A UAV countermeasure method and system based on the identification of illegal UAVs

Abstract

The present invention provides a drone countermeasure method and system based on the identification of illegal drones. Among them, the present invention monitors the flight behavior of drones in a preset airspace, obtains the identity identification and control instruction sequence, compares the identity identification to detect whether there is an abnormal control mode for the drones. When it is confirmed that there is an abnormal control mode for the drones, an electromagnetic interference signal of a preset frequency band is emitted to make the drones enter a preset safe response mode, and the directional interference system is activated. The multi-source information fusion technology is used to lock the take-off position of the drones and the area where the operator is located, and the key data and the information of the take-off position and the area where the operator is located are summarized to generate an incident report. The technical solution provided by the present invention improves the accuracy of identification, can effectively distinguish legal and illegal drones, reduces the false alarm rate, improves the efficiency and safety of dealing with drone threats, ensures the secure transmission of information, and supports post-event investigation and accountability.

Description

Technical Field

[0001] The embodiments of the present invention relate to the technical field of unmanned aerial vehicles, and in particular, to an unmanned aerial vehicle countermeasure method and system based on the identification of illegal unmanned aerial vehicles. Background Art

[0002] With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles are increasingly widely used in civilian, commercial, and military fields. However, unauthorized or maliciously operated unmanned aerial vehicles also pose security risks, such as invading privacy, interfering with air traffic, and even being used for illegal activities. Therefore, for application scenarios such as critical infrastructure, sensitive areas, and large public event venues, there is an urgent need for an efficient unmanned aerial vehicle countermeasure method that can monitor the flight behavior of unmanned aerial vehicles in the airspace in real time, identify illegal unmanned aerial vehicles, and take effective countermeasure measures to ensure public safety;

[0003] Currently, there are various unmanned aerial vehicle countermeasure systems on the market, mainly relying on technical means such as radio spectrum monitoring, radar detection, and optical tracking to detect and locate unmanned aerial vehicles. Once a suspicious unmanned aerial vehicle is detected, existing systems usually use physical interception (such as net guns, capturing unmanned aerial vehicles) or electronic interference (such as emitting electromagnetic interference signals) for countermeasures. Some advanced systems also incorporate artificial intelligence algorithms to analyze the behavior patterns of unmanned aerial vehicles to improve the identification accuracy;

[0004] Although existing solutions can cope with unmanned aerial vehicle threats to a certain extent, they still have some limitations. First, traditional countermeasure methods often focus on post-processing and lack the ability to quickly lock the takeoff position of unmanned aerial vehicles and the operators, making it difficult to hold them accountable. Second, many systems rely on a single type of sensor and are easily affected by environmental factors, reducing the reliability and accuracy of the systems. Finally, the intelligence level of existing systems is limited, and they may not be able to accurately distinguish legal and illegal unmanned aerial vehicles in complex environments, increasing the risk of misoperation. Summary of the Invention

[0005] The embodiments of the present invention provide an unmanned aerial vehicle countermeasure method and system based on the identification of illegal unmanned aerial vehicles to solve the problems in the prior art that there is a lack of the ability to quickly lock the takeoff position of unmanned aerial vehicles and the operators, making it difficult to hold them accountable, being easily affected by environmental factors, reducing the reliability and accuracy of the systems, and may not be able to accurately distinguish legal and illegal unmanned aerial vehicles in complex environments, increasing the risk of misoperation.

[0006] In a first aspect, the embodiments of the present invention provide an unmanned aerial vehicle countermeasure method based on the identification of illegal unmanned aerial vehicles, including:

[0007] Monitor the flight behavior of drones in a preset airspace, capture the communication signals emitted by the drones in real time, analyze the communication signals, and use a preset communication protocol to decode and obtain the identity identification and control instruction sequence of the drones;

[0008] Compare and verify the identity identification with the records in a preset authorized drone database, and use a behavior analysis algorithm to analyze the control instruction sequence to detect whether there is an abnormal control mode for the drone;

[0009] When it is confirmed that the drone has an abnormal control mode, activate the directional interference system, transmit an electromagnetic interference signal of a preset frequency band to the drone, and realize targeted suppression of the drone's navigation system by adjusting the intensity and frequency characteristics of the electromagnetic interference signal, so that the drone enters a preset safety response mode, and the safety response mode includes: the specific positions of automatic return and safe landing;

[0010] When activating the directional interference system, activate the intelligent tracking mechanism, and lock the take-off position of the drone and the area where the operator is located by integrating data from multiple sensor nodes and using multi-source information fusion technology;

[0011] Summarize the key data in the countermeasure process and the information on the take-off position and the area where the operator is located determined by the intelligent tracking mechanism to generate an event report, and send it to the command center of the regulatory agency through an encrypted communication channel. The key data includes: the identity identification of the drone, the initial flight path, and the behavior changes and safety response mode after being interfered with.

[0012] Optionally, when it is confirmed that the drone has an abnormal control mode, activate the directional interference system, transmit an electromagnetic interference signal of a preset frequency band to the drone, and realize targeted suppression of the drone's navigation system by adjusting the intensity and frequency characteristics of the electromagnetic interference signal, so that the drone enters a preset safety response mode, and the safety response mode includes: the specific positions of automatic return and safe landing, including:

[0013] Transmit an electromagnetic interference signal of a preset frequency band to the drone, where the frequency and intensity characteristics of the electromagnetic interference signal are optimized and configured to realize targeted suppression of the drone's navigation system;

[0014] Based on the real-time monitored drone response feedback, dynamically adjust the parameters of the electromagnetic interference signal to obtain a strengthened electromagnetic interference signal;

[0015] Utilize the enhanced electromagnetic interference signal to make the UAV enter a preset safety response mode, where the safety response mode includes: an automatic return mode and a safe landing mode, and the selection of the safety response mode depends on the distance evaluation result between the current position of the UAV and the nearest safe landing area;

[0016] After the UAV enters the safety response mode, monitor the behavior changes of the UAV in real time, so that the UAV returns along a predetermined path and record the return arrival point of the UAV;

[0017] When it is detected during the directional interference that the UAV attempts to resume the original control instruction sequence, adjust the interference strategy in real time and enhance the interference intensity until the UAV loses the response ability to the original control instructions, ensuring that the UAV is in a controlled state in real time until the safety response mode is completed.

[0018] Optionally, based on the real-time monitored UAV response feedback, dynamically adjust the parameters of the electromagnetic interference signal to obtain an enhanced electromagnetic interference signal, including:

[0019] Use multi-sensor nodes deployed in a preset airspace monitoring network to collect the behavior data of the UAV, where the behavior data includes: flight speed, altitude change, direction change, and communication frequency fluctuation;

[0020] Use data analysis algorithms to perform real-time analysis on the behavior data, evaluate the reaction degree and mode of the UAV to the electromagnetic interference signal, and detect the change trend of the UAV's control instructions to generate an analysis result;

[0021] Based on the analysis result, use a closed-loop control system to adjust the key parameters of the electromagnetic interference signal to obtain an adjusted electromagnetic interference signal, and obtain the real-time response of the UAV through the closed-loop control system in real time, so as to optimize the adjusted electromagnetic interference signal according to the real-time response to obtain an optimized enhanced electromagnetic interference signal.

[0022] Optionally, adjust the key parameters of the electromagnetic interference signal through a closed-loop control system to obtain an adjusted electromagnetic interference signal, and obtain the real-time response of the UAV through the closed-loop control system in real time, so as to optimize the adjusted electromagnetic interference signal according to the real-time response to obtain an optimized enhanced electromagnetic interference signal, including:

[0023] Based on the analysis result, use a closed-loop control system to perform an initial adjustment on the key parameters of the electromagnetic interference signal to generate a preliminarily adjusted electromagnetic interference signal;

[0024] Collect and evaluate the real-time response data of the UAV according to the preliminarily adjusted electromagnetic interference signal to obtain the behavior change information of the UAV. The evaluation content includes: changes in flight trajectory, speed, altitude, and communication status;

[0025] Based on the behavior change information of the UAV, dynamically adjust the preliminarily adjusted electromagnetic interference signal so that the response of UAVs other than the existing targets can be monitored immediately after the dynamic adjustment, and optimize the preliminarily adjusted electromagnetic interference signal according to the response of UAVs other than the existing targets to obtain a preliminarily optimized electromagnetic interference signal;

[0026] Combine the historical response data with the prediction model to adapt the parameters of the preliminarily optimized electromagnetic interference signal, and apply machine learning algorithms to analyze the behavior pattern of the UAV, predict the avoidance strategy of the UAV, and accordingly adjust the intensity and frequency characteristics of the preliminarily optimized electromagnetic interference signal to obtain an optimized electromagnetic interference signal configuration;

[0027] Based on the optimized electromagnetic interference signal configuration, perform multiple rounds of iterative optimization. In each round of iteration, re-adjust the key parameters of the preliminarily optimized electromagnetic interference signal according to the feedback of the response of UAVs other than the existing targets. By comparing the UAV response effects under different parameter settings, select the optimal parameter combination to obtain an optimized enhanced electromagnetic interference signal.

