Unmanned aerial vehicle countering method and system based on illegal unmanned aerial vehicle identification

Abstract

The invention provides an unmanned aerial vehicle countering method and system based on illegal unmanned aerial vehicle recognition, and the method comprises the steps: monitoring the flight behavior of an unmanned aerial vehicle in a preset airspace, obtaining an identity label and a control instruction sequence, comparing the identity labels, and detecting whether the unmanned aerial vehicle has an abnormal control mode or not. And when it is confirmed that the unmanned aerial vehicle has an abnormal control mode, emitting an electromagnetic interference signal of a preset frequency band to enable the unmanned aerial vehicle to enter a preset safety response mode, starting the directional interference system, and locking a take-off position of the unmanned aerial vehicle and an area where an operator is located by adopting a multi-source information fusion technology. Summarizing the key data, the take-off position and the information of the area where the operator is located to generate an event report; according to the technical scheme provided by the invention, the identification accuracy is improved, legal and illegal unmanned aerial vehicles can be effectively distinguished, the false alarm rate is reduced, the efficiency and safety of coping with the threats of the unmanned aerial vehicles are improved, the secure transmission of information is ensured, and post investigation and responsibility investigation are supported.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of drone technology, and in particular to a drone countermeasure method and system based on illegal drone identification. Background Art

[0002] With the rapid development of drone technology, drones are increasingly used in civil, commercial and military fields. However, unauthorized or maliciously operated drones also pose security risks, such as privacy violations, interference with air traffic, and even use 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 drone countermeasure method that can monitor drone flight behavior in the airspace in real time, identify illegal drones, and take effective countermeasures to ensure public safety; Currently, there are a variety of drone countermeasure systems on the market, which mainly rely on radio spectrum monitoring, radar detection, optical tracking and other technical means to detect and locate drones. Once a suspicious drone is detected, existing systems usually use physical interception (such as net guns, capturing drones) or electronic interference (such as transmitting electromagnetic interference signals) to counter it. Some advanced systems also combine artificial intelligence algorithms to analyze the behavior patterns of drones to improve recognition accuracy; Although existing solutions can deal with drone threats to a certain extent, they still have some limitations. First, traditional countermeasures often focus on post-processing and lack the ability to quickly lock the drone's take-off position and operator, making it difficult to hold anyone 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 system. Finally, the existing systems are limited in their intelligence and may not be able to accurately distinguish between legal and illegal drones in complex environments, increasing the risk of misoperation. Summary of the invention

[0003] The embodiments of the present invention provide a method and system for countering drones based on illegal drone identification, so as to solve the problems in the prior art of lacking the ability to quickly lock the take-off position and operator of the drone, making it difficult to hold the operator accountable, being easily affected by environmental factors, reducing the reliability and accuracy of the system, and possibly being unable to accurately distinguish between legal and illegal drones in complex environments, thereby increasing the risk of misoperation.

[0004] In a first aspect, an embodiment of the present invention provides a method for countering a drone based on identifying illegal drones, including: 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; When the directional jamming system is activated, the intelligent tracking mechanism is activated, and the take-off position of the UAV and the area where the operator is located are locked by fusing data from multiple sensor nodes and using multi-source information fusion technology; 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.

[0005] Optionally, 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, and the intensity and frequency characteristics of the electromagnetic interference signal are adjusted to achieve targeted suppression of the drone navigation system, so that the drone enters a preset safety response mode, and 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.

[0006] Optionally, based on the real-time monitored UAV response feedback, dynamically adjusting the parameters of the electromagnetic interference signal to obtain an enhanced electromagnetic interference signal includes: 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.

[0007] Optionally, adjusting key parameters of the electromagnetic interference signal through a closed-loop control system to obtain an adjusted electromagnetic interference signal, and obtaining a real-time response of the UAV 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, 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 drone outside the existing target. By comparing the drone response effects under different parameter settings, the optimal parameter combination is selected to obtain the optimized enhanced electromagnetic interference signal.

[0008] Optionally, the identity is compared and verified with records in a preset authorized drone database, and the control instruction sequence is analyzed using a behavior analysis algorithm 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.

[0009] Optionally, monitoring the flight behavior of a drone in a preset airspace, capturing the communication signal sent by the drone in real time, parsing the communication signal, and obtaining the identity and control instruction sequence of the drone by decoding using a preset communication protocol, 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.

[0010] Optionally, when the directional jamming system is started, an intelligent tracking mechanism is started to lock the take-off position of the UAV and the area where the operator is located by fusing data from multiple sensor nodes and using multi-source information fusion technology, including: Initiate an intelligent tracking mechanism, and when the directional jamming system is activated, trigger the intelligent tracking mechanism to collect dynamic data of the UAV and the environment around the UAV 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; Applying advanced positioning algorithms to process the fused sensor data to determine the flight path and real-time coordinates of the drone, and inferring the take-off position of the drone by back-analyzing the historical flight trajectory of the drone; Combining the location information of the UAV with the geographical environment in combination with the geographic information system and the terrain database, predicting 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. In the second aspect, an embodiment of the present invention provides a drone countermeasure system based on illegal drone identification, including: 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 start-up module is used to start the intelligent tracking mechanism when the directional jamming system is started, and to lock the take-off position of the UAV and the area where the operator is located by fusing data from multiple sensor nodes and using multi-source information fusion technology; 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.

