Unmanned aerial vehicle control method and related product thereof

Abstract

The invention discloses an unmanned aerial vehicle control method. According to the method, identity recognition information and flight data information of an unmanned aerial vehicle can be obtained in real time; and processing the flight data information by using the dynamic queue processing model to obtain a control instruction for controlling the unmanned aerial vehicle to execute operation. The scheme can dynamically allocate the priority of the unmanned aerial vehicle, so that the unmanned aerial vehicle can automatically complete landing, the labor cost is reduced, and the landing safety risk is reduced. The scheme can be integrated and matched with an unmanned aerial vehicle take-off and landing platform and a low-altitude flight scheduling control platform, a platform system does not need to be additionally designed, the research and development cost can be reduced, and integrated automatic scheduling of route planning, task execution and queuing landing of the unmanned aerial vehicle can be met.

Description

Technical Field

[0001] This solution relates to the field of drone scheduling technology. More specifically, it relates to a drone control method for dynamic queue landing control of drones and related products. Background Technology

[0002] With the rise of the low-altitude economy, drones are playing an increasingly important role in various industries, and their use is becoming more frequent. The use of drones involves the drone itself, flight path planning, take-off and landing platforms (automatic or manual), and flight control platforms. Providing each drone with a separate hangar or take-off and landing platform would significantly increase operating costs and space requirements. If multiple drones use the same landing platform or automated hangar, strict human monitoring and control are necessary to prevent serious safety accidents such as collisions.

[0003] Currently, the scheduling methods for drones include the following:

[0004] 1. Each drone is equipped with one hangar or landing pad, with the hangar being dedicated to its intended use;

[0005] 2. The route locking method prevents other drones from executing the same route before the entire route is completed;

[0006] 3. Manually controlled landing: When the aircraft arrives at the landing area, it hovers and waits. The airspace is confirmed to be safe. After the landing pad is safe, the drone is manually controlled to land from the flight dispatch control platform. Summary of the Invention

[0007] The purpose of this invention is to provide a drone control method and related products for dynamic queue landing control of drones, in order to solve problems such as low drone landing efficiency, high labor costs, and frequent safety accidents.

[0008] To achieve the above objectives, the following technical solution is adopted:

[0009] Firstly, this solution provides a drone control method that, upon the drone entering a target area, can acquire flight data information corresponding to the drone's identification information within the target area in real time. Utilizing a dynamic queue processing model, combined with the drone's current battery level and task priority, a landing priority score is calculated. Based on the priority score and / or the occupancy status of the landing point, a drone replacement command, a drone hovering standby command, or a queue-jumping command is triggered, thereby controlling the drone to complete replacement, queue-jumping, hovering, or emergency landing operations.

[0010] Secondly, this solution provides a drone control module, which includes:

[0011] The airspace perception unit detects drones entering the target area in real time.

[0012] The tag tracking unit assigns queue tags to drones entering a designated airspace and tracks them in real time.

[0013] The information acquisition unit acquires the drone's identification information and flight data in real time.

[0014] The data processing unit uses a dynamic queue processing model to process flight data information and obtain control commands for controlling the UAV to perform operations.

[0015] The command output unit sends control commands to the drone to control it to perform operations.

[0016] Thirdly, a low-altitude flight scheduling and control platform, the platform comprising:

[0017] The flight control scheduling service module is used to support one or more of the following: UAV basic data processing, UAV library data processing, UAV scheduling logic services, and UAV scheduling communication.

[0018] The control module described above is used to control the dynamic queuing landing of UAVs.

[0019] Fourthly, a drone dispatching system, comprising: a drone, a low-altitude flight dispatching and control platform and a take-off and landing platform as described above;

[0020] The UAV interacts with the low-altitude flight scheduling and control platform via a wireless communication network.

[0021] The UAV executes control commands issued by the dynamic landing control module in the low-altitude flight scheduling and control platform, and completes operations such as filling in, cutting in line, hovering, or emergency landing on the landing pad.

