Unmanned aerial vehicle airport charging scheduling method and system

Abstract

The invention discloses an unmanned aerial vehicle airport charging scheduling method and system, and the method comprises the steps: S1, obtaining the state data of each charging pile and the state data of each returning unmanned aerial vehicle, and constructing a compatibility matrix and a time cost matrix; s2, performing preliminary screening and sorting on the returning unmanned aerial vehicles based on the task emergency degree to obtain a to-be-scheduled queue; s3, based on the compatibility matrix and the time cost matrix, adopting an optimal allocation algorithm to allocate charging piles to the to-be-scheduled queue to obtain pile position allocation data; and S4, based on the pile position allocation data, allocating departure time to each unmanned aerial vehicle and planning a conflict-free path to obtain time-space scheduling information. According to the invention, the optimal distribution algorithm is adopted to distribute the charging pile for each unmanned aerial vehicle, and reasonable scheduling is carried out when multiple unmanned aerial vehicles return to the field for charging at the same time through departure time distribution and a time slot reservation mechanism, so that path conflicts are avoided, collision or blockage among the unmanned aerial vehicles is prevented, and the utilization rate and the overall scheduling efficiency of airport channels of the unmanned aerial vehicles are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle cluster scheduling, in particular to an unmanned aerial vehicle airport charging scheduling method and system. BACKGROUND

[0002] With the wide application of unmanned aerial vehicles in the fields of power inspection, security monitoring, logistics transportation, etc., the unmanned aerial vehicle airport needs to manage the return charging operation of multiple unmanned aerial vehicles at the same time. The existing technology has the following problems in the scheduling of multiple unmanned aerial vehicles returning to the unmanned aerial vehicle airport for charging: 1. Collision or congestion risk when multiple unmanned aerial vehicles return to the airport at the same time: most existing unmanned aerial vehicle airports schedule charging according to the "first come, first charge" principle or sort according to the remaining power, which is prone to collision or congestion when multiple unmanned aerial vehicles return to the airport at the same time.

[0003] 2. Simple scheduling rules: most unmanned aerial vehicle airports use "first come, first charge" or simple sorting based on distance, and the priority rules are rigid, which cannot ensure the optimal global charging efficiency.

[0004] 3. Different models of unmanned aerial vehicles and charging piles matching problem: there are different models of unmanned aerial vehicles and charging piles in the airport, and some unmanned aerial vehicles can only be charged at specific charging piles. If the scheduling is not proper, it will increase the waiting time or cause congestion.

[0005] 4. Large difference in charging time: the remaining power of unmanned aerial vehicles is different, combined with the difference in model and power of different charging piles, which leads to significant difference in charging time of each unmanned aerial vehicle at different charging piles; 5. Path conflict: when multiple unmanned aerial vehicles move close to the charging pile or go out to perform tasks at the same time, path conflict is prone to occur, and there are interlacing, meeting, etc. in the internal passage of the airport, which will cause ground congestion or collision if the path planning is not reasonable. SUMMARY

[0006] In view of one or more of the above problems of the prior art, the present application provides an unmanned aerial vehicle airport charging scheduling method and system, which realizes scientific scheduling and can effectively deal with the situation of multiple unmanned aerial vehicles returning to the airport at the same time or multiple unmanned aerial vehicles leaving the airport at the same time, avoids collision between unmanned aerial vehicles, shortens the overall movement time of unmanned aerial vehicles, and effectively improves the return charging efficiency of multiple unmanned aerial vehicles.

[0007] In order to achieve the above purpose, the present application adopts one or more of the following technical solutions: In a first aspect, an unmanned aerial vehicle airport charging scheduling method is provided, comprising the following steps: S1, acquiring state data of each charging pile and state data of each returning unmanned aerial vehicle, and constructing a compatibility matrix and a time cost matrix; S2, based on the task urgency, the returning unmanned aerial vehicle is preliminarily screened and sorted, and a to-be-scheduled queue is obtained; S3, based on the compatibility matrix and the time cost matrix, the to-be-scheduled queue is distributed to the charging pile by using an optimal distribution algorithm, and pile distribution data is obtained; S4, based on the pile distribution data, a time slot reservation method is used to distribute the departure time and plan a conflict-free path for each unmanned aerial vehicle, and time-space scheduling information is obtained.

