Public network unmanned aerial vehicle countermeasure method and device based on base station signal adsorption

Abstract

This invention relates to the field of secure communication, specifically disclosing a method and apparatus for countering public network drones based on base station signal attraction. The method includes: a public network link component dividing frequency bands, counting the number of frequency band switching, and obtaining frequency division results; collecting network topology-related parameters, optimizing the frequency division results, and filtering out prohibited drones; a public network traffic control component monitoring the communication traffic characteristics of the prohibited drones, allocating limited bandwidth resources, and simultaneously sending redundant data packets to occupy communication bandwidth and suppress communication between the prohibited drones and the public network; a base station simulator countermeasure component simulating public network signal characteristics, guiding the prohibited drones to attract, transmitting simulated navigation data, and guiding them to leave the prohibited area; and a base station simulator feedback component detecting the communication status and final location of the prohibited drones, providing feedback to correct the countermeasure results, and completing the public network drone countermeasure. This invention reduces interference from irrelevant equipment, improves countermeasure accuracy, and reduces countermeasure difficulty.

Description

Technical Field

[0001] This invention relates to the field of countering secure communication against unmanned aerial vehicles (UAVs), specifically to a method and apparatus for countering UAVs over public networks based on base station signal absorption. Background Technology

[0002] Currently, drone countermeasures work by emitting signals at specific frequencies and power levels to disrupt the normal signal interaction between drones and public networks, causing them to lose control or become unpositionable, ultimately leading to forced landings, return trips, or hovering. This disrupts drone control by damaging signal transmission. While emitting high-power electromagnetic signals to interfere with drone communication links or navigation systems can be effective quickly, can counter multiple drones without directly destroying the target and reducing collateral damage, it may interfere with other legitimate network equipment and is less effective against drones with anti-jamming capabilities.

[0003] The drone needs to acquire, track, and demodulate satellite navigation signals through a receiver module, converting the radio waves broadcast by the satellite into digital information that can be used for calculation. During the drone's flight, the flight control system compares the drone's calculated actual operating position with the expected position on the planned path to the destination frame by frame. Based on the calculated deviation data and the drone's current attitude, specific path correction commands are generated to determine the attitude parameters that need to be adjusted, such as heading angle, pitch angle, roll angle, and dynamic parameters.

[0004] For example, the invention patent with announcement number CN111800216B provides a system and method for generating electromagnetic waveforms to counter unauthorized drone flights. The system includes a monitoring module, a link feature identification module, and an interference handling module. The monitoring module receives electromagnetic wave signals between the drone and the remote controller and inputs them to the link feature identification module. The link feature identification module blindly identifies the modulation, frame format, encoding, and signal bandwidth of the data link used by the drone from the intercepted electromagnetic wave signals. The interference handling module regenerates the interference waveform based on the identification results and sends it into the air to counter the drone.

[0005] For example, the invention patent with announcement number CN107566079B discloses a precise jamming countermeasure system and method for flight control signals of civilian unmanned aerial vehicles (UAVs) across the entire frequency band. The system includes: a computer, a high-performance signal transceiver, a computing module, a power amplification module, and a wireless signal transceiver module. After detecting the radio signal of the target UAV, the computer controls the high-performance signal transceiver to generate a time-synchronized and frequency-synchronized jamming signal based on the frequency and spectral characteristics of the received radio signal. After the jamming signal is amplified by the power amplification module, the computer controls the wireless signal transceiver module through the computing module to send the jamming signal to the target UAV, thereby interfering with the operation of the target UAV.

[0006] Based on the above technical solutions, it was found that in complex urban environments, the interference intensity, coverage, and complexity of numerous unrelated devices are high, significantly increasing the difficulty of drone identification and severely affecting the accuracy and stability of identification. The anti-interference capability and countermeasures of the countermeasure system have low practical application effectiveness. Countermeasures against drones by directly interfering with drones with electrical signals and tampering with drone communication commands rely on the analysis and cracking of drone signals and communication protocols, ignoring the convenience of countermeasures using the drone's own communication signals during operation. This approach cannot effectively solve the problem of the high difficulty of countermeasures when drones use non-public dynamic parameter configurations and complex anti-interference technologies. The development cycle of cracking solutions is lengthy and not conducive to efficient countermeasures. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides a method and apparatus for countering public network drones based on base station signal absorption, which can effectively solve the problems mentioned in the background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a public network drone countermeasure method based on base station signal absorption, comprising: a public network link component counting the number of frequency band switching times of each access device within a preset time window under a preset frequency band determination interval, and obtaining a frequency division result based on the number of frequency band switching times; collecting network topology mobility parameters and topology morphology change parameter sets of each access device, coupling them to obtain a topology dynamic evaluation value for each access device, optimizing the frequency division result, and filtering out prohibited drones; and a public network traffic control component monitoring the communication traffic characteristics of the prohibited drones, and comprehensively analyzing the topology... Dynamic evaluation values ​​are used to determine bandwidth limitation limits, which are then allocated to restricted-fly drones. Simultaneously, redundant data packets are sent to the connection link, consuming communication bandwidth and suppressing communication between the restricted-fly drones and the public network. The base station simulator countermeasure component simulates public network signal characteristics to guide the restricted-fly drones to the network. Based on a path planning function, the simulated position coordinates of the restricted-fly drones are inferred, and simulated navigation data is transmitted to replace the current restricted-fly position coordinates, guiding the drones away from the restricted-fly zone. The base station simulator feedback component monitors the communication status and final position of the restricted-fly drones in real time, optimizes bandwidth resource allocation based on the monitoring results, and corrects the simulated navigation data to complete the countermeasure against drones operating from the public network.

[0009] The second aspect of this invention provides a public network drone countermeasure device based on base station signal absorption, comprising: a public network link component for dividing frequency band determination intervals, analyzing the topology dynamic evaluation values ​​of each access device, and filtering to identify prohibited drones; a public network traffic control component for evaluating bandwidth limit thresholds, allocating bandwidth resources to prohibited drones, and suppressing communication between the drone and the public network; a public network timer for controlling the data packet transmission interval and cyclically sending redundant data packets to the communication link between the prohibited drone and the public network; a base station simulator countermeasure component for simulating public network signal characteristics, guiding the prohibited drone to access the simulated communication link, sending simulated navigation data, and guiding the prohibited drone to leave the prohibited area; and a base station interference feedback unit for monitoring the communication status and final location of the prohibited drone and optimizing the countermeasure process.