[0022] Fifthly, this solution provides a drone scheduling device, characterized in that it includes: a memory, one or more processors; the memory and processors are connected via a communication bus; the processors are configured to execute instructions in the memory; the storage medium stores instructions for executing the various steps of the method described above.

[0023] Sixthly, this solution provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the method described above.

[0024] The beneficial effects of this invention are as follows:

[0025] This solution can dynamically allocate drone priorities, enabling drones to land automatically, reducing labor costs and lowering landing safety risks.

[0026] This solution can be integrated and adapted with UAV take-off and landing platforms and low-altitude flight scheduling and control platforms without the need for additional platform system design. This not only reduces R&D costs but also enables integrated automatic scheduling of UAV route planning, mission execution, and queuing for landing.

[0027] This solution eliminates the need for shared drone storage, allowing multiple drones to fly and land freely. The drone storage and drones are not bound together, enabling free allocation between drones and drone storage, thereby improving the utilization rate of both. Attached Figure Description

[0028] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 A schematic diagram of the UAV control method described in this scheme is shown;

[0030] Figure 2 This diagram illustrates the construction of the flight data structure set described in this scheme;

[0031] Figure 3 This diagram illustrates the process of processing flight data using the dynamic queue processing model described in this scheme.

[0032] Figure 4 This diagram illustrates the UAV scheduling system described in this solution.

[0033] Figure 5 A schematic diagram of the UAV scheduling method described in this scheme is shown;

[0034] Figure 6 A schematic diagram of the UAV control module described in this solution is shown;

[0035] Figure 7 A schematic diagram of the UAV control device described in this solution is shown. Detailed Implementation

[0036] To make the present invention, its technical solutions, and advantages clearer, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0037] Analysis and research of existing technologies have revealed the following problems in current drone technology:

[0038] 1. The common practice is to configure one drone with one dedicated drone hangar. This approach significantly increases the cost of using drones, occupies more parking space, and renders them unusable in many projects.

[0039] 2. Typically, drones use a locked flight path, which cannot flexibly meet the needs of switching between multiple tasks. This means that multiple drones cannot fly on the same flight path at the same time, which greatly reduces the flight efficiency of drones.

[0040] 3. Typically, drones return to the hangar after completing their missions. However, manually controlling multiple drones returning to the hangar increases labor costs and affects return efficiency. Furthermore, human error due to factors such as fatigue can easily lead to collisions and other safety accidents, causing property damage.

[0041] Therefore, this solution aims to provide a drone control method for dynamic queue landing control. This method utilizes a dynamic queue processing model to process drone flight data and generate control commands. Based on these commands, the drones can automatically and orderly complete the landing operation. This method can control multiple drones to automatically and orderly queue for landing, enabling shared drone hangars while reducing the number of hangars needed, minimizing space usage, reducing labor costs, and mitigating safety risks such as drone damage.

[0042] The following section, with reference to the accompanying drawings, provides a detailed description of a UAV control method for dynamic platoon landing control proposed in this scheme. Specifically, as shown in the attached drawings... Figure 1 As shown, the control method includes:

[0043] S1. Acquire drone identification information and flight data in real time;

[0044] S2. The flight data information is processed using a dynamic queue processing model to obtain control commands for controlling the UAV to perform operations.

[0045] In this scheme, the drone can be an unmanned mechanical device, such as an unmanned aerial vehicle (UAV). Typically, after completing a flight mission, a drone needs to return to a designated location to recharge or await its next mission assignment. The designated location can be a hangar for parking drones or multiple landing platforms for drone landing. The landing platform is the designated landing location for the drone. Before use, the landing platform needs to be calibrated by a low-altitude flight scheduling and control platform, and a landing airspace centered on the landing platform needs to be demarcated. The low-altitude flight scheduling and control platform begins controlling the drone before it approaches the landing airspace, ensuring a stable, orderly, and safe landing on the landing platform. Therefore, for better drone management, a predetermined airspace can be extended beyond the landing airspace. This predetermined airspace can serve as the area where the low-altitude flight scheduling and control platform captures the drone, or as a hovering and waiting area for the drone. During the drone's flight to the landing airspace, the low-altitude flight scheduling and control platform maintains stable communication with the drone, using flight data such as altitude, speed, and latitude / longitude to determine the drone's position relative to the landing airspace. When a drone enters the designated airspace, the low-altitude flight dispatch and control platform can send a hovering and waiting command to the drone, enabling it to hover stably and orderly outside the landing airspace while awaiting further dispatch. The designated airspace should be set outside the landing airspace, and its size can be appropriately adjusted according to the size of the landing airspace and the number of drones to ensure that drones can wait safely and orderly before entering the landing airspace.