[0028] Optionally, compare and verify the identity identifier with the records in the preset authorized UAV database, and use behavior analysis algorithms to analyze the control instruction sequence to detect whether there is an abnormal control mode for the UAV, including:

[0029] Use the preset authorized UAV database to compare and verify the identity identifier of the UAV to obtain a comparison and verification result to confirm whether the UAV has obtained a legal flight authorization;

[0030] Based on the comparison and verification result, if it is found that the identity identifier of the UAV is not in the authorized UAV database, mark the UAV as a potentially unauthorized aircraft;

[0031] Use behavior analysis algorithms to analyze the control instruction sequence parsed from the communication signal to evaluate the flight behavior pattern of the UAV to generate an analysis result. Among them, the behavior analysis algorithms include: state transition diagram, Markov decision process, and reinforcement learning model;

[0032] According to the analysis result, identify and quantify the behavior characteristics of the UAV, and judge whether there are signs that the control instruction sequence of the UAV does not conform to the preset flight mode.

[0033] Optionally, monitor the flight behavior of drones in a preset airspace, capture the communication signals emitted by the drones in real time, analyze the communication signals, and decode using a preset communication protocol to obtain the identity identification and control instruction sequence of the drones, including:

[0034] Utilize a multi-sensor network deployed in the preset airspace to monitor the flight behavior of the drones in real time and collect the dynamic information of the drones;

[0035] Based on the multi-sensor network, capture the communication signals emitted by the drones in real time, transmit the communication signals to a central processing unit, and the central processing unit is equipped with a dedicated signal processing module for initially filtering and sorting the communication signals, removing noise and irrelevant data to generate optimized communication signals;

[0036] According to the preset communication protocol, analyze the optimized communication signals to obtain the identity identification and control instruction sequence of the drones.

[0037] Optionally, when starting the directional interference system, start an intelligent tracking mechanism. By fusing data from multiple sensor nodes and adopting multi-source information fusion technology, lock the takeoff position of the drone and the area where the operator is located, including:

[0038] Start the intelligent tracking mechanism. When the directional interference system is activated, trigger the intelligent tracking mechanism, and utilize a multi-type sensor network deployed in the preset airspace to collect the dynamic data of the drone and the environment around the drone in real time;

[0039] Based on multi-sensor data fusion technology, perform time synchronization and spatial alignment on the dynamic data from different sensors to obtain the fused data;

[0040] Apply an advanced positioning algorithm to process the fused sensor data to determine the flight path and real-time coordinates of the drone, and infer the takeoff position of the drone by backtracking and analyzing the historical flight trajectory of the drone;

[0041] Combine the geographic information system and the terrain database, combine the position information of the drone with the geographical environment, and predict the takeoff point and the area of the operator;

[0042] Start the collaborative tracking mode. When the takeoff point is initially locked, coordinate the sensor nodes in the surrounding area of the preset airspace to form a dense monitoring network to locate the area where the operator is located. In the second aspect, an embodiment of the present invention provides a drone countermeasure system based on the identification of illegal drones, including:

[0043] A capture module, configured to monitor the flight behavior of drones in a preset airspace, capture in real time the communication signals sent by the drones, analyze the communication signals, and decode using a preset communication protocol to obtain the identity identifiers and control instruction sequences of the drones;

[0044] A verification module, configured to compare and verify the identity identifiers with the records in a preset authorized drone database, and analyze the control instruction sequences using a behavior analysis algorithm to detect whether there are abnormal control modes for the drones;

[0045] A transmission module, configured to, when it is confirmed that there are abnormal control modes for the drones, activate a directional interference system, transmit electromagnetic interference signals of a preset frequency band to the drones, and achieve targeted suppression of the navigation systems of the drones by adjusting the intensity and frequency characteristics of the electromagnetic interference signals, so that the drones enter a preset safe response mode, and the safe response mode includes: the specific positions of automatic return and safe landing;

[0046] A start module, configured to, when starting the directional interference system, activate an intelligent tracking mechanism, and lock the take-off position of the drones and the area where the controllers are located by integrating data from multiple sensor nodes and using multi-source information fusion technology;

[0047] A summary module, configured to summarize the key data in the countermeasure process and the information on the take-off position and the area where the controllers are located determined by the intelligent tracking mechanism to generate an event report, and send it to the command center of the regulatory agency through an encrypted communication channel, and the key data includes: the identity identifiers of the drones, the initial flight paths, and the behavior changes and safe response modes after being interfered with.

[0048] In a third aspect, an embodiment of the present invention provides a computing device, including a processor and a memory, where a computer program is stored in the memory, and the processor is configured to run the computer program to execute any one of the methods for countering drones based on the identification of illegal drones in the first aspect.

[0049] In a fourth aspect, an embodiment of the present invention provides a computer storage medium, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the methods for countering drones based on the identification of illegal drones in any one of the first aspect are implemented.

[0050] In the embodiments of the present invention, the flight behavior of an unmanned aerial vehicle (UAV) in a preset airspace is monitored, and the communication signal emitted by the UAV is captured in real time. The communication signal is analyzed, and the identity identifier and control instruction sequence of the UAV are obtained by decoding using a preset communication protocol. The identity identifier is compared and verified with the records in a preset authorized UAV database, and a behavior analysis algorithm is used to analyze the control instruction sequence to detect whether there is an abnormal control mode for the UAV. When it is confirmed that the UAV has an abnormal control mode, a directional interference system is activated to transmit an electromagnetic interference signal of a preset frequency band to the UAV. By adjusting the intensity and frequency characteristics of the electromagnetic interference signal, targeted suppression of the UAV navigation system is achieved, so that the UAV enters a preset safety response mode. The safety response mode includes the specific positions of automatic return and safe landing. When the directional interference system is activated, an intelligent tracking mechanism is activated. By fusing data from multiple sensor nodes and using multi-source information fusion technology, the take-off position of the UAV and the area where the operator is located are locked. The key data in the countermeasure process and the information on the take-off position and the area where the operator is located determined by the intelligent tracking mechanism are summarized to generate an event report, which is sent to the command center of the regulatory agency through an encrypted communication channel. The key data includes the identity identifier of the UAV, the initial flight path, and the behavioral changes and safety response mode after being interfered with. The technical solution provided by the present invention improves the accuracy of identification, can effectively distinguish between legal and illegal UAVs, reduces the false alarm rate, improves the efficiency and safety in dealing with UAV threats, ensures the secure transmission of information, and supports post-event investigation and liability tracing.

[0051] Further, an optimized configured electromagnetic interference signal is transmitted to the UAV to ensure that the frequency and intensity characteristics can specifically suppress the UAV navigation system, thereby improving the effectiveness and reliability of the countermeasure.

[0052] Dynamic adjustment and enhanced interference: Based on the real-time monitored response feedback of the UAV, the parameters of the electromagnetic interference signal are dynamically adjusted to generate an enhanced electromagnetic interference signal. This adaptive adjustment mechanism can continuously optimize the interference effect according to the actual reaction of the UAV, ensuring the successful implementation of the countermeasure. According to the evaluation result of the distance between the current position of the UAV and the nearest safe landing area, the automatic return or safe landing mode is flexibly selected. This design takes into account the actual environmental factors and maximally guarantees public safety. After the UAV enters the safety response mode, its behavior changes are continuously monitored in real time to ensure that the return or landing operation is executed according to the predetermined path, and the final arrival point is recorded. In addition, when it is detected that the UAV attempts to restore the original control instruction sequence, the interference strategy is immediately adjusted, and the interference intensity is increased until the UAV completely loses the response ability to the original control instruction, ensuring that the UAV is always under control.

