Unmanned aerial vehicle intrusion real-time interception method and system based on airspace electronic fence

Abstract

The invention provides an unmanned aerial vehicle intrusion real-time interception method and system based on an airspace electronic fence. The method comprises the steps of firstly obtaining real-time flight monitoring data of an unmanned aerial vehicle in a target airspace, then analyzing predefined airspace electronic fence configuration information to generate a virtual boundary constraint set, and then combining three-dimensional space coordinates and the virtual boundary constraint set to judge whether the unmanned aerial vehicle invades a no-fly zone or not. When it is judged that the unmanned aerial vehicle invades the no-fly zone, a collaborative interception instruction sequence is generated, finally, the collaborative interception instruction sequence is sent to the unmanned aerial vehicle through a radio frequency communication link, the unmanned aerial vehicle is driven to execute flight path correction, and real-time interception of the unmanned aerial vehicle is completed; according to the technical scheme, the real-time performance and safety of sensitive airspace protection are improved, meanwhile, the risk of mistakenly intercepting legal aircrafts is avoided, and non-lethal precise expelling is completed on the premise of completely eradicating the crash risk.

Description

Technical Field

[0001] The present application relates to the technical field of drone collaborative control, and in particular to a real-time interception method and system for drone intrusion based on airspace electronic fences. Background Art

[0002] In sensitive airspace such as airport clear zones, there is an urgent need to achieve three-dimensional dynamic interception capabilities for illegally invading drones. This scenario requires that the boundaries and altitude thresholds of the no-fly zone can respond to changes in airspace scheduling in real time (such as actively lowering the altitude limit during flight take-off and landing periods). At the same time, it supports protocol-adaptive interception of multiple types of drones and avoids crash risks through non-lethal path correction, meeting the core requirements of the ICAO safety guidelines.

[0003] The current mainstream interception solution uses radar-based optoelectronic fusion tracking combined with protocol jamming technology. This approach uses a phased array radar to capture the target's spatial position and optoelectronic equipment to identify the drone's posture. When the target enters a pre-set geofence, it emits a broadband electromagnetic signal to suppress the remote control link, forcing the drone to lose control and return home or make an emergency landing. This solution relies on a static boundary trigger mechanism and is unable to adapt to dynamic airspace scheduling strategies.

[0004] However, this solution has three key flaws. First, static geofencing cannot respond to temporary airspace adjustment commands, resulting in the inadvertent interception of legitimate aircraft or the missed detection of dynamic intrusion targets during major events. Second, electromagnetic interference is ineffective against the new frequency-hopping communication protocol and cannot distinguish between friendly and foe targets, creating an interception blind spot. Third, crude signal suppression can easily trigger a drone to lose control and crash, violating non-lethal safety standards. The lack of heading correction accuracy also poses a risk of secondary accidents. Summary of the Invention

[0005] The present application provides a real-time interception method and system for drone intrusion based on airspace electronic fences, which is used to solve the problems in the existing technology that static geographic fences cannot respond to airspace dynamic scheduling instructions, resulting in false interception and missed detection, electromagnetic interference solutions are ineffective for new frequency hopping communication protocols and cannot distinguish between friendly and enemy targets, and rough signal suppression triggers the risk of drones losing control and crashing.

[0006] In a first aspect, the present application provides a method for real-time interception of drone intrusions based on airspace electronic fences, comprising: Acquire real-time flight monitoring data of the UAV in the target airspace, the real-time flight monitoring data including the three-dimensional spatial coordinates, motion vector, and heading deflection angle of the UAV; Parsing predefined airspace geo-fence configuration information to generate a virtual boundary constraint set, the virtual boundary constraint set including geo-fence boundary geometry and dynamic altitude restrictions; Determining whether the UAV intrudes into a no-fly zone defined by the virtual boundary constraint set by combining the three-dimensional space coordinates and the virtual boundary constraint set; When it is determined that the UAV has intruded into a no-fly zone, generating a coordinated interception instruction sequence; The coordinated interception instruction sequence is sent to the UAV via a radio frequency communication link, driving the UAV to perform flight path correction to complete real-time interception of the UAV.

[0007] Optionally, obtain real-time flight monitoring data of drones in the target airspace, including: Synchronously collecting the spatial position signal and motion characteristic signal of the UAV through multi-source collaborative sensing nodes; Using a radar detection node to obtain the original echo signal of the UAV, and performing spatial position analysis on the original echo signal to generate an initial three-dimensional positioning coordinate set of the UAV; Using an optoelectronic tracking node to capture a continuous image sequence of the UAV, and extracting an attitude angle change trajectory and a velocity direction vector of the UAV from the continuous image sequence; Using a radio frequency detection node to intercept the remote control link characteristics of the drone to identify the drone's identification code and control instruction type; Performing spatiotemporal alignment of the initial three-dimensional positioning coordinate set with the attitude angle change trajectory to generate target three-dimensional spatial coordinates; Combining the velocity direction vector and the control instruction type, calculating the motion acceleration vector and the heading deflection angle of the UAV; The target three-dimensional space coordinates, motion acceleration vector and heading deflection angle are integrated to form the real-time flight monitoring data.

[0008] Optionally, predefined airspace electronic fence configuration information is parsed to generate a virtual boundary constraint set, including: Parsing the airspace electronic fence configuration information to obtain geographic boundary description parameters and altitude dynamic change constraints; Extracting a polygon vertex coordinate sequence from the geographic boundary description parameters to construct a closed geometric boundary of the horizontally projected no-fly zone; Synchronously analyzing the time-related elements and the space-related elements in the height dynamic change constraint to establish a vertical height threshold mapping rule; Spatially superimposing the closed geometric boundary with the height threshold mapping rule to generate a three-dimensional dynamic no-fly zone body; The geometric structure and constraint rules of the three-dimensional dynamic no-fly zone body are integrated to form the virtual boundary constraint set.

