A method for scheduling unmanned aerial vehicles (UAVs) for overlapping coverage areas of a cluster of airports

Abstract

The application discloses a method for scheduling unmanned aerial vehicles (UAVs) aiming at overlapping coverage areas of cluster deployment airports, and belongs to the technical field of UAV control. In order to solve the problem of accurately generating executable sub-routes for multiple airports, the application comprises the following steps: collecting airport cluster set data, grid side length, orthographic parameter and UAV basic data; establishing a circular domain polygon of each airport; identifying a candidate airport set of each grid unit, then performing attribution judgment on the grid unit of the overlapping candidate airport to obtain a unique execution airport corresponding to each grid unit; performing airport scheduling sequencing according to the grid offset of the grid unit relative to the center of the unique execution airport to obtain a sequential number of the grid unit corresponding to each unique execution airport, and forming a scheduling sequence; constructing an airport execution sub-area; solving a corresponding orthographic strip interval, then generating a continuous route for completely covering the airport execution sub-area; and performing candidate sub-route splitting for UAV scheduling.

Description

Technical Field

[0001] This invention belongs to the field of unmanned aerial vehicle (UAV) control technology, specifically relating to a UAV scheduling method for cluster deployments in overlapping airport coverage areas. Background Technology

[0002] In scenarios such as forestry patrols, nature reserve supervision, border inspections, disaster verification, and routine inspections around airports, it is often not a single airport that performs the task, but rather multiple fixed-deployment airports that work together to cover the same target area. In order to ensure that each airport remains replaceable during severe weather, temporary shutdown of a single airport, or peak periods of key tasks, the standard coverage radius of each airport is often set to a fixed value in engineering.

[0003] When the circular coverage areas of multiple airports overlap, traditional methods typically involve either "independent planning for each airport, manual review of conflicts, and subsequent modification of flight routes" or "manual segmentation followed by separate flight route planning." This approach suffers from at least four problems: First, overlapping areas are prone to repeated data collection by multiple drones; second, manually defined boundaries struggle to ensure load balancing, leading to some airports being overloaded while others remain idle; third, the execution order is often determined solely by airport lists or manual experience, lacking a robust, layer-by-layer scheduling mechanism; and fourth, even after area allocation, subsequent manual generation of orthogonal flight routes and subtasks for each airport is still required, resulting in low overall efficiency. Therefore, a comprehensive technical solution is needed that can start from the overall airport cluster perspective, first determining the unique ownership and execution order of overlapping coverage areas, and then automatically generating orthogonal flight routes and splitting tasks for the assigned areas. Summary of the Invention

[0004] The problem this invention aims to solve is to achieve accurate generation of executable sub-routes from multiple airports, and proposes a drone scheduling method for overlapping coverage areas of clustered airports.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A method for scheduling drones in overlapping airport coverage areas for cluster deployment includes the following steps:

[0007] S1. Collect airport cluster data, including grid edge length, orthogonal parameters, and basic UAV data;

[0008] S2. Based on the airport cluster set data obtained in step S1, construct the circular polygon for each airport;

[0009] S3. Generate airport coverage area grid cells based on the grid side length obtained in step S1. Then, based on the circular domain polygon of each airport obtained in step S2, identify the candidate airport set of each grid cell. Then, determine the ownership of the grid cells of overlapping candidate airports to obtain the unique execution airport corresponding to each grid cell.

[0010] S4. For the unique execution airport obtained in step S3, sort the airport scheduling according to the grid offset of the grid cell relative to the center of the unique execution airport, and obtain the sequential number of the grid cell corresponding to each unique execution airport to form a scheduling sequence.

[0011] S5. Based on the unique execution airport corresponding to each grid cell obtained in step S3 and the scheduling sequence obtained in step S4, construct the airport execution sub-region;

[0012] S6. Based on the airport execution sub-region obtained in step S5, solve for the corresponding orthophoto strip spacing, and then generate continuous flight routes that completely cover the airport execution sub-region;

[0013] S7. Comprehensively evaluate the scan coverage length, adjacent scan line connection length, airport departure transfer length, return length, and safety margin of the continuous routes of the execution sub-region obtained in step S6, calculate the estimated total duration of each candidate sub-route, and split the candidate sub-route for UAV scheduling.

[0014] Furthermore, the specific implementation method of step S1 includes the following steps:

[0015] S1.1. Collect airport cluster set data M, where the a-th airport set data M_a includes the center coordinates C_a, coverage radius r_a, and priority P_a of the a-th airport;

[0016] S1.2. Collect the grid side length g_c;

[0017] S1.3. Collect orthophoto parameters, including main heading angle α_m, flight altitude h_f, camera sensor width w_s, camera focal length f_l, horizontal pixel count n_px, and lateral overlap rate o_lat;

[0018] S1.4. Collect basic data of the UAV, including operating speed V_op, transfer speed V_tr, nominal duration of a single flight T_nom, and safety margin T_safe.

[0019] Furthermore, the specific implementation method of step S2 includes the following steps:

[0020] S2.1. For the a-th airport, with a preset number of points n_v, Earth radius R_e, and azimuth angle of the i-th fixed point θ_i, the local coordinates and latitude / longitude coordinates are obtained, expressed as:

[0021] θ_i=2πi / n_v

[0022] x_{a,i}=r_a×sin(θ_i)

[0023] y_{a,i}=r_a×cos(θ_i)

[0024] φ_{a,i}=φ_a+(y_{a,i} / R_e)×180 / π

[0025] λ_{a,i}=λ_a+[x_{a,i} / (R_e×cosφ_a)]×180 / π

[0026] Where θ_i represents the azimuth of the i-th fixed point; x_{a,i} and y_{a,i} represent the x-axis and y-axis coordinates of the plane with the center of the a-th airport as the origin; φ_{a,i} and λ_{a,i} represent the latitude and longitude of the i-th fixed point, respectively; φ_a is the latitude of the a-th airport, and λ_a is the longitude of the a-th airport;

[0027] S2.2. For the a-th airport, connect the data of each fixed point obtained in step S2.1 to obtain the circular domain polygon Ω_a of the a-th airport. Then, perform the operations of step S2.1 and step S2.2 on each airport in the airport cluster set to obtain the circular domain polygon of each airport.

