Cooperative countering control method and system for cluster unmanned aerial vehicles

Abstract

The invention provides a collaborative countering control method and system for a cluster unmanned aerial vehicle, and relates to the technical field of unmanned aerial vehicle countering, and the method comprises the steps: firstly, sensing the dynamic interaction information of a cluster unmanned aerial vehicle and a countering equipment cluster in a target region in real time, and generating a dynamic interaction sensing result containing various kinds of information; executing hierarchical collaborative decision processing based on the dynamic interactive perception result, generating a hierarchical collaborative decision result, performing adaptive scheduling on countering device cluster resources according to the hierarchical collaborative decision result, generating a scheduling scheme, generating a dynamic countering instruction set according to the scheduling scheme, and sending the instruction set to a countering device cluster. And in combination with the real-time countering effect and the sensing result, closed-loop adjustment is performed on the instruction set, so that the countering device cluster continuously and cooperatively counters the cluster unmanned aerial vehicle. According to the invention, the countering efficiency and success rate are improved.

Description

Technical Field

[0001] This invention relates to the field of drone countermeasures technology, and more specifically, to a collaborative countermeasure control method and system for swarm drones. Background Technology

[0002] With the rapid development of drone technology, swarm drones, with their strong collaborative operation capabilities and high mission execution efficiency, have been widely used in many fields such as logistics and environmental monitoring. However, the widespread use of swarm drones has also brought a series of security risks, such as illegal intrusion and malicious interference, posing a serious threat to the security and normal order of important areas.

[0003] Currently, countermeasures against swarm drones mainly suffer from the following problems. Firstly, most existing countermeasures target individual drones, lacking consideration for the overall collaborative characteristics of swarm drones. This makes them ineffective in dealing with the complex collaborative operation modes of swarm drones, resulting in poor countermeasure performance. Secondly, countermeasure equipment often employs fixed resource allocation and operational strategies when performing countermeasure tasks, failing to flexibly adjust based on the real-time dynamic interaction between the swarm drones and the countermeasure equipment. This leads to low resource utilization and low countermeasure efficiency. Furthermore, existing countermeasure systems lack a closed-loop feedback mechanism, unable to adjust countermeasure strategies promptly based on countermeasure effectiveness, thus failing to achieve continuous and effective collaborative countermeasure operations. Summary of the Invention

[0004] In view of the aforementioned problems, and in conjunction with the first aspect of the present invention, embodiments of the present invention provide a cooperative countermeasure control method for swarmed unmanned aerial vehicles (UAVs), the method comprising: The system performs real-time perception of the dynamic interaction information between the cluster of UAVs and the countermeasure equipment cluster within the target area, and generates dynamic interaction perception results. The dynamic interaction perception results include real-time motion correlation information and signal interaction information of each unit of the cluster of UAVs, as well as real-time resource status information and range of action information of each device in the countermeasure equipment cluster. Based on the dynamic interactive perception results, hierarchical collaborative decision processing is performed to generate hierarchical collaborative decision results. The hierarchical collaborative decision results include countermeasure strategy directions for different levels of units in the cluster of drones, as well as the rules for the collaborative cooperation of different devices in the countermeasure device cluster. Based on the hierarchical collaborative decision-making results, the resources of the countermeasure equipment cluster are adaptively scheduled to generate an adaptive resource scheduling scheme. The adaptive resource scheduling scheme includes the cluster UAV units corresponding to each device in the countermeasure equipment cluster, as well as the resource allocation method of each device. Based on the adaptive resource scheduling scheme, a dynamic countermeasure instruction set is generated, which includes the real-time operation parameters and execution timing scheduling of each device in the countermeasure device cluster. The set of dynamic countermeasure commands is sent to the countermeasure equipment cluster, and the real-time countermeasure effect information fed back by the countermeasure equipment cluster is received. The set of dynamic countermeasure commands is adjusted in a closed loop based on the real-time countermeasure effect information and the dynamic interactive perception results, so that the countermeasure equipment cluster can continuously perform coordinated countermeasure operations against the cluster of UAVs.

[0005] Furthermore, embodiments of the present invention also provide a cooperative countermeasure control system for swarm drones, characterized in that it includes: A processor; a machine-readable storage medium for storing machine-executable instructions of the processor; wherein the processor is configured to execute the aforementioned cooperative countermeasure control method against swarmed drones by executing the machine-executable instructions.

[0006] In another aspect, embodiments of the present invention also provide a computer program product, the computer program product including machine-executable instructions, the machine-executable instructions being stored in a computer-readable storage medium, the processor of the cooperative countermeasure control system for swarming drones reading the machine-executable instructions from the computer-readable storage medium, the processor executing the machine-executable instructions, causing the cooperative countermeasure control system for swarming drones to execute the aforementioned cooperative countermeasure control method for swarming drones.

[0007] Based on the above, by sensing the dynamic interaction information between the swarm of UAVs and the countermeasure equipment cluster within the target area in real time, the real-time motion correlation and signal interaction of each unit of the swarm of UAVs, as well as the real-time resource status and operational range of each device in the countermeasure equipment cluster, are obtained. Then, based on the dynamic interaction sensing results, hierarchical collaborative decision-making is performed to generate countermeasure strategy directions for different levels of units of the swarm of UAVs and collaborative rules for different devices in the countermeasure equipment cluster. This fully considers the hierarchical structure of the swarm of UAVs and the collaborative relationship between the countermeasure devices. Based on the hierarchical collaborative decision-making results, the resources of the countermeasure equipment cluster are adaptively scheduled, generating an adaptive resource scheduling scheme, which optimizes the allocation of countermeasure resources and improves resource utilization. A dynamic countermeasure command set is generated based on the adaptive resource scheduling scheme, clarifying the real-time operation parameters and execution timing of each device in the countermeasure equipment cluster. The dynamic countermeasure command set is sent to the countermeasure equipment cluster, and the command set is adjusted in a closed loop based on the real-time countermeasure effect information and the dynamic interaction sensing results, enabling the countermeasure equipment cluster to continuously perform collaborative countermeasure operations against the swarm of UAVs, effectively improving countermeasure efficiency and success rate. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the execution flow of the collaborative countermeasure control method for swarm drones provided in an embodiment of the present invention.

[0009] Figure 2 This is a schematic diagram of exemplary hardware and software components of a collaborative countermeasure control system for swarm drones provided in an embodiment of the present invention. Detailed Implementation

[0010] The present invention will now be described in detail with reference to the accompanying drawings. Figure 1 This is a flowchart illustrating a cooperative countermeasure control method for swarm drones provided in one embodiment of the present invention. The following is a detailed description of the cooperative countermeasure control method for swarm drones.

[0011] Step S110: Real-time sensing of the dynamic interaction information between the cluster of UAVs and the countermeasure equipment cluster within the target area, generating dynamic interaction sensing results. The dynamic interaction sensing results include real-time motion association information and signal interaction information of each unit of the cluster of UAVs, as well as real-time resource status information and range of action information of each device in the countermeasure equipment cluster.

[0012] This embodiment uses a scenario of coordinated countermeasures against a swarm of drones in a critical security area as its background. This area deploys multiple swarm drones with reconnaissance and communication relay capabilities, and is equipped with a multi-source sensing network consisting of radar, optical, infrared, and signal monitoring equipment, as well as various countermeasures such as signal jamming, electromagnetic suppression, and energy interference. The following steps will be described in detail within this scenario.

[0013] Step S111: Deploy a multi-source sensing device group to perform coverage monitoring of the target area. The multi-source sensing device group includes radar sensing devices, optical sensing devices, infrared sensing devices, and signal sensing devices.

[0014] Within this security zone, radar sensing equipment employs a phased array system, deployed at three high points around the perimeter to form a triangular monitoring network. Each radar has a horizontal detection angle of 0-360 degrees and a vertical detection angle of -10 to 90 degrees, achieving omnidirectional scanning of the target area through time-division multiplexing. Optical sensing equipment utilizes high-definition zoom cameras, mounted on the rooftops of buildings and surrounding poles within the area. The lenses cover a focal length range from wide-angle to telephoto, automatically adjusting according to target distance and cooperating with a pan-tilt unit for 360-degree rotation and pitch adjustment. Infrared sensing equipment employs cooled focal plane array detectors, operating in the mid-wave infrared band, deployed near the radar sensing equipment, scanning synchronously with the radar to ensure continuous monitoring of high-temperature targets. Signal sensing equipment consists of multiple omnidirectional and directional antennas. Omnidirectional antennas are distributed at the center and edges of the area to monitor signals from all directions, while directional antennas track signal sources in specific directions through mechanical rotation or electronic scanning. All sensing equipment is connected to the data processing center via fiber optic transmission links for real-time data aggregation.

[0015] Step S112: Obtain the real-time position coordinates and speed information of each unit of the swarm of UAVs through radar sensing equipment to form radar sensing data; obtain the real-time shape characteristics and formation pattern information of each unit of the swarm of UAVs through optical sensing equipment to form optical sensing data; obtain the real-time thermal radiation characteristics and energy consumption correlation information of each unit of the swarm of UAVs through infrared sensing equipment to form infrared sensing data; capture the signal waveform and signal transmission direction information emitted by each unit of the swarm of UAVs through signal sensing equipment, and simultaneously capture the countermeasure signals and feedback signals emitted by each device in the countermeasure equipment cluster to form signal sensing data.

[0016] Radar sensing equipment transmits electromagnetic waves and receives echoes. It processes the echo signals through pulse compression, moving target detection, and Doppler filtering to extract the target's range, azimuth, and elevation angles. Combined with the radar's own position coordinates, it calculates the three-dimensional Cartesian coordinates of each unit in the swarm of UAVs. By performing differential calculations on the coordinate data from multiple consecutive scan cycles, it obtains the velocity vector of each unit, including its magnitude and direction. The aforementioned position coordinates and velocity information are organized in timestamp order to form radar sensing data.

[0017] The optical sensing device continuously captures images of the target area. After image preprocessing, including denoising, contrast enhancement, and distortion correction, the images are used by a target detection algorithm to identify each unit of the swarm drone and extract its external features such as bounding rectangles, contour features, and texture features. Simultaneously, based on the pixel coordinates of each unit in the image and the camera's intrinsic and extrinsic parameters, the relative positional relationships in the actual scene are converted, and the arrangement patterns between units, such as matrix, wedge, and circular formations, are analyzed to form optical sensing data.

[0018] Infrared sensing equipment detects the infrared radiation energy of each unit in a swarm of UAVs. After photoelectric conversion, signal amplification, and A / D conversion, thermal image data is obtained. Thermal radiation characteristics such as temperature distribution, thermal radiation intensity, and hotspot locations of each unit are extracted from the thermal images. Combined with the UAV's power system type (e.g., electric, fuel-powered), a correlation model between thermal radiation intensity and energy consumption is established. The energy consumption rate and remaining flight time are inferred from the changing trends of thermal radiation intensity, forming infrared sensing data.