[0009] Optionally, combining the three-dimensional space coordinates and the virtual boundary constraint set to determine whether the drone intrudes into a no-fly zone defined by the virtual boundary constraint set includes: Constructing a spatial position mapping relationship of a three-dimensional dynamic no-fly zone body in the virtual boundary constraint set; Performing a geometric inclusion judgment on the three-dimensional spatial coordinates of the drone and the spatial position mapping relationship to calculate the spatial relative position state of the drone and the three-dimensional dynamic no-fly zone body, wherein the spatial relative position state includes the spatial relative position state within the no-fly zone body and the spatial relative position state outside the no-fly zone body; Synchronously obtaining dynamic altitude limit parameters in the virtual boundary constraint set, and associating the flight time identifier and spatial position coordinates of the UAV; Extracting a real-time altitude threshold from the dynamic altitude limit parameter according to the current flight time identifier; When the spatial relative position state indicates that the drone is located within the no-fly zone and the current altitude value of the drone exceeds the real-time altitude threshold, it is determined that the drone has invaded the no-fly zone.

[0010] Optionally, performing a geometric inclusion judgment on the three-dimensional spatial coordinates of the drone and the spatial position mapping relationship to solve the spatial relative position state of the drone and the three-dimensional dynamic no-fly zone body includes: Dividing the three-dimensional dynamic no-fly zone into a set of spatial grid cells, and marking the position attribute state of each spatial grid cell within the no-fly zone; Matching the three-dimensional spatial coordinates of the drone to corresponding spatial grid cells, and reading the position attribute state value of the spatial grid cells; When the position attribute state value is a mark within the no-fly zone, outputting the spatial relative position state of the UAV within the no-fly zone; When the position attribute state value is a mark outside the no-fly zone, the spatial relative position state of the UAV outside the no-fly zone is output.

[0011] Optionally, when it is determined that the UAV has intruded into a no-fly zone, a coordinated interception instruction sequence is generated, including: Calculating a spatial avoidance vector direction based on a positional relationship between the three-dimensional spatial coordinates of the drone and the geo-fence boundary geometry included in the virtual boundary constraint set; Calculating a path correction value by combining the motion vector and the heading deflection angle; Synchronously associating a dynamic altitude limit parameter in the virtual boundary constraint set with a current altitude value of the UAV to determine an altitude adjustment threshold; The spatial avoidance vector direction, path correction value and altitude adjustment threshold are encoded into instructions to generate a basic interception instruction unit; Multiple basic interception instruction units are serially arranged at preset time intervals to form a coordinated interception instruction sequence.

[0012] Optionally, sending the coordinated interception instruction sequence to the UAV via a radio frequency communication link to drive the UAV to perform flight path correction to complete real-time interception of the UAV includes: A protocol format conversion module is embedded in the collaborative interception instruction sequence to convert the collaborative interception instruction sequence into a flight control protocol compatible format of the target UAV, thereby obtaining a converted collaborative interception instruction sequence; sending the converted cooperative interception instruction sequence to the UAV via a radio frequency communication link, and using the converted cooperative interception instruction sequence to suppress the original control link of the UAV to take over the flight control of the UAV; After taking over the flight control of the UAV, the UAV is driven to perform flight path correction to complete the real-time interception of the UAV.

[0013] In a second aspect, the present application provides a real-time drone intrusion interception system based on airspace electronic fence, comprising: An acquisition module is used to acquire real-time flight monitoring data of the UAV in the target airspace, wherein the real-time flight monitoring data includes the three-dimensional spatial coordinates, motion vector, and heading deflection angle of the UAV; a parsing module for parsing predefined airspace geo-fence configuration information to generate a virtual boundary constraint set, the virtual boundary constraint set including geo-fence boundary geometry and dynamic altitude restrictions; a determination module, configured to determine whether the drone has intruded into a no-fly zone defined by the virtual boundary constraint set, by combining the three-dimensional space coordinates and the virtual boundary constraint set; A generation module, configured to generate a coordinated interception instruction sequence when determining that the UAV has intruded into a no-fly zone; The sending module is used to send the coordinated interception instruction sequence to the UAV through a radio frequency communication link, driving the UAV to perform flight path correction to complete real-time interception of the UAV.

[0014] In a third aspect, the present application provides a computing device comprising 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 real-time interception method for drone intrusion based on airspace electronic fence as described in the first aspect above.

[0015] In a fourth aspect, the present application provides a computer storage medium storing a computer program. When the computer program is executed by a computer, it implements a real-time interception method for drone intrusion based on airspace electronic fence as described in the first aspect.

[0016] This application generates a virtual boundary constraint set by dynamically analyzing the airspace electronic fence configuration information, and combines the real-time three-dimensional spatial coordinates and motion status of the drone to make intrusion judgments, thereby realizing the dynamic response capability of airspace scheduling; breaking through the rigid boundary defects of traditional static geographic fences, accurately identifying intrusion behaviors through a spatiotemporal collaborative judgment mechanism, and generating a collaborative interception instruction sequence to drive the drone to autonomously correct its path, forming a "perception-judgment-interception" closed-loop control, significantly improving the real-time and safety of sensitive airspace protection, while avoiding the risk of accidentally intercepting legitimate aircraft.