[0008] Preferably, in step S1, the state data of the returning unmanned aerial vehicle includes unmanned aerial vehicle ID, battery model, current position, current power, battery capacity and task urgency; the state data of the charging pile includes current state, model and power information.

[0009] Preferably, the specific process of step S3 is: S301, based on the time cost matrix, the priority charging returning unmanned aerial vehicle and charging pile combination is determined according to the greedy algorithm; S302, the allocated returning unmanned aerial vehicle and charging pile are removed from the time cost matrix, and a reduced time cost matrix is obtained; S303, the reduced time cost matrix is reduced and the optimal solution is found, and the pile distribution data is obtained.

[0010] Preferably, in step S301, the minimum value in the time cost matrix is selected, and the returning unmanned aerial vehicle and charging pile combination corresponding to the minimum value is selected as the priority determined pile distribution scheme; When the minimum value is greater than one, the returning unmanned aerial vehicle and charging pile combination corresponding to the minimum value with the maximum row and column sum is selected as the priority determined pile distribution scheme.

[0011] Preferably, in step S303, the reduced time cost matrix is reduced by row and column, and at least one zero element is generated; The minimum covering line method is used to find the minimum number of straight lines covering all zero elements, if the number of straight lines is equal to the order of the matrix, the optimal distribution scheme is found, that is, each unmanned aerial vehicle can be distributed to a unique zero-cost charging pile; otherwise, the matrix is further reduced and then searched; The optimal distribution scheme in the reduced matrix is mapped back to the original time cost matrix, and the final pile distribution data is obtained.

[0012] Preferably, step S4 specifically includes: S401, a static shortest path is planned for each unmanned aerial vehicle, the airport channel is divided into multiple road sections, and potential conflict points between all static shortest paths are identified; S402, calculate a time window allowing departure for each unmanned aerial vehicle, including the earliest departure time and the latest departure time, and assign a specific departure time for the unmanned aerial vehicle using a greedy insertion algorithm; S403, according to the departure time of each unmanned aerial vehicle, reserve an exclusive time window for each potential conflict point on the static shortest path of the unmanned aerial vehicle, and generate time-space scheduling information.

[0013] Preferably, in step S401, the potential conflict points include path intersection nodes, path overlapping sections, and shared sections.

[0014] Preferably, in step S402, the specific process of assigning a specific departure time to the unmanned aerial vehicle using a greedy insertion algorithm is as follows: Obtain the current system time, select all allocated unmanned aerial vehicles with the earliest departure time no later than the system time, simulate their departure at the system time, and calculate the charging completion time based on their static shortest path and time slot reservation method; Select the unmanned aerial vehicle with the earliest charging completion time and set its departure time as the system time; Update the system state and repeat the above process until the departure time of all unmanned aerial vehicles is determined.

[0015] On the other hand, the present application also provides a charging scheduling system for unmanned aerial vehicle airport, comprising: A data acquisition module for acquiring state data of each charging pile and state data of each returning unmanned aerial vehicle, and constructing a compatibility matrix and a time cost matrix; A preprocessing module for preliminarily screening and sorting the returning unmanned aerial vehicles based on the task urgency, and obtaining a to-be-scheduled queue; A pile allocation module for allocating charging piles to the to-be-scheduled queue based on the compatibility matrix and the time cost matrix, and obtaining pile allocation data using an optimal allocation algorithm; A path planning module for allocating departure times to each unmanned aerial vehicle and planning conflict-free paths based on the pile allocation data, and obtaining time-space scheduling information.