[0011] In a third aspect, an embodiment of the present invention provides a computing device, including a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a drone countermeasure method based on illegal drone identification as described in any one of the first aspects.

[0012] In a fourth aspect, an embodiment of the present invention provides a computer storage medium having computer program instructions stored thereon, wherein the computer program instructions, when executed by a processor, implement a drone countermeasure method based on illegal drone identification as described in any one of the first aspects.

[0013] In an embodiment of the present invention, the flight behavior of a drone in a preset airspace is monitored, and the communication signal emitted by the drone is captured in real time, the communication signal is parsed, and the identity and control instruction sequence of the drone are obtained by decoding using a preset communication protocol; the identity is compared and verified with the records in a preset authorized drone database, and the control instruction sequence is analyzed using a behavior analysis algorithm 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 started to transmit an electromagnetic interference signal of a preset frequency band to the drone, and 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, and the safety response mode includes: automatic return and safety The specific landing location; when the directional interference system is started, the intelligent tracking mechanism is started, and the take-off location of the UAV and the area where the operator is located are locked by fusing data from multiple sensor nodes and using multi-source information fusion technology; the key data in the countermeasure process and the information on the take-off location and the area where the operator is located determined by the intelligent tracking mechanism are summarized to generate an event report, and the report is sent to the command center of the regulatory agency through an encrypted communication channel. The key data includes: the identity of the UAV, the initial flight path, and the behavioral changes and safety response modes after being interfered with; the technical solution provided by the present invention improves the accuracy of identification, and can effectively distinguish between legal and illegal UAVs, reduce the false alarm rate, improve the efficiency and safety of responding to UAV threats, ensure the safe transmission of information, and support post-investigation and accountability; Furthermore, an electromagnetic interference signal with optimized configuration is transmitted to the UAV, ensuring that the frequency and intensity characteristics can specifically suppress the UAV navigation system, thereby improving the effectiveness and reliability of the countermeasures.

[0014] Dynamic adjustment and enhanced interference: Based on the real-time monitored drone response feedback, the electromagnetic interference signal parameters are dynamically adjusted to generate enhanced electromagnetic interference signals. This adaptive adjustment mechanism can continuously optimize the interference effect according to the actual response of the drone to ensure the successful implementation of countermeasures; according to the distance assessment results between the current position of the drone and the nearest safe landing area, the automatic return or safe landing mode can be flexibly selected. This design takes into account actual environmental factors and maximizes public safety; after the drone enters the safety response mode, its behavior changes continue to be monitored in real time to ensure that the return or landing operation is performed according to the predetermined path, and the final arrival point is recorded. In addition, when it is detected that the drone attempts to restore the original control command sequence, the interference strategy is immediately adjusted to increase the interference intensity until the drone completely loses the ability to respond to the original control commands, ensuring that the drone is always under control; Furthermore, based on the analysis results, the closed-loop control system is used to make the initial adjustment to the key parameters of the electromagnetic interference signal to generate the electromagnetic interference signal after preliminary adjustment. This step ensures that the initial interference signal has basic suppression capability, laying the foundation for subsequent optimization; based on the electromagnetic interference signal after preliminary adjustment, the real-time response data of the drone is collected and evaluated to obtain detailed behavior change information. This information is not only used to evaluate the current interference effect, but also provides a basis for subsequent adjustments, enhancing the adaptability of the system; based on the behavior change information of the drone, the electromagnetic interference signal after preliminary adjustment is dynamically fine-tuned, so that the drone response outside the existing target 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 and improves the effectiveness of countermeasures; combining historical response data with the prediction model, adapting the preliminary optimized electromagnetic interference signal parameters, and applying machine learning algorithms to analyze the behavior pattern of the drone and predicting the possible avoidance strategies it may take. This not only enhances the initiative of the system, but also improves the predictability and response capabilities of future threats; based on the optimization of the electromagnetic interference signal configuration, multiple rounds of iterative optimization are performed, and the key parameters of the signal are adjusted again in each round of iteration 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 pertinence and adaptability of the interference signal, greatly improving the success rate and safety of the countermeasure.

[0015] These and other aspects of the present invention will become more apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flowchart of a drone countermeasure method based on illegal drone identification provided by an embodiment of the present invention; Figure 2 A schematic diagram of the structure of a drone countermeasure system based on illegal drone identification provided by an embodiment of the present invention; Figure 3 A schematic diagram of the structure of a computing device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0018] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0019] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the first, second, etc. descriptions in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit the first and second to be different types.