[0017] Furthermore, a protocol conversion module is embedded in the coordinated interception command sequence, converting the commands into a format compatible with the target drone's native protocol. This module then suppresses and takes over the target drone's original control link via a radio frequency communication link. This overcomes the lack of universality and crash risk associated with traditional electromagnetic interference protocols, enabling protocol-adaptive control of drones across manufacturers and ensuring precise delivery of interception commands. Furthermore, through a safe takeover mechanism for flight control, the system drives drones to execute coordinated corrections to their heading, speed, and altitude, achieving non-lethal precision removal without the risk of a crash, thus meeting the core requirements of ICAO safety guidelines.

[0018] These and other aspects of the present application will become more readily apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the present application or the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0020] Figure 1 A flowchart of a method for real-time interception of drone intrusion based on airspace electronic fences provided by the present application is shown; Figure 2 The present invention provides a schematic diagram of a real-time drone intrusion interception system based on an airspace electronic fence. Figure 3 A schematic structural diagram of a computing device provided by the present application is shown. DETAILED DESCRIPTION

[0021] In order to enable people skilled in the art to better understand the solution of this application, the technical solution of this application will be clearly and completely described below in conjunction with the drawings in this application.

[0022] In some of the processes described in the specification and claims of this application and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be 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 order of execution. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this document are used to distinguish different messages, devices, modules, etc., and do not represent a sequential order, nor do they limit "first" and "second" to being different types.

[0023] The following will be combined with the accompanying drawings to clearly and completely describe the technical solutions in this application. Obviously, the embodiments described are only some of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of this application.

[0024] Figure 1 This application provides a flowchart of a real-time interception method for drone intrusion based on airspace electronic fence, such as Figure 1 As shown, the method includes: Step 101: Acquire real-time flight monitoring data of a UAV in a target airspace, wherein the real-time flight monitoring data includes the three-dimensional spatial coordinates, motion vector, and heading deflection angle of the UAV.

[0025] Optionally, step 101 may specifically include the following steps: 1011. Synchronously collect spatial position signals and motion characteristic signals of the UAV through a multi-source collaborative sensing node; 1012. Utilize a radar detection node to obtain an original echo signal of the UAV, and perform spatial position analysis on the original echo signal to generate an initial three-dimensional positioning coordinate set of the UAV; 1013. Capture a continuous image sequence of the UAV using an optoelectronic tracking node, and extract an attitude angle change trajectory and a velocity direction vector of the UAV from the continuous image sequence; 1014. Using a radio frequency detection node to intercept the remote control link characteristics of the UAV to identify the UAV's identification code and control instruction type; 1015. Perform spatiotemporal alignment on the initial three-dimensional positioning coordinate set and the attitude angle change trajectory to generate target three-dimensional space coordinates; 1016. Calculate the motion acceleration vector and heading deflection angle of the UAV by combining the velocity direction vector and the control instruction type; 1017. Integrate the target three-dimensional space coordinates, motion acceleration vector and heading deflection angle to form the real-time flight monitoring data.

[0026] In the above scheme, multi-source collaborative sensing nodes refer to network nodes composed of multiple sensors deployed within the target airspace, used to synchronously collect spatial position signals and motion signature signals from drones. Spatial position signals refer to physical quantities that reflect the drone's position changes in three-dimensional space. Motion signature signals refer to physical quantities that characterize the drone's motion state, including velocity and acceleration. Radar detection nodes refer to specialized equipment that uses radar technology to detect the drone's position. Raw echo signals refer to the unprocessed signals reflected from electromagnetic waves emitted by the radar and then reflected by the drone. The initial three-dimensional positioning coordinate set refers to the preliminary set of drone position coordinates generated by analyzing the raw echo signals. Optoelectronic tracking nodes refer to equipment that uses optical imaging technology to track drones. Continuous image sequences refer to the set of motion image frames of the drone captured continuously by optoelectronic equipment. Attitude angle change trajectories refer to the continuous record of the drone's pitch, roll, and yaw angles over time. Velocity direction vectors refer to directed line segments that describe the direction and magnitude of the drone's motion. Radio frequency detection nodes refer to specialized equipment that intercepts drone remote control signals. Remote control link characteristics refer to the unique identifiers of communication signals between the drone and the remote controller. The identification code refers to the drone's unique identification code. The control command type refers to the type of control command sent by the remote controller to the drone. Spatiotemporal alignment refers to the process of aligning data collected at different times within a unified time coordinate system. The target's 3D spatial coordinates refer to the precise position coordinates obtained after spatiotemporal alignment. The motion acceleration vector refers to a directed line segment describing the magnitude and direction of the drone's acceleration. The heading angle refers to the angle between the drone's current flight direction and a reference direction. Real-time flight monitoring data refers to the integrated data set of the drone's real-time position, motion status, and heading information.

[0027] In this embodiment, step 1011 first activates multi-source collaborative sensing nodes to synchronously collect signals: radar detection nodes deployed in the target airspace transmit electromagnetic waves and receive reflected signals, optoelectronic tracking nodes activate high-speed video capture, and radio frequency detection nodes scan the communication frequency band. These three nodes synchronously collect spatial position signals and motion signature signals based on GPS time. Next, step 1012 processes the raw radar echo signals: the radar nodes perform time delay analysis and phase calculation on the received reflected signals, calculate the distance, azimuth, and elevation angles between the drone and the radar, and generate an initial three-dimensional positioning coordinate set containing longitude, latitude, and altitude. Next, step 1013 analyzes the optoelectronic continuous image sequence: the optoelectronic equipment tracks feature points and analyzes the target contours of the captured image sequence, calculates the velocity direction vector based on the displacement of feature points between adjacent frames, and calculates the attitude angle change trajectory consisting of pitch and roll angles based on the deflection angle of the aircraft axis. Then, step 1014 intercepts the radio frequency remote control link signature: the radio frequency node identifies the modulation mode and frequency hopping pattern of the communication signal, compares it with a pre-stored protocol library to parse the drone's identification code, and simultaneously decodes the signal payload to determine the type of control command. Then, in step 1015, spatiotemporal alignment is performed: the initial 3D positioning coordinate set generated by the radar and the attitude angle change trajectory extracted by optoelectronics are input into the time synchronization module. The data streams are aligned with microsecond timestamps and unified to the WGS84 geographic coordinate system using a coordinate system conversion matrix, outputting the precise 3D spatial coordinates of the target. Then, in step 1016, motion parameters are calculated: the velocity direction vector and control command type are input into the kinematic model. When a sharp rise command is identified, the vertical acceleration weight is increased. The 3D motion acceleration vector is calculated by combining the velocity components, and the heading deflection angle is deduced based on the attitude angle change. Finally, in step 1017, data integration is performed: the target 3D spatial coordinates, motion acceleration vector, and heading deflection angle are packaged into a structured data package in a time series, forming real-time flight monitoring data.