[0016] On the other hand, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program stored in the memory to perform the steps of the unmanned aerial vehicle airport charging scheduling method provided in embodiment one.

[0017] On the other hand, a non-transitory computer readable storage medium is provided for storing a readable computer program, which can perform the steps of the unmanned aerial vehicle airport charging scheduling method provided in embodiment one when run by a processor.

[0018] By using the above technical solution, the present application has the following advantages: 1. The optimal allocation algorithm is adopted in one aspect of the present application to allocate charging piles for the current scheduling batch of unmanned aerial vehicles, so as to minimize the total time cost of completing the charging task by all the allocated unmanned aerial vehicles, while taking into account the state information of the unmanned aerial vehicles and charging piles and the matching of model compatibility to obtain the optimized pile allocation data; and through triggering time allocation and time slot reservation mechanism, reasonable scheduling can be performed when multiple unmanned aerial vehicles return to the field for charging at the same time, which fundamentally avoids path conflicts and prevents collisions or blockages between multiple unmanned aerial vehicles, ensures the safety of unmanned aerial vehicles, and significantly improves the utilization rate and overall scheduling efficiency of the unmanned aerial vehicle airport channel.

[0019] 2. The time slot reservation method is adopted to allocate the departure time of the unmanned aerial vehicle and plan the ground driving path of the unmanned aerial vehicle from the current position to the target charging pile, and the unmanned aerial vehicle allocated with priority is prioritized for planning, combined with the pile allocation data, which not only shortens the overall movement time of the unmanned aerial vehicle and effectively improves the multi-unmanned aerial vehicle charging efficiency, but also makes it possible to have available unmanned aerial vehicles in the shortest time, ensuring the rapid response of unmanned aerial vehicles to emergency tasks. BRIEF DESCRIPTION OF DRAWINGS

[0020] The accompanying drawings, which form a part of this specification, are included to provide a further understanding of the application, and are incorporated herein by reference. The embodiments illustrated in the drawings are presented by way of example in explaining the present application and are not meant to limit the present application.

[0021] Figure 1 is a method flowchart in the embodiment of the present application; Figure 2 is a schematic diagram of the driving path of the unmanned aerial vehicle in the embodiment of the present application; Figure 3 is a system schematic diagram in the embodiment of the present application. DETAILED DESCRIPTION

[0022] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the present application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0023] It should be noted that the terms used herein are only for the purpose of describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form, and in addition, it should be understood that when the terms "comprise" and / or "include" are used in the specification, there is a feature, step, operation, device, component and / or combination thereof. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative labor are within the scope of protection of the present application.

[0024] Embodiment one In a typical embodiment of the present application, a method for scheduling charging of unmanned aerial vehicles (UAVs) at an airport is provided, as shown in the following steps: Figures 1-2 S1, obtain state data of each charging pile and state data of each returning UAV, and construct a compatibility matrix and a time consumption matrix.

[0025] Specifically, the main control system of the UAV airport obtains real-time state data of all returning UAVs and state data of all charging piles, wherein the state data of the returning UAVs includes UAV ID, battery model, current location, current power, battery capacity and task urgency; the state data of the charging piles includes current state, model and power information. The state data of the charging piles located in the field includes the current state of the charging pile: idle / occupied / fault, and the model and power information of the charging pile.

[0026] In this embodiment, a binary compatibility matrix M is constructed, wherein element Mij=1 indicates that UAV i and charging pile j are compatible in model, i.e. the charging power matches and can be charged; Mij=0 indicates that they are not compatible.

[0027] For all combinations of Mij=1, the total time consumption C of UAV i using charging pile j is calculated. In this embodiment, all UAVs enter the airport from the same entrance and leave the airport from the same exit, so the calculation formula for the total time consumption of a UAV i from landing on the apron to reaching a UAV charging station j at the entrance of the UAV airport and then leaving from the exit is: T t(i,j) + T c(i,j) + T b(i,j) , wherein, T t(i,j) is the driving time from the entrance of the UAV airport to the charging pile j based on the road network model, T c(i,j) is the charging time calculated according to the remaining power of UAV i, the battery capacity and the power information of charging pile j, T b(i,j) is the driving time from charging pile j back to the exit of the UAV airport based on the road network model.