[0053] Further, based on the parsing results, a closed-loop control system is used to initially adjust the key parameters of the electromagnetic interference signal, generating a preliminarily adjusted electromagnetic interference signal. This step ensures that the initial interference signal has basic suppression capabilities, laying the foundation for subsequent optimization; according to the preliminarily adjusted electromagnetic interference signal, real-time response data of the drone is collected and evaluated to obtain detailed behavior change information. This information is not only used for the evaluation of the current interference effect but also provides a basis for subsequent adjustments, enhancing the system's self-adaptability; based on the drone's behavior change information, the preliminarily adjusted electromagnetic interference signal is dynamically fine-tuned so that the response of drones outside the existing targets can be immediately monitored after each adjustment, and the signal is further optimized accordingly. This process achieves the goal of gradually approaching the optimal suppression state, improving the effectiveness of countermeasures; by combining historical response data with the prediction model, the parameters of the preliminarily optimized electromagnetic interference signal are adapted, and machine learning algorithms are applied to analyze the behavior patterns of drones and predict the evasion strategies they may adopt. This not only enhances the initiative of the system but also improves the predictability and response ability to future threats; based on the optimized electromagnetic interference signal configuration, multiple rounds of iterative optimization are performed. In each round of iteration, the key parameters of the signal are readjusted according to the latest drone response feedback. By comparing the drone response effects under different parameter settings, the optimal parameter combination is selected and finally determined as the optimized enhanced electromagnetic interference signal. This process ensures the high targeting and adaptability of the interference signal, greatly improving the success rate and security of countermeasures.

[0054] These aspects or other aspects of the present invention will be more clearly understood in the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0056] Figure 1 It is a flowchart of an unmanned aerial vehicle countermeasure method based on illegal unmanned aerial vehicle identification provided by an embodiment of the present invention;

[0057] Figure 2 It is a schematic structural diagram of an unmanned aerial vehicle countermeasure system based on illegal unmanned aerial vehicle identification provided by an embodiment of the present invention;

[0058] Figure 3 It is a schematic structural diagram of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0059] To enable those skilled in the art to better understand the solution of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

[0060] In some processes described in the specification, claims and the above-mentioned drawings of the present invention, a plurality of operations appear in a specific order. However, it should be clearly understood that these operations may not be executed in the order in which they appear herein or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions such as first and second herein are used to distinguish different messages, devices, modules, etc., and do not represent a sequence, nor do they limit that the first and the second are of different types.

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts belong to the scope of protection of the present invention.

[0062] Figure 1 A flowchart of a drone countermeasure method based on illegal drone identification is provided for an embodiment of the present invention, as Figure 1 shown. The method includes:

[0063] Step 101: Monitor the flight behavior of drones in a preset airspace, and capture the communication signals emitted by the drones in real time, analyze the communication signals, and decode the identity identification and control instruction sequence of the drones by using a preset communication protocol;

[0064] In this step, monitoring refers to continuously monitoring the flight behavior of drones through a multi-type sensor network (such as radar, optical camera, infrared sensor, etc.) deployed in the preset airspace. These sensors are not only distributed on the ground, but may also be installed on airborne floating platforms (such as balloons or small drones) and satellites to ensure full coverage. At the same time, the communication signals include control instructions, status reports, identity verification information, and possibly encrypted communication content emitted by the drones. Through a distributed antenna array and spectrum analysis equipment, efficient capture of broadband communication signals can be achieved;

[0065] This step aims to establish a comprehensive monitoring system. By monitoring the flight behavior of drones in a preset airspace, it collects their dynamic data and captures the communication signals emitted by the drones in real time. Then, the captured communication signals are transmitted to the central processing unit and deeply analyzed using preset communication protocols to decode and obtain the identity identifier (ID) and control instruction sequence of the drones. This step provides the necessary basic data for subsequent identity verification and behavior analysis;

[0066] For example, during a large-scale public event, after the system is launched, it starts monitoring the airspace above the event area. When a drone is detected approaching, the system automatically activates the sensor network and captures the communication signal emitted by the drone. By analyzing the communication signal, it is found that the drone is flying along a predetermined path, but the identity identifier it emits shows as unknown. At this time, the system has obtained the identity identifier and detailed control instruction sequence of the drone, preparing for the next comparison and verification.

[0067] Step 102: Compare and verify the identity identifier with the records in the preset authorized drone database, and analyze the control instruction sequence using behavior analysis algorithms to detect whether there are abnormal control patterns in the drone;

[0068] In this step, the authorized drone database is a database containing the identity identifiers of all registered legal drones, used to compare and verify the legality of drones. The behavior analysis algorithms are a series of complex algorithms, such as state transition diagrams, Markov decision processes, and reinforcement learning models, used to evaluate the behavior patterns of drones and detect whether there are abnormal control patterns or non-compliant flight paths;

[0069] In this step, the identity identifier obtained above is compared with the authorized drone database to confirm whether the drone has been legally authorized. At the same time, based on the behavior analysis algorithms, the control instruction sequence is deeply analyzed to identify the flight behavior characteristics of the drone and judge whether there are signs of deviation from the normal flight mode in its control instruction sequence, such as irregular flight trajectories, frequent altitude changes, or abnormal acceleration and deceleration, etc.;

[0070] Continuing the above example, the system compares the captured identity identifier with the authorized drone database, and the result shows that the drone is not authorized. Subsequently, the system applies behavior analysis algorithms to analyze the control instruction sequence and finds that there are abnormalities in the flight trajectory and speed changes of the drone, indicating that there may be malicious control. The system immediately marks the drone as a potential threat and prepares to enter the next countermeasure.

[0071] Step 103: When it is confirmed that the drone has an abnormal control mode, activate the directional interference system, transmit an electromagnetic interference signal of a preset frequency band to the drone, and achieve targeted suppression of the drone's navigation system by adjusting the intensity and frequency characteristics of the electromagnetic interference signal, so that the drone enters a preset safe response mode, and the safe response mode includes: the specific positions of automatic return and safe landing;

[0072] In this step, the directional interference system is a device specifically designed to transmit electromagnetic interference signals, which can suppress the navigation system of a specific drone and force the drone to enter a safe response mode. The safe response mode refers to the actions taken by the drone after being interfered, including the selection of two specific positions: automatic return and safe landing;

[0073] Once it is confirmed that the drone has an abnormal control mode, the system will activate the directional interference system and transmit an electromagnetic interference signal of a preset frequency band to the drone. By precisely adjusting the intensity and frequency characteristics of the electromagnetic interference signal, targeted suppression of the drone's navigation system is achieved, forcing it to enter a preset safe response mode, that is, automatic return or safe landing;

[0074] After the system confirms that the above drone has an abnormal control mode, it immediately activates the directional interference system and transmits an optimally configured electromagnetic interference signal to the drone. As the interference signal is transmitted, the drone gradually loses control of its navigation system and finally is forced to select the automatic return mode. The drone returns to a safe area near the takeoff point according to the predetermined path, avoiding the impact on the event site.

[0075] Step 104: When activating the directional interference system, activate the intelligent tracking mechanism, and lock the takeoff position of the drone and the area where the operator is located by integrating data from multiple sensor nodes and using multi-source information fusion technology;

[0076] In this step, the intelligent tracking mechanism is a technical means to accurately locate the takeoff position of the drone and the area where the operator is located by integrating data from multiple sensors (such as radar, camera, etc.) and combining with a geographic information system (GIS) and a terrain database. Multi-source information fusion technology refers to the process of synchronizing data from different sources in time and aligning them in space, eliminating redundant information, and improving the accuracy and reliability of the data;

[0077] When the directional interference system is activated, the intelligent tracking mechanism is also triggered to integrate data from multiple sensor nodes and use multi-source information fusion technology to lock the takeoff position of the drone and the area where the operator is located. Through advanced positioning algorithms (such as Kalman filtering, particle filtering, etc.), the system can provide a more comprehensive airspace situation awareness and enhance the monitoring ability of the drone's behavior;

[0078] While the drone enters the automatic return mode, the intelligent tracking mechanism is also activated. By integrating data from multiple sensor nodes, it successfully locks the takeoff position of the drone. Further analysis shows that the takeoff position is in a hidden corner outside the activity venue, suspected to be an illegal operation location. The system immediately sends the relevant information to the on-site security personnel to assist them in quickly going to the area for investigation.