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0021] Figure 1 A flowchart of a drone countermeasure method based on illegal drone identification is provided for an embodiment of the present invention, such as Figure 1 As shown, the method includes: Step 101: monitor the flight behavior of the drone in the preset airspace, and capture the communication signal sent by the drone in real time, analyze the communication signal, and obtain the identity and control instruction sequence of the drone by decoding using a preset communication protocol; In this step, monitoring refers to the continuous monitoring of the flight behavior of the drone 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 floating platforms in the air (such as balloons or small drones) and satellites to ensure full coverage. At the same time, communication signals include control commands, status reports, authentication information, and possible encrypted communication content issued by the drone. Through distributed antenna arrays and spectrum analysis equipment, efficient capture of broadband communication signals can be achieved; This step aims to establish a comprehensive monitoring system, which collects dynamic data by monitoring the flight behavior of drones in the preset airspace, and captures the communication signals sent by drones in real time. Then, the captured communication signals are transmitted to the central processing unit, and the preset communication protocol is used for deep analysis to decode the drone's identity (ID) and control command sequence. This step provides the necessary basic data for subsequent identity authentication and behavior analysis; For example, during a large public event, the system 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 from the drone. By analyzing the communication signal, it is found that the drone is flying along the planned path, but the identity it sends is displayed as unknown. At this point, the system has obtained the drone's identity and detailed control command sequence, ready for the next step of comparison and verification.

[0022] Step 102: Compare and verify the identity with the records in the preset authorized drone database, and use a behavior analysis algorithm to analyze the control instruction sequence to detect whether the drone has an abnormal control mode; In this step, the authorized drone database is a database containing the identities of all registered legal drones, which is used to compare and verify the legality of the drone. Behavior analysis algorithms are a series of complex algorithms, such as state transition diagrams, Markov decision processes, and reinforcement learning models, which are used to evaluate the behavior patterns of drones and detect whether there are abnormal control patterns or non-compliant flight paths; This step uses the identity obtained above to compare with the authorized drone database to confirm whether the drone has been legally authorized. At the same time, based on the behavior analysis algorithm, the control instruction sequence is deeply analyzed to identify the flight behavior characteristics of the drone and determine whether its control instruction sequence has signs of deviation from the normal flight mode, such as irregular flight trajectory, frequent altitude changes, or abnormal acceleration and deceleration; Continuing with the above example, the system compares the captured ID with the authorized drone database and shows that the drone is not authorized. The system then applies a behavioral analysis algorithm to analyze the control command sequence and finds that the drone's flight trajectory and speed changes are abnormal, indicating possible malicious manipulation. The system immediately marks the drone as a potential threat and prepares to take the next step of countermeasures.

[0023] Step 103: 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, and the intensity and frequency characteristics of the electromagnetic interference signal are adjusted to achieve targeted suppression of the drone navigation system, so that the drone enters a preset safety response mode, and the safety response mode includes: automatic return and specific location of safe landing; In this step, the directional jammer system is a device specially designed to emit 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 with, including the choice of two specific locations: automatic return and safe landing; Once the drone is confirmed to have an abnormal control mode, the system will activate the directional interference system to transmit electromagnetic interference signals of a preset frequency band to the drone. By precisely adjusting the strength and frequency characteristics of the electromagnetic interference signal, the drone's navigation system can be suppressed in a targeted manner, forcing it to enter a preset safety response mode, i.e., automatically return home or land safely. After the system confirmed that the above-mentioned drone had an abnormal control mode, it immediately activated the directional interference system and emitted an optimized electromagnetic interference signal to the drone. As the interference signal was emitted, the drone gradually lost control of its navigation system and was eventually forced to choose the automatic return mode. The drone returned to a safe area near the take-off point along the predetermined path, avoiding any impact on the event site.

[0024] Step 104: When the directional jamming system is activated, the intelligent tracking mechanism is activated, and the take-off position of the UAV and the area where the operator is located are locked by fusing data from multiple sensor nodes and using multi-source information fusion technology; In this step, the intelligent tracking mechanism is a technical means to accurately locate the take-off position of the drone and the area where the operator is located by integrating data from multiple sensors (such as radar, cameras, etc.) and combining geographic information systems (GIS) and terrain databases. Multi-source information fusion technology refers to the time synchronization and spatial alignment of data from different sources to eliminate redundant information and improve data accuracy and reliability; When the directional jamming system is activated, the intelligent tracking mechanism is also triggered, integrating data from multiple sensor nodes and using multi-source information fusion technology to lock the take-off 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 more comprehensive airspace situational awareness and enhance the ability to monitor drone behavior; When the drone entered the automatic return mode, the intelligent tracking mechanism was also activated. By integrating data from multiple sensor nodes, the take-off position of the drone was successfully locked. Further analysis showed that the take-off position was located in a hidden corner outside the event venue, which was suspected to be an illegal operation site. The system immediately sent relevant information to the on-site security personnel to assist them in quickly going to the area for investigation.