[0019] Signal sensing equipment receives electromagnetic signals in space via antennas. After low-noise amplification, filtering, and frequency conversion, the signals are demodulated and parameters measured by a digital signal processor. For signals emitted by each unit of the swarm of UAVs, waveform characteristics such as carrier frequency, bandwidth, modulation scheme, code rate, and signal amplitude are extracted, and the signal transmission direction is determined using an angle-of-arrival estimation algorithm. Simultaneously, countermeasure signals emitted by countermeasure equipment (such as jamming signals and suppression signals) and feedback signals generated after countermeasures (such as UAV response signals and abnormal signals) are captured and their parameters extracted in the same way. All signal parameters are classified according to signal source type and timestamp to form signal sensing data.

[0020] Step S113: Perform interactive correlation processing on radar perception data, optical perception data, infrared perception data and signal perception data, and associate and bind the perception data of the same cluster UAV unit under different perception devices, and associate and bind the perception data of the same countermeasure device under different perception devices.

[0021] The interactive association processing employs an association algorithm based on multi-source data fusion. First, a unified time reference system is established, synchronizing the timestamps of each sensing device to the standard clock of the data processing center. For swarm UAV units, the position coordinates and velocity from radar sensing data are used as initial features. In optical sensing data, matching is performed based on shape features and position information, calculating the positional deviation between the target in the image and the radar target. When the deviation is less than a set threshold, they are determined to be the same unit. In infrared sensing data, association is performed based on the spatial distribution of thermal radiation features and the radar target position. If the infrared target position coincides spatially with the radar target position, and the thermal radiation features match the characteristics of a UAV, then binding is performed. In signal sensing data, association is performed based on the consistency of the signal transmission direction with the azimuth angle of the radar target position. Combining the uniqueness of signal waveform features, the association binding of different sensing data within the same unit is achieved, assigning a unique identifier ID to each unit. For countermeasure devices, multi-source association is performed based on their fixed position coordinates (the device's detectable location by radar), shape features in the optical image, and countermeasure signal features captured by the signal sensing device (such as known transmission frequency and modulation method). A unique identifier ID is also assigned to complete the association binding.

[0022] Step S114: Extract the real-time relative position change patterns and motion direction coordination relationships between each unit from the perception data of the clustered UAV units after association and binding, and form the real-time motion association information of each unit of the clustered UAV.

[0023] From the associated and bound perception data, the position coordinates of each cluster of UAV units are extracted over a continuous time series. The relative distance (Euclidean distance in three-dimensional space) and relative azimuth angle (azimuth angle with one of the units as a reference) between any two units are calculated. The changing trends of these relative position parameters over time are analyzed, and real-time relative position change patterns are summarized, such as stable distance, periodic changes, and gradual increases or decreases. Simultaneously, the motion direction vectors of each unit are extracted, and the cosine value of the angle between the directions is calculated. When the cosine value is close to 1, it indicates the same direction; close to -1, it indicates opposite directions; and close to 0, it indicates perpendicular directions. By analyzing the distribution of the direction cosine values ​​of multiple units, the motion direction coordination relationships between units, such as same-direction motion, opposite-direction motion, and following motion, are determined. These changing patterns and coordination relationships are organized by unit ID to form real-time motion association information.

[0024] Step S115: Extract the waveform characteristics of the transmitted signals of each unit, the signal transmission path and the signal interaction frequency with other units from the perception data of the clustered UAV units after association and binding, and form the signal interaction information of each unit of the clustered UAV.

[0025] For the associated and bound signal sensing data, waveform features such as carrier frequency drift characteristics, modulation depth, code structure, and spectral purity of the transmitted signal are extracted for each cluster of UAV units based on their identifier ID. These features constitute the signal "fingerprint" of that unit. Using signal arrival time difference and direction of arrival information, combined with terrain data of the target area (such as building height and location) and electromagnetic wave propagation models (such as ray tracing models), the propagation path of the signal from the transmitting unit to the receiving unit (which may be other UAV units or a ground control station) is simulated and calculated, including direct path, reflected path, and diffracted path. The number of signal interactions between each unit and other units per unit time is counted, i.e., the signal interaction frequency. The number of interactions is counted through the frame synchronization header or specific identifier fields in the detected signal. The waveform features, signal transmission path, and interaction frequency are organized by unit ID and interaction object ID to form signal interaction information.

[0026] Step S116: Extract the current resource occupancy ratio, remaining resource quantity, and resource consumption rate of each device from the associated and bound countermeasure device perception data to form real-time resource status information of each device in the countermeasure device cluster.

[0027] The associated and bound countermeasure device sensing data includes resource parameters such as device power supply voltage, current, power, storage space utilization, and CPU utilization. The current resource utilization ratio is calculated by dividing the used resources by the total resources; for example, the power resource utilization ratio is the ratio of current output power to rated power, and the storage space utilization ratio is the ratio of used storage space to total storage space. The remaining resource amount is calculated by subtracting the used resources from the total resources; for example, the remaining battery capacity is the total battery capacity multiplied by (1 - power resource utilization ratio). The resource consumption rate is calculated by linearly fitting or averaging the resource utilization data over continuous time, obtaining the amount of resource reduction per unit time, such as the power consumption per unit time and the storage space utilization growth rate. These parameters are organized by device ID to form real-time resource status information.

[0028] Step S117: Extract the effective coverage range, signal propagation attenuation law and effective distance limit of each device's transmitted countermeasure signal from the associated and bound countermeasure device sensing data to form the effective range information of each device in the countermeasure device cluster.

[0029] The effective coverage area of ​​the countermeasures device is determined through field strength testing and simulation calculations. In an anechoic environment, the field strength of the signal emitted by the countermeasures device at different directions and distances is measured. Combining the free-space propagation model, antenna pattern, and environmental attenuation factors of the target area (such as building obstruction loss and vegetation attenuation), the spatial region where the signal field strength is greater than or equal to the interference threshold is calculated. This spatial region is the effective coverage area, typically represented by a polyhedron or distance-angle range in three-dimensional space. The signal propagation attenuation law is obtained by curve fitting the measured field strength data. The fitting model includes parameters such as path loss factor, frequency attenuation coefficient, and distance exponent, used to describe the relationship between signal strength and propagation distance. The effective range limit considers factors such as the maximum transmit power, receive sensitivity, and interference margin of the countermeasures device. When the signal propagates to a certain distance, the field strength is lower than the interference threshold or cannot meet the interference suppression ratio requirement; this distance is the upper limit of the effective range. The effective coverage area, signal propagation attenuation law parameters, and effective range limit are organized according to the device identifier ID to form the effective range information.

[0030] Step S118: Integrate the real-time motion association information and signal interaction information of each unit of the clustered UAV with the real-time resource status information and range of action information of each device in the countermeasure equipment cluster to generate dynamic interactive perception results.

[0031] The dynamic interactive sensing results are organized using a hierarchical data structure. The top layer contains metadata such as timestamps, data version numbers, and device status. The next layer consists of two sub-modules: swarm drone information and countermeasure device information. The swarm drone information sub-module is indexed by unit ID, with each unit entry containing real-time motion correlation information (relative position change patterns, motion direction coordination relationships) and signal interaction information (waveform characteristics, signal transmission path, interaction frequency). The countermeasure device information sub-module is indexed by device identifier ID, with each device entry containing real-time resource status information (current resource occupancy ratio, remaining resource amount, resource consumption rate) and effective coverage information (effective coverage area, signal propagation attenuation patterns, effective distance limitations). The information modules are linked through identifier IDs. For example, in a swarm drone unit entry, it can be linked to other unit IDs with which it has signal interactions; in a countermeasure device entry, it can be linked to swarm drone unit IDs within its effective coverage area. The integrated data is provided to the subsequent hierarchical collaborative decision-making module through a standardized interface.

[0032] Step S120: Based on the dynamic interactive perception results, perform hierarchical collaborative decision processing to generate hierarchical collaborative decision results. The hierarchical collaborative decision results include countermeasure strategy directions for different levels of units in the cluster of drones, as well as the collaborative rules for different devices in the countermeasure device cluster.

[0033] Step S121: Extract real-time motion association information of each unit of the swarm UAV from the dynamic interactive perception results, analyze the motion dominance relationship of each unit in the formation, determine the hierarchical structure of the swarm UAV, and divide it into dominant hierarchical units, cooperative hierarchical units and subordinate hierarchical units.

[0034] Step S1211: Extract the frequency of motion direction change and the magnitude of motion speed adjustment of each unit from the real-time motion association information of each unit of the swarm UAV.

[0035] The real-time motion correlation information records the changes in the motion direction of each unit over time. The frequency of motion direction changes is obtained by counting the number of times the direction angle change exceeds a set threshold per unit time. The motion speed adjustment amplitude is obtained by calculating the absolute value of each speed change and then taking the average or root mean square value of all the amplitudes, reflecting the severity of the speed adjustment.

[0036] Step S1212: Count the number of times each unit causes changes in the direction of motion of other units within a continuous time period, and the total magnitude of the speed adjustments caused by each unit.

[0037] Select a continuous time window (e.g., 30 seconds). For each unit, iterate through all other units and determine whether other units change their direction of motion within a set time window (e.g., 2 seconds) after the unit's direction of motion changes. If so, count the number of times the unit causes a direction change. For the total speed adjustment amplitude, also within this time window, calculate the sum of the speed adjustment amplitudes of other units that follow the unit's motion.

[0038] Step S1213: The unit that causes the most changes in the direction of motion of other units and causes the largest sum of the speed adjustments of other units is marked as the candidate dominant unit.

[0039] The units are ranked according to the number of directional changes and the total magnitude of velocity adjustments. The unit that ranks first in both metrics is selected as the candidate dominant unit. If two units rank first in each metric, a weighted sum is calculated (the weights are set according to the importance of directional dominance and velocity dominance in the actual scenario), and the unit with the largest weighted sum is selected as the candidate dominant unit.

[0040] Step S1214: Analyze the real-time relative position change pattern between the candidate dominant unit and other units, and calculate the time delay of other units following the movement after the candidate dominant unit moves.

[0041] Based on the real-time relative position change pattern, the moment when the motion state of the candidate dominant unit changes (such as a change in direction or speed) is extracted. Then, the moment when each of the other units subsequently undergoes a corresponding motion change is extracted. The difference between the two moments is the time delay of the following motion. If other units do not undergo a corresponding change, the time delay is recorded as infinite.