[0079] Step 105: Summarize the key data during the countermeasure process, the takeoff position determined by the intelligent tracking mechanism, and the information of the area where the operator is located to generate an incident report, and send it to the command center of the regulatory agency through an encrypted communication channel. The key data includes: the identity identifier of the drone, the initial flight path, the behavioral changes after being interfered with, and the safety response mode.

[0080] In this step, the incident report refers to summarizing all the key data generated during the countermeasure process to form a detailed document for recording the whole process and results of the incident. The encrypted communication channel ensures the security of information during transmission and prevents the leakage of sensitive information.

[0081] This step is responsible for organizing and summarizing all the key data during the countermeasure process, including the identity identifier of the drone, the initial flight path, the behavioral changes after being interfered with, the safety response mode, as well as the takeoff position determined by the intelligent tracking mechanism and the information of the area where the operator is located. The generated incident report is sent to the command center of the regulatory agency through an encrypted communication channel to ensure the secure transmission and timely processing of information.

[0082] The system automatically generates a detailed incident report recording the whole process from when the drone was first detected until it enters the automatic return mode. The report details the identity identifier of the drone, the flight path, the behavioral changes after being interfered with, and the final safety response mode, and is accompanied by the takeoff position determined by the intelligent tracking mechanism and the information of the area where the operator is located. This report is sent to the command center of the relevant regulatory agency through an encrypted communication channel for subsequent investigation and liability determination.

[0083] To solve the problem that the directional interference system in the prior art lacks the ability of adaptive adjustment and to improve the suppression effect on the drone navigation system in the prior art, in one embodiment, according to what is described in step 103, when it is confirmed that the drone has an abnormal operation mode, the directional interference system is activated to emit electromagnetic interference signals of a preset frequency band to the drone. By adjusting the intensity and frequency characteristics of the electromagnetic interference signals, targeted suppression of the drone navigation system is achieved, so that the drone enters a preset safety response mode, specifically including:

[0084] Transmit an electromagnetic interference signal of a preset frequency band to the drone, where the frequency and intensity characteristics of the electromagnetic interference signal are optimized and configured to achieve targeted suppression of the drone's navigation system; based on the real-time monitored response feedback of the drone, dynamically adjust the parameters of the electromagnetic interference signal to obtain an enhanced electromagnetic interference signal; use the enhanced electromagnetic interference signal to make the drone enter a preset safe response mode, where the safe response mode includes: an automatic return mode and a safe landing mode, and the selection of the safe response mode depends on the distance evaluation result between the current position of the drone and the nearest safe landing area; after the drone enters the safe response mode, monitor the behavior changes of the drone in real time, so that the drone returns along a predetermined path, and record the return arrival point of the drone; when it is detected that the drone attempts to restore the original control instruction sequence during the implementation of directional interference, adjust the interference strategy in real time and increase the interference intensity until the drone loses the response ability to the original control instruction, ensuring that the drone is under control in real time until the safe response mode is completed;

[0085] In this embodiment, the enhanced electromagnetic interference signal refers to a more effective interference signal generated by dynamically adjusting the key parameters (such as frequency range, power level, and modulation method) of the initial electromagnetic interference signal. This signal can be continuously optimized according to the actual reaction of the drone to ensure continuous suppression of its navigation system and force it to enter the safe response mode. At the same time, the selection of the safe response mode is based on the distance evaluation result between the current position of the drone and the nearest safe landing area, ensuring that the drone takes the safest action path;

[0086] In the embodiment of the present application, first, an optimized and configured electromagnetic interference signal is transmitted to the drone, aiming to perform the first suppression on its navigation system. Subsequently, the system dynamically adjusts the parameters of the electromagnetic interference signal based on the real-time monitored response feedback of the drone, thereby generating an enhanced electromagnetic interference signal. This process is realized through a closed-loop control system, ensuring that the new response of the drone can be immediately obtained after each adjustment, and the interference signal is further optimized based on this until the best suppression state is reached. After the drone enters the safe response mode, the system continues to monitor its behavior changes in real time to ensure that it returns or lands along a predetermined path and records the final arrival point. If it is detected that the drone attempts to restore the original control instruction sequence during the implementation of directional interference, the system will immediately adjust the interference strategy and increase the interference intensity to ensure that the drone is always under control until the safe response mode is completed;

[0087] For example, at an outdoor concert site, the system detects an unauthorized drone approaching the event area. After confirming its abnormal control mode, the system immediately activates the directional interference system and emits an optimized electromagnetic interference signal towards the drone. As the interference signal is emitted, the drone gradually loses control of its navigation system. The system monitors the drone's response in real time and dynamically adjusts the parameters of the electromagnetic interference signal to generate an enhanced electromagnetic interference signal, ultimately forcing the drone to select the automatic return mode. The drone returns to a safe area near the takeoff point along a predetermined path, avoiding any impact on the event site. During this process, the system also detects that the drone attempts to resume the original control instruction sequence and immediately increases the interference intensity to ensure that the drone remains under control until it successfully completes the automatic return.

[0088] To address the problem of the lack of adaptive adjustment ability in the existing directional interference system and improve the suppression effect on the drone navigation system in the prior art, as described in the previous embodiment, based on the real-time monitored drone response feedback, the parameters of the electromagnetic interference signal are dynamically adjusted to obtain an enhanced electromagnetic interference signal, which specifically includes:

[0089] Using multi-sensor nodes deployed in a preset airspace monitoring network to collect the drone's behavior data, where the behavior data includes flight speed, altitude change, direction change, and communication frequency fluctuation; using data analysis algorithms to perform real-time analysis on the behavior data, evaluate the degree and pattern of the drone's response to the electromagnetic interference signal, and detect the changing trend of the drone's control instructions to generate an analysis result; based on the analysis result, using a closed-loop control system to adjust the key parameters of the electromagnetic interference signal to obtain an adjusted electromagnetic interference signal, and obtaining the real-time response of the drone through the closed-loop control system in real time to optimize the adjusted electromagnetic interference signal according to the real-time response to obtain an optimized enhanced electromagnetic interference signal;

[0090] In this embodiment, the multi-sensor nodes refer to various types of sensors distributed in the preset airspace, such as radar, optical cameras, infrared sensors, etc., which are used to capture the drone's behavior data in real time. These behavior data are not limited to the basic flight parameters of the drone (such as flight speed, altitude change, direction change), but also include information such as communication frequency fluctuation, etc., for comprehensively evaluating the state of the drone. The data analysis algorithms are a series of complex algorithms for analyzing and evaluating the behavior data, aiming to identify the degree and pattern of the drone's response to the electromagnetic interference signal, while detecting the changing trend of the control instructions, and ultimately generating a detailed analysis result. The closed-loop control system is a control system that can automatically adjust the output according to the feedback. In this embodiment, it is used to adjust the key parameters of the electromagnetic interference signal to ensure that the new response of the drone can be immediately obtained after each adjustment, and the interference signal can be further optimized based on this until the best suppression state is reached;

[0091] In the embodiments of the present application, first, the behavior data of the unmanned aerial vehicle (UAV) is collected in real time by multiple sensor nodes, including but not limited to flight speed, altitude change, direction change, and communication frequency fluctuation. Then, data analysis algorithms are used to parse these behavior data in real time, evaluate the response degree and pattern of the UAV to electromagnetic interference signals, detect the change trend of its control commands, and generate parsing results. Based on this parsing result, the closed-loop control system makes a primary adjustment to the key parameters of the electromagnetic interference signal (such as frequency range, power level, and modulation method), and generates a preliminarily adjusted electromagnetic interference signal. The system continues to obtain the real-time response of the UAV in real time through the closed-loop control system, continuously optimizes the adjusted electromagnetic interference signal according to these feedbacks, and finally obtains an optimized enhanced electromagnetic interference signal to ensure continuous suppression of the UAV's navigation system and force it to enter a safe response mode;

[0092] For example, during a public event in the city center, the system detected an unauthorized UAV approaching the event site. After confirming its abnormal control mode, the system activated the directional interference system and transmitted an initial electromagnetic interference signal to the UAV. As the UAV was gradually interfered, the system collected its behavior data in real time through multiple sensor nodes, including information such as flight speed and altitude change. The data analysis algorithm was used to parse these data and found that the UAV tried to avoid interference by changing its flight altitude. The system then adjusted the key parameters of the electromagnetic interference signal based on the parsing result through the closed-loop control system to generate an enhanced electromagnetic interference signal. The system continued to monitor the response of the UAV in real time and found that it began to lose control and turn to the automatic return mode. During this process, when it was detected that the UAV tried to restore the original control command sequence, the system immediately increased the interference intensity to ensure that the UAV remained under control until the automatic return was successfully completed, avoiding potential safety hazards.