[0028] wherein, T t(i,j) , T b(i,j) only related to charging pile j; T c(i,j) ​The target charging current of the UAV i and the charging power of the charging pile j are related. Therefore, when a certain UAV i selects the charging pile j for charging, the total time cost C can be determined. When m UAVs enter the UAV airport with n charging piles, considering the charging compatibility of the UAV and the charging pile, the time cost matrix can be represented as: (1) Wherein, C ij is the total time cost of the UAV i entering the UAV charging pile j.

[0029] Wherein, for Mij =0 combination, set Cij =INF, INF is a maximum value, such as the maximum value that the system can identify, representing that the combination cannot be allocated.

[0030] S2, based on the task urgency, the returning UAV is preliminarily screened and sorted, and the to-be-scheduled queue is obtained.

[0031] Specifically, the system determines the UAVs that need to complete the return charging task in priority according to the task urgency (such as "urgent", "general", "low priority"), for example, the UAVs with task marked as "urgent" and remaining power less than 20%. These high-priority UAVs are preferentially included in the current scheduling batch, and the low-priority UAVs are temporarily stored in the to-be-scheduled pool. Then, the UAVs in the current scheduling batch are sorted in descending order of urgency, forming the to-be-scheduled queue Dpriority, for example: Drone_3 (urgent) → Drone_1 (urgent) → Drone_5 (general).

[0032] S3, based on the compatibility matrix and the time cost matrix, the optimal allocation algorithm is used to allocate the charging pile to the to-be-scheduled queue, and the pile allocation data is obtained.

[0033] Specifically, the following steps are included: S301, based on the time cost matrix, the returning UAV and the charging pile combination for priority charging are determined according to the greedy algorithm.

[0034] The UAVs that need to complete the return charging task in priority are determined, so that the available UAVs can be obtained in the shortest time, for example, the time cost matrix : (2) Wherein, represents that the model of the UAV i and the charging pile j does not match.

[0035] If one UAV needs to be charged in priority at this time, according to the greedy algorithm, the scheme corresponding to the minimum value is found, that is, the allocation scheme with the minimum time cost. In this embodiment, C 14= 1 and C 26 = 1. When the minimum value is greater than one, i.e. there are multiple options with the same time cost, the option is selected according to the following formula: (3) That is, after removing the mismatched data in the row and column where the minimum value is located, the option corresponding to the minimum value with the largest sum of the row and column is selected, so as to reduce the overall time cost of the remaining unmanned aerial vehicles, optimize the scheduling option, and maximize the overall recharging efficiency.

[0036] In this embodiment, = 13, = 15. Therefore, the unmanned aerial vehicle No. 2 is preferentially allocated to enter the charging station No. 6 for recharging.

[0037] S302, remove the allocated unmanned aerial vehicles and charging piles from the time cost matrix to obtain a reduced time cost matrix.

[0038] Since the unmanned aerial vehicle No. 2 has been allocated to the charging station No. 6, the time cost matrix is recalculated, the problem size is reduced, and the adjusted time cost matrix becomes: (4) Similarly, according to the greedy algorithm, the current minimum time cost option is searched, i.e. = 1, the unmanned aerial vehicle No. 1 has been allocated to the charging station No. 4, and the reduced matrix becomes: (5) S303, reduce the reduced time cost matrix and find the optimal solution to obtain the pile allocation data.