[0042] Step S1215: Classify the other units with the shortest time delay as candidate cooperative units, and classify the other units with time delays exceeding the preset follow delay interval as candidate subordinate units.

[0043] The preset follow delay range is determined based on the typical response time of the swarm drones (e.g., 0.5 to 2 seconds). The N units with the smallest follow delay values ​​within this range (N is set according to the formation size, such as 20% of the total number of units) are classified as candidate cooperating units, which have the highest cooperation with the candidate leader unit. Units with delays exceeding this range are classified as candidate subordinate units, which respond slowly or not at all to the leader unit.

[0044] Step S1216: Repeat steps S1211 to S1215 above to verify all candidate dominant units, so that the number of candidate cooperative units and candidate subordinate units corresponding to each candidate dominant unit conforms to the formation motion logic.

[0045] If multiple candidate dominant units exist (e.g., multiple sub-formations within a formation), steps S1211 to S1215 are executed for each candidate dominant unit to obtain its respective candidate coordinating units and candidate subordinate units. The number of units in each sub-formation is verified to be reasonable, such as whether the number of candidate dominant units matches the number of sub-formations, and whether the ratio of candidate coordinating units to candidate subordinate units conforms to common formation structures (e.g., 1:3:6). If not, the selection criteria for candidate dominant units or the preset following delay interval are readjusted until the formation motion logic is met.

[0046] Step S1217: Divide the finally determined candidate leading units into leading hierarchical units, candidate cooperating units into cooperating hierarchical units, and candidate subordinate units into subordinate hierarchical units to form a hierarchical structure of the swarm drones.

[0047] After verification, candidate leading units, candidate coordinating units, and candidate subordinate units for each sub-formation are determined. A hierarchical identifier is assigned to each unit (e.g., leading level is L1, coordinating level is L2, and subordinate level is L3), and the ID list of each unit is recorded to form the hierarchical structure of the swarm drones. This hierarchical structure of the swarm drones reflects the command and control relationship of the units within the formation.

[0048] Step S122: Extract signal interaction information of each unit of the swarm UAV from the dynamic interactive perception results, analyze the signal transmission frequency and signal dependence between each unit, verify and correct the hierarchical structure, and make the hierarchical division consistent with the signal interaction relationship.

[0049] The signal transmission frequency between units is extracted from the signal interaction information, i.e., the number of interactions per unit time, to construct a signal interaction frequency matrix. Simultaneously, signal dependency is analyzed by calculating the impact of a unit's signal interruption on the signal transmission of other units; a high impact indicates a strong dependency. The signal interaction frequency matrix and signal dependency matrix are compared with the hierarchical structure obtained in step S1217 to verify whether the dominant hierarchical unit has the highest signal transmission frequency and signal dependency with other hierarchical units, and whether the signal interaction frequency between cooperating hierarchical units is higher than the frequency between cooperating and subordinate hierarchical units. If contradictions exist, such as a subordinate hierarchical unit having a higher signal transmission frequency with the dominant hierarchical unit than a cooperating hierarchical unit, the hierarchical division of that unit is adjusted, or it is reassigned to a new sub-group, until the hierarchical structure and signal interaction relationships are consistent.

[0050] Step S123: For the dominant level unit, based on the range of action information of each device in the countermeasure device cluster in the dynamic interactive perception results, analyze the types and quantities of countermeasure devices that can cover the dominant level unit, and determine the countermeasure strategy direction for the dominant level unit. This countermeasure strategy direction focuses on cutting off the signal interaction between the dominant level unit and other level units.

[0051] The real-time location coordinates of the dominant hierarchical unit and the effective range information of the countermeasures equipment are extracted from the dynamic interactive sensing results. For each dominant hierarchical unit, it is determined whether the effective coverage range of each countermeasures device includes the unit's location. The effective coverage range is determined by substituting the unit's location coordinates into the coverage range model of the countermeasures equipment (e.g., distance-angle range). If the coordinates are within the range, the device can cover the unit. The types of countermeasures equipment that can cover the dominant hierarchical unit are counted, such as communication jamming equipment and navigation jamming equipment, as well as the number of each type of equipment. Since the dominant hierarchical unit communicates with other units via signals, cutting off its signal interaction can paralyze the entire cluster. Therefore, the countermeasure strategy is determined to be cutting off signal interaction, specifically including interfering with its communication links and navigation signals.

[0052] Step S124: For the collaborative level unit, based on the real-time resource status information of each device in the countermeasure device cluster in the dynamic interactive perception results, analyze the distribution of countermeasure devices with remaining resources, and determine the countermeasure strategy direction for the collaborative level unit. This countermeasure strategy direction focuses on interfering with the motion coordination relationship of the collaborative level unit.

[0053] Countermeasure devices with remaining resources exceeding a set threshold are selected from real-time resource status information. The types and geographical distribution of these devices in the target area are then statistically analyzed. Coordination hierarchical units are responsible for coordinated movement within the formation. Their movement depends on information exchange with the dominant hierarchical unit and other coordination units. Disrupting their coordinated movement can loosen and disrupt the formation. Based on the distribution of devices with remaining resources, if there are numerous electromagnetic pulse devices or acoustic interference devices in the area, these devices can be used to interfere with the sensors or power systems of coordination units, thereby disrupting their coordinated movement. Therefore, the countermeasure strategy is determined to be disrupting the coordinated movement relationship.

[0054] Step S125: For subordinate level units, based on the resource consumption rate of each device in the countermeasure device cluster in the dynamic interactive perception results, analyze the countermeasure device capabilities that can continue to function, and determine the countermeasure strategy direction for subordinate level units. This countermeasure strategy direction focuses on weakening the energy supply correlation of subordinate level units.

[0055] The resource consumption rate and remaining resource quantity of the countermeasure devices are extracted from real-time resource status information. The continuous operating time of each device is calculated, i.e., the remaining resource quantity divided by the resource consumption rate. Devices with a continuous operating time exceeding a set threshold are selected, and their countermeasure capabilities are analyzed, such as whether they possess continuous energy interference capabilities (e.g., laser irradiation, microwave heating). Subordinate level units have relatively low energy reserves and are highly dependent on dominant and cooperative level units. Weakening their energy supply can cause them to lose their endurance and be unable to perform tasks. Therefore, the countermeasure strategy is determined to weaken the energy supply dependence, causing the subordinate unit's battery power to be rapidly depleted or its power system to overheat and fail through continuous energy interference.

[0056] Step S126: Extract real-time resource status information and scope information of each device in the countermeasure device cluster from the dynamic interactive perception results, analyze the resource complementarity and scope overlap of each device, and determine the coordination rules between countermeasure devices, including resource sharing rules, scope connection rules and signal interference avoidance rules.

[0057] Analyze the resource types of each countermeasure device, such as power resources, computing resources, storage resources, and signal resources, to determine which devices have complementary resources. For example, if one device has sufficient power resources but insufficient computing resources, while another device has sufficient computing resources but limited power resources, then the two can share resources. The resource sharing rules specify the types of resources to be shared, the sharing conditions (such as triggering sharing when one party's resources fall below a threshold), the sharing priority, and the method for calculating the sharing amount.

[0058] The coverage connection rules are determined by analyzing the overlapping and gap areas of the effective coverage of each device. For overlapping areas, the working priority or time-sharing mode of the devices is specified to avoid mutual signal interference. For gap areas, adjacent devices are scheduled to adjust their coverage range (such as adjusting the antenna azimuth angle or increasing the transmission power) to achieve coverage connection and ensure that there are no blind spots in the target area.

[0059] The signal interference avoidance rules are formulated based on parameters such as the operating frequency and modulation method of the countermeasures equipment. For equipment operating in the same or adjacent frequency bands, methods such as frequency division, time slot allocation, or spatial isolation are used to avoid mutual interference. For example, if equipment A operates in frequency band f1 and equipment B operates in frequency band f2, ensure that the interval between f1 and f2 is greater than the set protection bandwidth; or if equipment A operates in time period t1 and equipment B operates in time period t2, t1 and t2 do not overlap.

[0060] Step S127: Integrate the countermeasure strategy directions for different levels of the clustered drones with the coordination rules of different devices in the countermeasure equipment cluster to generate hierarchical collaborative decision results.

[0061] The hierarchical collaborative decision-making results are organized in the form of a decision tree or rule base. The top layer represents the overall decision objective (e.g., paralyzing the combat capability of a cluster of drones). The next layer is divided into branches representing countermeasure strategies for the dominant, collaborative, and subordinate levels. Each branch includes information such as the strategy objective, the type of countermeasure resources required, and priority. Rules for the coordinated operation of countermeasure equipment serve as constraints on the decision-making process and are embedded in the execution steps of each strategy direction. For example, when executing a strategy to cut off signal interaction at the dominant level, communication interference equipment must be scheduled according to signal interference avoidance rules. Simultaneously, the results also include the temporal relationship of strategy execution (e.g., interfering with the dominant level first, then the collaborative level) and resource scheduling priorities, forming a complete hierarchical collaborative decision-making result.

[0062] Step S130: Based on the hierarchical collaborative decision-making results, adaptive scheduling processing is performed on the resources of the countermeasure equipment cluster to generate an adaptive resource scheduling scheme. The adaptive resource scheduling scheme includes the cluster UAV units corresponding to each device in the countermeasure equipment cluster, as well as the resource allocation method of each device.

[0063] Step S131: Extract the countermeasure strategy direction for different levels of the clustered drones from the hierarchical collaborative decision-making results, and determine the type and total amount of resources required for countermeasures at each level.

[0064] For the "cut off signal interaction" strategy of the dominant hierarchical unit, the required resource types include communication interference resources (such as interference signal power and bandwidth) and navigation interference resources (such as GPS / LBS interference power). The total amount of resources is calculated based on the number of dominant hierarchical units, communication link bandwidth, and navigation signal strength. For example, the communication interference power requirement of each unit is determined based on the link budget and interference suppression ratio. The total amount of communication interference resources is the number of units multiplied by the requirement of a single unit.

[0065] For the strategy of "interfering with motion coordination" at the collaborative hierarchical level, the required resource types include electromagnetic pulse resources (such as pulse energy and repetition frequency) and acoustic interference resources (such as sound pressure level and frequency range). The total amount of resources is calculated based on the number of collaborative hierarchical units and the sensitivity of motion sensors; for example, the electromagnetic pulse energy requirement is determined based on the sensor's damage threshold.

[0066] For the strategy of "weakening energy supply dependencies" targeting subordinate hierarchical units, the required resource types include laser energy resources (such as laser power and irradiation time) and microwave heating resources (such as microwave power and action time). The total resource amount is calculated based on the number of subordinate hierarchical units, battery capacity, and power system thermal capacity. For example, the laser power requirement is determined based on the energy required to heat the battery to its failure temperature and the irradiation time.