[0010] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0011] (1) This invention provides a method and device for countering UAVs on the public network based on base station signal attraction. The public network link component presets the frequency band judgment interval, extracts the frequency band switching record within a periodic time, obtains the frequency division result, collects the network topology related parameters of the access device, optimizes the frequency division result, and filters out the no-fly UAVs. The public network traffic control device monitors the communication traffic characteristics of the no-fly UAVs, allocates bandwidth limit standard values ​​for the no-fly UAVs, sends redundant data packets to occupy bandwidth, and suppresses communication between the UAVs and the public network. The base station countermeasure unit simulates the characteristics of the public network signal, attracts the UAVs to access the network, collects the real-time navigation signals of the UAVs, analyzes the signal parameter format, reverse-engineers the no-fly position based on the path planning function, and transmits simulated navigation data to guide the UAVs to leave the no-fly zone. The base station interference feedback unit detects the communication status of the UAVs, compares the planned and actual positions of the UAVs, obtains the relevant adjustment parameters for UAV countermeasure, optimizes the bandwidth allocation link, provides feedback adjustment of the preset frequency band judgment interval, and corrects the simulated navigation data.

[0012] (2) This invention uses a bandwidth limit to define a value and dynamically adjusts the preset transmission bandwidth according to the actual communication needs of the no-fly drone. It allocates specific bandwidth resources with a maximum bandwidth limit to the communication link between the no-fly drone and the public network, suppresses the communication between the no-fly drone and the public network from the communication level, interferes with the operation of the no-fly drone, and the processing is convenient and fast, increasing the possibility of the base station simulator guiding the no-fly drone to successfully access and absorb.

[0013] (3) This invention further refines the identification of no-fly drones by reusing the dynamic evaluation value of the topology parameter, based on the degree of change in the network topology, thereby improving the accuracy of identification. At the same time, it adjusts the bandwidth allocation suppression strategy in a timely manner according to the dynamic evaluation value of the topology, avoiding the repeated calculation of parameters, dynamically adjusting the suppression intensity, and enhancing the countermeasure effect. The reuse of the dynamic evaluation value of the topology can avoid data errors during cross-component transmission, reduce interference that may be caused during transmission, facilitate unified modification, and reduce the risk of missed or incorrect modifications. At the same time, it ensures the data consistency of each countermeasure device during operation, avoids logical errors caused by inconsistent parameters, simplifies the logical processing process, makes the entire countermeasure process more tightly linked, and improves operating efficiency.

[0014] (4) Compared with the existing technology, this solution can eliminate interference from irrelevant network devices in complex urban environments, has a strong ability to handle complex environments, accurately identify no-fly drones, ensure the accuracy and stability of identification, and significantly improve the anti-interference capability of the countermeasure method. By utilizing the signal transmission characteristics of the drone and satellite navigation operation process itself, it avoids the difficulty of directly cracking the drone's non-public dynamic parameter configuration and complex anti-interference technology. The countermeasure process is fast and short, and can achieve efficient countermeasure against no-fly drones. Attached Figure Description

[0015] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0017] Figure 2 This is a flowchart illustrating the operational process of countering no-fly drones according to the present invention. Detailed Implementation

[0018] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0019] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0020] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, they convey the same meaning.

[0021] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0022] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0023] According to the embodiments of the present invention Figure 2 The process described provides a technical solution for countering no-fly drones: A public network link component divides the frequency bands and determines the number of frequency band switching intervals, obtaining the frequency division result based on the number of switching intervals; it calculates the topology dynamic evaluation value of each access device, optimizes the frequency division result, and filters out no-fly drones; a public network traffic control component calculates the bandwidth limit threshold, allocates limited bandwidth resources to the no-fly drones, and simultaneously sends redundant data packets to the connection link to suppress communication between the no-fly drones and the public network; a base station simulator countermeasure component simulates public network signal characteristics, guides the no-fly drones to attach, reverses the simulated position coordinates of the no-fly drones, and transmits simulated navigation data to guide the no-fly drones away from the no-fly zone; a base station simulator feedback component optimizes bandwidth resource allocation based on the countermeasure results and corrects the simulated navigation data, completing the public network drone countermeasure. Figure 2 This is a flowchart illustrating the methods and devices for countering no-fly drones.

[0024] Reference Figure 1 As shown, this invention provides a method and apparatus for countering public network drones based on base station signal absorption, comprising:

[0025] Within a preset time window, the public network connection component counts the number of frequency band switching times of each access device in a preset frequency band determination interval, and obtains the frequency division result based on the number of frequency band switching times.

[0026] In this embodiment of the invention, the coverage area of ​​the public network is equal to the no-fly zone for drones. Drones can communicate remotely with ground control terminals through the public network. Access devices are all network devices connected to the public network within the coverage area of ​​the public network.

[0027] Specifically, the process of obtaining the frequency partitioning results is as follows:

[0028] The public network connection component pre-determines the frequency bands, including the first frequency band determination interval, the second frequency band determination interval, and the third frequency band determination interval, and divides the frequency bands of all access devices according to the three frequency band determination intervals.

[0029] The above-mentioned frequency bands are defined one by one according to the frequency value, and the frequency value increases with the judgment level.

[0030] Based on the preset time window, the frequency band switching times of each access device in the second frequency band determination interval and the third frequency band determination interval within the time window are extracted. The switching frequencies of each access device are sorted out and arranged in ascending order to form the device switching frequency fluctuation set within the time window. The average frequency of the device switching frequency fluctuation set is selected as the high-frequency switching benchmark value.

[0031] The switching frequency of the access device is compared with the high-frequency switching benchmark value to obtain the first frequency division result and the second frequency division result.

[0032] The first frequency division result mentioned above specifically refers to the access device switching frequency being less than or equal to the high-frequency switching benchmark value.

[0033] The second frequency division result mentioned above specifically indicates that the switching frequency of the access device is greater than the high-frequency switching reference value.