[0025] Step 105: Summarize the key data during the countermeasure process and 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 includes: the identity of the drone, the initial flight path, and the behavior changes and safety response mode after being disturbed; In this step, the incident report refers to the summary of all key data generated during the countermeasure process to form a detailed document to record the process and results of the entire incident. The encrypted communication channel ensures the security of information during transmission and prevents the leakage of sensitive information; This step is responsible for collating and summarizing all key data in the countermeasure process, including the drone's identity, initial flight path, behavioral changes after interference and security response mode, as well as the take-off location and operator's area determined by the intelligent tracking mechanism. The generated event report is sent to the regulatory agency's command center through an encrypted communication channel to ensure the secure transmission and timely processing of information; The system automatically generated a detailed incident report, recording the entire process from the first capture of the drone to the automatic return mode. The report details the drone's identification, flight path, behavioral changes after interference, and the final safety response mode, as well as the take-off location and operator's area determined by the intelligent tracking mechanism. This report is sent to the command center of the relevant regulatory agency through an encrypted communication channel for subsequent investigation and accountability.

[0026] In order to solve the problem that the directional interference system in the prior art lacks adaptive adjustment capability and improve the suppression effect on the drone navigation system in the prior art, in a certain embodiment, according to step 103, 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, and the intensity and frequency characteristics of the electromagnetic interference signal are adjusted to achieve targeted suppression of the drone navigation system, so that the drone enters a preset safety response mode, specifically including: An electromagnetic interference signal of a preset frequency band is transmitted 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 drone response feedback monitored in real time, the parameters of the electromagnetic interference signal are dynamically adjusted to obtain an enhanced electromagnetic interference signal; using the enhanced electromagnetic interference signal, the drone is caused to enter 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, the behavior changes of the drone are monitored in real time so that the drone returns according to the predetermined path, and the return arrival point of the drone is recorded; when it is detected that the drone attempts to restore the original control instruction sequence during the implementation of directional interference, the interference strategy is adjusted in real time to enhance the interference intensity until the drone loses the ability to respond to the original control instructions, ensuring that the drone is in a controlled state in real time until the safety response mode is completed; In this embodiment, the enhanced electromagnetic interference signal refers to a more effective interference signal generated by dynamically adjusting the key parameters of the initial electromagnetic interference signal (such as frequency range, power level and modulation mode). This signal can be continuously optimized according to the actual response of the drone to ensure continuous suppression of its navigation system and force it to enter a safe response mode. At the same time, the selection of the safe response mode is based on the distance assessment result between the current position of the drone and the nearest safe landing area to ensure that the drone takes the safest action path; In an embodiment of the present application, an electromagnetic interference signal with an optimized configuration is first transmitted to the drone, aiming at the initial suppression of its navigation system. Subsequently, the system dynamically adjusts the parameters of the electromagnetic interference signal based on the real-time monitored drone response feedback, thereby generating an enhanced electromagnetic interference signal. This process is implemented through a closed-loop control system to ensure that the new response of the drone can be obtained immediately after each adjustment, and the interference signal is further optimized accordingly until the optimal suppression state is achieved. After the drone enters the safe response mode, the system continues to monitor its behavior changes in real time to ensure that it performs return or landing operations according to the predetermined path and records the final arrival point. If during the implementation of directional interference, it is detected that the drone attempts to restore the original control command sequence, the system will immediately adjust the interference strategy and increase the interference intensity to ensure that the drone is always in a controlled state until the safe response mode is completed; For example, at an outdoor concert, the system detected an unauthorized drone approaching the event area. After confirming that it had an abnormal control mode, the system immediately activated the directional interference system and transmitted an optimized electromagnetic interference signal to the drone. As the interference signal was transmitted, the drone gradually lost control of its navigation system. The system monitored the drone's response in real time and dynamically adjusted the parameters of the electromagnetic interference signal to generate an enhanced electromagnetic interference signal, which eventually forced the drone to select the automatic return mode. The drone returned to a safe area near the take-off point along the predetermined path, avoiding any impact on the event site. During this process, the system also detected that the drone tried to restore the original control command sequence, and then increased the interference intensity to ensure that the drone always remained under control until it successfully completed the automatic return.