[0028] Furthermore, the specific implementation method of step S3 includes the following steps:

[0029] S3.1. Establish a uniform grid with side length g_c within the smallest outer perimeter that includes the entire airport coverage area, and denote each grid cell as G_q;

[0030] For the a-th airport and grid cell q, calculate the intersection area A_{a,q} of the circular polygon Ω_a and G_q. If A_{a,q}>0, then the grid cell is within the candidate coverage area of ​​airport a, thus obtaining the candidate airport set for each grid cell, expressed as:

[0031] A_{a,q}=Area(Ω_a∩G_q)

[0032] Where A_{a,q} is the set of candidate airports for grid cell q, and Area(·) represents the area operation.

[0033] S3.2. If a grid cell G_q corresponds to only one airport such that A_{a,q}>0, then the cell is directly assigned to that airport; if G_q falls within the coverage areas of multiple airports, then a weighted assignment score S_{a,q} is calculated for each candidate airport, and the airport with the lowest score is determined as the unique executing airport for that grid cell. The calculation process is as follows:

[0034]

[0035] P_min=min(P_1,P_2,...,P_a,P_n)

[0036] S_{a,q}=d_{a,q}×[1+β_p×(P_a-P_min)]

[0037]

[0038] Where X_q and Y_q are the center x-axis coordinates and y-axis coordinates of grid cell G_q, respectively; X_a and Y_a are the center x-axis coordinates and y-axis coordinates of airport a, respectively; d_{a,q} is the distance term; β_p is the priority bias coefficient; and P_a is the airport priority of the a-th airport. This represents the unique execution airport for grid cell G_q;

[0039] When all airports have the same priority, the nearest airport takes precedence; when the priorities are different, priority is incorporated into the assignment decision through β_p.

[0040] Furthermore, the expression for the specific implementation method of step S4 is as follows:

[0041] u_{a,q}=round[(X_q-X_a) / g_c]

[0042] v_{a,q}=round[(Y_q-Y_a) / g_c]

[0043] k_{a,q}=max(|u_{a,q}|,|v_{a,q}|)

[0044] s_{a,q}=Ψ(u_{a,q},v_{a,q})

[0045] Where u_{a,q} and v_{a,q} represent the grid column offset and row offset of grid cell G_q relative to the a-th airport, respectively; k_{a,q} represents the concentric circle number, and the smaller k_{a,q} is, the closer it is to the airport center; Ψ(·) represents the preset spiral sequence mapping function, which is used to form a continuous traversal order within the same concentric circle; during scheduling, k_{a,q} is sorted in ascending order first, and then s_{a,q} is sorted in ascending order to form an inside-outside scheduling sequence.

[0046] Furthermore, the specific implementation method of step S5 includes the following steps:

[0047] S5.1. First, extract all grid cells belonging to airport a, forming the set Q_a of the grid cells belonging to airport a, where ;

[0048] For any q∈Q_a, the intersection of the airport coverage area Ω_a and the grid cell G_q is defined as the effective execution segment F_{a,q} corresponding to the q grid cell, where F_{a,q}=Ω_a∩G_q. Then, the total execution area of ​​airport a after deduplication is defined as... ;

[0049] S5.2. When setting up tasks to proceed step by step according to concentric circles, the grid units to which they belong are grouped according to the concentric circle number. The set of grid units to which airport a belongs in the kth concentric circle is defined as Q_{a,k}={q|q∈Q_a,k_{a,q}=k}, and the outermost concentric circle number of airport a is defined as K_a=max_{q∈Q_a}k_{a,q}.

[0050] For two assigned grid cells G_{q_i} and G_{q_j} within the same concentric circle, if the adjacency relation q_i~q_j⇔max(|u_{a,q_i}-u_{a,q_j}|,|v_{a,q_i}-v_{a,q_j}|)=1, then they are considered to be adjacent within that concentric circle, where u_{a,q} and v_{a,q} represent the grid column offset and grid row offset of the assigned grid cell relative to airport a, respectively. Based on the above adjacency relation, the set Q_{a,k} is divided into m_{a,k} connected components, denoted as Q_{a,k,1}, Q_{a,k,2}, ..., Q_{a,k,m_{a,k}}.

[0051] Define the execution sub-region corresponding to the c-th connected component of the k-th layer as... Where c = 1, 2, ..., m_{a, k};

[0052] S5.3. Extract the minimum spiral sequence number for each execution sub-region:

[0053] Defined as s_{a,k,c}{min}=min_{q∈Q_{a,k,c}}s_{a,q}, where s_{a,q} represents the spiral sequence number of the grid cell G_q within its respective concentric circle. Following the rule of increasing concentric circle number k and increasing the minimum spiral sequence number s_{a,k,c}{min} within the same concentric circle, this applies to all sub-regions. Sort the data and renumber it sequentially according to the sorting results. The total number of execution sub-regions of airport a is defined as n_{z,a}=Σ_{k=0}{K_a}m_{a,k}.

[0054] Furthermore, the specific implementation method of step S6 includes the following steps:

[0055] S6.1. For the execution sub-region of airport a, the ground resolution, single-strip coverage width, and spacing between adjacent scan lines are calculated based on the flight altitude h_f, camera sensor width w_s, focal length f_l, horizontal pixel count n_px, and lateral overlap rate o_lat. The calculation formula is as follows:

[0056] GSD=(h_f×w_s) / (f_l×n_px)

[0057] W_cov=GSD×n_px

[0058] L_sep = W_cov × (1 - o_lat)

[0059] Where GSD represents the ground resolution; W_cov represents the coverage width of a single strip on the ground; and L_sep represents the scan line spacing.

[0060] S6.2. Rotate the execution sub-region to the scanning coordinate system according to the main heading angle α_m, and then generate parallel scan lines in this coordinate system according to L_sep obtained in step S6.1; then calculate the intersection point of each scan line with the boundary of the execution sub-region, and pair the intersection points as effective coverage segments; then connect adjacent scan lines in alternating directions to form a serpentine flight path, expressed as:

[0061] X'=X×cosα_m-Y×sinα_m

[0062] Y'=X×sinα_m+Y×cosα_m

[0063] n_l=max{1,ceil[(Y_max-Y_min) / L_sep]}

[0064] W_fill=(n_l-1)×L_sep

[0065] Y_1=(Y_min+Y_max-W_fill) / 2

[0066] Y_j=Y_1+(j-1)×L_sep

[0067] Where X and Y are the coordinates before rotation, and X' and Y' are the coordinates after rotation; n_l is the number of scan lines; W_fill represents the total width of the scan line fill; Y_1 represents the starting y-coordinate of the first scan line; Y_min and Y_max are the upper and lower boundaries of the bounding box after rotation; Y_j represents the position of the j-th scan line. Odd-numbered scan lines are connected in opposite directions to reduce the connection distance.