[0046] In this plan, such as Figure 2 As shown, the steps for acquiring the drone's identification information and flight data in real time may include:

[0047] S101. Obtain flight data information corresponding to the current identification information of the UAV;

[0048] S102. Assign queue tags to drones based on drone identification information and construct flight data structure sets from flight data information;

[0049] S103. Add the UAV's queue label and corresponding flight data structure set to the dynamic processing queue;

[0050] S104. Acquire flight data information corresponding to the UAV's identification information in real time, and update the flight data structure set.

[0051] In some instances, it's necessary to acquire unmanned aerial vehicle (UAV) identification and flight data in real time. Identification information allows for the identification of UAVs, distinguishing them from different drones. It also reveals the UAV model, mission type, and other information. For example, a UAV could be a small delivery drone, a medium-sized cargo drone, or even an unmanned aerial vehicle. Identification enables more accurate control and scheduling of UAVs, preventing congestion, collisions, and mission redundancy. Based on the UAV's identification information, a unique queue tag with a unique identifier is assigned to each UAV. Simultaneously, the UAV's flight data is constructed into a flight data structure set. This flight data structure set is updated based on the latest real-time flight data to ensure the accuracy of the UAV's dynamic data. The flight data includes the UAV's real-time latitude and longitude, altitude, relative altitude, battery level, mission priority, and flight path.

[0052] In this scheme, the queue tags of multiple UAVs and their corresponding flight data structure sets are added to a dynamic processing queue in the low-altitude flight scheduling and control platform. A pre-built dynamic queue processing model is used to process the flight data information in the dynamic processing queue to obtain control commands for controlling the UAVs to perform operations. Specifically, the dynamic queue processing model may include a scoring model constructed using the UAV's current battery level and the task priority. This scoring model can be pre-constructed using machine learning, deep learning neural networks, or by pre-setting weights. Alternatively, machine learning, deep learning neural networks, etc., can be used to predict the weight coefficients of the first-order linear equation constructed based on the UAV's current battery level and the task priority; the scoring model is then constructed based on the predicted weights, the UAV's current battery level, and the task priority.

[0053] In this plan, such as Figure 3 As shown, the steps for processing flight data using a dynamic queue processing model to obtain control commands for controlling the UAV to perform operations may include:

[0054] S201. Obtain the drone's current battery level and task priority.

[0055] S202. Based on the drone's current battery level and the task priority of the mission, a dynamic queue processing model is used to calculate the drone's landing priority score.

[0056] S203. Form a landing queue for the UAVs according to the priority scoring order;

[0057] S204. Trigger drone replacement command, drone hovering standby command or queue-jumping command in real time based on the priority score and / or the occupancy status of the landing point;

[0058] S205. If the current battery level of the drone is less than the predetermined minimum threshold, an emergency queue-jumping command is triggered to insert the drone at the head of the landing queue, or a forced landing command is triggered to land at a temporarily assigned emergency landing point.