[0027] In order to solve the problem that the directional interference system in the prior art lacks the adaptive adjustment capability and improve the suppression effect on the drone navigation system in the prior art, according to 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, specifically including: Using multi-sensor nodes deployed in a preset airspace monitoring network, the behavior data of the UAV is collected, and the behavior data includes flight speed, altitude change, direction change, and communication frequency fluctuation; using a data analysis algorithm to analyze the behavior data in real time, evaluate the reaction degree and mode of the UAV to the electromagnetic interference signal, and detect the change trend of the control command of the UAV 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 obtain the real-time response of the UAV in real time through the closed-loop control system, so as to optimize the adjusted electromagnetic interference signal according to the real-time response, and obtain an optimized enhanced electromagnetic interference signal; In this embodiment, multi-sensor nodes refer to various types of sensors distributed in a preset airspace, such as radars, optical cameras, infrared sensors, etc., which are used to capture the behavioral data of the drone in real time. These behavioral 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 fluctuations, which are used to comprehensively evaluate the status of the drone. The data analysis algorithm is a series of complex algorithms used to parse and evaluate behavioral data. It aims to identify the degree and pattern of the drone's response to electromagnetic interference signals, while detecting the changing trend of control instructions, and finally generating detailed analysis results. A closed-loop control system is a control system that can automatically adjust the output based on 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 obtained immediately after each adjustment, and the interference signal is further optimized accordingly until the optimal suppression state is achieved; In an embodiment of the present application, the behavior data of the UAV is first collected in real time through multiple sensor nodes, including but not limited to flight speed, altitude changes, direction changes, and communication frequency fluctuations. Then, a data analysis algorithm is used to analyze these behavior data in real time, evaluate the degree and mode of the UAV's response to the electromagnetic interference signal, detect the changing trend of its control instructions, and generate an analysis result. Based on this analysis result, the closed-loop control system makes an initial adjustment to the key parameters of the electromagnetic interference signal (such as frequency range, power level, and modulation method) to generate a preliminary adjusted electromagnetic interference signal. The system continues to obtain the real-time response of the UAV through the closed-loop control system, and continuously optimizes the adjusted electromagnetic interference signal based on these feedbacks, and finally obtains the optimized enhanced electromagnetic interference signal to ensure continuous suppression of the UAV's navigation system, forcing it to enter a safe response mode; For example, during a public event in the city center, the system detected an unauthorized drone approaching the event site. After confirming that it had an abnormal control mode, the system activated the directional interference system and sent an initial electromagnetic interference signal to the drone. As the drone was gradually interfered with, the system collected its behavior data in real time through multiple sensor nodes, including flight speed, altitude change and other information. The data was analyzed using a data analysis algorithm, and it was found that the drone tried to avoid interference by changing its flight altitude. Based on the analysis results, the system then adjusted the key parameters of the electromagnetic interference signal through a closed-loop control system to generate an enhanced electromagnetic interference signal. The system continued to monitor the drone's response in real time and found that it began to lose control and switch to automatic return mode. During this process, when it was detected that the drone tried to restore the original control command sequence, the system immediately increased the interference intensity to ensure that the drone always remained under control until it successfully completed the automatic return, avoiding potential safety hazards.

[0028] In order to solve the problem that the directional interference system in the prior art is not flexible enough to adjust, and to enhance the suppression effect on the UAV navigation system, according to the previous embodiment, based on the analysis results, 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, so as to optimize the adjusted electromagnetic interference signal according to the real-time response, and obtain the optimized enhanced electromagnetic interference signal, which specifically includes: Based on the analysis results, the key parameters of the electromagnetic interference signal are adjusted for the first time 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 the 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 monitored immediately 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 historical response data with the prediction model, the parameters of the preliminarily optimized electromagnetic interference signal are adapted, and a machine learning algorithm is applied to analyze the behavior pattern of the UAV, predict the avoidance strategy of the UAV, and adjust the intensity and frequency characteristics of the preliminarily 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, and in each round of iteration, the key parameters of the preliminarily optimized electromagnetic interference signal are adjusted again according to the response feedback of the UAV outside the existing target, and the optimal parameter combination is selected by comparing the response effects of the UAV under different parameter settings to obtain an optimized enhanced electromagnetic interference signal; In this embodiment, the behavior change information refers to the changes in the flight trajectory, speed, altitude, and communication status of the drone after it is 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 based on historical response data, which is used to predict the next action or avoidance strategy that the drone may take. The machine learning algorithm is used to analyze the behavior patterns of the drone, predict future behavior by learning from past data, and thus adapt the electromagnetic interference signal parameters in advance to enhance the initiative and efficiency of the system; In the embodiment of the present application, firstly, based on the analysis results, a closed-loop control system is used to make an initial adjustment to 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 drone response outside the existing target can be immediately monitored after each adjustment, and further optimizes the signal accordingly to obtain a preliminarily optimized electromagnetic interference signal. On this basis, the system combines historical response data with the prediction model to adapt the preliminarily optimized electromagnetic interference signal parameters, applies machine learning algorithms to analyze the behavior pattern of the drone, predicts the avoidance strategy it may take, and adjusts the signal strength and frequency characteristics accordingly 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, 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 the optimized enhanced electromagnetic interference signal is obtained to ensure a continuous and efficient suppression effect. For example, during an international sports event, the system detected an unauthorized drone approaching the competition venue. After confirming that it had an abnormal control mode, the system activated the directional interference system and sent an initial electromagnetic interference signal to the drone. As the drone was gradually interfered with, the system made initial adjustments to the key parameters of the electromagnetic interference signal through a closed-loop control system based on the analysis results, generating a preliminary 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 a certain effect. Then, the system dynamically adjusted the preliminary adjusted electromagnetic interference signal based on these behavioral change information to ensure that the new response of the drone can be immediately monitored after each adjustment and further optimize the signal. In this process, the system combines historical response data with prediction models to predict the avoidance strategies that the drone may take, such as trying to avoid interference by changing the flight altitude. The system applies machine learning algorithms to analyze the behavior patterns of drones 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, avoiding the impact on the event.