[0067] Step S132: Extract the coordination rules of different devices in the countermeasure device cluster from the hierarchical collaborative decision results, and determine the types of resources that each device can provide and the maximum supply of resources.

[0068] The rules for coordination clearly define the functional types of each countermeasure device. For example, communication jamming devices can provide communication jamming resources, while laser jamming devices can provide laser energy resources. The maximum resource supply is determined based on the technical parameters of the devices. For instance, the maximum output power of a communication jamming device is its maximum supply of communication jamming resources, and the product of the maximum laser power and continuous operating time of a laser jamming device is its maximum supply of laser energy resources. The rules also consider constraints on the maximum supply, such as the device's heat dissipation limitations and power supply capacity.

[0069] Step S133: Based on the type and total amount of resources required for countermeasures by the dominant hierarchical unit, and combined with the type and maximum supply of resources available by each device, prioritize the allocation of resources to countermeasure devices that can cover the dominant hierarchical unit, and determine the dominant hierarchical unit served by the countermeasure device.

[0070] For each resource type required for countering dominant hierarchical units, such as communication interference resources, the system iterates through countermeasure devices that can cover any dominant hierarchical unit and provide that resource type. Based on the device's maximum resource supply and the number of dominant hierarchical units it covers, a greedy algorithm or integer programming method is used for resource allocation. Devices with larger maximum resource supply and covering more dominant hierarchical units are prioritized for resource allocation until the total resource requirement of the dominant hierarchical units is met. After allocation, a list of dominant hierarchical unit IDs allocated to each device is recorded, thus determining the dominant hierarchical unit served by each device.

[0071] Step S134: Based on the resource type and total amount required for countermeasures by the collaborative level unit, and combined with the resource type and maximum supply of the remaining countermeasure equipment, allocate resources to the countermeasure equipment with remaining resources, and determine the collaborative level unit corresponding to the service of the countermeasure equipment.

[0072] After completing the resource allocation for the dominant hierarchical unit, the remaining countermeasure devices (devices with unallocated or unused resources) are tallied to determine their available resource types and remaining resource quantities. For resource types required for countermeasures at the collaborative hierarchical level, such as electromagnetic pulse resources, devices that can provide this resource type and have a remaining resource quantity greater than the requirement are selected from the remaining devices. These devices are then allocated resources in descending order of remaining quantity, ensuring that the allocated resource quantity for each device does not exceed its remaining resource quantity and the resource requirement of the collaborative hierarchical unit. After allocation, a list of collaborative hierarchical unit IDs corresponding to each device is recorded.

[0073] Step S135: Based on the resource type and total amount required for countermeasures by subordinate hierarchical units, and combined with the resource type and maximum resource supply available from the remaining countermeasure equipment, allocate resources to countermeasure equipment that can operate continuously, and determine the subordinate hierarchical unit corresponding to the service provided by the countermeasure equipment.

[0074] For the resource type required for countermeasures by subordinate hierarchical units, such as laser energy resources, devices capable of continuous operation (working time greater than a set threshold) and providing this resource type are selected from the remaining countermeasure devices. Based on the distribution location of subordinate hierarchical units and the operational range of the devices, units are assigned to the nearest device with the best operational effect. The amount of resources allocated is determined based on the number of units and the needs of individual units, ensuring that the continuous operating time of the devices meets the countermeasure requirements. A list of subordinate hierarchical unit IDs corresponding to each device is recorded.

[0075] Step S136: Extract resource sharing rules between countermeasure devices from the hierarchical collaborative decision-making results, dynamically adjust the resources allocated to each device, and when any device has insufficient resources, allocate resources from devices with surplus resources to supplement them, while updating the cluster drone units served by the corresponding device.

[0076] The resource sharing rules define the triggering conditions, sharing methods, and priorities for resource sharing. When a device's remaining resources fall below a set warning threshold (resource insufficiency) during a countermeasure mission, the system queries the resource sharing rules to find devices that can provide the same type of resources and have surplus resources (remaining resources greater than the sharing threshold). Based on sharing priorities (prioritizing devices of the same type and those closest), the system selects the resource-surplus device, calculates the required resource allocation (not exceeding the shareable amount of the surplus device and the demand of the insufficient device), and allocates resources from the surplus device to the insufficient device. After resource allocation, the cluster drone units served by the two devices are adjusted accordingly, such as transferring some units originally served by the insufficient device to the surplus device, ensuring the continuity of the countermeasure mission.

[0077] Step S137: Extract the rules for connecting the scope of action between countermeasures devices from the hierarchical collaborative decision-making results.

[0078] The rules for coordinating the effective ranges of different countermeasures devices specify in detail the methods for coordinating these ranges, including spatial and temporal coordination. Spatial coordination rules require that the overlapping area of ​​the effective ranges of adjacent devices be no less than a set width (e.g., 50 meters) to avoid coverage gaps; for devices with height differences, a vertical coordination gradient is specified (e.g., each device is responsible for a 100-meter height layer). Temporal coordination rules, for devices with intermittent operating characteristics, such as electromagnetic pulse devices, stipulate that their operating times are staggered with those of adjacent devices to avoid interference caused by countermeasures being applied to the same area at the same time.

[0079] Step S138: Determine the resource allocation method for each device, including the resource allocation ratio, resource replenishment frequency and resource adjustment triggering conditions. Integrate the cluster UAV units corresponding to each device in the countermeasure device cluster with the resource allocation method of each device to generate an adaptive resource scheduling scheme.

[0080] Step S1381: Extract the number of clustered UAV units served by each device and the amount of resources required for countermeasures by each clustered UAV unit from the preliminary allocation results of the adaptive resource scheduling scheme.

[0081] The initial allocation result of the adaptive resource scheduling scheme includes a list of unit IDs for each device service, and the number of units in the list is the number of clustered UAV units for each device service. For each unit, based on its hierarchical type and countermeasure strategy direction, the resource amount required for countermeasure by a single unit is decomposed from the total resource amount determined in step S131, such as the single communication interference resource amount for the dominant hierarchical unit, the single electromagnetic pulse resource amount for the cooperative hierarchical unit, etc.

[0082] Step S1382: Calculate the total resource requirement for each device, which is the sum of the resources required for countermeasures by all clustered UAV units served by each device.

[0083] The total resource requirement of a device is obtained by summing the individual resource quantities of all clustered drone units served by each device. For example, if a communication jamming device serves 3 dominant level units, and the communication jamming resource quantity of each unit is R, then the total resource requirement of the device is 3R.

[0084] Step S1383: Determine the resource allocation ratio based on the total resource demand of each device and the maximum resource supply of each device. The resource allocation ratio is the ratio of the total resource demand to the maximum resource supply.

[0085] The formula for calculating the resource allocation ratio is: Resource allocation ratio = Total resource demand / Maximum resource supply. This resource allocation ratio reflects the intensity of equipment resource utilization. The closer the ratio is to 1, the more fully the resources are utilized, but it must be less than or equal to 1 to avoid resource overload.

[0086] Step S1384: Extract the resource consumption rate of each device in the countermeasure device cluster from the dynamic interaction perception results, and determine the current amount of resources to be consumed for each device by combining the resource allocation ratio and the maximum resource supply in the resource allocation method.

[0087] Current resource consumption = resource allocation ratio × maximum resource supply, which is the total amount of resources that the equipment needs to consume under the current resource allocation method. This value is equal to the total resource demand (when the resource allocation ratio is less than or equal to 1).

[0088] Step S1385: Calculate the resource consumption cycle for each device based on the current amount of resources to be consumed and the resource consumption rate of each device.

[0089] Resource consumption cycle = current amount of resources to be consumed / resource consumption rate. This resource consumption cycle represents the time required for the device to consume all the resources to be consumed under the current resource allocation method.

[0090] Step S1386: Set half of the resource consumption cycle as the resource replenishment time interval. When the remaining resource amount drops to half of the current amount of resources to be consumed, resource replenishment is triggered to maintain the continuity of countermeasures.

[0091] The resource replenishment interval is equal to the resource consumption cycle divided by 2. When the remaining resources of the device drop to half of the current amount of resources to be consumed (i.e., remaining resources = current amount of resources to be consumed / 2), the resource replenishment mechanism is triggered. Resource replenishment can be achieved through switching the device's built-in backup power supply, accessing external power supply, or allocating resources from other devices through a resource scheduling system, ensuring that replenishment is completed before resources are exhausted and avoiding counter-interruption.

[0092] Step S1387: Set resource adjustment trigger conditions. When the resource occupancy ratio of the device exceeds the preset normal occupancy range, or the movement of the cluster drone unit served by the device exceeds the effective range of the device, or the countermeasure effect fed back by the device does not reach the preset effect range, resource adjustment is triggered.

[0093] The preset normal resource usage range is [0.3, 0.8]. When the device's resource usage ratio is less than 0.3, it indicates that the resource utilization rate is too low and resources need to be reduced; when it is greater than 0.8, it indicates that resources are scarce and resources need to be increased or some tasks need to be transferred. The movement of clustered drone units served by the device exceeds its effective range, which is determined by real-time location monitoring. Adjustment is triggered when the unit's location coordinates are outside the device's effective coverage area. The preset range for countermeasure effects is set according to the countermeasure strategy objectives. For example, the effective range for communication interference is a signal blocking rate ≥90%. Adjustment is triggered when the signal blocking rate reported by the device is lower than 90%.

[0094] Step S1388: Integrate the determined resource allocation ratio, resource replenishment time interval and resource adjustment trigger conditions to form the resource allocation method for each device.

[0095] Organize the resource allocation ratio, resource replenishment time interval (or resource replenishment frequency, frequency = 1 / time interval), and resource adjustment trigger conditions (including the threshold and judgment logic of each condition) of each device according to the device identifier ID to form a structured description of the resource allocation method, such as JSON or XML format.

[0096] Step S1389: Integrate the cluster UAV units corresponding to each device in the countermeasure equipment cluster with the resource allocation methods of each device to generate an adaptive resource scheduling scheme.

[0097] The adaptive resource scheduling scheme comprises two main parts: a device-unit service relationship table and a device resource allocation method table. The device-unit service relationship table records each device ID and its corresponding list of service unit IDs; the device resource allocation method table records each device ID and its corresponding resource allocation ratio, resource replenishment frequency, and resource adjustment trigger conditions. The scheme also includes metadata such as the scheme generation timestamp and validity period, as well as resource scheduling priority rules and conflict resolution mechanisms (e.g., the scheduling order when multiple devices request resources simultaneously).

