Unmanned aerial vehicle airspace safety monitoring system and method

Abstract

The invention discloses an unmanned aerial vehicle airspace safety monitoring system and method in the technical field of unmanned aerial vehicle monitoring. The method comprises the following steps: reading basic data associated with unmanned aerial vehicle airspace safety monitoring; receiving real-time position data of the unmanned aerial vehicle; unmanned aerial vehicle airspace safety monitoring is carried out based on the basic data associated with unmanned aerial vehicle airspace safety monitoring and the unmanned aerial vehicle real-time position data; alarm information generated by unmanned aerial vehicle airspace safety monitoring is summarized and then uniformly sent to the outside through message middleware. According to the method, the actual application environment of an unmanned aerial vehicle safety monitoring platform is fully considered, and the basic data associated with unmanned aerial vehicle airspace safety monitoring is imported at one time and used for multiple times, so that the time complexity of unmanned aerial vehicle airspace safety monitoring is greatly reduced, and the situation that the monitoring area is divided into multiple nodes in the prior art is avoided; and the hardware configuration and cost of the platform and the scheduling overhead between the monitoring platform and the node are increased.

Description

Technical Field

[0001] The present invention relates to a system and method for monitoring the airspace safety of an unmanned aerial vehicle (UAV), and belongs to the technical field of UAV monitoring. Background Art

[0002] In recent years, with the rapid development of the domestic low-altitude economy, the construction of drone safety monitoring platforms has been increasingly put on the agenda. The drone safety monitoring platform needs to maintain real-time monitoring of all drones over a designated area, and any abnormal drones need to be issued an alarm message and reported for processing.

[0003] In existing technologies, drone security monitoring requires dividing the monitoring area into multiple nodes, so that the processing capacity of each node is controlled below a certain number of drones. The cost of this is to increase the hardware configuration of the platform, significantly increasing the cost and scheduling overhead between the monitoring platform and nodes. Summary of the Invention

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a drone airspace safety monitoring system and method, which greatly reduces the time complexity of drone airspace safety monitoring by importing basic data associated with drone airspace safety monitoring once and using it multiple times.

[0005] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0006] In a first aspect, the present invention provides a method for monitoring the safety of drone airspace, comprising:

[0007] Read basic data related to drone airspace safety monitoring;

[0008] Receive real-time location data of drones;

[0009] Carry out drone airspace safety monitoring based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones;

[0010] The warning information generated by drone airspace safety monitoring is summarized and sent to the outside through the message middleware.

[0011] Furthermore, the basic data associated with drone airspace safety monitoring includes geo-fences, buildings and terrain, preset routes and sub-airspace data. Drone airspace safety monitoring is performed based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones, including:

[0012] Perform security checks between the drone’s real-time location and geo-fences;

[0013] Conduct safety checks on the real-time location of the drone and the relationship between buildings and terrain;

[0014] Conduct safety checks between the drone's real-time location and the preset route;

[0015] Perform safety checks on the real-time location of drones and between sub-airspaces.

[0016] Furthermore, security checks are performed between the real-time location of the drone and the geofence, including:

[0017] After gridding the geofence, mark the fence grid;

[0018] Matching drone grids based on real-time drone location data;

[0019] Determine the positional relationship between the drone and the fence area based on the drone grid and the fence grid;

[0020] In response to the drone being inside the fence, calculating the distance between the drone and the fence boundary and comparing it with a preset threshold;

[0021] In response to the drone being outside the fence, the drone coinciding with the fence, and the distance between the drone and the fence boundary being less than a preset threshold, an alarm message is generated.

[0022] Furthermore, safety checks are performed on the real-time location of the drone and the relationship between buildings and terrain, including:

[0023] Perform gridding processing based on terrain data to obtain a terrain grid set;

[0024] Based on the building data, the grid elevation values ​​in the terrain grid set are increased to obtain the drone detection grid set;

[0025] Determine the distance from the ground based on the real-time position of the drone and the drone detection grid set;

[0026] In response to the drone's distance from the ground being less than the minimum height above the ground, a safety warning message is issued; otherwise, no warning prompt is issued.

[0027] Furthermore, a safety check is performed between the real-time position of the drone and the preset route, including:

[0028] Calculate the projection coefficient of the drone in the direction of the preset route segment based on the real-time position of the drone;

[0029] Calculate the normalized distance from the drone to the current segment based on the projection coefficient of the drone in the direction of the preset route segment;

[0030] Dynamic yaw decision-making is performed based on the normalized distance from the UAV to the current flight segment.