[0059] In some instances, a scoring model is used to calculate a priority score for the drone based on its real-time battery level and task priority. Higher task priority and lower battery level result in a higher score and a higher landing priority. Different control commands are triggered based on the scores. For example, if there are six drones, their queue tags and flight data structures are added to a dynamic processing queue. The dynamic queue processing model calculates the priority scores of the six drones as follows: second drone score > fourth drone score > first drone score > sixth drone score > third drone score > fifth drone score. Then, according to the calculated priority score order, the six drones are arranged into a landing queue, and landing operations are performed in the order of the landing queue. Subsequently, the seventh drone approaches the landing airspace. At this point, the real-time flight data structures of the first six drones, along with the queue label and flight data structure of the seventh drone, are added to a dynamic processing queue. The dynamic queue processing model calculates the priority scores of the seven drones as follows: second drone score > sixth drone score > fourth drone score > first drone score > seventh drone score > third drone score > fifth drone score. Based on this newly calculated priority score, the six drones are arranged into a landing queue. Specifically, the sixth drone is moved before the fourth drone, and the seventh drone is moved before the third drone, and they land in the new order. Furthermore, if a drone's current battery level is below the minimum battery threshold, it is moved directly to the front of the landing queue. In this way, priorities are dynamically assigned to multiple drones, enabling automatic queuing and landing scheduling for multiple drones.

[0060] It's important to note that the landing queue described above can be either a logical execution queue or a physical arrangement. If designed as a logical execution queue, drones approaching the landing airspace from different directions, altitudes, and positions can hover freely without affecting other drones, calculate their priority scores, and continue hovering until they receive commands to fill in, cut in line, or land. If designed as a logical execution queue, drones approaching the landing airspace from different directions, altitudes, and positions need to have their priority scores calculated. Then, based on these priority scores, all drones are controlled to form a physical arrangement and hover before moving to receive commands to fill in, cut in line, or land, thus adjusting the overall drone queue.

[0061] In this scheme, a priority score for drones is calculated using a scoring model within the dynamic queue processing model, enabling the sorting of landings for multiple drones. However, beyond the orderly scheduling of drone landings, the availability of landing sites needs further consideration. If no landing site is currently available, or if a new available landing site is added, the drone scheduling strategy needs to be adjusted as quickly as possible. Therefore, the dynamic queue processing model can also include a strategy for adjusting drone landings based on the occupancy status of landing sites. Specifically, starting from all currently available landing sites, the first unoccupied landing site is searched and allocated to drones according to the order of the landing queue. Drones land on unoccupied landing sites according to landing control commands. If multiple drones have the same priority score, each time a drone that has completed landing lands and leaves the landing pad, a move-forward command is issued to the drones in the landing queue, moving one landing site forward in sequence. If no landing site is currently available, a command is issued to the drones to hover and wait at their current positions. If no landing site is currently available, and a drone's current battery level is below the minimum battery threshold, an emergency landing command is issued to that drone to land at a temporarily assigned emergency landing site. By employing the above methods, drones can be dynamically prioritized, enabling them to automatically fill queuing gaps and execute tasks, reducing human intervention, labor costs, and landing safety risks. Furthermore, this solution allows for the sharing of drone hangars, freeing them from being tied to individual drones. Hangars can be deployed anywhere in the city, allowing drones to fly and land freely, and enabling flexible allocation between drones and hangars, thereby improving the utilization rate of both drones and hangars.

[0062] The following example further illustrates this solution.

[0063] like Figure 4 As shown in the example, this example uses a drone scheduling system consisting of multiple drones, a drone hangar with landing pads, and a low-altitude flight scheduling and control platform to illustrate the control method for dynamic drone queue landing control. Specifically,

[0064] In this example, the low-altitude flight scheduling and control platform may include a flight scheduling service module. This module supports operations such as UAV data processing, hangar data processing, scheduling logic services, and scheduling communication support. To better adapt the control method used for UAV dynamic queue landing control to the low-altitude flight scheduling and control platform, the control method can be designed as a dynamic queue landing module and integrated into the platform, making the control method more versatile. Communication between the UAV, landing pad, and low-altitude flight scheduling and control platform can be achieved via wireless communication methods such as 4G, 5G, 6G, and BeiDou satellite communication.