[0068] Furthermore, the specific implementation method of step S7 includes the following steps:

[0069] S7.1. Let the total length of the complete route coverage segment of airport a be L_cov,a, the total length of the internal connecting segments of the complete route be L_link,a, the transfer length from the airport to the start of the complete route be L_out,a, and the return length from the end of the complete route back to the airport be L_back,a. Then the estimated total duration T_all,a of the complete execution area of ​​airport a is defined as follows:

[0070] T_all,a=(L_cov,a / V_op)+(L_link,a / V_op)+[(L_out,a+L_back,a) / V_tr]+T_safe

[0071] Where V_op represents the operating speed, V_tr represents the relocation speed, and T_safe represents the safety margin per flight;

[0072] S7.2. When determining the effective mission duration that a single sortie can carry, the effective single sortie mission duration T_eff is calculated based on the nominal mission duration T_nom and the safety margin T_safe, i.e.:

[0073] T_eff = T_nom - T_safe;

[0074] Then, based on the estimated total duration T_all,a of the complete execution area of ​​airport a, the number of sub-routes n_{r,a} that airport a needs to be split into is deduced, and the calculation formula is as follows:

[0075] n_{r,a}=max{1,ceil(T_all,a / T_eff)}

[0076] Where ceil(·) represents rounding up, and max{1,·} indicates that the number of sub-routes is at least 1;

[0077] S7.3. Divide the complete serpentine route of airport a into n_{r,a} sub-routes according to the scan line order. Let the m-th sub-routes of airport a be denoted as R_{a,m}, where m=1,2,...,n_{r,a}. Correspondingly, let the total length of the coverage segment of the m-th sub-routes be L_cov,a,m, the total length of the internal connecting segment be L_link,a,m, the transfer length from the airport to the starting point of the sub-routes be L_out,a,m, and the return length from the end point of the sub-routes back to the airport be L_back,a,m. Then the estimated total duration T_{a,m} of the sub-routes is defined as:

[0078] T_{a,m}=(L_cov,a,m / V_op)+(L_link,a,m / V_op)+[(L_out,a,m+L_back,a,m) / V_tr]+T_safe

[0079] Where L_cov,a,m represents the length of the coverage segment of the mth sub-line of airport a, L_link,a,m represents the connection length between adjacent scan lines within the mth sub-line of airport a, L_out,a,m represents the transfer length from airport a to the starting point of the mth sub-line, and L_back,a,m represents the return length from the end point of the mth sub-line back to the airport.

[0080] S7.4. To ensure that the load on each of the split sub-routes is as balanced as possible, the target average sub-routes duration T_avg for airport a is defined as follows:

[0081] T_avg,a=T_all,a / n_{r,a};

[0082] During the segmentation process, the scan lines are added one by one in order to make the total estimated duration T_{a,m} of each sub-route as close as possible to T_avg,a, thereby achieving a balanced split among multiple sub-routes within airport a.

[0083] If the user has manually specified n_{r,a}, the system will directly use this specified value as the number of splits and perform balanced splitting according to the above T_{a,m} calculation rules.

[0084] Furthermore, after the split is completed, each sub-route is written into a unified task structure, including information such as airport number, sub-region number, circle order, waypoint sequence, boundary point set, flight altitude, speed, GSD, total flight distance, estimated duration and execution sequence number, and task files, storage records and instructions are generated.

[0085] The beneficial effects of this invention are:

[0086] This invention describes a UAV scheduling method for clustered deployments of overlapping airport coverage areas. The overall approach is "first, cluster deduplication; then, layered scheduling; finally, route generation; and lastly, balanced splitting." Compared to the traditional method of "each airport drawing its own circle, planning its own schedule, and then manually deduplicating," this invention first places the circular coverage areas of all airports into the same local coordinate system, establishing a unified grid as the smallest unit for overlap identification and attribution determination. Then, a unique attribution score is calculated on the overlapping units, forming a unique execution sub-region for each airport. Next, a layered scheduling order from the inside out is established based on the sub-region's position relative to the airport center. Finally, the orthogonal strip spacing is calculated on each airport's attribution sub-region to generate a serpentine route, which is then split into multiple executable sub-routes based on duration and distance load.

[0087] This invention discloses a UAV scheduling method for clustered deployments in areas with overlapping airport coverage. When multiple airport coverage areas overlap, it can automatically determine unique attribution, significantly reducing the probability of duplicate data collection. By incorporating priority and distance into the attribution formula, it balances the principles of proximity to the nearest airport and emergency support. Establishing an inward-to-outward concentric scheduling order allows UAVs to prioritize execution in neighboring areas, reducing the risks of long-distance transfers and overlapping flights. After deduplication, the system can automatically generate orthogonal flight paths for each airport's assigned sub-region, avoiding manual replanning. Integrating coverage length, connection length, outbound flight, return flight, and safety margin into the duration assessment facilitates load balancing and task executability control. It is applicable to both single-airport tasks and complex scenarios such as collaborative patrols, replacement flights, and joint emergency support across multiple airports. Attached Figure Description

[0088] Figure 1 This is a flowchart of a drone scheduling method for cluster-deployed airport overlapping coverage areas, as described in this invention.

[0089] Figure 2 This is a schematic diagram of the multi-airport circular area overlap identification method of the present invention;

[0090] Figure 3 This is a schematic diagram illustrating the deduplication attribution determination method of the present invention;

[0091] Figure 4 This is a schematic diagram of the layered scheduling of the present invention;

[0092] Figure 5 This is a schematic diagram illustrating the generation of orthogonal flight paths for the sub-regions to which this invention belongs;

[0093] Figure 6 This is a schematic diagram of load balancing splitting according to the present invention. Detailed Implementation

[0094] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention; that is, the described specific embodiments are merely a part of the embodiments of the invention, and not all of them. The components of the specific embodiments of the invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations, and the invention may also have other embodiments.