[0034] The network topology mobility parameters and topology morphology change parameter sets of each access device are collected, coupled to obtain the topology dynamic evaluation value of each access device, the frequency division results are optimized, and the no-fly drones are filtered out.

[0035] It should be explained that the public network link component directly collects network topology mobility parameters and topology change parameters of each access device through the built-in counting sensor of the public network.

[0036] Specifically, the process of coupling to obtain the topology dynamic evaluation values ​​of each access device is as follows:

[0037] The network topology mobility parameters of each access device include the number of times topology establishment frames and topology disconnection frames are identified during the connection process between each access device and the public network, as well as the identification time points.

[0038] The topology disconnection frequency and link existence time entropy of each access device are calculated based on the network topology mobility parameters of each access device.

[0039] Compare the number of times the topology establishment frame and topology disconnection frame are identified for each access device, and take the smaller value as the number of topology disconnection times for each access device. The difference between the time points of the first and last topology establishment frames and topology disconnection frames is taken as the total topology disconnection time. The topology disconnection frequency is obtained by dividing the number of topology disconnection times by the total topology disconnection time.

[0040] The difference between the time points of two adjacent topology establishment frames and topology disconnection frames identified for each access device is recorded as the link existence time. The product of the ratio of link existence time to total link existence time and the logarithm of this ratio is accumulated, and the negative is taken to obtain the link existence time entropy of each access device.

[0041] The set of topology change parameters for each access device includes the average number of topological nodes in the network topology of each access device and the directed node degree of the network topology of each access device.

[0042] It should be explained that the mean number of nodes in the network topology of each access device refers to the average number of adjacent nodes that each access device connects to in each network topology; the directed node degree of each access device network topology refers to the total number of edges that are counted starting from each access device when traversing all directed edges of each access device's nodes.

[0043] The topology disconnection frequency, link existence time entropy, mean number of topology nodes, and directed node degree of each access device are normalized. Then, the feature influence parameters and the normalization results are weighted and aggregated to obtain the topology dynamic evaluation value of each access device.

[0044] It should be explained that the feature influence parameter refers to the quantitative parameter that the feature of the parameter affects the key parameters of the calculation result.

[0045] The specific analysis process is as follows:

[0046] ;

[0047] In the formula, TP i This is the topology dynamic evaluation value for the i-th access device. n is the total number of connected devices, F i H is the topology disconnection frequency for the i-th access device. i For the connection of the i-th access device, there exists a time entropy, num i Let E be the mean value of the network topology nodes of the i-th access device. i Let q1 be the directed node degree of the network topology of the i-th access device, q2 be the characteristic influence parameter corresponding to the preset topology disconnection frequency in the UAV countermeasure database, q3 be the characteristic influence parameter corresponding to the preset link existence time entropy in the UAV countermeasure database, q4 be the characteristic influence parameter corresponding to the preset topology node mean in the UAV countermeasure database, and q5 be the characteristic influence parameter corresponding to the preset directed node degree in the UAV countermeasure database.

[0048] The model takes the frequency of network disconnection for each access device, the time entropy of link existence for each access device, the average number of nodes in the network topology for each access device, and the directed node degree of the network topology for each access device as inputs, and the corresponding feature influence parameters as output targets. The LSTM algorithm updates the weights of all parameters in reverse, gradually reducing the error. After the weight model stabilizes, the corresponding influence parameters are output. For example, the directed node degree of each access device's network topology can be used as input in a logical sequence to construct training data. Combined with the logical feature input, the cumulative influence of directed node degree is captured, and the feature influence parameters corresponding to the directed node degree are output. These feature influence parameters are then organized into a mapping table and stored in the UAV countermeasure database for component access.

[0049] In this embodiment of the invention, multivariate analysis is performed on the frequency of topology disconnection, the entropy of link existence time, the mean of topology nodes in the network topology, and the degree of directed nodes in the network topology. Specifically, the correlation between these parameters is considered. The mean of topology nodes in the network topology and the degree of directed nodes in the network topology jointly affect the entropy of link existence time. The frequency of topology disconnection is negatively correlated with the time of link existence, and the mean of topology nodes in the network topology is positively correlated with the degree of directed nodes in the network topology.

[0050] The mean dynamic evaluation value of the topology of each access device is obtained by averaging the dynamic evaluation values ​​of the topology of each access device. Based on the mapping relationship set between the mean dynamic evaluation value of the topology of the access devices and the dynamic partitioning percentage, the specific mapping process is as follows: the mean dynamic evaluation value of the topology of the access devices is input into the LSTM algorithm to capture the temporal dependency of the dynamic partitioning process, extract the feature influence parameters, output the dynamic partitioning percentage, and organize it into a mapping relationship set. The mean dynamic evaluation value of the topology of the access devices is then substituted into the mapping relationship set to obtain the dynamic partitioning percentage. The dynamic partitioning percentage is recorded as the dynamic partitioning standard percentage. The frequency partitioning results are optimized and filtered to obtain the no-fly drones.

[0051] Specifically, the process of optimizing the frequency classification results and filtering them to obtain the no-fly drones is as follows:

[0052] The device switching frequency fluctuation set is divided according to the dynamic division standard percentage to obtain the topology stable part, and the rest is recorded as the topology active part. The switching frequency of the access devices in the topology active part is greater than that of the access devices in the topology stable part.

[0053] It should be explained that the above-mentioned division of the device handover frequency fluctuation set according to the dynamic division standard percentage means multiplying the dynamic division standard percentage by the total number of access devices in the device handover frequency fluctuation set, and taking the number of access devices in the product in sequence to obtain the topology stable part.

[0054] It should be explained that the above order refers to the order in which the switching frequencies of each access device are arranged from smallest to largest.

[0055] 1) If the minimum handover frequency of the access devices in the active part of the topology is less than or equal to the high-frequency handover reference value, then all access devices in the active part of the topology are directly marked as no-fly drones.

[0056] 2) If the maximum switching frequency of the access devices in the stable part of the topology is greater than the high-frequency switching benchmark value, then all devices in the active part of the topology, as well as a number of access devices selected in the stable part of the topology in descending order of the preset expansion quantity based on the arrangement order, will be marked as no-fly drones.