[0029] In order to solve the problem of insufficient precision in drone identity authentication and behavior analysis in the prior art, and to improve the effect of detecting abnormal control modes in the prior art, as another embodiment, according to step 102, the identity identifier is compared and verified with the records in the preset authorized drone database, and the control instruction sequence is analyzed using a behavior analysis algorithm to detect whether the drone has an abnormal control mode, specifically including: Using a preset authorized drone database, the identity of the drone is compared and verified to obtain a comparison and verification result to confirm whether the drone has obtained a legal flight authorization; based on the comparison and verification result, 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; using a behavior analysis algorithm, the control instruction sequence parsed from the communication signal is analyzed to evaluate the flight behavior pattern of the drone 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 result, the behavior characteristics of the drone are identified and quantified to determine whether the control instruction sequence of the drone has signs that it does not conform to the preset flight mode; In this embodiment, the preset authorized drone database is a database containing the identities of all registered legal drones, which is used to compare and verify the legitimacy of the drone. Through comparison and verification, it is possible to quickly determine whether the drone has been authorized, thereby preliminarily screening out potential unauthorized aircraft. Behavioral analysis algorithms are a series of complex algorithms designed to evaluate the flight behavior patterns of drones, including but not limited to state transition diagrams (used to model transitions between drone states), Markov decision processes (used to predict possible actions that drones may take in the future), and reinforcement learning models (used to learn drone behavior patterns from historical data). These algorithms work together to generate detailed analysis results to help identify and quantify the behavioral characteristics of drones, and then determine whether there are abnormalities in their control instruction sequences; In an embodiment of the present application, the identity of the drone is first compared and verified using a preset authorized drone database to obtain a comparison and verification result to confirm whether the drone has obtained a legal flight authorization. If it is found that the identity of the drone is not in the authorized drone database, the drone is immediately marked as a potential unauthorized aircraft. Next, the system uses a behavioral analysis algorithm to perform an in-depth analysis of the control instruction sequence parsed from the communication signal to evaluate the flight behavior pattern of the drone. This process not only takes into account 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 behavioral characteristics of the drone to determine whether its control instruction sequence shows signs of not conforming to the preset flight mode, such as irregular flight trajectories, frequent altitude changes, or abnormal acceleration and deceleration. This step can effectively distinguish between normal flight and abnormal control, providing a basis for subsequent countermeasures; For example, during an urban airspace monitoring mission, the system captured a communication signal from a drone and successfully decoded its identity and control command sequence. The system immediately compared the identity with the preset authorized drone database and found that the drone was not registered in the database, so it was marked as a potential unauthorized aircraft. Subsequently, the system applied a behavioral analysis algorithm to analyze the control command sequence and found that the drone's flight trajectory had multiple sharp turns and sudden altitude changes, which did not match the preset normal flight mode. Further quantitative analysis showed that these behavioral characteristics were highly abnormal, indicating possible malicious manipulation. Based on the above analysis results, the system determined that the drone had an abnormal control mode and immediately prepared to start the directional interference system to prevent potential security threats. In this way, the system can accurately identify and deal with illegal drones at an early stage, enhancing the safety management capabilities of the airspace.