[0065] In this example, a low-altitude flight control platform is used to mark targets for the landing platform, and a landing airspace is established centered on the landing platform. Additionally, a predetermined airspace can be established by extending a certain range outward from the boundary of the landing airspace. This predetermined airspace allows the low-altitude flight control platform to take over control of the UAV earlier, facilitating subsequent control and scheduling.

[0066] like Figure 5 As shown, after completing its flight mission, the drone flies to the drone hangar. Before the drone approaches the landing airspace, the low-altitude flight dispatch and control platform communicates with the drone in real time to obtain the drone's identification information and flight data.

[0067] The low-altitude flight scheduling and control platform assigns a uniquely identified queue tag to each UAV based on its identification information and creates a flight data structure set corresponding to that queue tag's flight data. The queue tags of all UAVs within the designated airspace are added to a dynamic processing queue. As the low-altitude flight scheduling and control platform acquires new flight data from UAVs, it can update the data structure values ​​in each UAV's flight data structure set in real time. The flight data structure set can include: latitude and longitude position, flight path / mission; altitude, relative altitude; remaining battery power, and mission priority.

[0068] The flight data of the UAV is processed using a dynamic queue processing model, as follows:

[0069] A comprehensive drone landing priority scoring formula is constructed using drone battery power and mission priority: S d =α*P d +β*(1−E d / E max ), where P is set. d ∈[1,3] represents the task priority; a larger value indicates a higher priority. d E represents the current remaining battery power. max To be fully charged, E min The minimum battery level threshold for allowing queuing; α and β are pre-set weighting coefficients, and α+β=1; d is the drone.

[0070] The drone landing priority scoring formula is used to calculate the score of drones in the dynamic processing queue. If there is drone d in the current queue... j Satisfaction rating S i >S j Then send the drone d i Issue a queue cut-off to the drone d j Previous instructions; if drone d i Current remaining battery power E di Satisfying Edi <E min Then send the drone d i Issue a command to insert at the head of the drone landing queue; where i and j are positive integers.

[0071] In addition, the dynamic queue processing model can also schedule unmanned aircraft based on the availability of landing sites. Specifically:

[0072] If there are k available landing points, start from the current available landing point P. k Begin searching for the first unoccupied landing point P. m , will P m Assigned to drone d i That is, to the drone d i Launched at landing point P m The landing instruction, where m ≤ k, and m, k, i, j are all positive integers;

[0073] If multiple drones have the same priority score, then whenever a drone that has completed its landing lands at landing point P1 and leaves the landing pad, a movement command is issued to the drones in the drone queue to move forward one landing point in sequence, i.e., P2 -> P1, P3 -> P2, ..., P n -> P n-1

[0074] If there is no available landing spot, issue a command to the drone to hover and wait at the current location;

[0075] If there are currently no available landing spots, and the drone d i The current battery level meets E di <E min If the landing conditions are met, then send a signal to drone d. i Issue an emergency landing order to land at a temporarily assigned emergency landing point.

[0076] After processing the drone flight data using a dynamic queue processing model, control commands can be output for each drone. For example, a drone with a high priority score executes a replacement command, moves to the head of the landing queue, and, depending on the availability of landing spots, flies to the first unoccupied landing spot to complete the landing. Among multiple drones, there is drone d. j Satisfaction rating S i >S j Then send the drone d i Issue a queue cut-off to the drone d j The previous queue-jumping instruction will be followed. Drones that do not meet the conditions for advancing will be instructed to hover and wait. In addition, drones requiring emergency landings will be instructed to execute the emergency landing instruction at the last landing point on the landing pad to complete the landing.

[0077] The solution described in this example can process UAV flight data through a dynamic queue processing model to generate corresponding control commands, thereby scheduling UAVs to automatically fill in queues, automatically prioritize and jump the queue, and automatically perform landing operations, making the system scheduling fully automated, with higher scheduling efficiency and better security.