[0057] In this embodiment of the invention, the results of multiple frequency band switching are filtered to identify no-fly drones. This allows for the elimination of interference from irrelevant network devices before countermeasures are taken against no-fly drones, improving the ability to handle complex environments and providing a good foundation for accurately identifying no-fly drones.

[0058] The public network traffic control component monitors the communication traffic characteristics of the no-fly drones, integrates the topology dynamic evaluation value, obtains the bandwidth limit definition value, allocates limited bandwidth resources to the no-fly drones, and sends redundant data packets to the connection link to occupy communication bandwidth and suppress the communication between the no-fly drones and the public network.

[0059] Specifically, the process of suppressing communication between no-fly drones and the public network is as follows:

[0060] The communication traffic characteristics of the aforementioned no-fly drones specifically include the communication throughput between the no-fly drone and the public network, the peak traffic of the communication link between the no-fly drone and the public network, and the communication transmission delay between the no-fly drone and the public network.

[0061] The public network traffic control component calls the communication data of the no-fly drone-public network communication link interface to directly view the communication throughput, peak traffic of the no-fly drone-public network communication link, and communication transmission latency of the no-fly drone-public network.

[0062] The communication throughput of the no-fly drone to the public network, the peak traffic of the communication link between the no-fly drone and the public network, the communication transmission delay between the no-fly drone and the public network, and the topology dynamic evaluation value of the no-fly drone are normalized respectively. The characteristic influence parameters are introduced and the normalization results are weighted and aggregated in sequence, and then coupled with the preset transmission bandwidth to obtain the bandwidth limit definition value.

[0063] The specific analysis process is as follows:

[0064] ;

[0065] In the formula, B' is the bandwidth limit threshold, B is the preset transmission bandwidth, T is the communication throughput between the no-fly drone and the public network, A is the peak traffic of the communication link between the no-fly drone and the public network, t is the communication transmission delay between the no-fly drone and the public network, TP is the topology dynamic evaluation value of the no-fly drone, p1 is the characteristic influence parameter corresponding to the preset communication throughput in the drone countermeasure database, p2 is the characteristic influence parameter corresponding to the preset peak traffic of the communication link in the drone countermeasure database, p3 is the characteristic influence parameter corresponding to the preset communication transmission delay in the drone countermeasure database, and p4 is the characteristic influence parameter corresponding to the preset topology dynamic evaluation value in the drone countermeasure database.

[0066] The model takes the communication throughput between the no-fly drone and the public network, the peak traffic of the communication link between the no-fly drone and the public network, the communication transmission latency between the no-fly drone and the public network, and the topology dynamic evaluation value of the no-fly drone as inputs. The corresponding feature impact parameters are used as output targets. The LSTM algorithm updates the feature weights of all parameters in reverse, gradually reducing feature errors. After the features stabilize, the corresponding impact parameters are output. For example, the communication throughput between the no-fly drone and the public network can be used as time series input to construct training data. Combined with time feature input, the cumulative impact of communication throughput is captured, and the feature impact parameters corresponding to the communication throughput are output, organized into a mapping table, and stored in the drone countermeasure database for component access.

[0067] In this embodiment of the invention, multivariate analysis is performed on communication throughput, peak communication link traffic, communication transmission delay, and topology dynamic evaluation value. Specifically, the correlation between these parameters is considered. The frequency of topology disconnection is negatively correlated with communication transmission delay. The topology dynamic evaluation value and the peak communication link traffic jointly affect the communication throughput. The multivariate topology dynamic evaluation value is positively correlated with the communication transmission delay.

[0068] Allocate specific bandwidth resources to no-fly drones, with bandwidth limit values ​​defining the maximum bandwidth.

[0069] By using a public network timer to control the data packet sending interval, redundant data packets are cyclically sent to the communication link between the no-fly drone and the public network. This ensures that the total data transmission volume of the communication link approaches the bandwidth limit, thereby occupying the transmission bandwidth of the communication link between the no-fly drone and the public network and suppressing the communication of the no-fly drone.

[0070] It should be explained that the above-mentioned requirement that the total data transmission volume of the communication link approaches the bandwidth limit means that the sum of the number of redundant data packets sent and the number of data packets sent by the no-fly drone itself approaches the total number of data packets that the communication link can transmit under the condition that the sum of the number of redundant data packets sent and the number of data packets sent by the no-fly drone itself approaches the bandwidth limit.

[0071] The base station simulator countermeasure component simulates public network signal characteristics to guide the no-fly drone to attach. Based on the path planning function, it reverse-engineers the simulated position coordinates of the no-fly drone, transmits simulated navigation data to replace the current no-fly drone's position coordinates, and guides it to leave the no-fly zone.

[0072] In this embodiment of the invention, by utilizing the characteristics of public network signals during the communication process between a no-fly drone and the public network, public network signals are simulated to guide the no-fly drone to access the base station simulator. The communication format of the no-fly drone is directly collected and simulated, avoiding the difficulty of directly cracking the drone's non-public dynamic parameter configuration and complex anti-interference technology. This rapidly shortens the countermeasure process and improves the efficiency of countermeasures against no-fly drones.

[0073] Specifically, the process by which the base station simulator countermeasure component simulates public network signal characteristics is as follows:

[0074] The base station simulator countermeasure component extracts the actual frequency bands used for communication between the public network and the no-fly drones, configures its own communication frequency band, and simulates the signal modulation method and signal frame structure of the public network.

[0075] It should be explained that the above-mentioned signal modulation method and signal frame structure of simulated public network refer to the process during which the no-fly drone will detect each public network signal. When the modulation method and frame structure of the detected signal conform to the standard protocols of 4G and 5G networks, the no-fly drone will identify it as a usable public network signal.

[0076] After receiving the public network signal, the base station simulator analyzes and obtains the public network signal transmission power and the public network antenna operating frequency.

[0077] The peak traffic of the communication link between the no-fly drone and the public network is mapped to obtain the percentage of the first influence of the signal simulation. The specific mapping process is as follows: the peak traffic of the communication link between the no-fly drone and the public network is input into the LSTM algorithm to capture the time dependence of the signal simulation process, extract the feature influence parameters, output the percentage of the first influence of the signal simulation, organize it into a mapping set, and then bring the peak traffic of the communication link between the no-fly drone and the public network into the mapping set to obtain the percentage of the first influence of the signal simulation.

