monitoring data stream obtained by the target UAV, identify the flight trajectory characteristics and electromagnetic signal spectrum of the target UAV, and build a three-dimensional space threat situation model in combination with multi-source heterogeneous sensor fusion technology. The three-dimensional space threat situation model is used to evaluate the threat level of the target UAV and generate a threat level evaluation result; A decomposition module is used to decompose the behavior patterns and attack intention parameters of the target UAV based on the threat level assessment results, and generate a dynamic decision factor set in combination with geographic fence constraints and environmental interference factors; A construction module is used to match the basic countermeasure plans preset in the plan knowledge graph through a dynamic decision factor set, generate multiple candidate countermeasure plans, and construct a plan logic tree according to the multiple candidate countermeasure plans; A generation module is used to generate a multi-dimensional evaluation parameter set based on a three-dimensional space threat situation model, a dynamic decision factor set, and a countermeasure effectiveness index preset in a plan knowledge graph; The synchronization module is used to collaboratively optimize the plan logic tree based on a multi-dimensional evaluation parameter set to generate an optimal countermeasure plan, convert the optimal countermeasure plan into an executable instruction set and synchronize it to the countermeasure device in the target area.

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 method for generating a UAV countermeasure plan based on artificial intelligence technology 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 method for generating a UAV countermeasure plan based on artificial intelligence technology as described in any one of claims 1 to 7 is implemented.

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