[0031] Furthermore, the projection coefficient of the UAV in the direction of the preset route segment is:

[0032]

[0033] Where λ is the projection coefficient of the UAV in the direction of the preset route segment; P UAV is the current position of the drone; P A 、P B The starting point and end point of the flight segment respectively;

[0034] The normalized distance from the drone to the current flight segment is:

[0035] D norm =|(P UAV -P A )-λ·(P B -P A )|

[0036] Where D norm is the normalized distance from the drone to the current flight segment;

[0037] The dynamic yaw decision is:

[0038]

[0039] Where, Alarm is the dynamic yaw decision, D H The preset safety threshold.

[0040] Furthermore, safety checks are performed on the real-time location of the drone and the sub-airspaces, including:

[0041] Based on the real-time position of the UAV and the sub-airspace data, grid coordinate mapping is performed to obtain integer grid coordinates. The calculation formula is:

[0042]

[0043] Where, Grid x ,Grid y is the integer grid coordinate; x,y is the coordinate of the drone; x min ,y min is the coordinate of the lower left corner of the sub-space; s is the preset grid side length;

[0044] The distance between the drone and the grid boundary is calculated based on the integer grid coordinates. The calculation formula is:

[0045]

[0046] Where, L x ,L y is the total number of grids in the X and Y directions of the sub-space, D g is the plane distance from the UAV to the nearest grid boundary; the UAV flight risk coefficient is calculated based on the distance from the UAV to the grid boundary, and the calculation formula is:

[0047]

[0048] Where R is the risk factor; v is the real-time speed of the UAV, and T is the system response time;

[0049] The warning information is determined based on the comparison between the flight risk coefficient and the preset risk threshold.

[0050] In a second aspect, the present invention provides a drone airspace safety monitoring system, comprising:

[0051] Data reading module: reads basic data related to drone airspace safety monitoring;

[0052] Data receiving module: receives real-time location data of the drone;

[0053] Safety monitoring module: performs drone airspace safety monitoring based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones;

[0054] Aggregate alarm module: After summarizing the alarm information generated by drone airspace safety monitoring, it is sent to the outside through the message middleware.

[0055] In a third aspect, the present invention provides a drone airspace safety monitoring device, comprising a processor and a storage medium;

[0056] The storage medium is used to store instructions;

[0057] The processor is configured to operate according to the instructions to execute the steps of any of the above methods.

[0058] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above methods when executed by a processor.

[0059] Compared with the prior art, the present invention has the following beneficial effects:

[0060] First, this solution fully considers the actual application environment of the drone safety monitoring platform. By importing the basic data related to drone airspace safety monitoring once and using it multiple times, it greatly reduces the time complexity of drone airspace safety monitoring. It also avoids the problem of existing technologies that divide the monitoring area into multiple nodes, which leads to increased platform hardware configuration, cost, and scheduling overhead between the monitoring platform and nodes.

[0061] Second, this solution eliminates the branching judgments of traditional methods through a unified calculation framework when performing safety checks on preset routes. The calculation formula covers all spatial position relationships, automatically adapting to the three states of forward, upward, and backward. The overall calculation workload is significantly reduced compared to traditional methods, making it suitable for low-power flight control platforms. The calculation reference point is automatically adjusted through the projection coefficient to ensure consistent measurement accuracy throughout the entire flight segment.

[0062] Third, this solution uses only integer addition, subtraction, and comparison in sub-airspace security checks, eliminating the need for squaring, square root, or floating-point operations, making it suitable for embedded devices. Planar distance and altitude detection are separated, avoiding complex spatial geometry calculations. The risk factor directly quantifies the out-of-bounds time window. The grid-based design supports arbitrary airspace shapes, requiring only pre-defined boundary grid coordinates. Grid boundaries are strictly aligned with geofences, eliminating the curve-fitting errors associated with traditional ray-based methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The accompanying drawings, which constitute part of the present invention, are intended to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are intended to explain the present invention and do not constitute an undue limitation of the present invention. In the accompanying drawings:

[0064] Figure 1 A geo-fence grid diagram of a drone airspace safety monitoring method provided in Example 1 of the present invention;

[0065] Figure 2 A no-fly zone grid identification diagram for a method for monitoring drone airspace safety provided in Example 1 of the present invention;

[0066] Figure 3 A schematic diagram of a line segment perpendicular foot of a method for monitoring the safety of drone airspace provided in the first embodiment of the present invention;