[0030] In order to solve the problem of inaccurate UAV flight behavior monitoring and communication signal parsing in the prior art, and to improve the effect of obtaining the UAV identity and control instruction sequence in the prior art, as another embodiment, according to step 101, the flight behavior of the UAV in the preset airspace is monitored, and the communication signal emitted by the UAV is captured in real time, the communication signal is parsed, and the identity and control instruction sequence of the UAV are obtained by decoding using a preset communication protocol, specifically including: using a multi-sensor network deployed in the preset airspace to monitor the flight behavior of the UAV in real time and collect dynamic information of the UAV; based on the multi-sensor network, the communication signal emitted by the UAV is captured in real time, and the communication signal is transmitted to a central processing unit, 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, so as to generate an optimized communication signal; according to the preset communication protocol, the optimized communication signal is parsed to obtain the identity and control instruction sequence of the UAV; In this embodiment, the multi-sensor network refers to various types of sensors (such as radars, optical cameras, infrared sensors, etc.) distributed in a preset airspace, which are used to monitor the flight behavior of drones in real time and collect their dynamic information. These sensors are not limited to ground base stations, but also include aerial floating platforms (such as balloons or small drones) and satellite monitoring systems to ensure all-round coverage and high-precision data collection. The central processing unit is a computing center that integrates dedicated signal processing modules. It is responsible for receiving data from the multi-sensor network, performing preliminary filtering and sorting, removing noise and irrelevant data, and generating optimized communication signals. This step is crucial for subsequent accurate analysis. The preset communication protocols cover a variety of international standards and proprietary formats to ensure compatibility with drone communications of different manufacturers and models, enabling the system to extract key information from the optimized communication signals, such as the drone's identity and control command sequence; In an embodiment of the present application, firstly, the flight behavior of the UAV is monitored in real time through a multi-sensor network deployed in a preset airspace, and its dynamic information, such as flight trajectory, speed, altitude change, etc., is collected. Then, the communication signals emitted by the UAV are captured in real time based on the multi-sensor network, and these signals are transmitted to the central processing unit. The dedicated signal processing module in the central processing unit performs preliminary filtering and sorting of the communication signals, removes noise and irrelevant data, and generates optimized communication signals. Finally, the optimized communication signals are deeply analyzed according to the preset communication protocol to decode and obtain the identity of the UAV and the control instruction sequence. This process ensures the effective acquisition and accurate analysis of the UAV communication signals, providing a solid foundation for subsequent operations; For example, in an airspace monitoring mission near an international airport, the system started to conduct comprehensive monitoring of the preset airspace around the airport. The multi-sensor network quickly captured an approaching drone and collected dynamic information about its flight behavior in real time. At the same time, the system captured the communication signal emitted by the drone in real time and transmitted it to the central processing unit. In the central processing unit, a dedicated signal processing module preliminarily filtered and sorted the communication signal, removed background noise and other irrelevant data, and generated an optimized communication signal. Subsequently, the system deeply analyzed the optimized communication signal according to the preset communication protocol and successfully obtained the drone's identity and detailed control instruction sequence. This enables the system to immediately confirm whether the drone is authorized and prepare to further analyze its flight behavior pattern to assess whether there is a risk of abnormal operation. In this way, the system can accurately identify the identity and behavior of the drone at an early stage, enhancing the safety management capabilities of the airspace.

[0031] In order to solve the problem of inaccurate locking of the take-off position of the drone and the area where the operator is located in the prior art, 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 the directional interference system is started, the intelligent tracking mechanism is started, and the take-off position of the drone and the area where the operator is located are locked by fusing data from multiple sensor nodes and using multi-source information fusion technology, which specifically includes: starting the intelligent tracking mechanism, when the directional interference system is activated, triggering the intelligent tracking mechanism, and using a multi-type sensor network deployed in a preset airspace to collect dynamic data of the drone and the environment around the drone in real time. ; 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 drone, and the take-off position of the drone is inferred by retrospectively analyzing the historical flight trajectory of the drone; the location information of the drone 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; In this embodiment, the intelligent tracking mechanism refers to an automated process that integrates multiple sensors and data sources to accurately lock the take-off position of the drone and the area where the operator is located. The mechanism is triggered at the same time as the directional jamming system is activated to ensure timely response and start tracking. The multi-type sensor network includes radar, optical cameras, infrared sensors, etc., which are used to collect dynamic data of the drone and its surroundings in real time, such as flight speed, altitude change, direction change, etc., which are crucial for subsequent analysis. Multi-sensor data fusion technology is a technical means for synchronizing and aligning data from different sensors in time and space, eliminating redundant information, improving data accuracy and reliability, and thus generating fused comprehensive data. Advanced positioning algorithms refer to a series of complex mathematical and technical methods, such as Kalman filtering, particle filtering, etc., which are used to process fused sensor data to determine the precise flight path and real-time coordinates of the drone, and infer the take-off position by back-analyzing its historical flight trajectory. Geographic Information Systems (GIS) and terrain databases are used to combine the location information of the drone with the specific geographical environment, provide detailed geographical references, and help predict the potential area of ​​take-off points and operators. The collaborative tracking mode is to coordinate sensor nodes in the surrounding area to form a dense monitoring network after initially locking the take-off point, further accurately locate the operator's area and enhance tracking accuracy; In the embodiment of the present application, the intelligent tracking mechanism is first started, which is immediately triggered when the directional interference system is activated, and the dynamic data of the UAV and its surrounding environment are collected in real time by using a multi-type sensor network deployed in the preset airspace. Then, based on the multi-sensor data fusion technology, the data from different sensors are synchronized in time and aligned in space, and the redundant information is removed to obtain the fused comprehensive data. Next, the fused sensor data is processed using an advanced positioning algorithm 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 its historical flight trajectory. On this basis, 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 potential area of ​​the operator. Finally, the collaborative tracking mode is started. 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 further accurately locate the area where the operator is located, ensuring the efficiency and accuracy of tracking; For example, during a major event in the city center, the system detected an unauthorized drone approaching the event site. After confirming that it had an abnormal control mode, the system activated the directional interference system and emitted an electromagnetic interference signal to the drone. At the same time, the intelligent tracking mechanism was also activated, using a multi-type sensor network deployed in a preset airspace to collect dynamic data of the drone and its surroundings in real time. The system synchronized and aligned these data in time and space through multi-sensor data fusion technology to generate fused comprehensive data. Subsequently, the fused data was processed using advanced positioning algorithms to determine the flight path and real-time coordinates of the drone, and by back-analyzing its historical flight trajectory, it was successfully inferred that the drone's take-off position was located in a hidden corner outside the event venue. Combined with the geographic information system and terrain database, the system predicted the possible location of the operator and activated the collaborative tracking mode, coordinating the sensor nodes in the nearby area to form a dense monitoring network, and finally locked the specific location of the operator. Security personnel quickly went to the 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 source of threats, enhancing the overall security management capabilities.