[0093] To solve the problem that the adjustment of the directional interference system in the prior art is not flexible enough and to enhance the suppression effect on the UAV's navigation system, as described in the previous embodiment, based on the parsing result, the key parameters of the electromagnetic interference signal are adjusted through the closed-loop control system to obtain an adjusted electromagnetic interference signal, and the real-time response of the UAV is obtained in real time through the closed-loop control system to optimize the adjusted electromagnetic interference signal according to the real-time response to obtain an optimized enhanced electromagnetic interference signal, which specifically includes:

[0094] Based on the parsing results, the key parameters of the electromagnetic interference signal are initially adjusted using a closed-loop control system to generate a preliminarily adjusted electromagnetic interference signal; based on the preliminarily adjusted electromagnetic interference signal, the real-time response data of the UAV is collected and evaluated to obtain the behavior change information of the UAV, and the evaluation content includes changes in flight trajectory, speed, altitude, and communication status; based on the behavior change information of the UAV, the preliminarily adjusted electromagnetic interference signal is dynamically adjusted so that the response of the UAV outside the existing target can be immediately monitored after the dynamic adjustment, and the preliminarily adjusted electromagnetic interference signal is optimized according to the response of the UAV outside the existing target to obtain a preliminarily optimized electromagnetic interference signal; combining the historical response data with the prediction model, the parameters of the preliminarily optimized electromagnetic interference signal are adapted, and machine learning algorithms are applied to analyze the behavior pattern of the UAV, predict the avoidance strategy of the UAV, and accordingly adjust the intensity and frequency characteristics of the preliminarily optimized electromagnetic interference signal to obtain an optimized electromagnetic interference signal configuration; based on the optimized electromagnetic interference signal configuration, multiple rounds of iterative optimization are performed. In each round of iteration, the key parameters of the preliminarily optimized electromagnetic interference signal are readjusted according to the feedback of the response of the UAV outside the existing target. By comparing the UAV response effects under different parameter settings, the optimal parameter combination is selected to obtain an optimized enhanced electromagnetic interference signal;

[0095] In this embodiment, the behavior change information refers to the changes in aspects such as the flight trajectory, speed, altitude, and communication status of the UAV after being affected by the electromagnetic interference signal. This information is used to evaluate the effect of the electromagnetic interference signal and provide a basis for subsequent adjustments. The prediction model is a mathematical model established based on historical response data, which is used to predict the next actions or avoidance strategies that the UAV may take. Machine learning algorithms are applied to analyze the behavior pattern of the UAV, predict future behavior by learning past data, so as to adapt the parameters of the electromagnetic interference signal in advance and enhance the initiative and efficiency of the system;

[0096] In the embodiments of the present application, first, based on the parsing results, a closed-loop control system is used to initially adjust the key parameters of the electromagnetic interference signal (such as frequency range, power level, and modulation mode) to generate a preliminarily adjusted electromagnetic interference signal. Then, based on the preliminarily adjusted electromagnetic interference signal, the system collects and evaluates the real-time response data of the drone to obtain detailed behavior change information, including changes in flight trajectory, speed, altitude, and communication status. Next, based on this behavior change information, the system dynamically adjusts the preliminarily adjusted electromagnetic interference signal to ensure that the response of drones other than the existing targets can be immediately monitored after each adjustment, and further optimizes the signal based on this to obtain a preliminarily optimized electromagnetic interference signal. On this basis, the system combines historical response data with a prediction model to adapt the parameters of the preliminarily optimized electromagnetic interference signal, applies machine learning algorithms to analyze the behavior patterns of drones, predicts the evasion strategies they may adopt, and accordingly adjusts the intensity and frequency characteristics of the signal to obtain an optimized electromagnetic interference signal configuration. Finally, based on the optimized electromagnetic interference signal configuration, the system performs multiple rounds of iterative optimization. In each round of iteration, the key parameters are adjusted again according to the latest drone response feedback. By comparing the drone response effects under different parameter settings, the optimal parameter combination is selected, and finally an optimized enhanced electromagnetic interference signal is obtained to ensure continuous and efficient suppression effects;

[0097] For example, during an international sports event, the system detected an unauthorized drone approaching the competition venue. After confirming its abnormal control mode, the system activated the directional interference system and transmitted an initial electromagnetic interference signal to the drone. As the drone was gradually interfered with, the system, based on the parsing results, initially adjusted the key parameters of the electromagnetic interference signal through a closed-loop control system to generate a preliminarily adjusted electromagnetic interference signal. The system continued to collect the real-time response data of the drone in real time and found that its flight trajectory became unstable and its speed slowed down, indicating that the initial interference had produced certain effects. Then, the system dynamically adjusted the preliminarily adjusted electromagnetic interference signal according to this behavior change information to ensure that the new response of the drone could be immediately monitored after each adjustment and further optimized the signal. During this process, the system combined historical response data with a prediction model to predict the evasion strategies that the drone might adopt, such as trying to avoid interference by changing the flight altitude. The system applied machine learning algorithms to analyze the behavior patterns of the drone and adapt the electromagnetic interference signal parameters in advance, enhancing the initiative of the system. After multiple rounds of iterative optimization, the system finally selected the optimal parameter combination and generated an optimized enhanced electromagnetic interference signal, forcing the drone to enter the automatic return mode according to the predetermined path and avoiding the impact on the event.

[0098] To address the issue of inaccurate UAV authentication and behavior analysis in the prior art, and to improve the detection effect of abnormal control modes in the prior art, as another embodiment, as described in step 102, the identity identifier is compared and verified with the records in a preset authorized UAV database, and a behavior analysis algorithm is used to analyze the control instruction sequence to detect whether there is an abnormal control mode for the UAV, specifically including:

[0099] Using the preset authorized UAV database, the identity identifier of the UAV is compared and verified to obtain a comparison and verification result to confirm whether the UAV has obtained legal flight authorization; based on the comparison and verification result, if it is found that the identity identifier of the UAV is not in the authorized UAV database, the UAV is marked as a potentially unauthorized aircraft; using a behavior analysis algorithm, the control instruction sequence parsed from the communication signal is analyzed to evaluate the flight behavior pattern of the UAV to generate an analysis result, where the behavior analysis algorithm includes: state transition diagram, Markov decision process, and reinforcement learning model; according to the analysis result, the behavior characteristics of the UAV are identified and quantified, and it is determined whether there are signs that the control instruction sequence of the UAV does not conform to the preset flight mode;

[0100] In this embodiment, the preset authorized UAV database is a database containing the identity identifiers of all registered legal UAVs, which is used to compare and verify the legality of UAVs. Through comparison and verification, it can be quickly determined whether a UAV has been authorized, thus initially screening out potentially unauthorized aircraft. The behavior analysis algorithm is a series of complex algorithms designed to evaluate the flight behavior pattern of UAVs, including but not limited to state transition diagrams (used to model the transitions between UAV states), Markov decision processes (used to predict the actions that a UAV may take in the future), and reinforcement learning models (used to learn the behavior pattern of UAVs from historical data). These algorithms work together to generate detailed analysis results to help identify and quantify the behavior characteristics of UAVs, and then determine whether there are abnormalities in their control instruction sequences;