[0039] The time cost matrix is respectively reduced in row and column: (6)That is, each element in the matrix is subtracted by the minimum value in the row and column where it is located, does not participate in the calculation, and more zero elements can be generated, where the zero element represents a relatively optimal allocation option, thereby facilitating the finding of the optimal solution. The matrix in equation (5) is reduced in row to ensure that each row has at least one zero element, and the time cost matrix after row reduction

[0040] is: (7)The matrix in equation (7) is reduced in column to ensure that each column has at least one zero element, and the time cost matrix after column reduction is: (8) Finding the optimal solution: for the matrix of formula (8), use the minimum covering line method to find the minimum number of straight lines covering all zero elements. If the number of straight lines is equal to the order of the matrix, that is, the number of unmanned aerial vehicles is equal to the number of charging piles, the optimal allocation scheme is found: each zero element corresponds to a set of "unmanned aerial vehicle-charging pile" allocation scheme, and there is no repetition. If the number of straight lines is less than the order of the matrix, the minimum value of the uncovered elements in the matrix needs to be calculated, the uncovered elements are subtracted by the minimum value, the elements at the intersection of the two covering lines are added by the minimum value, and the step is repeated until the number of straight lines is equal to the order of the matrix.

[0041] Map the zero element allocation scheme in the reduced matrix above back to the original time cost matrix , to obtain the final pile allocation data , }.

[0042] According to the optimal solution obtained, the pile allocation data is obtained, and unmanned aerial vehicle No. 2 is allocated to charging station No. 6 to ensure that there is an available unmanned aerial vehicle in the shortest time. Then, unmanned aerial vehicle No. 1 is allocated to charging station No. 4; unmanned aerial vehicle No. 3 is allocated to charging station No. 1; and unmanned aerial vehicle No. 4 is allocated to charging station No. 3. According to the original time cost matrix, the total time cost under this allocation scheme is 1+1+2+2=6.

[0043] S4, based on the pile allocation data, time slot reservation method is used to allocate the departure time for each unmanned aerial vehicle and plan a conflict-free path to obtain time-space scheduling information.

[0044] In this embodiment, in the two-dimensional plane road network model, the time slot reservation method is used to plan the shortest conflict-free path for the unmanned aerial vehicles allocated to the charging piles, which can avoid channel occupation conflicts and reduce the risk of unmanned aerial vehicle collision and congestion.

[0045] In the two-dimensional plane road network model, the internal channels of the airport are abstracted as edges and divided into multiple road segments, such as "road segment 1-2" and "road segment 3-4", and key positions such as intersections, buffer sites, and charging pile entrances are abstracted as vertices V, such as "airfield exit V1" and "charging pile entrance V5". The occupation state r(e,t)∈{0,1} of each channel e∈E at time t is defined, where r(e,t)=1 indicates that the channel e is occupied at time t, and r(e,t)=0 indicates that it is idle. For a two-way channel, an additional opposite channel constraint is added: r(e,t)+r(eo,t)≤1 (eo is the opposite channel of e), that is, the two-way channel cannot be occupied at the same time slot.

[0046] In this embodiment, the specific process of allocating the departure time for each unmanned aerial vehicle and planning a conflict-free path to obtain time-space scheduling information is as follows: S401. Plan a static shortest path for each UAV, divide the airport channel into multiple road segments, and identify potential conflict points between all static shortest paths.

[0047] Based on the road network model, use Dijkstra algorithm to plan the static shortest path from the current location to the target charging pile.

[0048] S402. Calculate a time window for each UAV to allow departure, including the earliest departure time and the latest departure time, and use the greedy insertion algorithm to assign a specific departure time for the UAV.

[0049] Specifically, the main control system calculates a time window for each UAV to allow departure [ESTi, LSTi], where ESTi is the earliest departure time, which means the UAV is ready after landing and self-checking on the parking apron, and LSTi is the latest departure time, which can be calculated by reversing the task deadline. For example, if the task requires readiness by 10:00 and the total charging time is about 30 minutes, then LSTi = 9:30.