[0067] Figure 4 A schematic diagram of a line-of-sight analysis method for monitoring airspace safety of unmanned aerial vehicles provided in the first embodiment of the present invention;

[0068] Figure 5 A grid elevation visibility detection map for a method for monitoring drone airspace safety provided in Example 1 of the present invention;

[0069] Figure 6 A real-time drone grid map of a drone airspace safety monitoring method provided in Example 1 of the present invention;

[0070] Figure 7 A UAV dynamic conflict detection diagram of a UAV airspace safety monitoring method provided in Example 1 of the present invention;

[0071] Figure 8 In a method for monitoring the safety of drone airspace provided in embodiment 1 of the present invention, a drone deviates from a preset route map. DETAILED DESCRIPTION

[0072] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that, in the absence of conflict, the embodiments and features of the embodiments of the present invention can be combined with each other.

[0073] The following detailed description is an exemplary description and is intended to provide further detailed description of the present invention. Unless otherwise indicated, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention.

[0074] Example 1:

[0075] This embodiment provides a method for monitoring the safety of drone airspace. First, the basic data such as geographic fences, no-fly zones, sub-airspaces, terrain, and buildings are obtained through a data reading module. Then, the drone real-time location data receiving module receives the real-time drone location status data. The geographic fence detection module is responsible for the safety detection between the received real-time drone status data and the fence. A drone that is too close to the fence or flies out of the fence will generate an alarm message. The no-fly zone detection module is responsible for the safety detection between the received real-time drone status data and the no-fly zone. A drone that is too close to the no-fly zone or flies into the no-fly zone will generate an alarm message. The sub-airspace detection module is responsible for the safety detection between the received specific drone and the sub-airspace. UAVs that leave the sub-airspace will generate an alarm message; the building and terrain detection module is responsible for the safety detection between the received real-time UAV status data and buildings (or terrain), and UAVs that are too low to buildings or terrain will generate an alarm message; the route deviation detection module is responsible for the safety detection between the received real-time UAV status data and the preset route, and UAVs that deviate too far from the preset route will generate an alarm message; the dynamic conflict detection module is responsible for the mutual safety detection of the received real-time UAV data, and UAVs with collision risks will generate an alarm message; the safety detection result summary module is responsible for summarizing the alarm messages generated by the above detection modules, and finally sending the alarm messages uniformly through the message middleware. Specifically including the following contents:

[0076] 1. Basic data reading

[0077] The basic data associated with the drone safety monitoring platform includes geofences, terrain, buildings, no-fly zones, and other data. This basic data is typically automatically read upon software startup and undergoes targeted preprocessing. The geofence data read includes the vertex latitude and longitude coordinates of the fence boundary line, fence alarm thresholds, and other information. The terrain data read is generally 30-meter-resolution DEM data covering the geofence area. The urban building data read is generally large in quantity, with hundreds of thousands or even millions of polygons being common. Each building contains information such as the vertex coordinates of the building's two-dimensional polygon and its height. The no-fly zone data read primarily includes the vertex coordinates of the two-dimensional polygon and the no-fly zone alarm thresholds. Furthermore, no-fly zones can be dynamically transmitted via message-based middleware; sub-airspaces and drone routes are also transmitted via message-based middleware.

[0078] 2. UAV real-time location data reception

[0079] Drone security monitoring platforms typically use message-based middleware to continuously receive real-time drone status data. This data includes key information such as the drone's ID, latitude and longitude, heading, pitch, speed, and more. For the same drone, the latest status data automatically overwrites the previous data.

[0080] The drone safety monitoring platform continuously obtains the latest drone status data in the monitoring area at regular intervals and simultaneously transmits this real-time status data to different detection modules for assessment. Each detection module combines pre-processed basic data with the real-time drone status data, applies corresponding detection algorithms, and generates alerts for risky drones.

[0081] 3. Geofence Detection

[0082] The algorithm process is as follows:

[0083] ①Geofence grid processing;

[0084] ②Mark the fence grid;

[0085] ③ Matching drone grids based on real-time drone location data;

[0086] ④ Based on the drone grid and fence grid, determine the position relationship between the drone and the fence area;

[0087] ⑤ When the drone is inside the fence, calculate the distance between the drone and the fence boundary and compare it with the preset threshold;

[0088] ⑥ Generate an alarm when the drone is outside the fence, the drone overlaps with the fence, or the distance between the drone and the fence boundary is less than a preset threshold;

[0089] ⑥The algorithm ends.