[0101] In the embodiments of the present application, first, the identity identifier of the drone is compared and verified using a preset authorized drone database to obtain a comparison and verification result, so as to confirm whether the drone has obtained legal flight authorization. If it is found that the identity identifier of the drone is not in the authorized drone database, the drone is immediately marked as a potentially unauthorized aircraft. Next, the system uses a behavior analysis algorithm to deeply analyze the control instruction sequence parsed from the communication signal to evaluate the flight behavior pattern of the drone. This process not only considers the current behavior of the drone, but also combines its historical flight data to ensure the comprehensiveness and accuracy of the analysis. Finally, based on the generated analysis results, the system identifies and quantifies the behavior characteristics of the drone to determine whether there are signs in its control instruction sequence that do not conform to the preset flight mode, such as irregular flight trajectories, frequent altitude changes, or abnormal acceleration and deceleration. This step can effectively distinguish normal flight from abnormal control and provide a basis for subsequent countermeasures;

[0102] For example, in a city airspace monitoring mission, the system captures the communication signal emitted by a drone and successfully decodes its identity identifier and control instruction sequence. The system immediately compares and verifies the identity identifier with the preset authorized drone database and finds that the drone is not registered in the database, so it is marked as a potentially unauthorized aircraft. Subsequently, the system applies a behavior analysis algorithm to analyze the control instruction sequence and finds that the flight trajectory of the drone has multiple sharp turns and sudden altitude changes, which do not conform to the preset normal flight mode. Further quantitative analysis shows that these behavior characteristics are highly abnormal, indicating possible malicious control. Based on the above analysis results, the system determines that the drone has an abnormal control mode and immediately prepares to activate the directional interference system to prevent potential security threats. In this way, the system can accurately identify and handle illegal drones at an early stage, enhancing the safety management ability of the airspace.

[0103] To address the problem of inaccurate monitoring of UAV flight behavior and communication signal analysis in the prior art, and to improve the effect of obtaining UAV identity identification and control instruction sequences in the prior art, as another embodiment, as described in step 101, monitor the flight behavior of UAVs in a preset airspace, and capture in real time the communication signals emitted by the UAVs, analyze the communication signals, and use a preset communication protocol to decode to obtain the UAV identity identification and control instruction sequences, specifically including: using a multi-sensor network deployed in the preset airspace to monitor the flight behavior of the UAVs in real time and collect the dynamic information of the UAVs; based on the multi-sensor network, capture in real time the communication signals emitted by the UAVs, and transmit the communication signals to a central processing unit, which is equipped with a dedicated signal processing module for initially filtering and sorting the communication signals, removing noise and irrelevant data to generate optimized communication signals; according to the preset communication protocol, analyze the optimized communication signals to obtain the UAV identity identification and control instruction sequences;

[0104] In this embodiment, the multi-sensor network refers to various types of sensors (such as radar, optical cameras, infrared sensors, etc.) distributed in the preset airspace, which are used to monitor the flight behavior of UAVs in real time and collect their dynamic information. These sensors are not limited to ground base stations, but also include airborne floating platforms (such as balloons or small UAVs) and satellite monitoring systems to ensure full coverage and high-precision data collection. The central processing unit is a computing center integrated with a dedicated signal processing module, which is responsible for receiving data from the multi-sensor network, performing initial filtering and sorting, and removing noise and irrelevant data to generate optimized communication signals. This step is crucial for subsequent accurate analysis. The preset communication protocol covers a variety of international standards and proprietary formats to ensure compatibility with UAV communications of different manufacturers and models, enabling the system to extract key information from the optimized communication signals, such as UAV identity identification and control instruction sequences;

[0105] In the embodiment of the present application, first, a multi-sensor network deployed in the preset airspace is used to monitor the flight behavior of UAVs in real time and collect their dynamic information, such as flight trajectories, speeds, altitude changes, etc. Then, based on the multi-sensor network, the communication signals emitted by the UAVs are captured in real time and transmitted to the central processing unit. The dedicated signal processing module in the central processing unit initially filters and sorts the communication signals, removing noise and irrelevant data to generate optimized communication signals. Finally, the optimized communication signals are deeply analyzed according to the preset communication protocol to decode and obtain the UAV identity identification and control instruction sequences. This process ensures the effective acquisition and accurate analysis of UAV communication signals, providing a solid foundation for subsequent operations;

[0106] For example, in an airspace monitoring mission near an international airport, after the system is started, it begins to comprehensively monitor the preset airspace around the airport. The multi-sensor network quickly captures an approaching drone and real-time collects dynamic information on its flight behavior. At the same time, the system captures the communication signal emitted by the drone in real time and transmits it to the central processing unit. In the central processing unit, the dedicated signal processing module preliminarily filters and organizes the communication signal, removes background noise and other irrelevant data, and generates an optimized communication signal. Subsequently, the system deeply analyzes the optimized communication signal according to the preset communication protocol and successfully obtains the identity identifier of the drone and the detailed control instruction sequence. This enables the system to immediately confirm whether the drone is authorized and prepares to further analyze its flight behavior pattern to evaluate the risk of abnormal control. In this way, the system can accurately identify the identity and behavior of the drone at an early stage, enhancing the safety management ability of the airspace.

[0107] To solve the problem in the prior art that the takeoff position of the drone and the area where the operator is located are not accurately locked, and to improve the effect of tracking the threat source of the drone in the prior art, as another embodiment, according to step 104, when starting the directional interference system, start the intelligent tracking mechanism, and lock the takeoff position of the drone and the area where the operator is located by fusing data from multiple sensor nodes, specifically including: start the intelligent tracking mechanism, when the directional interference system is activated, trigger the intelligent tracking mechanism, and use the multi-type sensor network deployed in the preset airspace to collect the dynamic data of the drone and the environment around the drone in real time; based on the multi-sensor data fusion technology, synchronize the dynamic data from different sensors in time and align them in space to obtain the fused data; apply the advanced positioning algorithm to process the fused sensor data to determine the flight path and real-time coordinates of the drone, and infer the takeoff position of the drone by backtracking and analyzing the historical flight trajectory of the drone; combine the geographic information system and the terrain database, combine the position information of the drone with the geographical environment to predict the takeoff point and the area of the operator; start the collaborative tracking mode, when the takeoff point is initially locked, coordinate the sensor nodes in the surrounding area of the preset airspace to form a dense monitoring network to locate the area where the operator is located;

[0108] In this embodiment, the intelligent tracking mechanism refers to an automated process that integrates multiple sensors and data sources, aiming to precisely lock the takeoff position of the drone and the area where the operator is located. This mechanism is triggered simultaneously when the directional interference system is activated, ensuring timely response and starting to track. The multi-type sensor network includes radar, optical cameras, infrared sensors, etc., which are used to collect real-time dynamic data of the drone and its surrounding environment, such as flight speed, altitude change, direction change, etc. These data are crucial for subsequent analysis. The multi-sensor data fusion technology is a technical means used to synchronize and align the data from different sensors in time and space, eliminate redundant information, improve the accuracy and reliability of the data, and thus generate the integrated data after fusion. The advanced positioning algorithm refers to a series of complex mathematical and technical methods, such as Kalman filtering, particle filtering, etc., which are used to process the fused sensor data to determine the precise flight path and real-time coordinates of the drone, and infer the takeoff position by backtracking and analyzing its historical flight trajectory. The Geographic Information System (GIS) and terrain database are used to combine the position information of the drone with the specific geographical environment, provide detailed geographical references, and help predict the takeoff point and the potential area of the operator. The collaborative tracking mode is to coordinate the sensor nodes in the adjacent areas to form a dense monitoring network after initially locking the takeoff point, further accurately locate the area where the operator is located, and enhance the tracking accuracy;

[0109] In the embodiment of the present application, first, the intelligent tracking mechanism is started, which is immediately triggered when the directional interference system is activated, and the multi-type sensor network deployed in the preset airspace is used to collect real-time dynamic data of the drone and its surrounding environment. Then, based on the multi-sensor data fusion technology, the data from different sensors are synchronized and aligned in time and space, and the redundant information is removed to obtain the integrated data after fusion. Next, the advanced positioning algorithm is applied to process the fused sensor data to determine the flight path and real-time coordinates of the drone, and infer the takeoff position of the drone by backtracking and analyzing its historical flight trajectory. On this basis, combined with the Geographic Information System and terrain database, the position information of the drone is combined with the geographical environment to predict the takeoff point and the potential area of the operator. Finally, the collaborative tracking mode is started. When the takeoff point is initially locked, the sensor nodes in the surrounding area of the preset airspace are coordinated to form a dense monitoring network, further accurately locate the area where the operator is located, and ensure the efficiency and accuracy of the tracking;

