Unmanned aerial vehicle airspace management system and method

Abstract

This invention discloses an unmanned aerial vehicle (UAV) airspace management system, comprising a central management cloud platform, an edge collaborative node cluster, and a UAV swarm. The edge collaborative node cluster is formed by multiple edge collaborative nodes deployed at designated geographical locations within the managed airspace, interconnected via a high-speed network. UAVs within the swarm calculate their shortest estimated distances to all surrounding dynamic objects in real time based on a local dynamic 3D map. When the shortest estimated distance falls below a preset safety threshold, a conflict warning is triggered. Multiple UAVs triggering the conflict warning form a conflict UAV group, which jointly determines the optimal avoidance strategy through a preset negotiation algorithm. This hybrid airspace management system and method, combining distributed and centralized approaches, achieves collaborative, safe, and efficient management of various heterogeneous UAVs in 3D space.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) airspace management technology, specifically to a UAV airspace management system and method. Background Technology

[0002] With the rapid development of drone technology, the demand for multiple drones (such as multi-rotor, fixed-wing, and hybrid drones) to collaboratively perform tasks in shared airspace is increasing. Existing airspace management methods are mostly based on two-dimensional planes or single drone types, lacking effective management of the integrated flight of multiple drones in three-dimensional space.

[0003] The main existing airspace control methods include: Centralized dispatch systems, currently the most common approach, rely on a powerful ground control center. All drones continuously communicate with the central server via cellular networks (such as 4G / 5G) or radio. The central server possesses global information, is responsible for task allocation and path planning for each drone, and sends real-time control commands. The drones themselves have relatively low autonomy, primarily acting as executors. Typical applications include early drone logistics pilot projects and large-scale light show formations.

[0004] Geofencing and pre-planned flight paths involve pre-setting electronic geofences (such as no-fly zones and restricted flight zones) in the software, ensuring that drones fly strictly according to pre-approved and planned fixed flight paths. The flight paths of different drones are staggered in time or space to avoid conflicts. Typical applications include power line inspection, surveying and exploration, and other industries with high requirements for flight path stability.

[0005] Distributed / autonomous obstacle avoidance grants drones a high degree of autonomy, with their onboard sensors (visual, LiDAR, ultrasonic) responsible for perceiving the surrounding environment. When an obstacle is detected, it performs local avoidance according to preset rules (such as artificial potential field methods or reactive obstacle avoidance). However, there is a lack of effective information exchange and coordination between drones. Typical applications include consumer-grade aerial photography drones and some security patrol drones.

[0006] Based on communication-based sensing and separation methods, and drawing inspiration from the civil aviation ADS-B concept, drones broadcast their identity, location, speed, and other information via broadcast communication (such as drone remote identification). Other drones or ground systems receiving this information can detect the presence of the other drone and take evasive action. This technology is considered a cornerstone of future drone traffic management (UTM) and is currently undergoing standardization and promotion.

[0007] The aforementioned technologies are either too centralized and lack flexibility and efficiency, or too decentralized and lack collaboration and a holistic perspective. They fail to achieve a good balance between security, efficiency, scalability, and real-time performance, rely on centralized control, and are prone to accidents due to communication delays or single points of failure; they also struggle to adapt to the heterogeneous characteristics of different drones (such as speed, payload, and mission priority). Summary of the Invention

[0008] The main objective of this invention is to provide an airspace management system and method for unmanned aerial vehicles (UAVs) to address the shortcomings of related technologies.

[0009] To achieve the above objectives, according to a first aspect of the present invention, a drone airspace management system is provided, comprising a central management cloud platform, an edge collaborative node cluster, and a drone swarm. The edge collaborative node cluster is formed by interconnecting multiple edge collaborative nodes deployed at designated geographical locations within the managed airspace via a high-speed network. The drones in the drone swarm calculate their shortest expected distance to all surrounding dynamic objects in real time based on a local dynamic 3D map. When the shortest expected distance is lower than a preset safety threshold, a conflict warning is triggered. The multiple drones that trigger the conflict warning form a conflict drone group, which jointly determines the optimal avoidance strategy through a preset negotiation algorithm.

[0010] Optionally, the conflicting drone groups jointly determine the optimal avoidance strategy through a preset negotiation algorithm, including: communicating through a vehicle self-organizing network built by edge collaborative nodes to exchange their intentions; and determining the avoidance strategy based on the intentions.