[0090] like Figure 1 Shown: Fence area polygon (ABCDEFA) mesh processing, the grid width is one D TH , the bounding rectangle of polygon (ABCDEFA) is: R(P LT ,P RT ,P RB ,P LB ), double D for R TH The distance is expanded, that is, Figure 1 The grid set shown in the figure is the grid set that intersects with the fence boundary line.

[0091] 4. No-fly zone detection

[0092] No-fly zones generally include permanent, temporary, and special control zones. No-fly zone safety detection primarily calculates the distance between a drone's real-time location and each no-fly zone. If the distance falls below a given no-fly zone threshold, an alert is generated and reported to the drone monitoring platform. Its core goal is to prevent drones from accidentally or illegally entering no-fly zone airspace through real-time detection.

[0093] The detection algorithm uses a grid-based approach, converting each no-fly zone into a corresponding grid set. During real-time detection, drones within these no-fly zone grids are further evaluated for their positional relationship and closest distance to the corresponding no-fly zone. This approach offers significant advantages: only drones near no-fly zones are identified and subsequently evaluated for no-fly zone proximity. The vast majority of drones within grids outside of no-fly zones are directly filtered out, resulting in high efficiency.

[0094] The algorithm process is as follows:

[0095] ① Gridding of no-fly zones

[0096] The gridding of the no-fly zone uses the same grid range and grid size as the geo-fence, denoted as: S GRID .

[0097] ②No-fly zone grid marking

[0098] Take out the no-fly zone objects one by one from the no-fly zone set, denoted as F GEO , note F GEO The envelope rectangle range is R, and a no-fly zone threshold (D F ) range, the expanded rectangular range is still recorded as R, such as Figure 2 The dotted box shown in the figure. The corresponding grid positions are obtained according to the positions of the four corner points of R, and the identification grid set corresponding to the no-fly zone is further determined. Figure 2 The oblique line grid sets shown in the figure are the no-fly zone identification grid sets corresponding to the no-fly zone (ABCDA) and the no-fly zone (EFGE), respectively, and are denoted as: S 1 GRID , S 2 GRID ;

[0099] ③ UAV grid matching

[0100] The algorithm for grid matching of the real-time received UAV position data is relatively simple. It only needs to calculate the row and column numbers corresponding to the UAV position coordinates in the grid set, and then obtain the corresponding grid information through the row and column numbers. Figure 2 The grids corresponding to P1, P2, P3, P4, P5, and P6 are G1, G2, G3, G4, G5, and G6 respectively.

[0101] ④ Determination of the position relationship between drones and no-fly zones

[0102] Obtain the properties of the drone's position mapping grid. If the grid is marked by one or more no-fly zones, such as G1, G2, G3, or G4, further determine the positional relationship between the drone's position and the corresponding no-fly zone. The determination algorithm uses a common ray method. If the drone falls within the no-fly zone, proceed directly to step ⑥ to generate an alarm message; if the drone falls outside the no-fly zone, proceed to step ⑤ to further calculate the closest distance between the drone and the no-fly zone.

[0103] If the grid is not marked by any no-fly zone, such as G5 or G6, it means there is no no-fly zone near the drone, and the assessment of the drone ends.

[0104] ⑤ Calculation of distance between drone and no-fly zone

[0105] After the above judgment ④, the drone that falls outside the no-fly zone needs to further calculate the minimum distance D between it and the no-fly zone. MIN , so as to match the given alarm threshold D F For comparison, if D MIN <D F , then an alarm message needs to be generated and reported to the monitoring platform. If D MIN >D F , then there is no need to generate an alarm message.

[0106] The distance calculation between the drone and the no-fly zone boundary is based on the distance calculation between points and line segments. Figure 3 As shown: The foot O of the perpendicular from point P to AB is inside AB, then L PO That is the shortest distance from P to line segment AB; the foot O of the perpendicular from P to CD is on the extension line of DC, then compare the distance from P to C and P to D, because L PC <LPD , then L PC is the shortest distance from P to line segment CD.

[0107] Take out the no-fly zone boundary line segment and the drone segment by segment to calculate the distance between the point and the line segment. The minimum value of all distances is the distance from the point to the no-fly zone boundary, D MIN =min{D 1 MIN D 2 MIN D 3 MIN …D n MIN}.

[0108] ⑥Alarm information generation

[0109] The judgment results obtained from steps ④ and ⑤ generate corresponding alarm information.

[0110] ⑦The algorithm ends.

[0111] 5. Building and Terrain Detection

[0112] When flying drones over cities or mountainous areas, terrain and buildings are significant factors affecting flight safety. While these factors are generally considered when planning drone routes, real-time dynamic monitoring of the impact of buildings and terrain on drone flight is essential for drone safety monitoring platforms.