[0095] Therefore, the following detailed description of specific embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected specific embodiments of the invention. All other specific embodiments obtained by those skilled in the art based on these specific embodiments without inventive effort are within the scope of protection of this invention.

[0096] To further understand the invention's content, features, and effects, the following specific embodiments are provided, along with accompanying drawings. Figure 1 - Appendix Figure 6 Detailed explanation is as follows:

[0097] Example 1:

[0098] A method for scheduling drones in overlapping airport coverage areas for cluster deployment includes the following steps:

[0099] S1. Collect airport cluster data, including grid edge length, orthogonal parameters, and basic UAV data;

[0100] The specific implementation method of step S1 includes the following steps:

[0101] S1.1. Collect airport cluster set data M, where the a-th airport set data M_a includes the center coordinates C_a, coverage radius r_a, and priority P_a of the a-th airport;

[0102] S1.2. Collect the grid side length g_c;

[0103] S1.3. Collect orthophoto parameters, including main heading angle α_m, flight altitude h_f, camera sensor width w_s, camera focal length f_l, horizontal pixel count n_px, and lateral overlap rate o_lat;

[0104] S1.4. Collect basic data of the UAV, including operating speed V_op, transfer speed V_tr, nominal duration of a single flight T_nom, and safety margin T_safe.

[0105] S2. Based on the airport cluster set data obtained in step S1, construct the circular polygon for each airport;

[0106] Furthermore, the specific implementation method of step S2 includes the following steps:

[0107] S2.1. For the a-th airport, with a preset number of points n_v, Earth radius R_e, and azimuth angle of the i-th fixed point θ_i, the local coordinates and latitude / longitude coordinates are obtained, expressed as:

[0108] θ_i=2πi / n_v

[0109] x_{a,i}=r_a×sin(θ_i)

[0110] y_{a,i}=r_a×cos(θ_i)

[0111] φ_{a,i}=φ_a+(y_{a,i} / R_e)×180 / π

[0112] λ_{a,i}=λ_a+[x_{a,i} / (R_e×cosφ_a)]×180 / π

[0113] Where θ_i represents the azimuth of the i-th fixed point; x_{a,i} and y_{a,i} represent the x-axis and y-axis coordinates of the plane with the center of the a-th airport as the origin; φ_{a,i} and λ_{a,i} represent the latitude and longitude of the i-th fixed point, respectively; φ_a is the latitude of the a-th airport, and λ_a is the longitude of the a-th airport;

[0114] Furthermore, the preset points refer to the discrete boundary sampling points / direction points generated on the circumference according to a preset number, which are used to form a circular domain polygon in the subsequent connection. They are not new objects introduced by the extra force. According to the preset number of points n_v, n_v fixed points are set at equal intervals according to the azimuth angle on the circumference with the center of the a-th airport C_a as the center and the coverage radius r_a as the radius, where i=0,1,…,n_v-1, the Earth radius is R_e, and the azimuth angle of the i-th fixed point is θ_i, so as to obtain the local coordinates and latitude and longitude coordinates.

[0115] S2.2. For the a-th airport, connect the data of each fixed point obtained in step S2.1 to obtain the circular domain polygon Ω_a of the a-th airport. Then, perform the operations of step S2.1 and step S2.2 on each airport in the airport cluster set to obtain the circular domain polygon of each airport.

[0116] S3. Generate airport coverage area grid cells based on the grid side length obtained in step S1. Then, based on the circular domain polygon of each airport obtained in step S2, identify the candidate airport set of each grid cell. Then, determine the ownership of the grid cells of overlapping candidate airports to obtain the unique execution airport corresponding to each grid cell.

[0117] Furthermore, the specific implementation method of step S3 includes the following steps:

[0118] S3.1. Establish a uniform grid with side length g_c within the smallest outer perimeter that includes the entire airport coverage area, and denote each grid cell as G_q;

[0119] For the a-th airport and grid cell q, calculate the intersection area A_{a,q} of the circular polygon Ω_a and G_q. If A_{a,q}>0, then the grid cell is within the candidate coverage area of ​​airport a, thus obtaining the candidate airport set for each grid cell, expressed as:

[0120] A_{a,q}=Area(Ω_a∩G_q)

[0121] Where A_{a,q} is the set of candidate airports for grid cell q, and Area(·) represents the area operation.

[0122] S3.2. If a grid cell G_q corresponds to only one airport such that A_{a,q}>0, then the cell is directly assigned to that airport; if G_q falls within the coverage areas of multiple airports, then a weighted assignment score S_{a,q} is calculated for each candidate airport, and the airport with the lowest score is determined as the unique executing airport for that grid cell. The calculation process is as follows:

[0123]

[0124] P_min=min(P_1,P_2,...,P_a,P_n)

[0125] S_{a,q}=d_{a,q}×[1+β_p×(P_a-P_min)]

[0126]

[0127] Where X_q and Y_q are the center x-axis coordinates and y-axis coordinates of grid cell G_q, respectively; X_a and Y_a are the center x-axis coordinates and y-axis coordinates of airport a, respectively; d_{a,q} is the distance term; β_p is the priority bias coefficient; and P_a is the airport priority of the a-th airport. This represents the unique execution airport for grid cell G_q;

[0128] When all airports have the same priority, the nearest airport takes precedence; when the priorities are different, priority is incorporated into the assignment decision through β_p.

[0129] S4. For the unique execution airport obtained in step S3, sort the airport scheduling according to the grid offset of the grid cell relative to the center of the unique execution airport, and obtain the sequential number of the grid cell corresponding to each unique execution airport to form a scheduling sequence.

[0130] Furthermore, the expression for the specific implementation method of step S4 is as follows:

[0131] u_{a,q}=round[(X_q-X_a) / g_c]

[0132] v_{a,q}=round[(Y_q-Y_a) / g_c]

[0133] k_{a,q}=max(|u_{a,q}|,|v_{a,q}|)

[0134] s_{a,q}=Ψ(u_{a,q},v_{a,q})

[0135] Where u_{a,q} and v_{a,q} represent the grid column offset and row offset of grid cell G_q relative to the a-th airport, respectively; k_{a,q} represents the concentric circle number, and the smaller k_{a,q} is, the closer it is to the airport center; Ψ(·) represents the preset spiral sequence mapping function, which is used to form a continuous traversal order within the same concentric circle; during scheduling, k_{a,q} is sorted in ascending order first, and then s_{a,q} is sorted in ascending order to form an inside-outside scheduling sequence.