[0098] Step S140: Based on the adaptive resource scheduling scheme, generate a dynamic countermeasure instruction set, which includes the real-time operation parameters and execution timing schedule of each device in the countermeasure device cluster.

[0099] Step S141: Extract the cluster UAV units corresponding to each device in the countermeasure device cluster and the resource allocation method of each device from the adaptive resource scheduling scheme.

[0100] By parsing the device-unit service relationship table of the adaptive resource scheduling scheme, a list of cluster UAV unit IDs for each device service is obtained; resource allocation method information such as resource allocation ratio, resource replenishment frequency, and resource adjustment trigger conditions for each device is extracted from the device resource allocation method table, laying the foundation for subsequent generation of operation parameters and execution sequence.

[0101] Step S142: For each device, determine the corresponding operation parameter type according to the hierarchical type of the cluster UAV unit served by the device. For devices serving the dominant hierarchical unit, the operation parameter type includes signal interference frequency, signal interference intensity, and signal duration. For devices serving the cooperative hierarchical unit, the operation parameter type includes motion interference amplitude, motion interference frequency, and interference coverage. For devices serving the subordinate hierarchical unit, the operation parameter type includes energy attenuation intensity, energy attenuation frequency, and duration of continuous action.

[0102] Step S1421: Distinguish whether the clustered drone units served by the device belong to the dominant level unit, the cooperative level unit, or the subordinate level unit.

[0103] Based on the list of cluster drone unit IDs served by the device, query the hierarchical structure determined in step S1217 to obtain the hierarchical identifier (L1, L2, L3) of each unit. If all units served by the device are of the same hierarchical type (which is usually the case in actual applications to ensure the consistency of the countermeasure strategy), then the service hierarchical type of the device is that level. If there are mixed levels, the main service hierarchical type needs to be determined according to the hierarchical priority (dominant hierarchical priority), or the device resources can be split to serve different levels respectively.

[0104] Step S1422: If the device serves a dominant hierarchical unit, the countermeasure strategy direction for the dominant hierarchical unit is determined in the hierarchical collaborative decision-making results. This countermeasure strategy direction focuses on cutting off the signal interaction between the dominant hierarchical unit and other hierarchical units. Therefore, the operation parameter type is determined to include signal interference frequency, signal interference intensity and signal duration.

[0105] The signal interaction of the dominant hierarchical unit is mainly achieved through the communication link. The signal interference frequency must cover the operating frequency of its communication signal, including the carrier frequency and upper and lower sidebands. The signal interference intensity must meet the interference suppression ratio requirement, that is, the ratio of the interference signal intensity to the target signal intensity is greater than the set threshold. The signal duration must cover the duration of the countermeasure task, while taking into account the signal opening and closing sequence to avoid unnecessary energy consumption.

[0106] Step S1423: If the device serves a collaborative level unit, the countermeasure strategy direction for the collaborative level unit is determined in the hierarchical collaborative decision result. This countermeasure strategy direction focuses on interfering with the motion collaboration relationship of the collaborative level unit. Therefore, the operation parameter type is determined to include motion interference amplitude, motion interference frequency and interference coverage.

[0107] The motion coordination of the collaborative hierarchical units relies on motion sensors such as accelerometers and gyroscopes. Motion interference amplitude refers to the amount of interference superposition on the sensor output signal, such as the peak value of the voltage interference signal. The motion interference frequency needs to match the sensor's operating frequency band or natural frequency to produce the maximum interference effect. The interference coverage range refers to the effective area of ​​the interference signal in space, which needs to cover the motion trajectory of the collaborative hierarchical units.

[0108] Step S1424: If the device serves a subordinate hierarchical unit, the countermeasure strategy direction for the subordinate hierarchical unit is determined in the hierarchical collaborative decision-making results. This countermeasure strategy direction focuses on weakening the energy supply relationship of the subordinate hierarchical unit. Therefore, the operation parameter type is determined to include energy weakening intensity, energy weakening frequency and duration of action.

[0109] The energy supply of subordinate hierarchical units mainly comes from batteries. Energy attenuation intensity refers to the energy transferred to the battery or power system per unit time, such as laser power density. Energy attenuation frequency refers to the repetition period of energy action, such as the repetition frequency of pulsed laser. Continuous action duration refers to the total time of energy action, which must ensure that the energy supply system fails within this time.

[0110] Step S1425: Record the type of operating parameters corresponding to each device.

[0111] Create a list of operating parameter types for each device. For example, the device list for the service-leading unit includes "signal interference frequency", "signal interference strength" and "signal duration", and indicate the unit and value range of each parameter (e.g., the unit of signal interference frequency is Hertz, and the value range is the device's operating frequency band).

[0112] Step S143: Extract the real-time motion association information and signal interaction information of the cluster of UAV units served by the device from the dynamic interactive perception results, and combine them with the resource allocation ratio in the resource allocation method to determine the specific value of each operation parameter and form the real-time operation parameters of the device.

[0113] For devices serving the dominant unit, the carrier frequency and bandwidth of the dominant unit's communication signal are extracted from the signal interaction information. The signal interference frequency is set to cover the frequency range of the carrier frequency and bandwidth. For example, if the carrier frequency is f0 and the bandwidth is B, the interference frequency range is from f0-B / 2 to f0+B / 2. The signal interference intensity is determined based on the resource allocation ratio and the device's maximum output power. For example, if the resource allocation ratio is 0.8 and the maximum interference intensity is I0, the actual interference intensity is 0.8×I0. The signal duration is determined based on the countermeasure task requirements and the resource replenishment frequency. If the task requires continuous interference for time T and the resource replenishment frequency is once every t, the signal duration is set to T, during which resource replenishment is performed according to the replenishment frequency.

[0114] For devices at the service collaboration level, the motion speed and acceleration of the collaboration level units are extracted from real-time motion correlation information. The motion interference amplitude is determined based on the resource allocation ratio. For example, if the resource allocation ratio is 0.7 and the maximum interference amplitude is A0, then the actual interference amplitude is 0.7 × A0. The motion interference frequency is determined based on the bandwidth of the unit's motion sensor. For example, if the sensor bandwidth is F, then the interference frequency is set to be near F. The interference coverage area is set to a spatial region containing the predicted trajectory, based on the unit's real-time position and motion trajectory prediction.

[0115] For the devices at the service subordinate level unit, extract the thermal radiation characteristics and energy consumption correlation information of the subordinate level unit from the infrared sensing data, and determine the energy weakening intensity in combination with the resource allocation ratio. For example, if the resource allocation ratio is 0.9 and the maximum energy weakening intensity is E0, the actual intensity is 0.9×E0. The energy weakening frequency is determined according to the technical parameters of the device (such as the laser pulse repetition frequency) and the thermal response time of the unit energy system. The continuous action duration is calculated based on the heat capacity of the unit energy system and the energy weakening intensity to ensure that the temperature rises to the failure threshold within this duration.

[0116] Step S144: Extract the cooperation rules of different devices in the countermeasure device cluster from the hierarchical collaborative decision result, and determine the execution start time of each device in combination with the resource replenishment frequency of each device in the adaptive resource scheduling scheme.

[0117] The action range connection rule and signal interference avoidance rule in the cooperation rule specify the execution order and time interval of the devices. For devices that require spatial connection, according to the positional relationship of their action ranges, determine the execution start time in the order from outside to inside or from left to right. For example, if the coverage area of device A is on the left and device B is on the right, then device A starts first, and device B starts after a delay of t time. t is calculated based on the size of the overlapping area of the action ranges of the two devices and the target movement speed. For devices that need to avoid signal interference, the time division multiplexing method is adopted. For example, if device C and device D operate in the same frequency band, then device C executes in the time period [t1, t2], and device D executes in the time period [t3, t4], where t2 < t3, and the time interval is determined according to the signal attenuation time. At the same time, in combination with the resource replenishment frequency, ensure that the execution start time does not conflict with the resource replenishment time. For example, if the resource replenishment is at time ts, the execution start time is set after ts.

[0118] Step S145: Analyze the execution start time of each device and the action duration or continuous action duration in the operation parameters to determine the execution end time of each device.

[0119] Execution end time = Execution start time + Action duration (or continuous action duration). For devices with resource replenishment, if the action duration includes multiple resource replenishment cycles, the execution end time is the end time of the last replenishment cycle.

[0120] Step S146: Schedule the execution timings of each device according to the execution start time and execution end time of each device, and the action range connection rule between the devices.

[0121] Mark the start and end times of each device's execution on the timeline and check for time conflicts (e.g., two devices operating in the same area within the same time period and potentially interfering with each other). Adjust the start times of conflicting devices according to the scope of action rules; for example, delay device B's start time until after device A's end time, or shorten device A's duration. For devices that need to work collaboratively (e.g., simultaneously interfering with different links of the same target), set their start and end times to be the same to ensure synchronous operation. The final execution sequence includes the execution time period of each device and its time relationship with other devices (e.g., parallel, serial, delayed).

[0122] Step S147: Integrate the real-time operation parameters and execution timing of each device to form a dynamic countermeasure instruction set that includes the real-time operation parameters and execution timing of each device in the countermeasure device cluster.

[0123] The dynamic countermeasure command set generates one countermeasure command for each device. The command format includes the device identifier ID, a list of real-time operation parameters (parameter name, value, unit), and execution timing schedule (execution start time, execution end time, and time relationship description). The command set uses binary encoding or structured text format and is sent to each countermeasure device through a communication link to ensure the integrity and accuracy of the commands.

[0124] Step S150: Send the dynamic countermeasure command set to the countermeasure device cluster, and at the same time receive the real-time countermeasure effect information fed back by the countermeasure device cluster. Combine the real-time countermeasure effect information with the dynamic interactive perception results, and make closed-loop adjustments to the dynamic countermeasure command set so that the countermeasure device cluster can continuously perform coordinated countermeasure operations on the clustered UAVs.

[0125] Step S151: Establish a dedicated communication link between the countermeasure control center and each device in the countermeasure equipment cluster, and send the device instructions in the dynamic countermeasure instruction set to the corresponding device through the dedicated communication link.

[0126] The countermeasure control center and each countermeasure device establish a dedicated communication link via wired Ethernet or 4G / 5G wireless communication based on the TCP / IP protocol. The communication link employs end-to-end encryption using the AES-256 encryption algorithm. The key is pre-allocated offline or dynamically generated via a key exchange protocol (such as Diffie-Hellman). The link establishment process includes device discovery, authentication, and parameter negotiation (such as transmission rate and timeout). Authentication uses a two-way digital certificate mechanism to ensure the legitimacy of both communicating parties. Command transmission employs a reliable transmission method. Each device command includes a command header (device ID, command length, checksum) and a command body (dynamic countermeasure command content). The control center sends commands sequentially by device ID or via multicast to multiple devices.