[0113] UAV terrain detection is generally carried out through line of sight analysis. The principle of line of sight analysis is as follows: Figure 4 As shown: O is the observation point, B and C are both target points, OG is the observation height of O, BG1 and CG1 are the observation heights of B and C respectively. Figure 4 As can be seen, there is no terrain obstruction between OC and OB, and they can see through. However, there is terrain obstruction between OB and O. Point O can see point B1 at the farthest point. Although segment B2B is higher than the terrain, it is blocked by B1B2 and cannot see through OB. Line-of-sight analysis is a common GIS analysis function, so its implementation details will not be elaborated here. Considering the similarities between building detection and terrain detection, this solution combines building and terrain detection into one. The terrain data uses the national 30-meter resolution DEM data. The specific algorithm process is described below:

[0114] ①Import building and terrain data

[0115] Specify the terrain data path and import the terrain data through the GDAL open source library; import the building data through internally specified format files (such as json files).

[0116] ② Terrain gridding

[0117] Terrain gridding uses the same grid range as the geofence, and the grid size is kept consistent with the resolution of the terrain data (usually 30 meters). GRID Take the center point of each grid, obtain the corresponding terrain elevation value according to the center point coordinates (need to be completed through the GDAL open source library related interface), and assign the same elevation value to the grid. After the terrain gridding process, S GRID Already has the ability to determine terrain using drones.

[0118] ③ Building gridding

[0119] Take out each building polygon in sequence, record it as: B GEO , B GEO The height is H GEO Calculate and B GEO Boundary lines intersect or are B GEO The set of all grids included is denoted as: S T GRID , for S T GRID The elevation value of each grid in the grid is added to H GEO , which is the actual height of the terrain after superimposing buildings. Due to the high density of building distribution, if the same grid is covered by multiple buildings, the added elevation value of the grid is the larger value of the building height.

[0120] ④UAV visual detection

[0121] Sequentially retrieve the real-time status data of the drone and record the current position of the drone to be detected as P UAV , according to the heading of the UAV (Heading) and the observation distance threshold (D O ), calculate and obtain P UAV The target observation point P T UAV , so that D O =||P T UAV -P UAV ||, P UAV ->P T UAV Keep the same as Heading. UAV ->P T UAV In S GRID Conduct line-of-sight analysis in Figure 5 As shown, set a minimum ground clearance height D of the drone H , note P UAV ->P T UAVThe observation height of a point A in the middle is H A , then: H A =H S +(H T –H S )*(L SA / L ST ); where H S 、H T P UAV and P T UAV Height value; L SA , L ST They are P UAV and the middle point A, the target observation point P T UAV Plane distance; L SA , L ST It can be directly calculated from the longitude and latitude coordinates of the two points; the elevation of grid G ​​where point A is located is H G , record the distance from the ground D AG =H A -H G , if D is satisfied H >=D AG , it means that point A is too close to the ground and a safety warning message needs to be returned; if D H <D AG , it means that the drone is flying safely and no warning is needed.

[0122] ⑤The algorithm ends.

[0123] 6. Dynamic Conflict Detection

[0124] Drone dynamic conflict detection is a technology that monitors and predicts potential collision risks with other drones, manned aircraft, or obstacles in real time during flight, allowing drones to take evasive action. Its core goal is to ensure flight safety and avoid mid-air or ground collisions.

[0125] Based on the conflict scenario, it can be divided into: conflict detection between single drones and single drones; conflict detection between single drones and multiple drones. The algorithm is as follows:

[0126] ① Real-time drone gridding

[0127] The gridding of drones uses the same grid range as the geofence, and the grid size is kept consistent with the resolution of the terrain data (usually 30 meters). It is denoted as: S GRID Sequentially take out the drone position coordinates and calculate the drone’s position in S GRID The row and column indexes in are matched to the corresponding grid according to the row and column indexes, and the drone ID is identified in the grid.

[0128] ②UAV cluster analysis

[0129] Sequentially extract the drone position coordinates and match them to the corresponding grid (same as the previous ①), denoted as: G P , get G P The peripheral distance is not greater than D H All grids of S P GRID , get S P GRID All the drone IDs identified in the image are collected into an array and recorded as: V P , if V P If there are other drone IDs in the list, go to step ③ below to perform accurate dynamic conflict detection. Figure 6 Shown: UAV P1 corresponding to V P1 = {B, C, D}, V corresponding to P2 P2 ={E, G, H}.