[0078] The signal transmission power of the public network is coupled with the percentage of the first influence of the signal simulation. Specifically, the signal transmission power of the public network is multiplied by the percentage of the first influence of the signal simulation to obtain the first influence value of the signal simulation value.

[0079] The communication throughput of the no-fly drone to the public network is mapped to obtain the percentage of the second influence of the signal simulation. The specific mapping process is as follows: input the communication throughput of the no-fly drone to the public network into the LSTM algorithm, capture the time dependence of the signal simulation process, extract the feature influence parameters, output the percentage of the second influence of the signal simulation, organize it into a mapping set, and then input the communication throughput of the no-fly drone to the public network into the mapping set to obtain the percentage of the second influence of the signal simulation.

[0080] The antenna operating frequency of the public network is coupled with the percentage of the second influence of the signal simulation. Specifically, the antenna operating frequency of the public network is multiplied by the percentage of the second influence of the signal simulation to obtain the second influence value of the signal simulation value.

[0081] The first influence value and the second influence value are superimposed to obtain the signal simulation value of the base station simulator, and the public network simulation signal is configured.

[0082] The base station simulator transmits public network simulated signals to the no-fly drone, guiding the no-fly drone to "attach" to the simulated communication link.

[0083] Allocate bandwidth resources greater than or equal to the bandwidth limit threshold for accessed no-fly drones.

[0084] Furthermore, the process of reversing the simulated position coordinates of the no-fly drone based on the path planning function is as follows:

[0085] The base station simulator extracts the boundary of a preset no-fly zone as the destination, where the coordinates of the destination fall within the boundary of the preset no-fly zone, and the boundary of the preset no-fly zone satisfies the requirement of minimizing the straight-line distance between the boundary and the no-fly drone.

[0086] The desired departure coordinates of the no-fly drone are calculated by measuring the distance to several original navigation satellites.

[0087] The base station simulator extracts the remaining battery power, maximum flight speed, and maximum pitch angle of the no-fly drone from the flight control component of the no-fly drone in real time, and uses these as constraints for generating the no-fly drone clearance path.

[0088] 1) The planned power consumption of the no-fly drone driving away path is less than the remaining power of the no-fly drone.

[0089] 2) The flight speed for setting the no-fly drone departure path is less than or equal to the maximum flight speed of the no-fly drone.

[0090] 3) The pitch angle of the no-fly drone drive-away path setting is less than or equal to the maximum pitch angle of the no-fly drone.

[0091] The coordinates of several obstacles in the flight environment are identified by the no-fly drone. Combined with the coordinates of the no-fly drone's desired departure location and the constraints on the no-fly drone's departure path generation, the results are input into the drone path planning algorithm, which outputs the globally optimal path for the no-fly drone to reach the set departure destination.

[0092] It should be explained that the aforementioned UAV path planning algorithm refers to enabling the UAV to learn the optimal strategy, treating the environment as the state space and the UAV's flight actions as the action space, guiding the UAV to gradually learn a safe and efficient flight strategy, directly inputting environmental information and outputting flight control commands.

[0093] The base station simulator extracts the corresponding scheduling instructions output by the no-fly drone based on the globally optimal path. The scheduling instructions include the actual predetermined power consumption of the no-fly drone, the actual set flight speed of the no-fly drone, and the actual set pitch angle of the no-fly drone.

[0094] Based on the corresponding scheduling instructions output by the no-fly drone, the simulated position coordinates of the no-fly drone are obtained, and the navigation signal corresponding to the simulated data is used to replace the current no-fly position coordinates.

[0095] In this invention, by simulating a no-fly drone and utilizing the signal transmission characteristics of satellite navigation, the position of the drone is replaced. This causes the no-fly drone to misidentify its own position, thus countering the no-fly drone. By utilizing the drone path planning algorithm, the starting position corresponding to the set destination coordinates is deduced, thereby efficiently driving the no-fly drone away from the no-fly zone.

[0096] Specifically, the navigation signals corresponding to the simulated data replace the current no-fly zone coordinates. The process is as follows:

[0097] The base station simulator countermeasure component collects the real-time navigation signals currently relied upon by the no-fly drone, and parses them to obtain the real-time navigation signal content parameters. The real-time navigation signal content parameters include the real-time positioning time of the no-fly drone, the real-time latitude and longitude of the no-fly drone, the real-time altitude of the no-fly drone, and the real-time operating speed of the no-fly drone.

[0098] Extract the target position coordinates of the no-fly drone at the next positioning time point of the current navigation, record them as the next target position coordinates of the no-fly drone, and compare them with the expected drive-away position coordinates of the no-fly drone. The comparison process includes real-time latitude and longitude comparison and real-time altitude comparison.

[0099] Calculate the difference between the real-time longitude of the no-fly drone and the longitude corresponding to the expected departure position coordinates of the no-fly drone. Then, calculate the ratio of this difference to the longitude corresponding to the expected departure position coordinates of the no-fly drone, and record it as the relative longitude difference.

[0100] It should be explained that the calculation process for the above relative difference is as follows:

[0101] ;

[0102] In the formula, RD is the relative difference, x is the expected parameter, and x' is the real-time parameter.

[0103] Calculate the difference between the real-time latitude of the no-fly drone and the latitude corresponding to the expected departure position coordinates of the no-fly drone. Then, calculate the ratio of this difference to the latitude corresponding to the expected departure position coordinates of the no-fly drone, and record it as the relative latitude difference.

[0104] Calculate the difference between the real-time altitude of the no-fly drone and the altitude corresponding to the expected departure position coordinates of the no-fly drone. Then, calculate the ratio of this difference to the altitude corresponding to the expected departure position coordinates of the no-fly drone, and denot it as the relative altitude difference.

[0105] The relative differences in longitude, latitude, and altitude are superimposed to obtain the relative differences in location coordinates.

[0106] 1) If the relative difference of position coordinates is less than or equal to the preset tolerance relative difference, the navigation signal corresponding to the simulated data will not be transmitted to the no-fly drone, and real-time comparison will be maintained while waiting for the no-fly drone to leave.

[0107] 2) If the relative difference of the position coordinates is greater than the preset tolerance relative difference, the base station simulator countermeasure component will encapsulate the content parameters corresponding to the simulated position coordinates of the no-fly drone according to the encapsulation format of the real-time navigation signal to obtain the simulated navigation signal.