[0110] For example, during a major event in the city center, the system detected an unauthorized drone approaching the event site. After confirming its abnormal control mode, the system activated the directional interference system and emitted electromagnetic interference signals towards the drone. At the same time, the intelligent tracking mechanism was also activated, using a multi-type sensor network deployed in the preset airspace to collect real-time dynamic data of the drone and its surrounding environment. The system synchronized these data in time and aligned them in space through multi-sensor data fusion technology, generating integrated data after fusion. Subsequently, advanced positioning algorithms were applied to process the fused data, determining the flight path and real-time coordinates of the drone, and successfully inferring the takeoff position of the drone was in a hidden corner outside the event venue by backtracking its historical flight trajectory. Combining with the geographic information system and terrain database, the system predicted the possible area where the operator was located and activated the collaborative tracking mode, coordinating sensor nodes in the nearby area to form a dense monitoring network, and finally locking the specific position of the operator. Security personnel quickly went to this area according to the guidance of the system, ensuring the safe and smooth progress of the event. In this way, the system can not only respond to drone threats in a timely manner, but also quickly lock the threat source, enhancing the overall security management ability.

[0111] Figure 2 The following is a schematic structural diagram of an unmanned aerial vehicle countermeasure system based on the identification of illegal unmanned aerial vehicles provided by an embodiment of the present invention, as Figure 2 shown, the system includes:

[0112] A capture module 21, configured to monitor the flight behavior of unmanned aerial vehicles in a preset airspace, and capture in real time the communication signals emitted by the unmanned aerial vehicles, analyze the communication signals, and decode using a preset communication protocol to obtain the identity identifier and control instruction sequence of the unmanned aerial vehicles;

[0113] A verification module 22, configured to compare and verify the identity identifier with the records in a preset authorized unmanned aerial vehicle database, and analyze the control instruction sequence using a behavior analysis algorithm to detect whether the unmanned aerial vehicle has an abnormal control mode;

[0114] A transmission module 23, configured to activate a directional interference system and emit electromagnetic interference signals of a preset frequency band towards the unmanned aerial vehicle when it is confirmed that the unmanned aerial vehicle has an abnormal control mode, and achieve targeted suppression of the navigation system of the unmanned aerial vehicle by adjusting the intensity and frequency characteristics of the electromagnetic interference signals, so that the unmanned aerial vehicle enters a preset safe response mode, and the safe response mode includes: the specific positions of automatic return and safe landing;

[0115] An activation module 24, configured to activate an intelligent tracking mechanism when activating the directional interference system, and lock the takeoff position of the unmanned aerial vehicle and the area where the operator is located by fusing data from multiple sensor nodes and adopting multi-source information fusion technology;

[0116] The summarization module 25 is configured to summarize the key data during the countermeasure process, the takeoff position determined by the intelligent tracking mechanism, and the information of the area where the operator is located, so as to generate an event report and send it to the command center of the regulatory agency through an encrypted communication channel. The key data includes: the identity identifier of the drone, the initial flight path, and the behavioral changes and safety response patterns after being interfered with. Figure 2 The described drone countermeasure system based on illegal drone identification can execute Figure 1 For the drone countermeasure method based on illegal drone identification described in the above embodiments, its implementation principle and technical effects will not be elaborated further. For the drone countermeasure system based on illegal drone identification in the above embodiments, the specific manners in which each module and unit perform operations have been described in detail in the embodiments related to the method, and will not be elaborated here.

[0117] In a possible design, Figure 2 The drone countermeasure system based on illegal drone identification in the above embodiments can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0118] The storage component 31 stores one or more computer instructions, where the one or more computer instructions are called and executed by the processing component 32.

[0119] The processing component 32 is configured to: monitor the flight behavior of the unmanned aerial vehicle (UAV) within a preset airspace, and capture in real time the communication signals emitted by the UAV, analyze the communication signals, and decode using a preset communication protocol to obtain the identity identifier and control instruction sequence of the UAV; compare and verify the identity identifier with the records in a preset authorized UAV database, and analyze the control instruction sequence using a behavior analysis algorithm to detect whether there is an abnormal control mode for the UAV; when it is confirmed that the UAV has an abnormal control mode, activate the directional interference system, transmit electromagnetic interference signals of a preset frequency band to the UAV, and achieve targeted suppression of the UAV's navigation system by adjusting the intensity and frequency characteristics of the electromagnetic interference signals, so that the UAV enters a preset safety response mode, and the safety response mode includes: the specific positions of automatic return and safe landing; when activating the directional interference system, activate the intelligent tracking mechanism, lock the take-off position of the UAV and the area where the operator is located by integrating data from multiple sensor nodes and adopting multi-source information fusion technology; summarize the key data in the countermeasure process and the information on the take-off position and the area where the operator is located determined by the intelligent tracking mechanism to generate an incident report, and send it to the command center of the regulatory agency through an encrypted communication channel, and the key data includes: the identity identifier of the UAV, the initial flight path, and the behavioral changes and safety response mode after being interfered with.

[0120] Among them, the processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components for executing the above method.

[0121] The storage component 31 is configured to store various types of data to support the operation of the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0122] Of course, the computing device may also necessarily include other components, such as input / output interfaces, display components, communication components, etc.

[0123] The input / output interface provides an interface between the processing component and the peripheral interface module, and the above peripheral interface module may be an output device, an input device, etc.

[0124] The communication component is configured to facilitate communication between the computing device and other devices in a wired or wireless manner, etc.

[0125] Wherein, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. At this time, the computing device can refer to a cloud server, and the above processing component, storage component, etc. can be basic server resources leased or purchased from a cloud computing platform.

[0126] An embodiment of the present invention also provides a computer storage medium storing a computer program, and when the computer program is executed by a computer, it can implement the above Figure 1 A drone countermeasure method based on illegal drone recognition shown in the embodiment.

[0127] Those skilled in the art can clearly understand that for the convenience and conciseness of description, the specific working processes of the above-described systems, devices, and units can refer to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0128] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0129] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disc, etc., including several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0130] Finally, it should be noted that: the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A drone countermeasure method based on illegal drone identification, characterized in that: include: Monitor the flight behavior of drones in a preset airspace, and capture the communication signals sent by the drones in real time, analyze the communication signals, and obtain the drone's identity and control instruction sequence by decoding using a preset communication protocol; Compare and verify the identity with the records in the preset authorized drone database, and use the behavior analysis algorithm to analyze the control instruction sequence to detect whether the drone has an abnormal control mode; When it is confirmed that the drone has an abnormal control mode, the directional interference system is activated to transmit an electromagnetic interference signal of a preset frequency band to the drone, and the intensity and frequency characteristics of the electromagnetic interference signal are adjusted to achieve targeted suppression of the drone's navigation system, so that the drone enters a preset safety response mode, which includes: automatic return and a specific location for safe landing; Start the intelligent tracking mechanism. When the directional jamming system is activated, the intelligent tracking mechanism is triggered, and the dynamic data of the UAV and the surrounding environment of the UAV are collected in real time by using the multi-type sensor network deployed in the preset airspace; based on the multi-sensor data fusion technology, the dynamic data from different sensors are synchronized in time and aligned in space to obtain the fused data; the advanced positioning algorithm is applied to process the fused sensor data to determine the flight path and real-time coordinates of the UAV, and the take-off position of the UAV is inferred by retrospectively analyzing the historical flight trajectory of the UAV; the location information of the UAV is combined with the geographical environment in combination with the geographic information system and the terrain database to predict the take-off point and the area of ​​the operator; start the collaborative tracking mode, and when the take-off point is initially locked, coordinate the sensor nodes in the surrounding area of ​​the preset airspace to form a dense monitoring network to locate the area where the operator is located; Summarize key data during the countermeasure process and information on the take-off location and the area where the operator is located determined by the intelligent tracking mechanism to generate an event report and send it to the command center of the regulatory agency through an encrypted communication channel. The key data include: the drone’s identity, initial flight path, and behavioral changes and safety response modes after interference.