[0136] S5. Based on the unique execution airport corresponding to each grid cell obtained in step S3 and the scheduling sequence obtained in step S4, construct the airport execution sub-region;

[0137] Furthermore, the specific implementation method of step S5 includes the following steps:

[0138] S5.1. First, extract all grid cells belonging to airport a, forming the set Q_a of the grid cells belonging to airport a, where ;

[0139] For any q∈Q_a, the intersection of the airport coverage area Ω_a and the grid cell G_q is defined as the effective execution segment F_{a,q} corresponding to the q grid cell, where F_{a,q}=Ω_a∩G_q. Then, the total execution area of ​​airport a after deduplication is defined as... ;

[0140] S5.2. When setting up tasks to proceed step by step according to concentric circles, the grid units to which they belong are grouped according to the concentric circle number. The set of grid units to which airport a belongs in the kth concentric circle is defined as Q_{a,k}={q|q∈Q_a,k_{a,q}=k}, and the outermost concentric circle number of airport a is defined as K_a=max_{q∈Q_a}k_{a,q}.

[0141] For two assigned grid cells G_{q_i} and G_{q_j} within the same concentric circle, if the adjacency relation q_i~q_j⇔max(|u_{a,q_i}-u_{a,q_j}|,|v_{a,q_i}-v_{a,q_j}|)=1, then they are considered to be adjacent within that concentric circle, where u_{a,q} and v_{a,q} represent the grid column offset and grid row offset of the assigned grid cell relative to airport a, respectively. Based on the above adjacency relation, the set Q_{a,k} is divided into m_{a,k} connected components, denoted as Q_{a,k,1}, Q_{a,k,2}, ..., Q_{a,k,m_{a,k}}.

[0142] Define the execution sub-region corresponding to the c-th connected component of the k-th layer as... Where c = 1, 2, ..., m_{a, k};

[0143] S5.3. Extract the minimum spiral sequence number for each execution sub-region:

[0144] Defined as s_{a,k,c}{min}=min_{q∈Q_{a,k,c}}s_{a,q}, where s_{a,q} represents the spiral sequence number of the grid cell G_q within its respective concentric circle. Following the rule of increasing concentric circle number k and increasing the minimum spiral sequence number s_{a,k,c}{min} within the same concentric circle, this applies to all sub-regions. Sort the data and renumber it sequentially according to the sorting results. The total number of execution sub-regions of airport a is defined as n_{z,a}=Σ_{k=0}{K_a}m_{a,k}.

[0145] S6. Based on the airport execution sub-region obtained in step S5, solve for the corresponding orthophoto strip spacing, and then generate continuous flight routes that completely cover the airport execution sub-region;

[0146] Furthermore, the specific implementation method of step S6 includes the following steps:

[0147] S6.1. For the execution sub-region of airport a, the ground resolution, single-strip coverage width, and spacing between adjacent scan lines are calculated based on the flight altitude h_f, camera sensor width w_s, focal length f_l, horizontal pixel count n_px, and lateral overlap rate o_lat. The calculation formula is as follows:

[0148] GSD=(h_f×w_s) / (f_l×n_px)

[0149] W_cov=GSD×n_px

[0150] L_sep = W_cov × (1 - o_lat)

[0151] Where GSD represents the ground resolution; W_cov represents the coverage width of a single strip on the ground; and L_sep represents the scan line spacing.

[0152] S6.2. Rotate the execution sub-region to the scanning coordinate system according to the main heading angle α_m, and then generate parallel scan lines in this coordinate system according to L_sep obtained in step S6.1; then calculate the intersection point of each scan line with the boundary of the execution sub-region, and pair the intersection points as effective coverage segments; then connect adjacent scan lines in alternating directions to form a serpentine flight path, expressed as:

[0153] X'=X×cosα_m-Y×sinα_m

[0154] Y'=X×sinα_m+Y×cosα_m

[0155] n_l=max{1,ceil[(Y_max-Y_min) / L_sep]}

[0156] W_fill=(n_l-1)×L_sep

[0157] Y_1=(Y_min+Y_max-W_fill) / 2

[0158] Y_j=Y_1+(j-1)×L_sep

[0159] Where X and Y are the coordinates before rotation, and X' and Y' are the coordinates after rotation; n_l is the number of scan lines; W_fill represents the total width of the scan line fill; Y_1 represents the starting y-coordinate of the first scan line; Y_min and Y_max are the upper and lower boundaries of the bounding box after rotation; Y_j represents the position of the j-th scan line. Odd-numbered scan lines are connected in opposite directions to reduce the connection distance.

[0160] Furthermore, W_fill represents the total span occupied by the n_l scan lines; Y_1 is used to center the scan line group within the rotated bounding box.

[0161] S7. Comprehensively evaluate the scan coverage length, adjacent scan line connection length, airport departure transfer length, return length, and safety margin of the continuous routes of the execution sub-region obtained in step S6, calculate the estimated total duration of each candidate sub-route, and split the candidate sub-route for UAV scheduling.

[0162] Furthermore, the specific implementation method of step S7 includes the following steps:

[0163] S7.1. Let the total length of the complete route coverage segment of airport a be L_cov,a, the total length of the internal connecting segments of the complete route be L_link,a, the transfer length from the airport to the start of the complete route be L_out,a, and the return length from the end of the complete route back to the airport be L_back,a. Then the estimated total duration T_all,a of the complete execution area of ​​airport a is defined as follows:

[0164] T_all,a=(L_cov,a / V_op)+(L_link,a / V_op)+[(L_out,a+L_back,a) / V_tr]+T_safe

[0165] Where V_op represents the operating speed, V_tr represents the relocation speed, and T_safe represents the safety margin per flight;

[0166] S7.2. When determining the effective mission duration that a single sortie can carry, the effective single sortie mission duration T_eff is calculated based on the nominal mission duration T_nom and the safety margin T_safe, i.e.:

[0167] T_eff = T_nom - T_safe;

[0168] Then, based on the estimated total duration T_all,a of the complete execution area of ​​airport a, the number of sub-routes n_{r,a} that airport a needs to be split into is deduced, and the calculation formula is as follows:

[0169] n_{r,a}=max{1,ceil(T_all,a / T_eff)}

[0170] Where ceil(·) represents rounding up, and max{1,·} indicates that the number of sub-routes is at least 1;