[0078] Based on the aforementioned UAV control method, this solution further provides a UAV control module 301 for dynamic queue landing control of UAVs. For example... Figure 6 As shown, this module includes an information acquisition unit 303 and a data processing unit 305. The information acquisition unit 303 acquires the UAV's identification information and flight data in real time. Then, the data processing unit 305 processes the flight data using a dynamic queue processing model to obtain control commands for controlling the UAV to perform operations.

[0079] In this solution, to adapt to the use of the low-altitude flight scheduling and control platform, an airspace perception unit 302, a tag tracking unit 304, and a command output unit 306 can be added to the UAV control module 301. The airspace perception unit 302 uses real-time sensing of the UAV's real-time position to capture UAVs entering the designated airspace. The tag tracking unit 304 assigns unique queue tags to the UAVs while simultaneously tracking their landing status in real time. The command output unit 306 outputs control commands as processing results, enabling wireless communication between the low-altitude flight scheduling and control platform and the UAVs and landing pads to schedule the orderly landing of the UAVs.

[0080] The aforementioned UAV control module can be designed as a standard functional module and integrated into the low-altitude flight scheduling and control platform. After configuring the UAV and take-off and landing platform into the system and planning the flight route, the system will automatically perform queuing and landing scheduling when executing flight missions.

[0081] Based on the above-described embodiments of the UAV control method, this solution further provides a computer-readable storage medium. This computer-readable storage medium is used to implement the program product of the above-described UAV control method. It may employ a portable compact disc read-only memory (CD-ROM) and include program code, and can run on a device, such as a personal computer. However, the program product of this solution is not limited to this. In this document, the readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0082] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0083] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0084] Program code contained on a computer-readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0085] Program code for performing the operations of this solution can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing devices can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or they can be connected to external computing devices (e.g., via the Internet using an Internet service provider).

[0086] Based on the above-described implementation of the UAV control method, this solution further provides an electronic device. For example... Figure 7 The electronic device shown is merely an example and should not impose any limitations on the functionality and scope of use of this embodiment.

[0087] like Figure 7As shown, electronic device 401 is presented in the form of a general-purpose computing device. The components of electronic device 401 may include, but are not limited to: at least one storage unit 402, at least one processing unit 403, a display unit 404, and a bus 405 for connecting different system components.

[0088] The storage unit 402 stores program code that can be executed by the processing unit 403, causing the processing unit 403 to perform the steps of the various exemplary embodiments described in the above-described UAV control method. For example, the processing unit 403 can perform actions such as... Figure 1 The steps are shown in the figure.

[0089] Storage unit 402 may include volatile storage units, such as random access memory (RAM) and / or cache storage units, and may further include read-only memory (ROM).

[0090] Storage unit 402 may also include programs / utilities with program modules, such program modules including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0091] Bus 405 may include a data bus, an address bus, and a control bus.

[0092] Electronic device 401 can also communicate with one or more external devices 407 (e.g., keyboard, pointing device, Bluetooth device, etc.), such communication can be made through input / output (I / O) interface 406. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 401, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0093] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the implementation of the present invention. For those skilled in the art, other variations or modifications can be made based on the above description. It is impossible to exhaustively list all the implementation methods here. All obvious variations or modifications derived from the technical solutions of the present invention are still within the protection scope of the present invention.

Claims

1. A method for controlling an unmanned aerial vehicle (UAV), characterized in that, The steps of this method include: Real-time acquisition of drone identification information and flight data; The flight data is processed using a dynamic queue processing model to obtain control commands for controlling the UAV to perform operations.

2. The control method according to claim 1, characterized in that, The steps for acquiring the drone's identification information and flight data in real time include: Obtain flight data information corresponding to the current identification information of the drone; Based on the drone identification information, queue tags are assigned to the drones, and flight data information is constructed into a flight data structure set; Add the UAV's queue label and corresponding flight data structure set to the dynamic processing queue; Real-time acquisition of flight data information corresponding to the UAV's identification information, and updating of the flight data structure set.