[0050] According to the greedy insertion strategy, obtain the current system time, filter all allocated UAVs with an earliest departure time no later than the system time, and according to the order of pile allocation in step S3, simulate the UAVs traveling from the current time T as the starting point, following the static shortest path obtained in step S401 from the current location to the target charging pile and the time slot reservation table of the current airport channel, and calculate the charging completion time. Select the UAV with the earliest charging completion time and set its departure time as the system time. Update the system state after setting, including the current system time, the time slot reservation table of the current airport channel, and the state of the remaining UAVs, and repeat the above process until the departure time of all allocated UAVs is determined.

[0051] S403. According to the departure time of each UAV, reserve an exclusive time window for each potential conflict point on its static shortest path, and generate time-space scheduling information.

[0052] According to the departure time of each UAV, reserve an exclusive time window for each potential conflict point on its static shortest path, and form a time slot reservation table at multiple potential conflict points. Among them, potential conflict points include path intersection nodes, path overlapping road segments, and shared road segments.

[0053] Wherein, when planning the time window for each UAV, the length of each channel on the static shortest path and the UAV driving speed need to be calculated, and then the time window is checked for conflict with existing reservations in the time slot reservation table.

[0054] If there is no conflict with the existing appointment, mark the time slot as "occupied (drone i)" in the time slot appointment table at the potential conflict point, and complete the appointment.

[0055] If there is a conflict with the existing appointment, the drone can be ensured to run strictly according to the appointment schedule by fine-tuning the driving speed or waiting in a non-conflict section, thereby avoiding any spatial conflict. Specifically, the driving speed of the drone is tried to be fine-tuned, such as being increased from 1 m / s to 1.2 m / s, so as to shorten the channel passing time. If the conflict cannot be solved by fine-tuning the driving speed, the drone is made to wait at a node on the path until the end of the conflict time slot, the time window is recalculated and the conflict is checked until a time slot scheme without conflict is found.

[0056] In all the time slot schemes without conflict, the scheme that enables the drone to complete the "driving-charging-return" whole process earliest is selected, the departure time is formally allocated, the time slot appointment result is written into the channel time slot appointment table, the time-space scheduling information is finally obtained, and the driving path of each drone is determined. Finally, the path instruction containing the detailed departure time and time slot appointment point is issued to each drone by the main control system of the drone airport according to the time-space scheduling information, and the drone executes the instruction to realize the efficient and conflict-free automatic charging process.

[0057] In another typical embodiment of the present application, with reference to Figure 3 Further provided is a drone airport charging scheduling system, comprising: A data acquisition module is configured to acquire state data of each charging pile and state data of each returning drone, and construct a compatibility matrix and a time cost matrix; A preprocessing module is configured to preliminarily screen and sort the returning drones based on task urgency, and obtain a to-be-scheduled queue; A pile allocation module is configured to allocate charging piles to the to-be-scheduled queue based on the compatibility matrix and the time cost matrix by using an optimal allocation algorithm, and obtain pile allocation data; A path planning module is configured to allocate departure times to each drone and plan a conflict-free path based on the pile allocation data, and obtain time-space scheduling information.

[0058] In another typical embodiment of the present application, an electronic device is provided, comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to run the computer program stored in the memory to execute the steps in the drone airport charging scheduling method provided in Embodiment I.

[0059] In still another typical embodiment of the present application, a non-transitory computer readable storage medium is provided for storing a readable computer program, which can execute the steps in the drone airport charging scheduling method provided in Embodiment I when run by a processor.

[0060] It should be noted that the above examples are only used to illustrate the technical solutions of the present application, and are not intended to limit the same. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the present application can have various modifications and changes. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for scheduling charging at an unmanned aerial vehicle (UAV) airport, characterized in that, Includes the following steps: S1. Obtain the status data of each charging pile and the status data of each returning drone, and construct a compatibility matrix and a time consumption matrix; S2. Based on the urgency of the task, perform preliminary screening and sorting of the returning drones to obtain the queue to be scheduled; S3. Based on the compatibility matrix and time consumption matrix, the optimal allocation algorithm is used to allocate charging piles to the queue to be scheduled, and the pile allocation data is obtained. S4. Based on the pile location allocation data, the time slot reservation method is used to allocate departure time to each UAV and plan conflict-free paths to obtain time-space scheduling information.