[0171] S7.3. Divide the complete serpentine route of airport a into n_{r,a} sub-routes according to the scan line order. Let the m-th sub-routes of airport a be denoted as R_{a,m}, where m=1,2,...,n_{r,a}. Correspondingly, let the total length of the coverage segment of the m-th sub-routes be L_cov,a,m, the total length of the internal connecting segment be L_link,a,m, the transfer length from the airport to the starting point of the sub-routes be L_out,a,m, and the return length from the end point of the sub-routes back to the airport be L_back,a,m. Then the estimated total duration T_{a,m} of the sub-routes is defined as:

[0172] T_{a,m}=(L_cov,a,m / V_op)+(L_link,a,m / V_op)+[(L_out,a,m+L_back,a,m) / V_tr]+T_safe

[0173] Where L_cov,a,m represents the length of the coverage segment of the mth sub-line of airport a, L_link,a,m represents the connection length between adjacent scan lines within the mth sub-line of airport a, L_out,a,m represents the transfer length from airport a to the starting point of the mth sub-line, and L_back,a,m represents the return length from the end point of the mth sub-line back to the airport.

[0174] S7.4. To ensure that the load on each of the split sub-routes is as balanced as possible, the target average sub-routes duration T_avg for airport a is defined as follows:

[0175] T_avg,a=T_all,a / n_{r,a};

[0176] During the segmentation process, the scan lines are added one by one in order to make the total estimated duration T_{a,m} of each sub-route as close as possible to T_avg,a, thereby achieving a balanced split among multiple sub-routes within airport a.

[0177] If the user has manually specified n_{r,a}, the system will directly use this specified value as the number of splits and perform balanced splitting according to the above T_{a,m} calculation rules.

[0178] Furthermore, after the split is completed, each sub-route is written into a unified task structure, including information such as airport number, sub-region number, circle order, waypoint sequence, boundary point set, flight altitude, speed, GSD, total flight distance, estimated duration and execution sequence number, and task files, storage records and instructions are generated.

[0179] Furthermore, the UAV scheduling method for cluster-deployed airport overlapping coverage areas described in this embodiment is illustrated with the following application example:

[0180] Example of 8km overlap coverage for multiple airports:

[0181] Taking the collaborative patrol of four fixed-deployment airports as an example, let n_a=4, the coverage radius of each airport r_a=8000 meters, the uniform grid side length g_c=1500 meters, the number of fixed points approximating the circular domain n_v=96, the priority bias coefficient β_p=0.1, the flight altitude h_f=120 meters, the lateral overlap rate o_lat=0.70, the operation speed V_op=15 meters / second, the transfer speed V_tr=15 meters / second, the nominal mission duration per sortie T_nom=3600 seconds, and the safety margin T_safe=600 seconds.

[0182] The system first generates circular polygon boundaries for each of the four airports and establishes a unified mesh in the global and local coordinate systems. For mesh cells that fall within the circular domain of a single airport, the system directly assigns them to that airport; for overlapping mesh cells that fall within the circular domains of two or more airports, the system sequentially calculates the distance term d_{a,q} and the weighted assignment score S_{a,q} for each candidate airport.

[0183] 2. Examples of overlapping attributions:

[0184] Suppose a grid cell G_q falls within the coverage areas of both airport 1 and airport 2, with d_{1,q}=4200 meters and d_{2,q}=3900 meters. When P_1=P_2=1, the system has S_{1,q}=4200 and S_{2,q}=3900, therefore G_q is assigned to airport 2. If P_1=1, P_2=2 and β_p=0.2, then S_{1,q}=4200×[1+0]=4200 and S_{2,q}=3900×[1+0.2]=4680, in which case the same grid cell will be preferentially assigned to the higher-priority airport 1. This example illustrates that the present invention not only considers the nearest airport principle but can also implement scheduling strategies oriented towards emergency support, differences in equipment capabilities, or administrative jurisdiction through priority bias.

[0185] Example of 3-layer scheduling and route output:

[0186] After assigning a unique location, the system calculates k_{a,q} and s_{a,q} for each airport's assigned grid cell, prioritizing scheduling inner-circle cells and then gradually moving towards the outer circle. For a specific execution sub-area of ​​the same airport, the system automatically calculates the scan line spacing L_sep based on h_f, w_s, f_l, n_px, and o_lat, and generates a serpentine flight path. Subsequently, the system estimates the number of airport-level sub-flight paths n_{r,a} based on T_eff=T_nom-T_safe=3000 seconds, and then breaks them down into multiple sub-flight paths according to T_{a,m}. Each sub-flight path includes the origin transfer distance, return distance, estimated operation time, and execution order, and can be directly incorporated into the scheduling system.

[0187] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0188] Although this application has been described above with reference to specific embodiments, various modifications can be made and components can be replaced with equivalents without departing from the scope of this application. In particular, as long as there is no structural conflict, the features in the specific embodiments disclosed in this application can be combined with each other in any way. The lack of an exhaustive description of these combinations in this specification is merely for the sake of brevity and resource conservation. Therefore, this application is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for scheduling unmanned aerial vehicles (UAVs) in clustered deployment areas with overlapping airport coverage, characterized in that, Includes the following steps: S1. Collect airport cluster data, including grid edge length, orthogonal parameters, and basic UAV data; S2. Based on the airport cluster set data obtained in step S1, construct the circular polygon for each airport; S3. Generate airport coverage area grid cells based on the grid side length obtained in step S1. Then, based on the circular domain polygon of each airport obtained in step S2, identify the candidate airport set of each grid cell. Then, determine the ownership of the grid cells of overlapping candidate airports to obtain the unique execution airport corresponding to each grid cell. S4. For the unique execution airport obtained in step S3, sort the airport scheduling according to the grid offset of the grid cell relative to the center of the unique execution airport, and obtain the sequential number of the grid cell corresponding to each unique execution airport to form a scheduling sequence. S5. Based on the unique execution airport corresponding to each grid cell obtained in step S3 and the scheduling sequence obtained in step S4, construct the airport execution sub-region; S6. Based on the airport execution sub-region obtained in step S5, solve for the corresponding orthophoto strip spacing, and then generate continuous flight routes that completely cover the airport execution sub-region; S7. Comprehensively evaluate the scan coverage length, adjacent scan line connection length, airport departure transfer length, return length, and safety margin of the continuous routes of the execution sub-region obtained in step S6, calculate the estimated total duration of each candidate sub-route, and split the candidate sub-route for UAV scheduling.