[0127] Step S152: During the instruction sending process, receive instruction reception status information from each device in real time. When all devices report that the instruction has been successfully received, stop the instruction retransmission operation.

[0128] After receiving a command, the device verifies it (e.g., checksum verification). If the verification passes, it sends a successful command reception status message to the control center, including the device ID, command sequence number, and reception timestamp. If the verification fails, it sends a reception failure status message, including the reason for the failure (e.g., checksum error, command format error). The control center maintains a command transmission status table, recording the number of times each device's command has been sent and its reception status. When all devices report a successful status, the retransmission queue is cleared, and command retransmission stops. If no successful feedback is received from a device within the set timeout period, the command for that device is retransmitted, with the number of retransmissions not exceeding a set limit (e.g., 3 times). If the retransmission still fails, the faulty device is recorded and an alarm is triggered.

[0129] Step S153: After each device in the countermeasure equipment cluster executes the countermeasure operation according to the instructions, it collects the equipment operation data and the status change data of the cluster drones in real time during the countermeasure process, forms real-time countermeasure effect information, and feeds it back to the countermeasure control center through a dedicated communication link.

[0130] Equipment operation data includes power supply voltage, current, power, temperature, CPU utilization, and storage space utilization, which are collected in real time through internal sensors and monitoring modules. The sampling frequency is set according to the parameter characteristics (e.g., 1kHz for voltage and current, and 1Hz for temperature). Status change data of the swarm drones are collected through multi-source sensing devices, such as radar sensing of position and speed changes, optical sensing of shape and formation changes, infrared sensing of thermal radiation changes, and signal sensing of signal waveform and transmission direction changes. The sampling frequency is consistent with the scanning frequency of the sensing devices. Equipment operation data and status change data are timestamped and packaged into real-time countermeasure effect information. The information format includes a data header (device ID, data length, timestamp) and a data body (names, values, and units of various parameters), which is periodically sent to the control center via a dedicated communication link. The sending period is set according to the real-time requirements of the countermeasure (e.g., 100ms).

[0131] Step S154: The countermeasure control center receives real-time countermeasure effect information and extracts countermeasure effect data of each device from the real-time countermeasure effect information, including the degree of signal interaction blocking of the dominant level unit, the degree of motion interference of the cooperative level unit, and the degree of energy weakening of the subordinate level unit.

[0132] The control center analyzes the received real-time countermeasure effect information and extracts parameters related to the countermeasure effect. For the dominant level unit, the degree of signal interaction blocking is calculated using signal sensing data, such as the bit error rate and packet loss rate of the communication signal. When the bit error rate is greater than a set threshold (e.g., 10^-3) or the packet loss rate is greater than 50%, it is determined that the blocking is successful. The degree of blocking is expressed as a percentage (e.g., blocking success rate = number of successful blocking / total number of interactions × 100%). For the cooperative level unit, the degree of motion cooperative interference is calculated using radar sensing data, such as the standard deviation of the relative distance between units and the variance of the angle between motion directions. The larger the standard deviation and variance, the higher the degree of interference. Normalized values ​​(0 to 1) are used, with 1 indicating complete interference. For the subordinate level unit, the degree of energy attenuation is calculated using infrared sensing data, such as the rate of decrease in thermal radiation intensity and the rate of increase in energy consumption rate. The larger the rate of decrease or increase, the higher the degree of attenuation. Normalized values ​​are also used.

[0133] Step S155: Extract the latest real-time motion association information, signal interaction information, and real-time resource status information and range of action information of each unit of the swarm UAV from the dynamic interactive perception results.

[0134] The control center periodically (e.g., every 1 second) obtains the latest version of the dynamic interactive sensing results from the data processing center, extracts updated information such as the real-time position, movement speed, signal transmission frequency, and thermal radiation intensity of each unit of the swarm drone, as well as the latest status information such as the remaining resources, resource consumption rate, and changes in the effective range of the countermeasure equipment (e.g., a shortened effective distance due to temperature changes), to ensure the timeliness of the data used for analysis.

[0135] Step S156: Perform correlation analysis between the countermeasure effect data and the latest information in the dynamic interaction perception results to determine whether the current dynamic countermeasure command set needs to be adjusted. If the signal interaction blocking degree of the dominant level unit does not reach the expected level, or the motion coordination of the cooperative level unit remains stable, or the energy of the subordinate level unit does not show a continuous weakening, or the resource consumption of the countermeasure equipment exceeds the preset consumption range, or the movement of the cluster drone unit exceeds the range of the equipment, then it is determined that adjustment is needed.

[0136] For example, step S1561: Extract the signal interaction blocking degree data of the dominant level unit from the countermeasure effect data. The signal interaction blocking degree data of the dominant level unit reflects the signal transmission success rate between the dominant level unit and other units. Compare the signal transmission success rate with the preset blocking success range. If the signal transmission success rate does not fall into the preset blocking success range, it is determined that the signal interaction blocking degree of the dominant level unit has not reached the expected level.

[0137] Signal transmission success rate = (1 - packet loss rate) × (1 - bit error rate). The preset blocking success interval is set according to the countermeasure strategy target (e.g., [0, 0.3], meaning a success rate below 30% is considered a successful block). The calculated signal transmission success rate is compared with this interval. If it is not within the interval (e.g., a success rate of 0.4), it is determined that the expected result has not been achieved.

[0138] Step S1562: Extract the motion coordination interference level data of the collaborative level unit from the countermeasure effect data. The motion coordination interference level data of the collaborative level unit reflects the consistency of motion direction and speed between collaborative level units. Compare the consistency data with the preset interference effective range. If the consistency data exceeds the preset interference effective range, it is determined that the motion coordination of the collaborative level unit remains stable.

[0139] Motion direction consistency is represented by the average cosine of the angle between the motion direction vectors of all cooperative hierarchical units; the closer the value is to 1, the higher the consistency. Velocity consistency is represented by the standard deviation coefficient of the velocity magnitude; the smaller the value, the higher the consistency. The preset effective interference range is the lower range of consistency data (e.g., direction cosine value [0, 0.5], velocity standard deviation coefficient [0.3, 1]). If the consistency data exceeds this range (e.g., direction cosine value 0.6, standard deviation coefficient 0.2), then the motion coordination is considered to remain stable.

[0140] Step S1563: Extract the energy weakening degree data of subordinate level units from the countermeasure effect data. The energy weakening degree data of subordinate level units reflects the energy reserve change rate of subordinate level units. Compare the energy reserve change rate with the preset effective weakening range. If the energy reserve change rate does not enter the preset effective weakening range, it is determined that the energy of subordinate level units has not been continuously weakened.

[0141] The rate of change of energy reserves is calculated by the amount of energy reduction per unit time, such as the rate of decrease in battery capacity. The preset effective attenuation range is the high range of the rate of change (e.g., rate of decrease in capacity [0.1C / h, 0.5C / h], where C is the battery capacity). If the rate of change does not enter this range (e.g., 0.05C / h), it is determined that the energy has not been continuously attenuated.

[0142] Step S1564: Extract the resource consumption rate and remaining resource amount of each device in the countermeasure device cluster from the real-time resource status information of each device in the dynamic interactive perception result; compare the resource consumption rate with the preset normal consumption rate range, and compare the remaining resource amount with the preset safe remaining amount range; if the resource consumption rate exceeds the preset normal consumption rate range, or the remaining resource amount is lower than the preset safe remaining amount range, then it is determined that the countermeasure device resource consumption exceeds the preset consumption range.

[0143] The preset normal consumption rate range is set according to the device type and countermeasure strategy. For example, the normal power consumption rate range of a communication jamming device is [Pmin, Pmax]. The safety surplus range is the low threshold of the remaining resources. For example, the remaining battery power ≥ 20% of the total capacity, and the remaining storage space ≥ 10% of the total capacity. If the resource consumption rate > Pmax or < Pmin, or the remaining resources < the safety threshold, it is determined that the preset consumption range is exceeded.

[0144] Step S1565: From the real-time motion association information of each unit of the cluster UAV in the dynamic interaction perception result and the action range information of each device in the countermeasure device cluster, extract the real-time position of the cluster UAV unit and the boundary coordinates of the device's action range, and determine whether the real-time position of the cluster UAV unit is within the boundary coordinates of the device's action range. If the real-time position of the cluster UAV unit exceeds the boundary coordinates of the device's action range, it is determined that the movement of the cluster UAV unit exceeds the device's action range.

[0145] The boundary coordinates of the device's action range are calculated through the effective coverage range model in the action range information. For example, the spherical boundary coordinates (x0, y0, z0, R), where (x0, y0, z0) is the center of the sphere and R is the radius. The real-time position coordinates of the cluster UAV unit are (x, y, z), and calculate the distance d from this coordinate to the center of the sphere as d = √[(x - x0)² + (y - y0)² + (z - z0)²]. If d > R, it is determined that the unit movement exceeds the device's action range. For non-spherical boundaries (such as polyhedrons), the ray method is used to determine whether a point is inside the boundary.

[0146] Step S1566: If any of the above situations in steps S1561 to S1565 occurs, that is, the signal interaction blocking degree of the leading hierarchical unit does not reach the expectation, or the motion coordination of the collaborative hierarchical unit remains stable, or the energy of the subordinate hierarchical unit does not show continuous weakening, or the resource consumption of the countermeasure device exceeds the preset consumption range, or the movement of the cluster UAV unit exceeds the device's action range, it is determined that the current dynamic countermeasure instruction set needs to be adjusted.

[0147] The control center performs a logical OR operation on the determination results of steps S1561 to S1565. As long as one condition is satisfied, the determination result that needs to be adjusted is output, and the specific conditions triggering the adjustment are recorded (such as "the signal transmission success rate of the leading hierarchical unit 0.4 > 0.3"). <​​​​If the signal interaction blocking of the dominant level unit fails to meet expectations, the cause may be insufficient interference strength. Based on the gap between the current signal interference strength and the degree of blocking, the signal interference strength is increased proportionally. For example, if the current strength is I and the strength corresponding to the target blocking success rate is I_target, then ΔI=(I_target-I), and the modified strength is I+ΔI. At the same time, it is checked whether the maximum interference strength of the device is exceeded. If it is exceeded, the signal interference frequency is adjusted (e.g., changed to the harmonic frequency of the target signal).

[0150] If the motion coordination of the collaborative level units remains stable, adjust the motion interference frequency to make it closer to the resonant frequency of the unit motion sensor, or increase the motion interference amplitude. For example, if the current amplitude is A, change it to A+ΔA, where ΔA is calculated based on the degree to which the consistency data exceeds the effective interference range.