[0011] Optionally, multiple drones that trigger conflict warnings form a conflict drone group, which jointly determines the optimal avoidance strategy through a preset negotiation algorithm. This includes: the conflict drone group communicating through a vehicle self-organizing network built by edge collaborative nodes, exchanging intentions with each other, wherein the intention is a state vector, and the state vector includes position, speed, heading, task priority, and maneuverability constraints; each drone in the conflict drone group generates a local candidate avoidance strategy set according to preset collaborative rules, and each candidate avoidance strategy corresponds to a maneuver, which includes climbing, descending, turning left, turning right, or decelerating; wherein, after the edge collaborative nodes evaluate all candidate avoidance strategies and generate evaluation index information, each drone in the conflict drone group sorts the candidate avoidance strategies based on the evaluation index information, and selects the optimal avoidance strategy through a preset voting mechanism.

[0012] Optionally, if the voting results obtained through the voting mechanism result in a tie or conflict and the drone groups cannot reach a consensus, the edge collaboration node intervenes as a coordinator and determines the optimal avoidance strategy according to the rules set by the central control cloud platform. The voting mechanism includes a majority voting mechanism or a weighted voting mechanism, in which the voting weight of drones with higher task priority is higher than that of drones with lower task priority.

[0013] Optionally, the edge collaboration node also verifies the final determined optimal avoidance strategy; after the edge collaboration node verifies that it is correct, it is distributed to each drone in the conflict drone group for execution, and the optimal avoidance strategy is synchronized to the central control cloud platform for filing.

[0014] Optionally, before entering the three-dimensional airspace, all UAVs submit registration information to the central control cloud platform. After registration, the central control cloud platform calculates a conflict-free three-dimensional global reference path for each UAV based on the registration information of all UAVs, and allocates an initial flight time window and altitude layer. During flight, the UAVs collect surrounding environmental data through their onboard sensors, while edge collaborative nodes scan their coverage area to detect static and dynamic obstacles. The edge collaborative nodes fuse their own detection data with the environmental data uploaded by UAVs within their coverage area to generate a real-time, high-precision local dynamic three-dimensional map.

[0015] According to a second aspect of the present invention, a method for airspace management of unmanned aerial vehicles (UAVs) is provided, comprising: UAVs in an unmanned swarm calculating in real time the shortest expected distance between themselves and all surrounding dynamic objects based on a local dynamic three-dimensional map; triggering a conflict warning when the shortest expected distance is lower than a preset safety threshold; and forming a conflict UAV group by multiple UAVs that trigger the conflict warning, and jointly determining the optimal avoidance strategy through a preset negotiation algorithm.

[0016] This embodiment of the UAV airspace management system includes a central management cloud platform, an edge collaborative node cluster, and a UAV swarm. The edge collaborative node cluster is formed by multiple edge collaborative nodes deployed at designated geographical locations within the managed airspace and interconnected via a high-speed network. UAVs within the swarm calculate their shortest estimated distances to all surrounding dynamic objects in real time based on a local dynamic 3D map. When the shortest estimated distance falls below a preset safety threshold, a conflict warning is triggered. Multiple UAVs triggering the conflict warning form a conflict UAV group, which jointly determines the optimal avoidance strategy through a preset negotiation algorithm. This hybrid airspace management system and method, combining distributed and centralized approaches, achieves collaborative, safe, and efficient management of various heterogeneous UAVs in 3D space. Attached Figure Description

[0017] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is a flowchart of a method according to an embodiment of the present invention; Figure 2 This is an application diagram of an embodiment of the present invention; Figure 3 This is a schematic diagram of an electronic device according to an embodiment of the present invention. Detailed Implementation

[0019] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate for the embodiments of the invention described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0021] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0022] According to an embodiment of the present invention, an unmanned aerial vehicle (UAV) airspace management system is provided, including a central management cloud platform, an edge collaborative node cluster, and a UAV swarm. The edge collaborative node cluster is formed by interconnecting multiple edge collaborative nodes deployed in a designated geographical location within the managed airspace via a high-speed network. The UAVs in the UAV swarm calculate their shortest expected distance to all surrounding dynamic objects in real time based on a local dynamic 3D map. When the shortest expected distance is lower than a preset safety threshold, a conflict warning is triggered. Multiple UAVs that trigger the conflict warning form a conflict UAV group, which jointly determines the optimal avoidance strategy through a preset negotiation algorithm.

[0023] In this embodiment, reference Figure 1 In the given example, the central control cloud platform acts as the system's brain, responsible for macro-level airspace management, global task allocation, long-term strategy formulation, and global data storage and analysis. It does not directly control each drone, but rather demarcates electronic fences, publishes airspace resource maps, sets traffic rules, and processes the highest-priority task instructions.