[0130] ③ UAV dynamic conflict determination

[0131] After obtaining the information of the UAV itself and the surrounding aircraft, this algorithm is responsible for the speed barrier method judgment between the UAV and the aircraft (if there are more than one aircraft, it will be judged one by one in a cycle). First, the height difference (H S ) and three-dimensional distance (d 3D ), if H S or d 3D If there is a threshold greater than the given one (the height difference threshold is different from the distance threshold), it indicates that there is no conflict between the UAV and the aircraft, and the processing with the aircraft is terminated. If it is not satisfied, further calculation and judgment are required, based on the respective velocity vectors of the UAV and the aircraft (respectively denoted as V A ,V O , the vector direction can be converted from the heading angle) velocity vector difference (V R =V A -V O ), using V R And the safety circle radius (R) of the aircraft, calculate the distance from the aircraft (center O) to V R Distance D L , if D L <=R, it means that there is a potential conflict between the UAV and the aircraft, and a dynamic conflict warning message needs to be given. L >R, then the processing with the aircraft is terminated. L When <= R, the latest collision position and collision time of the drone can be returned.

[0132] like Figure 7Shown: relative speed V R =V A– V O ;

[0133] The distance from the drone to the nearest collision point is: D AB =(||V A || / ||V R ||)*(D AO *D AO –D L *D L ) 1 / 2 ;

[0134] Flight time: T AB =D AB / ||V A ||;

[0135] ④The algorithm ends.

[0136] 7. UAV yaw detection

[0137] The main purpose of drone deviation detection is to detect whether a drone has strayed too far from its preset route during flight. If this occurs, not only may the drone's mission not be completed, but it may also cause a conflict with other drones. Therefore, it is necessary to issue a timely warning before the drone deviates too far from its preset route and quickly restore its track to ensure mission safety and efficiency.

[0138] Since there is a one-to-one correspondence between drones and routes, route deviation detection can actually be converted into: calculating the spatial distance between the drone's real-time position and the corresponding route. When the deviation distance is greater than a given deviation threshold, an alarm message needs to be given.

[0139] The algorithm process is as follows:

[0140] Sequentially retrieve the real-time position of the drone, recorded as: P UAV , the corresponding route is: R. Take P UAV Calculate the closest distance to each segment of R one by one. The closest distance calculation between points and line segments can be referred to Figure 3 The segment with the shortest distance is the segment the drone is currently in, and this shortest distance is D dev , and the drone route deviation threshold is set as: D H , then when D dev >D H When the drone deviates from the target, a warning message of drone deviation needs to be issued.

[0141] like Figure 8As shown: The drone starts from point A, and the preset route is ABC. However, after passing point A1, the drone gradually deviates from the preset route. When the drone flies to point A2, the deviation distance D between the drone and the original route is dev Exceeded the deviation threshold.

[0142] To further reduce the amount of calculation, this solution achieves accurate yaw detection through the three steps of "vector projection - boundary constraint - dynamic matching", avoiding complex mathematical operations. The calculation formula is as follows:

[0143] 1. Projection coefficient calculation

[0144]

[0145] Where λ is the projection coefficient of the UAV in the direction of the preset route segment; the projection coefficient is used to quantify the relative position of the UAV in the direction of the preset route segment, λ<0: the UAV is in the P A Behind; 0≤λ≤1: the UAV is directly above the flight segment; λ>1: the UAV has surpassed P B .P UAV is the current position of the drone; P A 、P B The starting point and end point of the flight segment respectively.

[0146] 2. Normalized distance calculation

[0147] D norm =|(P UAV -P A )-λ·(P B -P A )|

[0148] Where D norm is the normalized distance from the drone to the current flight segment.

[0149] 3. Dynamic Yaw Decision

[0150]

[0151] Where, Alarm is the dynamic yaw decision, D H is the preset safety threshold. When the normalized distance D between the drone and the current segment norm If the value is greater than the preset safety threshold, the drone is judged to be yawed; otherwise, the drone is not judged to be yawed.

[0152] The above solution unifies the calculation framework, eliminating the branch judgment of traditional methods. The three formulas cover all spatial position relationships, namely, automatic adaptation to the three states of front / upward / backward. The overall calculation amount is greatly reduced compared with traditional methods. It is suitable for low-power flight control platforms. The calculation reference point is automatically adjusted through the projection coefficient to ensure that the measurement accuracy remains consistent throughout the entire flight segment.