[0032] Figure 2 A schematic diagram of a UAV countermeasure system based on illegal UAV identification is provided for an embodiment of the present invention. Figure 2 As shown, the system includes: The capture module 21 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 the preset communication protocol; A verification module 22 is used to compare and verify the identity with the records in the preset authorized drone database, and use a behavior analysis algorithm to analyze the control instruction sequence to detect whether the drone has an abnormal control mode; The transmitting module 23 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 location of safe landing; The start module 24 is used to start the intelligent tracking mechanism when the directional jamming system is started, and to lock the take-off position of the UAV and the area where the operator is located by fusing data from multiple sensor nodes and using multi-source information fusion technology; The summary module 25 is used to summarize the key data in the countermeasure process and the take-off position determined by the intelligent tracking mechanism and the information of the operator's area 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. Figure 2 The drone countermeasure system based on illegal drone identification can perform Figure 1 The implementation principle and technical effect of the drone countermeasure method based on illegal drone identification described in the illustrated embodiment will not be repeated. The specific manner in which each module and unit performs operations in the drone countermeasure system based on illegal drone identification in the above embodiment has been described in detail in the embodiment of the method, and will not be elaborated here.

[0033] In one possible design, Figure 2 The drone countermeasure system based on illegal drone identification of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32; The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0034] The processing component 32 is used to: monitor the flight behavior of the UAV in the preset airspace, and 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 the preset communication protocol; compare and verify the identity with the record in the preset authorized UAV database, and use the behavior analysis algorithm to analyze the control instruction sequence to detect whether the UAV has an abnormal control mode; when it is confirmed that the UAV has an abnormal control mode, start the directional interference system to transmit an electromagnetic interference signal of a preset frequency band to the UAV, and adjust the intensity and frequency characteristics of the electromagnetic interference signal to achieve the interference of the navigation system of the UAV. Targeted suppression is used to make the UAV enter a preset safety response mode, which includes: automatic return and specific location for safe landing; when the directional interference system is activated, the intelligent tracking mechanism is activated, and the take-off position of the UAV and the area where the operator is located are locked by fusing data from multiple sensor nodes and using multi-source information fusion technology; key data in the countermeasure process and 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, and the report is sent to the command center of the regulatory agency through an encrypted communication channel. The key data includes: the UAV's identity, initial flight path, behavioral changes after interference, and safety response mode.

[0035] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above method.

[0036] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0037] Of course, the computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0038] The input / output interface provides an interface between the processing component and the peripheral interface module, which may be an output device, an input device, etc.

[0039] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0040] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0041] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 The illustrated embodiment is a method for countering drones based on identifying illegal drones.

[0042] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0043] The device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Ordinary technicians in this field can understand and implement it without paying creative labor.

[0044] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0045] 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 it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present 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; When the directional jamming system is activated, the intelligent tracking mechanism is activated, and the take-off position of the UAV and the area where the operator is located are locked by fusing data from multiple sensor nodes and using multi-source information fusion technology; 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 drone outside the existing target. By comparing the drone 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. The method according to claim 1, characterized in that When the directional jamming system is activated, the intelligent tracking mechanism is activated, and the take-off position of the UAV and the area where the operator is located are locked by fusing data from multiple sensor nodes and using multi-source information fusion technology, including: Initiate an intelligent tracking mechanism, and when the directional jamming system is activated, trigger the intelligent tracking mechanism to collect dynamic data of the UAV and the environment around the UAV 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; Applying advanced positioning algorithms to process the fused sensor data to determine the flight path and real-time coordinates of the drone, and inferring the take-off position of the drone by back-analyzing the historical flight trajectory of the drone; Combining the location information of the UAV with the geographical environment in combination with the geographic information system and the terrain database, predicting the take-off point and the area of ​​the operator; Start the collaborative tracking mode. 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.

8. 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 start-up module is used to start the intelligent tracking mechanism when the directional jamming system is started, and to lock the take-off position of the UAV and the area where the operator is located by fusing data from multiple sensor nodes and using multi-source information fusion technology; 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.

9. 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 7.

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

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