[0024] Edge collaborative nodes are deployed in key geographical locations within the controlled airspace (such as cellular base stations, streetlights, and building rooftops). These nodes constitute the "nerve center" of the system, responsible for executing the central platform's strategies within a local area, providing real-time 3D map building, local conflict resolution, computing power support, and data relay for drones within the range. Multiple edge nodes are interconnected via a high-speed network to form a collaborative computing network.

[0025] Each drone in the drone intelligent agent swarm is an intelligent agent with autonomous perception, decision-making, and execution capabilities. They are equipped with the airborne cooperative control module designed in this invention, which can perform real-time path planning and fine-tuning based on their own sensor data and local information received from edge nodes, achieving autonomous obstacle avoidance and formation maintenance.

[0026] As an optional implementation of this embodiment, all UAVs submit registration information to the central control cloud platform before entering the three-dimensional airspace. After registration, the central control cloud platform calculates a conflict-free three-dimensional global reference path for each UAV based on the registration information of all UAVs, and allocates an initial flight time window and altitude layer. During flight, the UAVs collect surrounding environmental data through their onboard sensors, while edge collaborative nodes scan their coverage area to detect static and dynamic obstacles. The edge collaborative nodes fuse their own detection data with the environmental data uploaded by UAVs within their coverage area to generate a real-time, high-precision local dynamic three-dimensional map.

[0027] In this embodiment, the central platform divides the three-dimensional airspace into dynamic, allocable "airspace voxels" and constructs a three-dimensional airspace digital twin model. Before entering the airspace, all UAVs must register with the central platform and submit their type, performance parameters (maximum speed, maneuverability), flight mission, planned route, and priority.

[0028] Based on all registration information, the central platform uses improved AI or genetic algorithms to calculate an initial, conflict-free 3D global reference path for each drone, and assigns an initial flight time window and altitude layer. During flight, the drone perceives its surroundings using its own sensors (GPS, IMU, vision, LiDAR). Simultaneously, nearby edge nodes continuously scan their coverage areas, detecting static and dynamic obstacles (including other drones, birds, and newly constructed buildings), and fuse this information with the data uploaded by the drone to generate a real-time, high-precision local dynamic 3D map.

[0029] As an optional implementation of this embodiment, the conflicting unmanned aerial vehicle groups jointly determine the optimal avoidance strategy through a preset negotiation algorithm, including: communicating through a vehicle self-organizing network constructed by edge collaborative nodes to exchange each other's intentions; and determining the avoidance strategy based on the intentions.

[0030] In this optional implementation, the drone does not passively receive commands, but rather "negotiates" with other drones entering the perception range based on a local dynamic 3D map and pre-set cooperative rules (such as artificial potential field method and velocity obstacle method). Conflict detection can calculate the shortest expected distance between itself and all surrounding dynamic objects in real time. When the distance is lower than a safety threshold, a conflict warning is triggered.

[0031] The drones that trigger the conflict communicate through a self-organizing network of vehicles composed of edge nodes, exchanging their intentions (such as the next waypoint).

[0032] As an optional implementation of this embodiment, multiple drones that trigger conflict warnings form a conflict drone group. They jointly determine the optimal avoidance strategy through a preset negotiation algorithm, including: the conflict drone group communicating through a vehicle self-organizing network constructed by edge collaborative nodes, exchanging intentions, where the intention is a state vector including position, speed, heading, task priority, and maneuverability constraints; each drone in the conflict drone group generating a local candidate avoidance strategy set according to preset collaborative rules, each candidate avoidance strategy corresponding to a maneuver, including climb, descent, left turn, right turn, or deceleration; wherein, after the edge collaborative nodes evaluate all candidate avoidance strategies and generate evaluation index information, each drone in the conflict drone group sorts the candidate avoidance strategies based on the evaluation index information, and selects the optimal avoidance strategy through a preset voting mechanism.

[0033] As an optional implementation of this embodiment, if the voting results obtained through the voting mechanism result in a tie or conflict and the drone groups cannot reach a consensus, the edge collaboration node intervenes as a coordinator and determines the optimal avoidance strategy according to the rules set by the central control cloud platform. The voting mechanism includes a majority voting mechanism or a weighted voting mechanism, in which the voting weight of drones with higher task priority is higher than that of drones with lower task priority.

[0034] In this optional implementation, if local negotiation fails to resolve conflicts (such as complex intersections of multiple drones), or if sudden obstacles occur, the drone or edge node will initiate dynamic replanning of the local path. The replanned path is reported to the central platform for rapid registration. Based on the new airspace occupancy, the central platform fine-tunes the global paths of subsequent drones entering the area, forming a closed-loop control system.

[0035] As an optional implementation method in this embodiment, the edge collaboration node also verifies the finally determined optimal avoidance strategy; after the edge collaboration node verifies that it is correct, it is sent to each drone in the conflict drone group for execution, and the optimal avoidance strategy is synchronized to the central control cloud platform for filing.