[0028] For example, in the cleared area of ​​an airport, when a brand B drone enters the monitored airspace, the radar detection node acquires its initial coordinates (116.3°E, 39.9°N, 120m). The optoelectronic tracking node captures its southeastward movement at a 15° pitch angle and extracts the velocity direction vector (0.87, 0.5, 0). The radio frequency detection node identifies its ID code DJI_12345 and intercepts the climb command. The system aligns the radar coordinates with the optoelectronic attitude data in time and space, revising the coordinates to (116.3002°E, 39.9005°N, 122m). Combined with the climb command, it calculates a vertical acceleration of +3m / s² and a heading deflection of 25°. Ultimately, it generates a real-time monitoring data packet: {coordinates (116.3002, 39.9005, 122), acceleration (0, 0, 3), heading 25°}, which is sent to the central processing unit.

[0029] This step realizes full-dimensional real-time monitoring of the drone's spatial position, motion status, and control intention through the synchronous spatiotemporal acquisition and intelligent data fusion of multi-source sensors, providing a high-precision dynamic data basis for intrusion judgment, effectively solving the problems of blind spots and data conflicts in traditional monitoring, and significantly improving airspace protection capabilities.

[0030] Step 102: Parse predefined airspace electronic fence configuration information to generate a virtual boundary constraint set.

[0031] Optionally, step 102 may specifically include the following steps: Step 1021: Parse the airspace electronic fence configuration information to obtain geographic boundary description parameters and altitude dynamic change constraints; Step 1022: extracting a polygon vertex coordinate sequence from the geographic boundary description parameters to construct a closed geometric boundary of the horizontally projected no-fly zone; Step 1023, synchronously analyzing the time-related elements and the space-related elements in the height dynamic change constraint to establish a vertical height threshold mapping rule; Step 1024: spatially superimpose the closed geometric boundary with the altitude threshold mapping rule to generate a three-dimensional dynamic no-fly zone volume; Step 1025: Integrate the geometric structure and constraint rules of the three-dimensional dynamic no-fly zone body to form the virtual boundary constraint set.

[0032] In the above scheme, airspace electronic fence configuration information refers to a set of pre-set airspace management rule data. Geographic boundary description parameters refer to the coordinate data of polygon vertices that define the plane extent of a no-fly zone. Dynamic altitude constraint refers to altitude restriction rules that change over time or spatial position. A polygon vertex coordinate sequence refers to a sequentially arranged set of latitude and longitude coordinates of the polygon corner points. A horizontally projected no-fly zone refers to the projection of a no-fly zone onto the horizontal ground. A closed geometric boundary refers to a closed plane figure formed by connecting polygon vertices. Time-related elements refer to time-related altitude adjustment triggers (such as specific time periods). Spatially-related elements refer to geographic location-related altitude adjustment triggers (such as areas near airports). Altitude threshold mapping rules refer to the functional relationship that maps spatiotemporal elements to specific altitude thresholds. Spatial overlay refers to the process of combining plane boundaries and altitude rules into a three-dimensional structure. A three-dimensional dynamic no-fly zone volume refers to a three-dimensional no-fly space composed of horizontal boundaries and dynamic altitude constraints. Geometric structure and constraint rules refer to the spatial form and altitude restriction conditions of a no-fly zone. A virtual boundary constraint set refers to the integrated set of three-dimensional dynamic no-fly zone rules.

[0033] In this embodiment, the configuration information is first parsed through step 1021: the system reads the airspace electronic fence configuration information file, separates the geographic boundary description parameters describing the plane boundary range and the height dynamic change constraints defining the height change rules.

[0034] Next, step 1022 constructs a planar boundary: extracts a sequence of polygon vertex coordinates from the geographic boundary description parameters and connects them sequentially to form a closed geometric boundary. This boundary represents the maximum coverage of the horizontally projected no-fly zone. Next, step 1023 establishes altitude rules: analyzes the temporal elements (such as daily peak hours) and spatial elements (such as distance from the airport core area) associated with the dynamic altitude change constraint, constructing a mapping function that assigns a unique altitude threshold to each spatiotemporal location. Then, step 1024 performs a three-dimensional overlay: using the closed geometric boundary as the base and the altitude threshold mapping rule as the vertical constraint, a three-dimensional dynamic no-fly zone volume is generated through spatial overlay. The altitude threshold within the horizontal boundary of this volume changes dynamically with time and location. Finally, step 1025 integrates the constraint set: encapsulating the spatial form and altitude change rules of the three-dimensional dynamic no-fly zone volume into a unified virtual boundary constraint set.