[0153] 8. Sub-airspace detection

[0154] A sub-airspace is typically a small area within the larger airspace monitored by a monitoring platform, designated for specific mission requirements. During the designated time period, designated drones will perform flight missions within the sub-airspace. For example, for drone spraying operations in the agricultural sector, a pre-defined spraying area is required, known as a sub-airspace. The drones then perform their spraying missions within the sub-airspace according to the designated route.

[0155] Sub-airspace safety detection refers to the real-time monitoring of drones flying within the sub-airspace. If a drone approaches the sub-airspace boundary or flies out of the sub-airspace, an alarm message must be issued immediately.

[0156] Since the scope of the sub-airspace and the number of drones performing flight missions in the sub-airspace are relatively limited, without compromising efficiency, the algorithm for real-time determination of the positional relationship between the drone and the sub-airspace adopts the traditional ray method, and the calculation of the closest distance between the drone and the sub-airspace boundary is completed using the general point and broken line distance calculation method.

[0157] To further improve computing efficiency, this solution provides a more convenient computing solution, including the following:

[0158] 1. Grid coordinate mapping for drones

[0159]

[0160] Where, Grid x ,Grid y is the integer grid coordinate; x,y is the coordinate of the drone; x min ,y min is the coordinate of the lower left corner of the sub-space; s is the preset grid side length;

[0161] 2. Calculate the distance from the drone to the grid boundary

[0162]

[0163] Where, L x ,L y is the total number of grids in the X and Y directions of the sub-space, D g It is the plane distance from the UAV to the nearest grid boundary. The height effect is handled by independent thresholding without the need for complex coupling calculations.

[0164] 3. Calculating the Drone Flight Risk Factor

[0165]

[0166] Where R is the risk factor, v is the real-time speed of the drone, and T is the system response time. When R ≥ 1, an immediate alert is required (imminent boundary crossing); 0.5 ≤ R < 1, an early warning state; and R < 0.5, no risk.

[0167] This solution uses only integer addition, subtraction, and comparison, eliminating the need for squaring, square root, or floating-point operations, making it suitable for embedded devices. Planar distance and height detection are separated, avoiding complex spatial geometry calculations. The risk factor directly quantifies the time window for boundary violations. The grid-based design supports arbitrary airspace shapes, requiring only predefined boundary grid coordinates. Grid boundaries are strictly aligned with geofences, eliminating the curve-fitting errors associated with traditional ray-based methods.

[0168] IX. Summary of Safety Testing Results

[0169] The security monitoring platform receives and aggregates the alarm information reported by each of the aforementioned submodules in real time. The types of fields contained in the alarm information for different detection modules vary, as shown in Table 1. Finally, through message middleware, messages are sent at fixed intervals, enabling other business platforms to respond promptly to the alarm information.

[0170]

[0171] Table 1

[0172] Example 2:

[0173] A drone airspace safety monitoring system, capable of implementing the drone airspace safety monitoring method described in Example 1, includes:

[0174] Data reading module: reads basic data related to drone airspace safety monitoring;

[0175] Data receiving module: receives real-time location data of the drone;

[0176] Safety monitoring module: performs drone airspace safety monitoring based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones;

[0177] Aggregate alarm module: After summarizing the alarm information generated by drone airspace safety monitoring, it is sent to the outside through the message middleware.

[0178] Example 3:

[0179] An embodiment of the present invention further provides a drone airspace safety monitoring device, which can implement the drone airspace safety monitoring method described in embodiment 1, including a processor and a storage medium;

[0180] The storage medium is used to store instructions;

[0181] The processor is configured to operate according to the instructions to execute the steps of the following method:

[0182] Read basic data related to drone airspace safety monitoring;

[0183] Receive real-time location data of drones;

[0184] Carry out drone airspace safety monitoring based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones;

[0185] The warning information generated by drone airspace safety monitoring is summarized and sent to the outside through the message middleware.

[0186] Example 4:

[0187] An embodiment of the present invention further provides a computer-readable storage medium that can implement the drone airspace safety monitoring method described in Example 1. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the following method steps:

[0188] Read basic data related to drone airspace safety monitoring;

[0189] Receive real-time location data of drones;

[0190] Carry out drone airspace safety monitoring based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones;

[0191] The warning information generated by drone airspace safety monitoring is summarized and sent to the outside through the message middleware.

[0192] It is understood from common technical knowledge that the present invention may be implemented by other embodiments that do not depart from its spirit or essential features. Therefore, the embodiments disclosed above are, in all respects, merely illustrative and not exclusive. All modifications within the scope of the present invention or equivalent to the scope of the present invention are intended to be encompassed by the present invention.