2. The method according to claim 1, characterized in that: When it is confirmed that the drone has an abnormal control mode, the directional interference system is started to transmit an electromagnetic interference signal of a preset frequency band to the drone. By adjusting the intensity and frequency characteristics of the electromagnetic interference signal, the navigation system of the drone is suppressed in a targeted manner so that the drone enters a preset safety response mode. The safety response mode includes: the specific location of automatic return and safe landing, including: Transmitting an electromagnetic interference signal of a preset frequency band to the drone, wherein the frequency and intensity characteristics of the electromagnetic interference signal are optimized to achieve targeted suppression of the drone navigation system; Based on the real-time monitored UAV response feedback, dynamically adjust the parameters of the electromagnetic interference signal to obtain an enhanced electromagnetic interference signal; Using the enhanced electromagnetic interference signal, the drone enters a preset safety response mode, wherein the safety response mode includes: an automatic return mode and a safe landing mode, and the selection of the safety response mode depends on the distance assessment result between the current position of the drone and the nearest safe landing area; After the drone enters the safety response mode, monitor the behavior changes of the drone in real time to make the drone return according to a predetermined path, and record the return arrival point of the drone; When it is detected during the implementation of directional interference that the UAV attempts to restore the original control command sequence, the interference strategy is adjusted in real time to increase the interference intensity until the UAV loses the ability to respond to the original control commands, ensuring that the UAV is in a controlled state in real time until the safety response mode is completed.

3. The method according to claim 2, characterized in that Based on the real-time monitored UAV response feedback, the parameters of the electromagnetic interference signal are dynamically adjusted to obtain an enhanced electromagnetic interference signal, including: Utilizing multi-sensor nodes deployed in a preset airspace monitoring network, collecting behavioral data of the drone, the behavioral data including: flight speed, altitude change, direction change, and communication frequency fluctuation; Analyze the behavior data in real time using a data analysis algorithm, evaluate the degree and mode of the drone's response to the electromagnetic interference signal, detect the change trend of the drone's control instructions, and generate analysis results; Based on the analysis results, a closed-loop control system is used to adjust the key parameters of the electromagnetic interference signal to obtain an adjusted electromagnetic interference signal, and the real-time response of the UAV is obtained in real time through the closed-loop control system to optimize the adjusted electromagnetic interference signal according to the real-time response to obtain an optimized enhanced electromagnetic interference signal.

4. The method according to claim 3, characterized in that Based on the analysis result, the key parameters of the electromagnetic interference signal are adjusted through the closed-loop control system to obtain the adjusted electromagnetic interference signal, and the real-time response of the UAV is obtained in real time through the closed-loop control system to optimize the adjusted electromagnetic interference signal according to the real-time response to obtain the optimized enhanced electromagnetic interference signal, including: Based on the analysis results, a closed-loop control system is used to make initial adjustments to key parameters of the electromagnetic interference signal to generate a preliminarily adjusted electromagnetic interference signal; According to the electromagnetic interference signal after the preliminary adjustment, the real-time response data of the UAV is collected and evaluated to obtain the behavior change information of the UAV, and the evaluation content includes: the change of flight trajectory, speed, altitude and communication status; Based on the behavior change information of the UAV, the electromagnetic interference signal after the preliminary adjustment is dynamically adjusted, so that the response of the UAV outside the existing target can be monitored immediately after the dynamic adjustment, and the electromagnetic interference signal after the preliminary adjustment is optimized according to the response of the UAV outside the existing target to obtain a preliminary optimized electromagnetic interference signal; Combining historical response data with the prediction model, adapting the initially optimized electromagnetic interference signal parameters, applying a machine learning algorithm to analyze the behavior pattern of the UAV, predicting the avoidance strategy of the UAV, and adjusting the intensity and frequency characteristics of the initially optimized electromagnetic interference signal accordingly to obtain an optimized electromagnetic interference signal configuration; Based on the optimized electromagnetic interference signal configuration, multiple rounds of iterative optimization are performed. In each round of iteration, the key parameters of the initially optimized electromagnetic interference signal are adjusted again according to the response feedback of the UAV outside the existing target. By comparing the UAV response effects under different parameter settings, the optimal parameter combination is selected to obtain the optimized enhanced electromagnetic interference signal.

5. The method according to claim 1, characterized in that: Compare and verify the identity with the records in the preset authorized drone database, and use the behavior analysis algorithm to analyze the control instruction sequence to detect whether the drone has an abnormal control mode, including: Using the preset authorized drone database, the drone's identity is compared and verified to obtain a comparison and verification result to confirm whether the drone has obtained legal flight authorization; Based on the comparison and verification results, if it is found that the identity of the drone is not in the authorized drone database, the drone is marked as a potential unauthorized aircraft; Analyzing the control instruction sequence parsed from the communication signal using a behavior analysis algorithm to evaluate the flight behavior pattern of the UAV to generate an analysis result, wherein the behavior analysis algorithm includes: a state transition diagram, a Markov decision process, and a reinforcement learning model; According to the analysis results, the behavioral characteristics of the UAV are identified and quantified, and it is determined whether the control instruction sequence of the UAV shows signs of not complying with a preset flight mode.

6. The method according to claim 1, characterized in that Monitor the flight behavior of drones in a preset airspace, and capture the communication signals sent by the drones in real time, parse the communication signals, and use the preset communication protocol to decode and obtain the drone's identity and control instruction sequence, including: Using a multi-sensor network deployed in a preset airspace, the flight behavior of the UAV is monitored in real time to collect dynamic information of the UAV; Based on a multi-sensor network, the communication signal emitted by the drone is captured in real time, and the communication signal is transmitted to a central processing unit, wherein the central processing unit is equipped with a dedicated signal processing module for preliminarily filtering and sorting the communication signal, removing noise and irrelevant data, and generating an optimized communication signal; According to a preset communication protocol, the optimized communication signal is parsed to obtain the identity identification and control instruction sequence of the UAV.

7. A drone countermeasure system based on illegal drone identification, characterized in that: include: A capture module is used to monitor the flight behavior of the UAV in the preset airspace, and to capture the communication signal sent by the UAV in real time, analyze the communication signal, and obtain the identity and control instruction sequence of the UAV by decoding using a preset communication protocol; A verification module, used to compare and verify the identity with the records in a preset authorized drone database, and to analyze the control instruction sequence using a behavior analysis algorithm to detect whether the drone has an abnormal control mode; The transmitting module is used to start the directional interference system when it is confirmed that the drone has an abnormal control mode, transmit an electromagnetic interference signal of a preset frequency band to the drone, and achieve targeted suppression of the drone navigation system by adjusting the intensity and frequency characteristics of the electromagnetic interference signal, so that the drone enters a preset safety response mode, and the safety response mode includes: automatic return and specific locations for safe landing; A startup module is used to start the intelligent tracking mechanism. When the directional jamming system is activated, the intelligent tracking mechanism is triggered, and the dynamic data of the UAV and the surrounding environment of the UAV are collected in real time by using a multi-type sensor network deployed in a preset airspace; based on multi-sensor data fusion technology, the dynamic data from different sensors are synchronized in time and aligned in space to obtain fused data; advanced positioning algorithms are applied to process the fused sensor data to determine the flight path and real-time coordinates of the UAV, and the take-off position of the UAV is inferred by retrospectively analyzing the historical flight trajectory of the UAV; the location information of the UAV is combined with the geographical environment in combination with the geographic information system and terrain database to predict the take-off point and the area of ​​the operator; the collaborative tracking mode is started, and when the take-off point is initially locked, the sensor nodes in the surrounding area of ​​the preset airspace are coordinated to form a dense monitoring network to locate the area where the operator is located; The summary module is used to summarize the key data in the countermeasure process and the take-off location determined by the intelligent tracking mechanism and the information on the area where the operator is located to generate an event report and send it to the command center of the regulatory agency through an encrypted communication channel. The key data includes: the drone’s identity, initial flight path, and behavioral changes and safety response modes after interference.

8. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a drone countermeasure method based on illegal drone identification as described in any one of claims 1 to 6.

9. A computer storage medium, characterized in that: A computer program is stored, and when the computer program is executed by a computer, a drone countermeasure method based on illegal drone identification as described in any one of claims 1 to 6 is implemented.

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