[0035] Continuing from the previous step, the system parses pre-configured configuration information: geographic boundary description parameters include a vertex sequence (116.30°E, 39.90°N) and (116.31°E, 39.91°N) that forms a pentagonal boundary. Dynamic altitude constraints stipulate a 100-meter altitude limit between 7:00 AM and 7:00 PM on weekdays and a 150-meter altitude limit at other times. After constructing the horizontally projected closed boundary of the no-fly zone, the system establishes altitude mapping rules (binding the time element to the weekday period and the spatial element to the core area). The planar boundary and altitude rules are superimposed to generate a three-dimensional no-fly zone volume: the area appears as a 100-meter-high cylinder during weekdays, rising to 150 meters at night. This is finally integrated into a virtual boundary constraint set and transmitted to the decision module.

[0036] This solution achieves real-time adaptive capabilities for airspace management through dynamic rule parsing and three-dimensional boundary generation. It combines multi-source monitoring with precise judgment to overcome the misjudgment limitations of traditional static fences. It also addresses the crash risks caused by rough signal suppression through protocol-adaptive interception and path correction, forming a safe and efficient closed-loop for drone intrusion interception.

[0037] Step 103 : combining the three-dimensional space coordinates and the virtual boundary constraint set to determine whether the drone has intruded into a no-fly zone defined by the virtual boundary constraint set.

[0038] Optionally, step 103 may specifically include the following steps: Step 1031: constructing a spatial position mapping relationship of a three-dimensional dynamic no-fly zone body in the virtual boundary constraint set; Step 1032: Performing a geometric inclusion check on the three-dimensional spatial coordinates of the drone and the spatial position mapping relationship to calculate the spatial relative position state of the drone and the three-dimensional dynamic no-fly zone body, wherein the spatial relative position state includes the spatial relative position state within the no-fly zone body and the spatial relative position state outside the no-fly zone body. Step 1032 specifically includes the following process: The three-dimensional dynamic no-fly zone body is divided into a set of spatial grid units, and the position attribute state of each spatial grid unit within the no-fly zone body is marked; the three-dimensional spatial coordinates of the UAV are matched to the corresponding spatial grid units, and the position attribute state value of the spatial grid unit is read; when the position attribute state value is marked within the no-fly zone body, the spatial relative position state of the UAV within the no-fly zone body is output; when the position attribute state value is marked outside the no-fly zone body, the spatial relative position state of the UAV outside the no-fly zone body is output.

[0039] Step 1033: synchronously obtain the dynamic height limit parameters in the virtual boundary constraint set, and associate the flight time identifier and spatial position coordinates of the UAV; Step 1034, extracting a real-time altitude threshold from the dynamic altitude limit parameter according to the current flight time identifier; Step 1035: When the spatial relative position state indicates that the drone is located within the no-fly zone and the current altitude value of the drone exceeds the real-time altitude threshold, it is determined that the drone has invaded the no-fly zone.

[0040] In the above scheme, the spatial position mapping relationship refers to the correspondence between the spatial coordinates and position attributes of a three-dimensional dynamic no-fly zone body. The spatial relative position state refers to the classification of the position relationship of the drone relative to the no-fly zone body. The no-fly zone body mark refers to the attribute value that indicates that the spatial grid cell is located inside the no-fly zone. The no-fly zone body mark refers to the attribute value that indicates that the spatial grid cell is located outside the no-fly zone. The position attribute state value refers to the position attribute mark value of the spatial grid cell. The dynamic altitude limit parameter refers to the data set containing the altitude threshold change rules. The flight time mark refers to the mark of the drone's current flight time. The real-time altitude threshold refers to the maximum flight altitude value allowed at the current time.

[0041] In this embodiment, a spatial position mapping relationship is first established in step 1031: the system reads the three-dimensional dynamic no-fly zone volume data from the virtual boundary constraint set and establishes a mapping rule library between spatial coordinates and position attributes. Next, a geometric inclusion determination is performed in step 1032: the three-dimensional dynamic no-fly zone volume is divided into a set of 1-cubic-meter spatial grid cells, and the position attribute status of each cell is marked (inside / outside the no-fly zone). The drone's three-dimensional spatial coordinates are matched to the corresponding grid cell, and its position attribute status value is read. If the status value is marked as within the no-fly zone, the spatial relative position status of the drone within the no-fly zone is output; if it is marked as outside the no-fly zone, the status of the drone outside the no-fly zone is output. Next, in step 1033, dynamic altitude parameter association is performed: the dynamic altitude limit parameter from the virtual boundary constraint set is obtained and associated with the drone's flight time identifier (e.g., UTC time) and spatial position coordinates (latitude and longitude). Then, in step 1034, a real-time altitude threshold is extracted: based on the current flight time identifier (e.g., 2:30 PM), the altitude threshold corresponding to the time is retrieved from the dynamic altitude limit parameter (e.g., a 100-meter altitude limit during peak hours on weekdays). Finally, the intrusion behavior is determined through step 1035: when the spatial relative position status is within the no-fly zone and the current altitude value of the drone (such as 120 meters) exceeds the real-time altitude threshold (100 meters), it is determined to be an intrusion into the no-fly zone.

[0042] Continuing from the previous example, the drone is located at the coordinates (116.30°E, 39.90°N, 120 meters). The system first constructs a three-dimensional grid mapping of the no-fly zone (with a 1-meter grid cell side). The coordinates are matched to the grid cells and the location attribute status value is read as "within the no-fly zone." The dynamic altitude parameter is then associated with the real-time altitude threshold of 100 meters at 2:30 PM (weekday peak hours). Because 120 meters is greater than 100 meters and the location is within the no-fly zone, the drone is determined to have intruded into the no-fly zone.