[0193] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0194] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0195] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0196] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0197] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring the safety of drone airspace, characterized in that: include: Read basic data related to drone airspace safety monitoring; Receive real-time location data of drones; Carry out drone airspace safety monitoring based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones; The warning information generated by drone airspace safety monitoring is summarized and sent to the outside through the message middleware.

2. The method for monitoring the safety of drone airspace according to claim 1, wherein: The basic data associated with drone airspace safety monitoring includes geo-fences, buildings and terrain, preset routes and sub-airspace data. Drone airspace safety monitoring is performed based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones, including: Perform security checks between the drone’s real-time location and geo-fences; Conduct safety checks on the real-time location of the drone and the relationship between buildings and terrain; Conduct safety checks between the drone's real-time location and the preset route; Perform safety checks on the real-time location of drones and between sub-airspaces.

3. The method for monitoring the safety of drone airspace according to claim 2, wherein: Perform security checks between the drone's real-time location and geofences, including: After gridding the geofence, mark the fence grid; Matching drone grids based on real-time drone location data; Determine the positional relationship between the drone and the fence area based on the drone grid and the fence grid; In response to the drone being inside the fence, calculating the distance between the drone and the fence boundary and comparing it with a preset threshold; In response to the drone being outside the fence, the drone coinciding with the fence, and the distance between the drone and the fence boundary being less than a preset threshold, an alarm message is generated.

4. The method for monitoring the safety of drone airspace according to claim 2, wherein: Perform safety checks on the real-time location of the drone and the relationship between buildings and terrain, including: Perform gridding processing based on terrain data to obtain a terrain grid set; Based on the building data, the grid elevation values ​​in the terrain grid set are increased to obtain the drone detection grid set; Determine the distance from the ground based on the real-time position of the drone and the drone detection grid set; In response to the drone's distance from the ground being less than the minimum height above the ground, a safety warning message is issued; otherwise, no warning prompt is issued.

5. The method for monitoring the safety of drone airspace according to claim 2, wherein: Perform safety checks on the drone's real-time location and preset routes, including: Calculate the projection coefficient of the drone in the direction of the preset route segment based on the real-time position of the drone; Calculate the normalized distance from the drone to the current segment based on the projection coefficient of the drone in the direction of the preset route segment; Dynamic yaw decision-making is performed based on the normalized distance from the UAV to the current flight segment.

6. The method for monitoring the safety of UAV airspace according to claim 5, characterized in that: The projection coefficient of the UAV in the direction of the preset route segment is: Where λ is the projection coefficient of the UAV in the direction of the preset route segment; P UAV is the current position of the drone; P A 、P B The starting point and end point of the flight segment respectively; The normalized distance from the drone to the current flight segment is: D norm =|(P UAV -P A )-λ·(P B -P A )| Where D norm is the normalized distance from the drone to the current flight segment; The dynamic yaw decision is: Where, Alarm is the dynamic yaw decision, D H The preset safety threshold.

7. The method for monitoring the safety of UAV airspace according to claim 2, wherein: Perform safety checks on the real-time location of drones and between sub-airspaces, including: Based on the real-time position of the UAV and the sub-airspace data, grid coordinate mapping is performed to obtain integer grid coordinates. The calculation formula is: Where, Grid x ,Grid y is the integer grid coordinate; x,y is the coordinate of the drone; x min ,y min is the coordinate of the lower left corner of the sub-space; s is the preset grid side length; The distance between the drone and the grid boundary is calculated based on the integer grid coordinates. The calculation formula is: Where, L x ,L y is the total number of grids in the X and Y directions of the sub-space, D g is the plane distance from the UAV to the nearest grid boundary; The UAV flight risk coefficient is calculated based on the distance between the UAV and the grid boundary. The calculation formula is: Where R is the risk factor; v is the real-time speed of the UAV, and T is the system response time; The warning information is determined based on the comparison between the flight risk coefficient and the preset risk threshold.

8. A drone airspace safety monitoring system, characterized by: include: Data reading module: reads basic data related to drone airspace safety monitoring; Data receiving module: receives real-time location data of the drone; Safety monitoring module: performs drone airspace safety monitoring based on the basic data associated with drone airspace safety monitoring and the real-time location data of drones; Aggregate alarm module: After summarizing the alarm information generated by drone airspace safety monitoring, it is sent to the outside through the message middleware.

9. A drone airspace safety monitoring device, characterized in that: including processors and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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