2. The method for scheduling unmanned aerial vehicles (UAVs) in clustered deployment areas with overlapping airport coverage, as described in claim 1, is characterized in that... The specific implementation method of step S1 includes the following steps: S1.

1. Collect airport cluster set data M, where the a-th airport set data M_a includes the center coordinates C_a, coverage radius r_a, and priority P_a of the a-th airport; S1.

2. Collect the grid side length g_c; S1.

3. Collect orthophoto parameters, including main heading angle α_m, flight altitude h_f, camera sensor width w_s, camera focal length f_l, image horizontal pixel count n_px, and lateral overlap rate o_lat; S1.

4. Collect basic data of the UAV, including operating speed V_op, transfer speed V_tr, nominal duration of a single flight T_nom, and safety margin T_safe.

3. The method for scheduling unmanned aerial vehicles (UAVs) in clustered deployment areas with overlapping airport coverage, as described in claim 2, is characterized in that... The specific implementation method of step S2 includes the following steps: S2.

1. For the a-th airport, with a preset number of points n_v, Earth radius R_e, and azimuth angle of the i-th fixed point θ_i, the local coordinates and latitude / longitude coordinates are obtained, expressed as: θ_i=2πi / n_v x_{a,i}=r_a×sin(θ_i) y_{a,i}=r_a×cos(θ_i) φ_{a,i}=φ_a+(y_{a,i} / R_e)×180 / π λ_{a,i}=λ_a+[x_{a,i} / (R_e×cosφ_a)]×180 / π Where θ_i represents the azimuth of the i-th fixed point; x_{a,i} and y_{a,i} represent the x-axis and y-axis coordinates of the plane with the center of the a-th airport as the origin; φ_{a,i} and λ_{a,i} represent the latitude and longitude of the i-th fixed point, respectively; φ_a is the latitude of the a-th airport, and λ_a is the longitude of the a-th airport; S2.

2. For the a-th airport, connect the data of each fixed point obtained in step S2.1 to obtain the circular domain polygon Ω_a of the a-th airport. Then, perform the operations of step S2.1 and step S2.2 on each airport in the airport cluster set to obtain the circular domain polygon of each airport.

4. The method for scheduling unmanned aerial vehicles (UAVs) in clustered deployment areas with overlapping airport coverage, as described in claim 3, is characterized in that... The specific implementation method of step S3 includes the following steps: S3.

1. Establish a uniform grid with side length g_c within the smallest outer perimeter that includes the entire airport coverage area, and denote each grid cell as G_q; For the a-th airport and grid cell q, calculate the intersection area A_{a,q} of the circular polygon Ω_a and G_q. If A_{a,q}>0, then the grid cell is within the candidate coverage area of ​​airport a, thus obtaining the candidate airport set for each grid cell, expressed as: A_{a,q}=Area(Ω_a∩G_q) Where A_{a,q} is the set of candidate airports for grid cell q, and Area(·) represents the area operation. S3.

2. If a grid cell G_q corresponds to only one airport such that A_{a,q}>0, then the cell is directly assigned to that airport; if G_q falls within the coverage areas of multiple airports, then a weighted assignment score S_{a,q} is calculated for each candidate airport, and the airport with the lowest score is determined as the unique executing airport for that grid cell. The calculation process is as follows: P_min=min(P_1,P_2,...,P_a,P_n) S_{a,q}=d_{a,q}×[1+β_p×(P_a-P_min)] Where X_q and Y_q are the center x-axis coordinates and y-axis coordinates of grid cell G_q, respectively; X_a and Y_a are the center x-axis coordinates and y-axis coordinates of airport a, respectively; d_{a,q} is the distance term; β_p is the priority bias coefficient; and P_a is the airport priority of the a-th airport. This represents the unique execution airport for grid cell G_q; When all airports have the same priority, the nearest airport takes precedence; when the priorities are different, priority is incorporated into the assignment decision through β_p.

5. A method for scheduling unmanned aerial vehicles (UAVs) in overlapping coverage areas of clustered airports according to claim 4, characterized in that, The specific implementation method of step S4 is expressed as follows: u_{a,q}=round[(X_q-X_a) / g_c] v_{a,q}=round[(Y_q-Y_a) / g_c] k_{a,q}=max(|u_{a,q}|,|v_{a,q}|) s_{a,q}=Ψ(u_{a,q},v_{a,q}) Where u_{a,q} and v_{a,q} represent the grid column offset and row offset of grid cell G_q relative to the a-th airport, respectively; k_{a,q} represents the concentric circle number, and the smaller k_{a,q} is, the closer it is to the airport center; Ψ(·) represents the preset spiral sequence mapping function, which is used to form a continuous traversal order within the same concentric circle; during scheduling, k_{a,q} is sorted in ascending order first, and then s_{a,q} is sorted in ascending order to form an inside-outside scheduling sequence.

6. A method for scheduling unmanned aerial vehicles (UAVs) in clustered deployment areas with overlapping airport coverage, as described in claim 5, is characterized in that... The specific implementation method of step S5 includes the following steps: S5.

1. First, extract all grid cells belonging to airport a, forming the set Q_a of the grid cells belonging to airport a, where ; For any q∈Q_a, the intersection of the airport coverage area Ω_a and the grid cell G_q is defined as the effective execution segment F_{a,q} corresponding to the q grid cell, where F_{a,q}=Ω_a∩G_q. Then, the total execution area of ​​airport a after deduplication is defined as... ; S5.

2. When setting up tasks to proceed step by step according to concentric circles, the grid units to which they belong are grouped according to the concentric circle number. The set of grid units to which airport a belongs in the kth concentric circle is defined as Q_{a,k}={q|q∈Q_a,k_{a,q}=k}, and the outermost concentric circle number of airport a is defined as K_a=max_{q∈Q_a}k_{a,q}. For two assigned grid cells G_{q_i} and G_{q_j} within the same concentric circle, if the adjacency relation q_i~q_j⇔max(|u_{a,q_i}-u_{a,q_j}|,|v_{a,q_i}-v_{a,q_j}|)=1, then they are considered to be adjacent within that concentric circle, where u_{a,q} and v_{a,q} represent the grid column offset and grid row offset of the assigned grid cell relative to airport a, respectively. Based on the above adjacency relation, the set Q_{a,k} is divided into m_{a,k} connected components, denoted as Q_{a,k,1}, Q_{a,k,2}, ..., Q_{a,k,m_{a,k}}. Define the execution sub-region corresponding to the c-th connected component of the k-th layer as... Where c = 1, 2, ..., m_{a, k}; S5.