[0108] It should be explained that the above encapsulation format is based on three parts: data carrier, pseudo-random code, and data code.

[0109] The base station simulator countermeasure component converts the encapsulated analog navigation signal into electromagnetic waves and sends them to the no-fly drone. It replaces the current no-fly drone's coordinates with the simulated coordinates of the no-fly drone. The no-fly drone then calculates its flight path based on the simulated coordinates. The actual destination of this flight path is the set departure point, which guides the no-fly drone away from the no-fly zone.

[0110] The base station simulator feedback component monitors the communication status and final location of the no-fly drones in real time, optimizes bandwidth resource allocation based on the monitoring results, and corrects the simulated navigation data to complete the countermeasures against drones on the public network.

[0111] Specifically, the process involves optimizing bandwidth resource allocation and correcting the simulated navigation data as follows:

[0112] The base station simulator feedback component obtains the communication throughput and peak communication link traffic of the no-fly drone to the base station simulator.

[0113] The base station simulator countermeasure component integrates the communication throughput of the no-fly drone to the base station simulator with the peak traffic of the communication link between the no-fly drone and the base station simulator to obtain a value reflecting the communication status of the no-fly drone.

[0114] It should be explained that the above comprehensive processing to obtain the communication status reflection value of the no-fly drone is specifically achieved by multiplying the communication throughput of the no-fly drone-base station simulator by the characteristic parameter corresponding to the predefined communication throughput to obtain the first component of the no-fly drone communication status reflection; multiplying the peak communication link traffic of the no-fly drone-base station simulator by the characteristic parameter corresponding to the predefined communication link traffic peak to obtain the second component of the no-fly drone communication status reflection; and adding the first component and the second component of the no-fly drone communication status reflection to obtain the no-fly drone communication status reflection value.

[0115] Based on the communication status feedback values ​​of the no-fly drones, the drone countermeasure parameter adjustment factor is obtained by mapping. The specific mapping process is as follows: input the communication status feedback values ​​of the no-fly drones into the LSTM algorithm, capture the logical dependency of the drone countermeasure parameter adjustment process, extract the feature influence parameters, output the drone countermeasure parameter adjustment factor, organize it into a mapping set, and substitute the communication status feedback values ​​of the no-fly drones into the mapping set to obtain the drone countermeasure parameter adjustment factor.

[0116] The drone countermeasure parameter adjustment factor is coupled with the bandwidth limit threshold. Specifically, the drone countermeasure parameter adjustment factor is multiplied by the bandwidth limit threshold to obtain the bandwidth optimization threshold, the bandwidth limit threshold is updated, and the communication link bandwidth resource allocation process for no-fly drones to access the public network is configured.

[0117] Based on the communication status feedback value of the no-fly drone, a reasonable fluctuation ratio is mapped. The specific mapping process is as follows: input the communication status feedback value of the no-fly drone into the LSTM algorithm to capture the temporal dependency of the countermeasure process, extract the feature influence parameters, output the reasonable fluctuation ratio, organize it into a mapping set, substitute the communication status feedback value of the no-fly drone into the mapping set to obtain the reasonable fluctuation ratio, and couple it with the coordinates of the specified driving position. Specifically, multiply the reasonable fluctuation ratio with the coordinates of the specified driving position to obtain the positive or negative difference with the specified coordinates, which is recorded as the allowable error range.

[0118] After completing the drone countermeasure, the final position coordinates of the no-fly drone are obtained. These coordinates are then compared with the coordinates of the designated removal position to obtain the position coordinate difference of the no-fly drone. If the position coordinate difference falls within the allowable error range, the original simulated navigation data is retained. If the position coordinate difference does not fall within the allowable error range, the difference between the position coordinate difference and the nearest boundary value of the allowable error range is calculated and recorded as an error correction factor. This factor is coupled with the desired removal position coordinates of the no-fly drone. Specifically, the error correction factor is multiplied by all parameters of the desired removal position coordinates of the no-fly drone. The corrected position coordinates are then used to correct the real-time latitude and longitude, real-time altitude, and real-time speed of the no-fly drone included in the simulated navigation, thus completing the public network drone countermeasure.

[0119] In this invention, by providing feedback on the results of countering no-fly drones, the data deviation in the process of countering no-fly drones is corrected. This not only allows for real-time adjustment of the process of driving away no-fly drones, but also serves as a basis for analyzing the feedback effect after the countermeasures are completed, helping to quickly locate the deviation position and optimize the design of the countermeasures process.

[0120] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A method for countering public network drones based on base station signal absorption, characterized in that, include: Within a preset time window, the public network connection component counts the number of frequency band switching times of each access device in a preset frequency band determination interval, and obtains the frequency division result based on the number of frequency band switching times. The network topology mobility parameters and topology shape change parameter sets of each access device are collected, coupled to obtain the topology dynamic evaluation value of each access device, the frequency division results are optimized, and the no-fly drones are filtered out. The public network traffic control component monitors the communication traffic characteristics of the no-fly drones, integrates the topology dynamic evaluation value, obtains the bandwidth limit definition value, allocates limited bandwidth resources to the no-fly drones, and sends redundant data packets to the connection link to occupy communication bandwidth and suppress the communication between the no-fly drones and the public network. The base station simulator countermeasure component simulates public network signal characteristics to guide the no-fly drone to attach, and reverses the simulated position coordinates of the no-fly drone based on the path planning function. It then transmits simulated navigation data to replace the current no-fly drone's no-fly position coordinates and guides it to leave the no-fly zone. The base station simulator feedback component monitors the communication status and final location of the no-fly drones in real time, optimizes bandwidth resource allocation based on the monitoring results, and corrects the simulated navigation data to complete the countermeasures against public network drones; The base station simulator feedback component optimizes bandwidth resource allocation and corrects simulated navigation data based on the countermeasure results.