3. The control method according to claim 1, characterized in that, The step of processing flight data using a dynamic queue processing model to obtain control commands for controlling the UAV to perform operations includes: Obtain the drone's current battery level and task priority; Based on the drone's current battery level and the task priority of the mission, a dynamic queue processing model is used to calculate the drone's landing priority score. The drones are arranged in a landing queue according to the priority scoring order. Based on the priority score and / or the occupancy status of the landing point, trigger drone replacement command, drone hovering standby command, or queue jumping command in real time.

4. The control method according to claim 3, characterized in that, The step of processing flight data using a dynamic queue processing model to obtain control commands for controlling the UAV to perform operations further includes: If the drone's current battery level is less than a predetermined minimum threshold, an emergency queue-jumping command will be triggered to insert it at the head of the landing queue, or a forced landing command will be triggered to land at a temporarily assigned emergency landing point.

5. The control method according to claim 1, 3, or 4, characterized in that, The dynamic queue processing model includes: A comprehensive landing priority scoring formula for drones, based on drone battery level and mission priority, is constructed as follows: S d =α*P d +β*(1−E d / E max ); Among them, P d ∈[1,3] represents the task priority; E d E represents the current remaining battery power. max To be fully charged, E min The minimum battery level threshold for allowing queuing; α and β are pre-set weighting coefficients, and α + β = 1; d represents the drone; If there is a drone d in the current queue j Satisfaction rating S i >S j Then send the drone d i Issue a queue cut-off to the drone d j Previous instructions; If the drone d i Current remaining battery power E di Satisfying E di <E min Then send the drone d i Issue the command to insert itself at the head of the drone landing queue; Where i and j are positive integers.

6. The control method according to claim 5, characterized in that, The dynamic queue processing model includes: From the currently available landing point P k Begin searching for the first unoccupied landing point P. m , will P m Assigned to drone d i That is, to the drone d i Launched at landing point P m The landing instruction, where m ≤ k, and m and k are positive integers; If multiple drones have the same priority score, then whenever a drone that has completed landing lands at landing point P1 and leaves the landing platform, a movement command is sent to the drones in the drone queue to move forward one landing point in sequence. If there is no available landing spot, issue a command to the drone to hover and wait at the current location; If there are currently no available landing spots, and the drone d i The current battery level meets E di <E min If the landing conditions are met, then send a signal to drone d. i Issue an emergency landing order to land at a temporarily assigned emergency landing point.

7. A drone control module, characterized in that, This module includes: The information acquisition unit acquires the drone's identification information and flight data in real time. The data processing unit uses a dynamic queue processing model to process flight data and obtain control commands for controlling the UAV to perform operations.

8. The control module according to claim 7, characterized in that, The data processing unit is used to execute the control method as described in claims 3 to 6.

9. The control module according to claim 7, characterized in that, This module also includes: The airspace perception unit detects drones entering the designated airspace in real time. The tag tracking unit assigns queue tags to drones entering a designated airspace and tracks them in real time. The command output unit sends control commands to the drone to control it to perform operations.

10. A low-altitude flight scheduling and control platform, characterized in that, The platform includes: The flight control scheduling service module is used to support one or more of the following: UAV basic data processing, UAV library data processing, UAV scheduling logic services, and UAV scheduling communication. The control module as described in claims 7 to 9 is used to control the dynamic queuing landing of UAVs.

11. A drone scheduling system, characterized in that, The system includes: a drone, a low-altitude flight scheduling and control platform and a take-off and landing platform as described in claim 10; The UAV interacts with the low-altitude flight scheduling and control platform via a wireless communication network. The UAV executes control commands issued by the dynamic landing control module in the low-altitude flight scheduling and control platform, and completes operations such as filling in, cutting in line, hovering, or emergency landing on the landing pad.

12. A dynamic landing control device for unmanned aerial vehicles (UAVs), characterized in that, include: Memory, one or more processors; The memory and processor are connected via a communication bus; the processor is configured to execute instructions from the memory. The storage medium stores instructions for performing each step of the method as described in any one of claims 1 to 6.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps of the method as described in any one of claims 1 to 6.

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