3. Extract the minimum spiral sequence number for each execution sub-region: Defined as s_{a,k,c}{min}=min_{q∈Q_{a,k,c}}s_{a,q}, where s_{a,q} represents the spiral sequence number of the grid cell G_q within its respective concentric circle. Following the rule of increasing concentric circle number k and increasing the minimum spiral sequence number s_{a,k,c}{min} within the same concentric circle, this applies to all sub-regions. Sort the data and renumber it sequentially according to the sorting results. , The total number of execution sub-regions of airport a is defined as n_{z,a}=Σ_{k=0}{K_a}m_{a,k}.

7. A method for scheduling unmanned aerial vehicles (UAVs) in clustered deployment areas with overlapping airport coverage, as described in claim 6, is characterized in that... The specific implementation method of step S6 includes the following steps: S6.

1. For the execution sub-region of airport a, the ground resolution, single-strip coverage width, and spacing between adjacent scan lines are calculated based on the flight altitude h_f, camera sensor width w_s, focal length f_l, horizontal pixel count n_px, and lateral overlap rate o_lat. The calculation formula is as follows: GSD=(h_f×w_s) / (f_l×n_px) W_cov=GSD×n_px L_sep = W_cov × (1 - o_lat) Where GSD represents the ground resolution; W_cov represents the coverage width of a single strip on the ground; and L_sep represents the scan line spacing. S6.

2. Rotate the execution sub-region to the scanning coordinate system according to the main heading angle α_m, and then generate parallel scan lines in this coordinate system according to L_sep obtained in step S6.1; then calculate the intersection point of each scan line with the boundary of the execution sub-region, and pair the intersection points as effective coverage segments; then connect adjacent scan lines in alternating directions to form a serpentine flight path, expressed as: X'=X×cosα_m-Y×sinα_m Y'=X×sinα_m+Y×cosα_m n_l=max{1,ceil[(Y_max-Y_min) / L_sep]} W_fill=(n_l-1)×L_sep Y_1=(Y_min+Y_max-W_fill) / 2 Y_j=Y_1+(j-1)×L_sep Where X and Y are the coordinates before rotation, and X' and Y' are the coordinates after rotation; n_l is the number of scan lines; W_fill represents the total width of the scan line fill; Y_1 represents the starting y-coordinate of the first scan line; Y_min and Y_max are the upper and lower boundaries of the bounding box after rotation; Y_j represents the position of the j-th scan line. Odd-numbered scan lines are connected in opposite directions to reduce the connection distance.

8. A method for scheduling unmanned aerial vehicles (UAVs) in clustered deployment areas with overlapping airport coverage, as described in claim 7, is characterized in that... The specific implementation method of step S7 includes the following steps: S7.

1. Let the total length of the complete route coverage segment of airport a be L_cov,a, the total length of the internal connecting segments of the complete route be L_link,a, the transfer length from the airport to the start of the complete route be L_out,a, and the return length from the end of the complete route back to the airport be L_back,a. Then the estimated total duration T_all,a of the complete execution area of ​​airport a is defined as follows: T_all,a=(L_cov,a / V_op)+(L_link,a / V_op)+[(L_out,a+L_back,a) / V_tr]+T_safe Where V_op represents the operating speed, V_tr represents the relocation speed, and T_safe represents the safety margin per flight; S7.

2. When determining the effective mission duration that a single sortie can carry, the effective single sortie mission duration T_eff is calculated based on the nominal mission duration T_nom and the safety margin T_safe, i.e.: T_eff = T_nom - T_safe; Then, based on the estimated total duration T_all,a of the complete execution area of ​​airport a, the number of sub-routes n_{r,a} that airport a needs to be split into is deduced, and the calculation formula is as follows: n_{r,a}=max{1,ceil(T_all,a / T_eff)} Where ceil(·) represents rounding up, and max{1,·} indicates that the number of sub-routes is at least 1; S7.

3. Divide the complete serpentine route of airport a into n_{r,a} sub-routes according to the scan line order. Let the m-th sub-routes of airport a be denoted as R_{a,m}, where m=1,2,...,n_{r,a}. Correspondingly, let the total length of the coverage segment of the m-th sub-routes be L_cov,a,m, the total length of the internal connecting segment be L_link,a,m, the transfer length from the airport to the starting point of the sub-routes be L_out,a,m, and the return length from the end point of the sub-routes back to the airport be L_back,a,m. Then the estimated total duration T_{a,m} of the sub-routes is defined as: T_{a,m}=(L_cov,a,m / V_op)+(L_link,a,m / V_op)+[(L_out,a,m+L_back,a,m) / V_tr]+T_safe Where L_cov,a,m represents the length of the coverage segment of the mth sub-line of airport a, L_link,a,m represents the connection length between adjacent scan lines within the mth sub-line of airport a, L_out,a,m represents the transfer length from airport a to the starting point of the mth sub-line, and L_back,a,m represents the return length from the end point of the mth sub-line back to the airport. S7.

4. To ensure that the load on each of the split sub-routes is as balanced as possible, the target average sub-routes duration T_avg for airport a is defined as follows: T_avg,a=T_all,a / n_{r,a}; During the segmentation process, the scan lines are added one by one in order to make the total estimated duration T_{a,m} of each sub-route as close as possible to T_avg,a, thereby achieving a balanced split among multiple sub-routes within airport a. If the user has manually specified n_{r,a}, the system will directly use this specified value as the number of splits and perform balanced splitting according to the above T_{a,m} calculation rules.

9. A method for scheduling unmanned aerial vehicles (UAVs) in clustered deployment areas with overlapping airport coverage, as described in claim 8, is characterized in that... After the split is completed, each sub-route is written into a unified task structure, including information such as airport number, sub-region number, circle order, waypoint sequence, boundary point set, flight altitude, speed, GSD, total flight distance, estimated duration and execution sequence number, and task files, storage records and instructions are generated.

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