[0151] If the energy of subordinate units is not continuously weakened, extend the duration of energy weakening. For example, if the current duration is T, change it to T+ΔT. ΔT is calculated based on the difference between the energy reserve change rate and the effective weakening range, or increase the energy weakening intensity (if resources allow).

[0152] In response to situations where the resource consumption of countermeasure equipment exceeds the preset range, if the consumption rate is too fast, the resource allocation ratio is reduced, thereby reducing the intensity and amplitude in the real-time operation parameters; if the remaining resource amount is too low, the resource replenishment interval is advanced, or resources are allocated from equipment with surplus resources, and the execution sequence is adjusted accordingly, such as shortening the execution time of the equipment or having other equipment take over.

[0153] In cases where a cluster of drone units moves beyond the device's effective range, the device's execution start time can be changed to activate when the unit enters its effective range, or the device's effective range can be adjusted (e.g., increasing transmission power to expand coverage, if resources allow), or the unit can be transferred to another device whose effective range includes its location.

[0154] Step S158: Integrate the modified device instructions to form a new set of dynamic countermeasure instructions, and send them to the corresponding device through a dedicated communication link to replace the original instructions.

[0155] The modified real-time operating parameters and execution sequence of the equipment are integrated into a new set of dynamic countermeasure instructions according to the format in step S147. The instruction sequence number is incremented to distinguish it from the original instructions. The control center sends the new instructions to the corresponding equipment through a dedicated communication link. After receiving the new instructions, the equipment stops executing the original instructions, performs countermeasure operations according to the new instruction parameters and timing, and reports the reception status of the new instructions.

[0156] Step S159: Repeat the process from steps S153 to S158 above to enable the countermeasure equipment cluster to continuously perform coordinated countermeasure operations against the clustered drones.

[0157] The control center sets a closed-loop adjustment cycle (e.g., 5 seconds). Steps S153 to S158 are executed once every cycle to continuously monitor the countermeasure effect and adjust countermeasure commands until the swarm of drones is effectively countered (e.g., loses control, makes an emergency landing, or returns to base) or the mission ends. During the countermeasure process, all adjustment operations, equipment status, and countermeasure effect data are recorded in real time to form a mission log for post-event analysis and strategy optimization.

[0158] During the aforementioned data collection process, sensitive data such as the location of countermeasure devices and communication link keys were involved. The following privacy protection techniques were employed for this data: During data storage, sensitive data was encrypted using the AES-256 encryption algorithm and stored in a secure database. The database was configured with access control policies, allowing only authorized personnel to access it via multi-factor authentication (e.g., password + USB key). During data transmission, in addition to encryption of the dedicated communication link, sensitive fields (such as keys) were transmitted through independent encrypted channels or physically isolated. When using the data, data anonymization techniques were employed, such as obfuscating the device location coordinates (retaining only regional information rather than precise coordinates), and storing keys in fragments and conducting access audits to ensure the privacy protection and prevention of leakage of sensitive data.

[0159] In one exemplary embodiment, a cooperative countermeasure control system for swarm drones is provided. This system can be a terminal, server, etc., and its internal structure diagram can be as follows: Figure 2As shown, the cooperative countermeasure control system for swarmed UAVs includes a processor, memory, input / output interface, communication interface, display unit, and input device. The processor, memory, and input / output interface are connected via a system bus, and the communication interface, display unit, and input device are also connected to the system bus via the input / output interface. The processor provides computational and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The input / output interface is used for information exchange between the processor and external devices. The communication interface is used for wired or wireless communication with external terminals; wireless communication can be achieved through Wi-Fi, mobile cellular networks, near-field communication, or other technologies. When the computer program is executed by the processor, it implements a cooperative countermeasure control method for swarmed UAVs. The display unit of this cooperative countermeasure control system for swarm drones is used to form a visually visible image and can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be an LCD screen or an e-ink screen. The input device of this cooperative countermeasure control system for swarm drones can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad set on the shell of the cooperative countermeasure control system for swarm drones, or an external keyboard, touchpad, or mouse, etc.

[0160] It should be noted that, in order to simplify the description of the present invention and thus help to understand one or more embodiments of the invention, multiple features may sometimes be grouped into one embodiment, drawing or description thereof in the foregoing description of the embodiments of the present invention.

Claims

1. A cooperative countermeasure control method for swarmed unmanned aerial vehicles (UAVs), characterized in that, The method includes: The system performs real-time perception of the dynamic interaction information between the cluster of UAVs and the countermeasure equipment cluster within the target area, and generates dynamic interaction perception results. The dynamic interaction perception results include real-time motion correlation information and signal interaction information of each unit of the cluster of UAVs, as well as real-time resource status information and range of action information of each device in the countermeasure equipment cluster. Based on the dynamic interactive perception results, hierarchical collaborative decision processing is performed to generate hierarchical collaborative decision results. The hierarchical collaborative decision results include countermeasure strategy directions for different levels of units in the cluster of drones, as well as the rules for the collaborative cooperation of different devices in the countermeasure device cluster. Based on the hierarchical collaborative decision-making results, the resources of the countermeasure equipment cluster are adaptively scheduled to generate an adaptive resource scheduling scheme. The adaptive resource scheduling scheme includes the cluster UAV units corresponding to each device in the countermeasure equipment cluster, as well as the resource allocation method of each device. Based on the adaptive resource scheduling scheme, a dynamic countermeasure instruction set is generated, which includes the real-time operation parameters and execution timing scheduling of each device in the countermeasure device cluster. The set of dynamic countermeasure commands is sent to the countermeasure equipment cluster, and the real-time countermeasure effect information fed back by the countermeasure equipment cluster is received. The set of dynamic countermeasure commands is adjusted in a closed loop based on the real-time countermeasure effect information and the dynamic interactive perception results, so that the countermeasure equipment cluster can continuously perform coordinated countermeasure operations against the cluster of UAVs.

2. The cooperative countermeasure control method for swarm drones according to claim 1, characterized in that, The process of real-time sensing of dynamic interaction information between the cluster of drones and the cluster of countermeasure equipment within the target area, and generating dynamic interaction sensing results, includes: A multi-source sensing device group is deployed to monitor the target area. The multi-source sensing device group includes radar sensing devices, optical sensing devices, infrared sensing devices, and signal sensing devices. The system acquires real-time position coordinates and speed information of each unit of the swarm drones using radar sensing equipment to form radar sensing data; it acquires real-time shape characteristics and formation pattern information of each unit of the swarm drones using optical sensing equipment to form optical sensing data; it acquires real-time thermal radiation characteristics and energy consumption correlation information of each unit of the swarm drones using infrared sensing equipment to form infrared sensing data; and it captures the signal waveforms and signal transmission direction information emitted by each unit of the swarm drones using signal sensing equipment, while simultaneously capturing the countermeasure signals and feedback signals emitted by each device in the countermeasure equipment cluster to form signal sensing data. Interactive correlation processing is performed on radar sensing data, optical sensing data, infrared sensing data and signal sensing data to associate and bind the sensing data of the same cluster of UAV units under different sensing devices, and to associate and bind the sensing data of the same countermeasure device under different sensing devices. From the perception data of the clustered UAV units after association and binding, the real-time relative position change patterns and motion direction coordination relationships between the units are extracted to form the real-time motion association information of each unit of the clustered UAV. From the perception data of the clustered UAV units after association and binding, the waveform characteristics of the signals transmitted by each unit, the signal transmission path and the signal interaction frequency with other units are extracted to form the signal interaction information of each unit of the clustered UAV. From the associated and bound countermeasures equipment perception data, extract the current resource occupancy ratio, remaining resource amount and resource consumption rate of each device to form real-time resource status information of each device in the countermeasures equipment cluster. From the sensing data of the associated and bound countermeasures equipment, the effective coverage range, signal propagation attenuation law and effective distance limit of the countermeasures signal emitted by each equipment are extracted to form the effective range information of each equipment in the countermeasures equipment cluster; The real-time motion correlation information and signal interaction information of each unit of the swarm drone are integrated with the real-time resource status information and range of action information of each device in the countermeasure equipment swarm to generate dynamic interactive perception results.

3. The cooperative countermeasure control method for swarm drones according to claim 1, characterized in that, The step of performing hierarchical collaborative decision-making processing based on the dynamic interactive perception results to generate hierarchical collaborative decision-making results includes: The real-time motion correlation information of each unit of the swarm UAV is extracted from the dynamic interactive perception results. The motion dominance relationship of each unit in the formation is analyzed to determine the hierarchical structure of the swarm UAV and divide it into dominant hierarchical units, cooperative hierarchical units and subordinate hierarchical units. The signal interaction information of each unit of the swarm UAV is extracted from the dynamic interactive perception results. The signal transmission frequency and signal dependence between each unit are analyzed to verify and correct the hierarchical structure so that the hierarchical division is consistent with the signal interaction relationship. For the dominant level unit, combined with the range of action information of each device in the countermeasure device cluster in the dynamic interactive perception results, the types and quantities of countermeasure devices that can cover the dominant level unit are analyzed, and the countermeasure strategy direction for the dominant level unit is determined. This countermeasure strategy direction focuses on cutting off the signal interaction between the dominant level unit and other level units. For the collaborative level unit, combined with the real-time resource status information of each device in the countermeasure device cluster in the dynamic interactive perception results, the distribution of countermeasure devices with remaining resources is analyzed to determine the countermeasure strategy direction for the collaborative level unit. This countermeasure strategy direction focuses on interfering with the motion coordination relationship of the collaborative level unit. For subordinate units, the resource consumption rate of each device in the countermeasure device cluster is analyzed in conjunction with the dynamic interactive perception results. The countermeasure device capabilities that can continue to operate are analyzed, and the countermeasure strategy direction for subordinate units is determined. This countermeasure strategy direction focuses on weakening the energy supply correlation of subordinate units. Extract real-time resource status and scope information of each device in the countermeasures equipment cluster from the dynamic interactive perception results, analyze the resource complementarity and overlapping areas of each device's scope, and determine the coordination rules between countermeasures equipment, including resource sharing rules, scope connection rules, and signal interference avoidance rules. The countermeasure strategies for different levels of swarm drones will be integrated with the rules for coordination between different devices in the countermeasure equipment cluster to generate hierarchical collaborative decision-making results.