[0043] This step achieves accurate identification of intrusion behavior through coordinated judgment of grid space mapping and dynamic height thresholds, breaking through the misjudgment limitations of static boundaries and fixed height thresholds of traditional solutions, and providing a reliable decision-making basis for airspace security protection.

[0044] Step 104: When it is determined that the UAV has intruded into a no-fly zone, a coordinated interception instruction sequence is generated.

[0045] Optionally, step 104 may specifically include the following steps: Step 1041 , calculating a spatial avoidance vector direction based on a positional relationship between the three-dimensional spatial coordinates of the drone and the geo-fence boundary geometry included in the virtual boundary constraint set; Step 1042, combining the motion vector and the heading deflection angle to calculate a path correction value; Step 1043 synchronously associates the dynamic altitude limit parameter in the virtual boundary constraint set with the current altitude value of the UAV to determine an altitude adjustment threshold; Step 1045 , encoding the spatial avoidance vector direction, path correction value, and altitude adjustment threshold into a command to generate a basic interception command unit; Step 1046, serially arrange multiple basic interception instruction units according to a preset time interval to form a coordinated interception instruction sequence.

[0046] In the above scheme, the spatial avoidance vector direction refers to the direction vector that the drone uses to escape the no-fly zone. The path correction value refers to the displacement and velocity change required to adjust the flight path. The altitude adjustment threshold refers to the safe altitude value to be achieved. Command encoding refers to the conversion of parameters into a command format recognizable by the device. The basic intercept command unit refers to the command unit containing the parameters for a single intercept action. The preset time interval refers to the time interval between command transmissions. Serialization refers to the chronological arrangement of command units. The coordinated intercept command sequence refers to a collection of command units combined in a chronological order.

[0047] In this embodiment, the spatial avoidance vector direction is first calculated in step 1041: the system obtains the three-dimensional spatial coordinates of the drone and the geometric location of the geofence boundary in the virtual boundary constraint set, calculates the vector from the drone's position to the nearest boundary point, and uses the opposite direction of this vector as the spatial avoidance vector direction (e.g., if the drone intrudes eastward, a westward avoidance vector is generated). Next, a path correction value is calculated in step 1042: the motion vector (speed magnitude and direction) and the heading angle (the deviation from the current flight direction) are combined. The lateral correction weight is increased as the heading angle increases. Vector decomposition is used to calculate the speed adjustment value (e.g., a 30% speed reduction) and the heading adjustment value (e.g., a 20-degree left turn) and output the path correction value. Next, step 1043 determines the altitude adjustment threshold: the dynamic altitude limit parameter in the virtual boundary constraint set is associated with the drone's current altitude. When the drone's current altitude exceeds the real-time threshold, a safe altitude below the real-time threshold is set as the altitude adjustment threshold (e.g., if the current altitude is 120 meters > the threshold of 100 meters, 80 meters is set as the adjustment target). Then, in step 1045, a basic command unit is generated: the spatial avoidance vector direction (e.g., vector angle 210°), path correction value (e.g., speed -30%, heading +20°), and altitude adjustment threshold (80 meters) are input into the command encoder and converted into a binary command frame to generate a basic intercept command unit. Finally, in step 1046, a command sequence is constructed: multiple basic command units (e.g., [Command 1: 210°, -30%, 80m], [Command 2: 215°, -25%, 75m]) are arranged in timeline order at preset time intervals (e.g., 0.5 seconds) to form a coordinated intercept command sequence.

[0048] Continuing from the previous step, the system calculates the spatial avoidance vector direction as 30° west-southwest (a 210° vector angle). Combining the motion vector (10 m / s eastward speed) and the heading deviation angle (15° right), the system calculates the path correction value (40% deceleration, 25° left turn). The dynamic altitude parameter (currently at a threshold of 100 meters) is then used to set the altitude adjustment threshold at 80 meters. The system then encodes the basic command unit {direction 210°, deceleration 40%, altitude 80 meters}. Three units are serialized at 0.5-second intervals (gradually adjusting direction and speed) to form a coordinated intercept command sequence, which is then sent to the drone.

[0049] This step achieves precise and progressive control of drone interception commands through the collaborative generation of three-dimensional parameters and dynamic sequence arrangement, breaking through the crude correction defects of traditional single commands and significantly improving the success rate of safe expulsion.

[0050] Step 105: Send the coordinated interception instruction sequence to the UAV via a radio frequency communication link, driving the UAV to perform flight path correction to complete real-time interception of the UAV.

[0051] Optionally, step 104 may specifically include the following steps: Step 1051: embedding a protocol format conversion module in the collaborative interception instruction sequence to convert the collaborative interception instruction sequence into a format compatible with the flight control protocol of the target UAV, thereby obtaining a converted collaborative interception instruction sequence; Step 1052: Send the converted cooperative interception instruction sequence to the UAV via the radio frequency communication link, and use the converted cooperative interception instruction sequence to suppress the original control link of the UAV to take over the flight control of the UAV; Step 1053: After taking over the flight control of the UAV, the UAV is driven to perform flight path correction to complete the real-time interception of the UAV.

[0052] In the above solution, the protocol format conversion module refers to the software component that converts the command sequence into the target drone's native protocol format. The flight control protocol compatible format refers to the command encoding format recognizable by the target drone. The converted collaborative interception command sequence refers to the command data stream after protocol conversion. The radio frequency communication link refers to the wireless signal transmission channel. The original control link refers to the communication channel between the drone and the remote controller. Flight control takeover refers to the process by which the interception system replaces the original remote controller to control the drone. Flight path correction refers to the action of adjusting the drone's heading, speed, and altitude.