2. The method for countering public network drones based on base station signal adsorption according to claim 1, characterized in that: The frequency division results are obtained, and the specific division process is as follows: The public network connection component pre-determines the frequency bands, including the first frequency band determination interval, the second frequency band determination interval, and the third frequency band determination interval, and divides the frequency bands of all access devices according to the three frequency band determination intervals; The frequency band determination intervals are defined one by one according to the frequency value, and the frequency value increases with the determination level. According to the preset time window, the frequency band switching times of each access device in the second frequency band determination interval and the third frequency band determination interval within the time window are extracted, the switching frequency of each access device is sorted out, and the switching frequency of each access device is arranged in ascending order to form the device switching frequency fluctuation set within the time window. The frequency average of the device switching frequency fluctuation set is selected as the high-frequency switching benchmark value. The switching frequency of the access device is compared with the high-frequency switching reference value to obtain the first frequency division result and the second frequency division result; The first frequency division result is specifically that the access device switching frequency is less than or equal to the high-frequency switching reference value; The second frequency division result is that the access device switching frequency is greater than the high-frequency switching reference value.

3. The method for countering public network drones based on base station signal adsorption according to claim 1, characterized in that: The coupling process yields the topology dynamic evaluation values ​​for each access device, and the specific calculation process is as follows: The network topology mobility parameters of each access device include the number of times topology establishment frames and topology disconnection frames are identified during the connection process between each access device and the public network, as well as the identification time points. The topology disconnection frequency and link existence time entropy of each access device are calculated based on the network topology mobility parameters of each access device. The set of topology change parameters for each access device includes the average number of topology nodes in the network topology of each access device and the directed node degree of the network topology of each access device. The topology disconnection frequency, link existence time entropy, topology node mean, and directed node degree of each access device are normalized. The feature influence parameter and the normalization result are then weighted and aggregated to obtain the topology dynamic evaluation value of each access device. The mean dynamic evaluation value of the topology of each access device is obtained by averaging the dynamic evaluation values ​​of each access device. Based on the mapping relationship set between the mean dynamic evaluation value of the topology of the access devices and the dynamic division percentage, the mean dynamic evaluation value of the topology of the access devices is substituted into the mapping relationship set to obtain the dynamic division percentage. The dynamic division percentage is recorded as the dynamic division standard percentage. The frequency division results are optimized and filtered to obtain the no-fly drones.

4. The method for countering public network drones based on base station signal adsorption according to claim 3, characterized in that: The optimization of the frequency division results, filtering out prohibited drones, is specifically performed as follows: The device handover frequency fluctuation set is divided according to the dynamic division standard percentage to obtain the topology stable part, and the rest is recorded as the topology active part. The handover frequency of the access devices in the topology active part is greater than that of the access devices in the topology stable part. If the minimum handover frequency of the access devices in the active part of the topology is less than or equal to the high-frequency handover reference value, then all access devices in the active part of the topology are directly marked as no-fly drones. If the maximum switching frequency of the access devices in the stable part of the topology is greater than the high-frequency switching benchmark value, then all devices in the active part of the topology, as well as a number of access devices selected in the stable part of the topology in descending order of the preset expansion quantity based on the arrangement order, will be marked as no-fly drones.

5. The method for countering public network drones based on base station signal adsorption according to claim 1, characterized in that: The specific process for suppressing communication between the prohibited drone and the public network is as follows: The communication traffic characteristics of the no-fly drones specifically include the communication throughput between the no-fly drone and the public network, the peak traffic of the communication link between the no-fly drone and the public network, and the communication transmission delay between the no-fly drone and the public network. The communication throughput of the no-fly drone to the public network, the peak traffic of the communication link between the no-fly drone and the public network, the communication transmission delay between the no-fly drone and the public network, and the topology dynamic evaluation value of the no-fly drone are normalized respectively. The characteristic influence parameters are introduced and the normalization results are weighted and aggregated in sequence, and then coupled with the preset transmission bandwidth to obtain the bandwidth limit definition value. Allocate specific bandwidth resources to no-fly drones, with bandwidth limitation values ​​defining the maximum bandwidth; By using a public network timer to control the data packet sending interval, redundant data packets are cyclically sent to the communication link between the no-fly drone and the public network. This ensures that the total data transmission volume of the communication link approaches the bandwidth limit, thereby occupying the transmission bandwidth of the communication link between the no-fly drone and the public network and suppressing the communication of the no-fly drone.

6. The method for countering public network drones based on base station signal adsorption according to claim 1, characterized in that: The base station simulator countermeasure component simulates public network signal characteristics. The specific simulation process is as follows: The base station simulator countermeasure component extracts the actual frequency bands used for communication between the public network and no-fly drones, configures its own communication frequency band, and simulates the signal modulation method and signal frame structure of the public network. The base station simulator countermeasure component obtains the signal transmission power and antenna operating frequency of the public network; The peak traffic mapping of the communication link between the no-fly drone and the public network yields the percentage of the first impact of signal simulation. The signal transmission power of the public network is coupled with the percentage of the first influence of the signal simulation to obtain the first influence value of the signal simulation value. The mapping of communication throughput between no-fly drones and the public network yields the second impact percentage from signal simulation. The antenna operating frequency of the public network is coupled with the percentage of the second influence of the signal simulation to obtain the second influence value of the signal simulation value. The first influence value and the second influence value are superimposed to obtain the signal simulation value of the base station simulator countermeasure component, and the public network simulation signal is configured. The base station simulator countermeasure component transmits public network simulated signals to the no-fly drone, guiding the no-fly drone to "attract" the drone, where "attract" refers to the no-fly drone connecting to the simulated communication link. Allocate bandwidth resources greater than or equal to the bandwidth limit threshold for accessed no-fly drones.