4. The cooperative countermeasure control method for swarm drones according to claim 3, characterized in that, The process involves extracting real-time motion correlation information of each unit in the swarm drone from the dynamic interactive perception results, analyzing the motion dominance relationship of each unit in the formation, determining the hierarchical structure of the swarm drone, and dividing it into dominant hierarchical units, cooperative hierarchical units, and subordinate hierarchical units, including: Extract the frequency of motion direction change and the magnitude of motion speed adjustment of each unit from the real-time motion correlation information of each unit in the swarm drone; Count the number of times each unit causes changes in the direction of motion of other units within a continuous time period, and the total magnitude of the speed adjustments caused by each unit. The unit that causes the most changes in the direction of motion of other units and causes the largest sum of the speed adjustments of other units is marked as a candidate dominant unit. Analyze the real-time relative position change pattern between the candidate dominant unit and other units, and calculate the time delay of other units following the movement after the candidate dominant unit moves. Other units with the shortest time delay are classified as candidate cooperative units, and other units with time delays exceeding the preset follow delay range are classified as candidate subordinate units. Repeat the above steps to verify all candidate dominant units, ensuring that the number of candidate cooperating units and candidate subordinate units corresponding to each candidate dominant unit conforms to the formation motion logic. The finalized candidate leading units are divided into leading hierarchical units, candidate cooperating units into cooperating hierarchical units, and candidate subordinate units into subordinate hierarchical units, forming a hierarchical structure for the swarm drones.

5. The cooperative countermeasure control method for swarm drones according to claim 1, characterized in that, The step of adaptively scheduling resources of the countermeasure equipment cluster based on the hierarchical collaborative decision-making results to generate an adaptive resource scheduling scheme includes: Extract countermeasure strategy directions for different levels of swarm drones from the hierarchical collaborative decision-making results, and determine the type and total amount of resources required for countermeasures at each level. Extract the coordination rules of different devices in the countermeasure equipment cluster from the hierarchical collaborative decision-making results, and determine the types of resources that each device can provide and the maximum supply of resources. Based on the type and total amount of resources required for countermeasures at the dominant level unit, and combined with the type and maximum supply of resources available from each device, resources are allocated preferentially to countermeasure devices that can cover the dominant level unit, and the dominant level unit corresponding to the countermeasure device is determined. Based on the type and total amount of resources required for countermeasures at the collaborative level unit, and combined with the type and maximum supply of resources available from the remaining countermeasure equipment, resources are allocated to countermeasure equipment with remaining resources, and the collaborative level unit corresponding to the service of the countermeasure equipment is determined. Based on the type and total amount of resources required for countermeasures by subordinate hierarchical units, and combined with the type and maximum supply of resources that the remaining countermeasure equipment can provide, resources are allocated to countermeasure equipment that can operate continuously, and the subordinate hierarchical units corresponding to the services provided by the countermeasure equipment are determined. Extract resource sharing rules between countermeasure devices from the hierarchical collaborative decision-making results, dynamically adjust the resources allocated to each device, and when any device has insufficient resources, allocate resources from devices with surplus resources to supplement them, while updating the cluster drone units served by the corresponding device. Extract the rules for connecting the scope of action of countermeasures devices from the results of hierarchical collaborative decision-making; Determine the resource allocation method for each device, including resource allocation ratio, resource replenishment frequency and resource adjustment trigger conditions. Integrate the cluster UAV units corresponding to each device in the countermeasure device cluster with the resource allocation method of each device to generate an adaptive resource scheduling scheme.

6. The cooperative countermeasure control method for swarmed UAVs according to claim 5, characterized in that, The determination of resource allocation methods for each device includes resource allocation ratios, resource replenishment frequency, and resource adjustment triggering conditions, including: From the initial allocation results of the adaptive resource scheduling scheme, extract the number of clustered UAV units served by each device and the amount of resources required for each clustered UAV unit to counterattack; Calculate the total resource requirement for each device, which is the sum of the resources required for countering all clustered UAV units served by each device; The resource allocation ratio is determined based on the total resource demand of each device and the maximum resource supply of each device. The resource allocation ratio is the ratio of the total resource demand to the maximum resource supply. Extract the resource consumption rate of each device in the countermeasure device cluster from the dynamic interaction perception results, and determine the current amount of resources to be consumed for each device by combining the resource allocation ratio and the maximum resource supply in the resource allocation method. Calculate the resource consumption cycle for each device based on the current amount of resources to be consumed and the resource consumption rate for each device; Set half of the resource consumption cycle as the resource replenishment interval. When the remaining resource amount drops to half of the current amount of resources to be consumed, resource replenishment is triggered to maintain the continuity of countermeasures. Set resource adjustment trigger conditions. When the resource usage ratio of the device exceeds the preset normal usage range, or the movement of the cluster drone unit served by the device exceeds the device's range of action, or the countermeasure effect reported by the device does not reach the preset effect range, resource adjustment is triggered. The determined resource allocation ratio, resource replenishment time interval, and resource adjustment trigger conditions are integrated to form the resource allocation method for each device.

7. The cooperative countermeasure control method for swarmed UAVs according to claim 1, characterized in that, The step of generating a dynamic countermeasure instruction set based on the adaptive resource scheduling scheme includes: Extract the cluster UAV units corresponding to each device in the countermeasure equipment cluster from the adaptive resource scheduling scheme, as well as the resource allocation method of each device; For each device, the corresponding operation parameter type is determined based on the hierarchical type of the cluster of drone units served by the device. For devices serving the dominant hierarchical unit, the operation parameter type includes signal interference frequency, signal interference intensity, and signal duration. For devices serving the cooperative hierarchical unit, the operation parameter type includes motion interference amplitude, motion interference frequency, and interference coverage. For devices serving the subordinate hierarchical unit, the operation parameter type includes energy attenuation intensity, energy attenuation frequency, and duration of continuous action. The real-time motion correlation information and signal interaction information of the cluster of UAV units serving the device are extracted from the dynamic interactive perception results. Combined with the resource allocation ratio in the resource allocation method, the specific value of each operation parameter is determined to form the real-time operation parameters of the device. Extract the coordination rules of different devices in the countermeasure device cluster from the hierarchical collaborative decision-making results, and determine the execution start time of each device by combining the resource replenishment frequency of each device in the adaptive resource scheduling scheme. Analyze the execution start time and the duration or continuous duration of the operation parameters of each device to determine the execution end time of each device. The execution sequence of each device is scheduled based on its start and end times, as well as the rules governing the scope of action between devices. The real-time operating parameters and execution timing of each device are integrated to form a dynamic countermeasure instruction set that includes the real-time operating parameters and execution timing scheduling of each device in the countermeasure device cluster.

8. The cooperative countermeasure control method for swarm drones according to claim 7, characterized in that, For each device, the corresponding operation parameter type is determined according to the hierarchical type of the cluster drone unit served by the device. The operation parameter type for the device serving the dominant hierarchical unit includes signal interference frequency, signal interference intensity, and signal duration. For devices in the service coordination level unit, the types of operating parameters include motion interference amplitude, motion interference frequency, and interference coverage. For devices serving subordinate units, the operating parameter types include energy attenuation intensity, energy attenuation frequency, and duration of action, including: Distinguish whether the clustered drone units serving the equipment belong to the dominant, collaborative, or subordinate level units; If the device serves a dominant level unit, the countermeasure strategy direction for the dominant level unit is determined in the hierarchical collaborative decision-making results. This countermeasure strategy direction focuses on cutting off the signal interaction between the dominant level unit and other level units. Therefore, the operation parameter type is determined to include signal interference frequency, signal interference intensity and signal duration. If the device serves a collaborative level unit, and the countermeasure strategy direction for the collaborative level unit is combined with the hierarchical collaborative decision results, the countermeasure strategy direction focuses on interfering with the motion collaboration relationship of the collaborative level unit. Therefore, the operation parameter type is determined to include motion interference amplitude, motion interference frequency and interference coverage. If the device serves a subordinate hierarchical unit, and the countermeasure strategy direction for the subordinate hierarchical unit is combined with the hierarchical collaborative decision-making results, the countermeasure strategy direction focuses on weakening the energy supply relationship of the subordinate hierarchical unit. Therefore, the operation parameter type is determined to include energy weakening intensity, energy weakening frequency and duration of action. Record the type of operating parameters for each device.

9. The cooperative countermeasure control method for swarm drones according to claim 1, characterized in that, The process involves sending the dynamic countermeasure command set to the countermeasure device cluster, simultaneously receiving real-time countermeasure effect information from the countermeasure device cluster, and combining the real-time countermeasure effect information with the dynamic interactive perception results to perform closed-loop adjustments to the dynamic countermeasure command set, enabling the countermeasure device cluster to continuously perform coordinated countermeasure operations against the clustered drones. This includes: Establish dedicated communication links between the countermeasure control center and each device in the countermeasure equipment cluster, and send the instructions of each device in the dynamic countermeasure instruction set to the corresponding device through the dedicated communication links; During the command transmission process, the command reception status information fed back by each device is received in real time. When all devices report that the command reception was successful, the command retransmission operation is stopped. After each device in the countermeasure equipment cluster executes the countermeasure operation according to the instructions, it collects the equipment operation data and the status change data of the cluster drones in real time during the countermeasure process, forms real-time countermeasure effect information, and feeds it back to the countermeasure control center through a dedicated communication link. The countermeasure control center receives real-time countermeasure effect information and extracts countermeasure effect data of each device from the real-time countermeasure effect information, including the degree of signal interaction blocking of the dominant level unit, the degree of motion coordination interference of the cooperative level unit, and the degree of energy weakening of the subordinate level unit. Extract the latest real-time motion correlation information and signal interaction information of each unit of the swarm drone from the dynamic interactive perception results, and real-time resource status information and range of action information of each device in the countermeasure equipment swarm. The countermeasure effect data is correlated with the latest information in the dynamic interaction perception results to determine whether the current dynamic countermeasure command set needs to be adjusted. If the signal interaction blocking degree of the dominant level unit does not reach the expected level, or the motion coordination of the cooperative level unit remains stable, or the energy of the subordinate level unit does not show a continuous weakening, or the resource consumption of the countermeasure equipment exceeds the preset consumption range, or the movement of the cluster drone unit exceeds the range of the equipment, then it is determined that adjustment is needed. If it is determined that adjustments are needed, the real-time operating parameters and execution sequence of the corresponding equipment will be modified according to the analysis results. The modifications include increasing the signal interference intensity, adjusting the motion interference frequency, extending the duration of energy attenuation, and changing the execution start time. The modified device commands are integrated to form a new set of dynamic countermeasure commands, which are then sent to the corresponding devices via a dedicated communication link to replace the original commands. Repeat the above process of receiving feedback, correlation analysis, adjusting instructions, and sending new instructions to enable the countermeasure equipment cluster to continuously perform coordinated countermeasure operations against the cluster of drones.

10. A cooperative countermeasure control system for swarm drones, characterized in that, include: processor; A machine-readable storage medium for storing machine-executable instructions of the processor; The processor is configured to execute the cooperative countermeasure control method for swarmed drones as described in any one of claims 1 to 9 by executing the machine-executable instructions.

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