[0053] In this embodiment, protocol conversion is first performed in step 1051: a protocol format conversion module is embedded in the coordinated interception command sequence. This module loads the target drone's protocol library (such as the DJI OcuSync protocol) and converts the command parameters (direction, speed, and altitude) into the target protocol's standard command frames, generating a converted coordinated interception command sequence. Next, in step 1052, commands are sent and the link is suppressed: the converted coordinated interception command sequence is continuously transmitted to the drone via the RF communication link, while simultaneously emitting a protocol-compatible suppression signal (such as an enhanced carrier signal) that overrides the communication frequency band of the drone's original control link, forcing the drone to switch to the interception system's control channel and take over flight control. Finally, in step 1053, path correction is driven: after flight control is taken over, the system gradually drives the drone to execute actions according to the command sequence: adjusting the heading angle according to the direction of the spatial avoidance vector, adjusting the speed vector based on the path correction value, and changing the flight altitude according to the altitude adjustment threshold, ultimately completing a safe departure.

[0054] Continuing from the previous step, the system converts the coordinated interception command sequence (direction 210°, deceleration 40%, altitude 80 meters) into the protocol format of Brand B drone (e.g., DJI OSDK command frames) via the protocol format conversion module. The command sequence is transmitted over a 2.4GHz radio link, while simultaneously transmitting a -10dBm suppression signal overriding the original remote control frequency band. This forces the drone to disconnect the original link and switch to the interception system's control channel. The system then directs it to a 210° direction, decelerates it to 6m / s, and descends to an altitude of 80 meters, completing a safe interception.

[0055] This step achieves seamless takeover of control of drones across manufacturers through adaptive protocol conversion and intelligent link suppression. It also ensures a smooth and safe interception process through progressive path correction command execution, completely resolving the poor protocol compatibility and crash risk issues of traditional electromagnetic interference solutions.

[0056] Figure 2 This application provides a structural diagram of a real-time drone intrusion interception system based on airspace electronic fences, such as Figure 2 As shown, the system includes: An acquisition module 21 is configured to acquire real-time flight monitoring data of the UAV in the target airspace, wherein the real-time flight monitoring data includes the three-dimensional spatial coordinates, motion vector, and heading deflection angle of the UAV; a parsing module 22 for parsing predefined airspace geo-fence configuration information to generate a virtual boundary constraint set, the virtual boundary constraint set including geo-fence boundary geometry and dynamic height limit; A determination module 23 is configured to determine whether the drone has intruded into a no-fly zone defined by the virtual boundary constraint set by combining the three-dimensional space coordinates and the virtual boundary constraint set; A generating module 24 is configured to generate a coordinated interception instruction sequence when determining that the UAV has intruded into a no-fly zone; The sending module 25 is used to send the coordinated interception instruction sequence to the UAV through a radio frequency communication link, driving the UAV to perform flight path correction to complete real-time interception of the UAV.

[0057] Figure 2 The real-time interception system for drone intrusion based on airspace electronic fence can be executed Figure 1 The implementation principles and technical effects of the method for real-time interception of drone intrusions based on airspace electronic fencing described in the illustrated embodiment are not further elaborated. The specific manner in which each module and unit performs operations in the system for real-time interception of drone intrusions based on airspace electronic fencing in the aforementioned embodiment has been described in detail in the relevant embodiments of the method and will not be further elaborated here.

[0058] In one possible design, Figure 2 The embodiment shown is a real-time interception system for drone intrusion based on airspace electronic fence, which 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 .

[0059] The processing component 32 is used for the above Figure 1 The embodiment provides a real-time interception method for drone intrusion based on airspace electronic fence.

[0060] 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 as 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.

[0061] 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 memory 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.

[0062] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0063] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0064] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0065] 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.

[0066] The present application also provides a computer storage medium storing a computer program, wherein the computer program can achieve the above-mentioned Figure 1 The illustrated embodiment provides a real-time interception method for drone intrusion based on airspace electronic fences.

[0067] Those skilled in the art will 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.

[0068] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0069] Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented using software plus a necessary general-purpose hardware platform, or of course, hardware. Based on this understanding, the essence of the above technical solution, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, or an optical disk, and includes a number of instructions for causing a computer device (such as a personal computer, server, or network device) to execute the methods described in each embodiment or certain portions of the embodiments.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A real-time interception method for drone intrusion based on airspace electronic fence, characterized in that: include: Acquire real-time flight monitoring data of the UAV in the target airspace, the real-time flight monitoring data including the three-dimensional spatial coordinates, motion vector, and heading deflection angle of the UAV; Parsing predefined airspace geo-fence configuration information to generate a virtual boundary constraint set, the virtual boundary constraint set including geo-fence boundary geometry and dynamic altitude restrictions; Determining whether the UAV intrudes into a no-fly zone defined by the virtual boundary constraint set by combining the three-dimensional space coordinates and the virtual boundary constraint set; When it is determined that the UAV has intruded into a no-fly zone, generating a coordinated interception instruction sequence; The coordinated interception instruction sequence is sent to the UAV via a radio frequency communication link, driving the UAV to perform flight path correction to complete real-time interception of the UAV.

2. The method according to claim 1, characterized in that Obtain real-time flight monitoring data of drones in the target airspace, including: Synchronously collecting the spatial position signal and motion characteristic signal of the UAV through multi-source collaborative sensing nodes; Using a radar detection node to obtain the original echo signal of the UAV, and performing spatial position analysis on the original echo signal to generate an initial three-dimensional positioning coordinate set of the UAV; Using an optoelectronic tracking node to capture a continuous image sequence of the UAV, and extracting an attitude angle change trajectory and a velocity direction vector of the UAV from the continuous image sequence; Using a radio frequency detection node to intercept the remote control link characteristics of the drone to identify the drone's identification code and control instruction type; Performing spatiotemporal alignment of the initial three-dimensional positioning coordinate set with the attitude angle change trajectory to generate target three-dimensional spatial coordinates; Combining the velocity direction vector and the control instruction type, calculating the motion acceleration vector and the heading deflection angle of the UAV; The target three-dimensional space coordinates, motion acceleration vector and heading deflection angle are integrated to form the real-time flight monitoring data.