2. The UAV airport charging scheduling method as described in claim 1, characterized in that, In step S1, the status data of the returning drone includes the drone ID, battery model, current location, current battery level, battery capacity, and mission urgency; the status data of the charging station includes the current status, model, and power information.

3. The UAV airport charging scheduling method as described in claim 1, characterized in that, The specific process of step S3 is as follows: S301. Based on the time cost matrix, determine the combination of returning drones and charging piles that should be prioritized for charging using a greedy algorithm. S302. Remove the allocated returning drones and charging stations from the time cost matrix to obtain the reduced time cost matrix; S303. Reduce the reduced time cost matrix and find the optimal solution to obtain the pile location allocation data.

4. The UAV airport charging scheduling method as described in claim 3, characterized in that, In step S301, the minimum value in the time cost matrix is ​​selected, and the combination of the returning drone and charging pile corresponding to the minimum value is taken as the priority allocation scheme for the charging pile location. When the minimum value is greater than one, the combination of returning drones and charging piles corresponding to the minimum value with the largest sum in the row and column is selected as the priority for determining the pile location allocation scheme.

5. The UAV airport charging scheduling method as described in claim 3, characterized in that, In step S303, the reduced time cost matrix is ​​reduced in rows and columns to produce at least one zero element. The minimum coverage line method is used to find the minimum number of straight lines covering all zero elements. If the number of straight lines is equal to the matrix order, the optimal allocation scheme is found, that is, each drone can be allocated a unique zero-cost charging station; otherwise, the matrix is ​​further reduced before searching. The optimal allocation scheme in the reduced matrix is ​​mapped back to the original time cost matrix to obtain the final pile allocation data.

6. The UAV airport charging scheduling method as described in claim 1, characterized in that, Step S4 specifically includes: S401. Plan the static shortest path for each UAV, divide the airport passage into multiple segments, and identify potential conflict points between all static shortest paths. S402. Calculate an allowed departure time window for each drone, including the earliest departure time and the latest departure time, and use a greedy insertion algorithm to assign a specific departure time to the drone. S403. Based on the departure time of each UAV, reserve an exclusive time window for each potential conflict point on its static shortest path and generate time-space scheduling information.

7. The UAV airport charging scheduling method as described in claim 1, characterized in that, In step S401, potential conflict points include path intersections, overlapping path segments, and shared road segments; In step S402, the specific process of assigning a departure time to the UAV using the greedy insertion algorithm is as follows: Obtain the current system time, filter all allocated drones whose earliest departure time is no later than the system time, simulate their departure at the system time, and calculate the charging completion time based on their static shortest path and time slot reservation method; Select the drone that completes charging earliest and set its departure time as the system time; Update the system status and repeat the above process until the departure time of all drones is determined.

8. A drone airport charging scheduling system, characterized in that, include: The data acquisition module is used to acquire the status data of each charging pile and the status data of each returning drone, and to construct a compatibility matrix and a time consumption matrix. The preprocessing module is used to perform preliminary screening and sorting of returning drones based on task urgency to obtain a queue to be scheduled. The charging pile allocation module is used to allocate charging piles to the queue to be scheduled based on the compatibility matrix and the time cost matrix, using the optimal allocation algorithm to obtain charging pile allocation data. The path planning module is used to allocate departure times to each UAV and plan conflict-free paths based on the pile location allocation data, thereby obtaining time-space scheduling information.

9. An electronic device comprising a memory and a processor, characterized in that, The memory is used to store a program, and the processor is coupled to the memory to execute the program stored in the memory to complete the steps in the UAV airport charging scheduling method as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium for storing a readable computer program, characterized in that, When executed by a processor, the computer program is able to perform the steps in the UAV airport charging scheduling method as described in any one of claims 1-7.

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