7. The method for countering public network drones based on base station signal adsorption according to claim 1, characterized in that: The specific analysis process for retrieving the simulated position coordinates of the no-fly drone based on the path planning function is as follows: The base station simulator countermeasure component extracts the boundary of a preset no-fly zone as the destination, wherein the location coordinates of the destination fall within the boundary of the preset no-fly zone, and the boundary of the preset no-fly zone satisfies the minimum straight-line distance between it and the no-fly drone. The desired departure coordinates of the no-fly drone are calculated by measuring the distance to several original navigation satellites. The base station simulator countermeasure component extracts the remaining battery power, maximum flight speed, and maximum pitch angle of the no-fly drone from the flight control component of the no-fly drone in real time, and uses these as constraints for generating the no-fly drone drive-away path. The planned power consumption of the no-fly drone's decoy path is less than the remaining power of the no-fly drone; The flight speed for setting the no-fly drone departure path must be less than or equal to the maximum flight speed of the no-fly drone. The pitch angle for setting the no-fly drone departure path should be less than or equal to the maximum pitch angle of the no-fly drone. The coordinates of several obstacles in the flight environment are identified by the no-fly drone. Combined with the coordinates of the no-fly drone's desired departure location and the constraints on the no-fly drone's departure path generation, the results are input into the drone path planning algorithm, which outputs the globally optimal path for the no-fly drone to reach the set departure destination. The base station simulator countermeasure component extracts the corresponding scheduling instructions output by the no-fly drone based on the globally optimal path. The scheduling instructions include the actual predetermined power consumption of the no-fly drone, the actual set flight speed of the no-fly drone, and the actual set pitch angle of the no-fly drone. Based on the corresponding scheduling instructions output by the no-fly drone, the simulated position coordinates of the no-fly drone are obtained, and the navigation signal corresponding to the simulated data is used to replace the current no-fly position coordinates.

8. The method for countering public network drones based on base station signal adsorption according to claim 7, characterized in that: The navigation signal corresponding to the simulated data is used to replace the current no-fly zone coordinates. The specific replacement process is as follows: The base station simulator countermeasure component collects the real-time navigation signals currently relied upon by the no-fly drone and parses them to obtain the real-time navigation signal content parameters. The real-time navigation signal content parameters include the real-time positioning time of the no-fly drone, the real-time latitude and longitude of the no-fly drone, the real-time altitude of the no-fly drone, and the real-time operating speed of the no-fly drone. Extract the target position coordinates of the no-fly drone at the next positioning time point of the current navigation, record them as the next target position coordinates of the no-fly drone, and compare them with the expected drive-away position coordinates of the no-fly drone. The comparison includes real-time latitude and longitude comparison and real-time altitude comparison. Calculate the difference between the real-time longitude of the no-fly drone and the longitude corresponding to the expected departure position coordinates of the no-fly drone, and then calculate the ratio of the difference to the longitude corresponding to the expected departure position coordinates of the no-fly drone, which is denoted as the relative longitude difference. Calculate the difference between the real-time latitude of the no-fly drone and the latitude corresponding to the expected departure position coordinates of the no-fly drone, and then calculate the ratio of the difference to the latitude corresponding to the expected departure position coordinates of the no-fly drone, which is denoted as the relative latitude difference. Calculate the difference between the real-time altitude of the no-fly drone and the altitude corresponding to the expected departure position coordinates of the no-fly drone, and then calculate the ratio of the difference to the altitude corresponding to the expected departure position coordinates of the no-fly drone, which is denoted as the relative altitude difference. The relative differences in longitude, latitude, and altitude are superimposed to obtain the relative differences in location coordinates; If the relative difference of position coordinates is less than or equal to the preset tolerance relative difference, the navigation signal corresponding to the simulated data will not be transmitted to the no-fly drone, and real-time comparison will be maintained while waiting for the no-fly drone to leave. If the relative difference of the position coordinates is greater than the preset tolerance relative difference, the base station simulator countermeasure component will encapsulate the content parameters corresponding to the simulated position coordinates of the no-fly drone according to the encapsulation format of the real-time navigation signal to obtain the simulated navigation signal. The base station simulator countermeasure component converts the encapsulated analog navigation signal into electromagnetic waves and sends them to the no-fly drone. It replaces the current no-fly drone's coordinates with the simulated coordinates of the no-fly drone. The no-fly drone then calculates its flight path based on the simulated coordinates. The actual destination of this flight path is the set departure point, which guides the no-fly drone away from the no-fly zone.

9. The method for countering public network drones based on base station signal adsorption according to claim 1, characterized in that: The specific feedback process for optimizing bandwidth resource allocation and correcting simulated navigation data based on monitoring results is as follows: The base station simulator feedback component obtains the communication throughput and peak communication link traffic of the no-fly drone-base station simulator feedback component; The base station simulator countermeasure component integrates the communication throughput of the no-fly drone-base station simulator feedback component with the peak communication link traffic of the no-fly drone-base station simulator feedback component to obtain the communication status reflection value of the no-fly drone; Based on the communication status feedback value of the no-fly drones, the drone countermeasure parameter adjustment factor is obtained by mapping. The drone countermeasure parameter adjustment factor is coupled with the bandwidth limit threshold to obtain the bandwidth optimization threshold, update the bandwidth limit threshold, and configure the communication link bandwidth resource allocation process for no-fly drones to access the public network; Based on the communication status of the no-fly drones, a reasonable fluctuation ratio is mapped and coupled with the coordinates of the designated drive-away position to obtain the allowable error range. After completing the drone countermeasure, the final position coordinates of the no-fly drone are obtained, and the difference between the final position coordinates and the coordinates of the designated driving-away position is processed to obtain the position coordinate difference of the no-fly drone. If the position coordinate difference is within the allowable error range, the original simulated navigation data is retained. When the position coordinate difference does not fall within the allowable error range, the difference between the position coordinate difference and the nearest boundary value of the allowable error range is calculated and recorded as the error correction factor. This factor is coupled with the expected position coordinates of the no-fly drone to correct the simulated navigation data and complete the countermeasure against public network drones.

10. An apparatus for applying the public network drone countermeasure method as described in any one of claims 1-9, characterized in that: include: Public network connection component, public network traffic control component, public network timer, base station simulator countermeasure component, and base station simulator feedback component; The public network link component is used to divide the frequency band determination interval, analyze the topology dynamic evaluation value of each access device, and filter out prohibited drones. The public network traffic control component is used to assess the bandwidth limit threshold, allocate bandwidth resources for no-fly drones, and suppress communication between drones and the public network. The public network timer is used to control the data packet sending interval and cyclically send redundant data packets to the communication link between the no-fly drone and the public network. The base station simulator countermeasure component is used to simulate public network signal characteristics, guide the no-fly drone to access the simulated communication link, send simulated navigation data, and guide the no-fly drone to leave the no-fly zone; The base station simulator feedback component is used to monitor the communication status and final location of the no-fly drones and optimize the countermeasure process.

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