3. The method according to claim 1, characterized in that Parse predefined airspace geo-fence configuration information to generate a virtual boundary constraint set, including: Parsing the airspace electronic fence configuration information to obtain geographic boundary description parameters and altitude dynamic change constraints; Extracting a polygon vertex coordinate sequence from the geographic boundary description parameters to construct a closed geometric boundary of the horizontally projected no-fly zone; Synchronously analyzing the time-related elements and the space-related elements in the height dynamic change constraint to establish a vertical height threshold mapping rule; Spatially superimposing the closed geometric boundary with the height threshold mapping rule to generate a three-dimensional dynamic no-fly zone body; The geometric structure and constraint rules of the three-dimensional dynamic no-fly zone body are integrated to form the virtual boundary constraint set.

4. The method according to claim 1, wherein Determining whether the drone intrudes into a no-fly zone defined by the virtual boundary constraint set by combining the three-dimensional space coordinates and the virtual boundary constraint set includes: Constructing a spatial position mapping relationship of a three-dimensional dynamic no-fly zone body in the virtual boundary constraint set; Performing a geometric inclusion judgment on the three-dimensional spatial coordinates of the drone and the spatial position mapping relationship to calculate the spatial relative position state of the drone and the three-dimensional dynamic no-fly zone body, wherein the spatial relative position state includes the spatial relative position state within the no-fly zone body and the spatial relative position state outside the no-fly zone body; Synchronously obtaining dynamic altitude limit parameters in the virtual boundary constraint set, and associating the flight time identifier and spatial position coordinates of the UAV; Extracting a real-time altitude threshold from the dynamic altitude limit parameter according to the current flight time identifier; When the spatial relative position state indicates that the drone is located within the no-fly zone and the current altitude value of the drone exceeds the real-time altitude threshold, it is determined that the drone has invaded the no-fly zone.

5. The method according to claim 4, characterized in that Performing a geometric inclusion judgment on the three-dimensional spatial coordinates of the drone and the spatial position mapping relationship to solve the spatial relative position state of the drone and the three-dimensional dynamic no-fly zone body, including: Dividing the three-dimensional dynamic no-fly zone into a set of spatial grid cells, and marking the position attribute state of each spatial grid cell within the no-fly zone; Matching the three-dimensional spatial coordinates of the drone to corresponding spatial grid cells, and reading the position attribute state value of the spatial grid cells; When the position attribute state value is a mark within the no-fly zone, outputting the spatial relative position state of the UAV within the no-fly zone; When the position attribute state value is a mark outside the no-fly zone, the spatial relative position state of the UAV outside the no-fly zone is output.

6. The method according to claim 1, characterized in that When the UAV is determined to have intruded into a no-fly zone, a coordinated interception instruction sequence is generated, including: Calculating a spatial avoidance vector direction based on a positional relationship between the three-dimensional spatial coordinates of the drone and the geo-fence boundary geometry included in the virtual boundary constraint set; Calculating a path correction value by combining the motion vector and the heading deflection angle; Synchronously associating a dynamic altitude limit parameter in the virtual boundary constraint set with a current altitude value of the UAV to determine an altitude adjustment threshold; The spatial avoidance vector direction, path correction value and altitude adjustment threshold are encoded into instructions to generate a basic interception instruction unit; Multiple basic interception instruction units are serially arranged at preset time intervals to form a coordinated interception instruction sequence.

7. The method according to claim 1, characterized in that The coordinated interception instruction sequence is sent to the UAV via a radio frequency communication link, driving the UAV to perform flight path correction to complete real-time interception of the UAV, including: A protocol format conversion module is embedded in the collaborative interception instruction sequence to convert the collaborative interception instruction sequence into a format compatible with the flight control protocol of the target UAV, thereby obtaining a converted collaborative interception instruction sequence; sending the converted cooperative interception instruction sequence to the UAV via a radio frequency communication link, and using the converted cooperative interception instruction sequence to suppress the original control link of the UAV to take over the flight control of the UAV; After taking over the flight control of the UAV, the UAV is driven to perform flight path correction to complete the real-time interception of the UAV.

8. A real-time interception system for drone intrusion based on airspace electronic fence, characterized by: include: An acquisition module is used to acquire real-time flight monitoring data of the UAV in the target airspace, wherein the real-time flight monitoring data includes the three-dimensional spatial coordinates, motion vector, and heading deflection angle of the UAV; a parsing module for parsing predefined airspace geo-fence configuration information to generate a virtual boundary constraint set, the virtual boundary constraint set including geo-fence boundary geometry and dynamic altitude restrictions; a determination module, configured to determine whether the drone has intruded into a no-fly zone defined by the virtual boundary constraint set, by combining the three-dimensional space coordinates and the virtual boundary constraint set; A generation module, configured to generate a coordinated interception instruction sequence when determining that the UAV has intruded into a no-fly zone; The sending module is used to send the coordinated interception instruction sequence to the UAV through a radio frequency communication link, driving the UAV to perform flight path correction to complete real-time interception of the UAV.

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 real-time interception method for drone intrusion based on airspace electronic fence 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 real-time interception method for drone intrusion based on airspace electronic fence according to any one of claims 1 to 7 is implemented.

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