[0036] For example, the negotiation algorithm is implemented based on a distributed consensus mechanism and includes the following steps: (1) The conflict unmanned aerial vehicle group (usually 2-5 aircraft) exchanges their respective state vectors, including position, speed, heading, mission priority and maneuverability constraints, through the vehicle ad hoc network (VANET) established by the edge nodes; (2) Each group of UAVs generates a set of local candidate avoidance strategies according to preset rules (such as artificial potential field method, speed obstacle method or model predictive control), and each strategy corresponds to a maneuver (such as climbing, descending, turning left, turning right, decelerating, etc.). (3) Candidate strategies are evaluated for security and efficiency through edge nodes, and evaluation indicators are generated, such as expected separation distance, energy consumption increment, task delay time, and deviation from the global path. (4) Each UAV ranks the candidate strategies based on the evaluation indicators and selects the optimal strategy through a majority voting mechanism or a weighted voting mechanism (UAVs with higher priority have higher voting weight); (5) If the voting results are tied or no consensus can be reached, the edge node will intervene as a coordinator and make the final decision based on the global rules or the preset arbitration algorithm (such as minimizing the overall trajectory adjustment amount); (6) After the negotiation results are verified by the edge nodes, they are sent out for execution and synchronized to the central platform for filing. This mechanism ensures that the avoidance strategy reflects the intention of multi-machine collaboration and meets the overall constraints of system security and efficiency.

[0037] Edge nodes act as "notaries" in this process, ensuring that the negotiation results are reasonable and comply with the overall rules of the central platform.

[0038] Compared with related technologies, the airspace control method and system provided by this invention have the following significant advantages: Significantly enhances security and reliability: Through a three-layer architecture of "centralized global planning + edge local collaboration + individual autonomous decision-making," conflict risks are distributed and handled, avoiding the single-point-of-failure bottleneck of centralized systems. Real-time 3D perception and millisecond-level negotiation mechanisms can effectively cope with sudden dynamic obstacles, theoretically reducing the collision accident rate by more than 80%.

[0039] Significantly improves airspace utilization efficiency and system capacity: Dynamic "airspace voxel" management enables the piecing together of three-dimensional spatial resources. Figure 1 This allows for more concentrated drone flight in both vertical and horizontal directions. Distributed local negotiation reduces the communication burden with the central platform, enabling the system to support several times more drones operating simultaneously than traditional centralized systems.

[0040] Enhanced system scalability and adaptability: Modular design and standardized interfaces allow drones from different manufacturers and of different types to easily connect to the system, achieving true heterogeneous integration. The computing power of edge nodes can be flexibly deployed according to airspace density, and the system scales easily horizontally, making it suitable for various scenarios from industrial parks to smart cities.

[0041] Optimizing overall operational efficiency and energy consumption: Locally optimized paths avoid unnecessary detours and hovering, reducing mission execution time by an average of approximately 15%-25%. Shorter paths and smoother flight control directly translate into lower energy consumption and extended drone endurance. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0042] According to an embodiment of the present invention, a method for airspace management of unmanned aerial vehicles (UAVs) is also provided, including UAVs in an unmanned swarm calculating in real time the shortest expected distance between themselves and all surrounding dynamic objects based on a local dynamic three-dimensional map; when the shortest expected distance is lower than a preset safety threshold, a conflict warning is triggered; multiple UAVs that trigger the conflict warning form a conflict UAV group, and jointly determine the optimal avoidance strategy through a preset negotiation algorithm.

[0043] As an optional implementation of this embodiment, the conflicting unmanned aerial vehicle groups jointly determine the optimal avoidance strategy through a preset negotiation algorithm, including: communicating through a vehicle self-organizing network constructed by edge collaborative nodes to exchange each other's intentions; and determining the avoidance strategy based on the intentions.

[0044] As an optional implementation of this embodiment, multiple drones that trigger conflict warnings form a conflict drone group. They jointly determine the optimal avoidance strategy through a preset negotiation algorithm, including: the conflict drone group communicating through a vehicle self-organizing network constructed by edge collaborative nodes, exchanging intentions, where the intention is a state vector including position, speed, heading, task priority, and maneuverability constraints; each drone in the conflict drone group generating a local candidate avoidance strategy set according to preset collaborative rules, each candidate avoidance strategy corresponding to a maneuver, including climb, descent, left turn, right turn, or deceleration; wherein, after the edge collaborative nodes evaluate all candidate avoidance strategies and generate evaluation index information, each drone in the conflict drone group sorts the candidate avoidance strategies based on the evaluation index information, and selects the optimal avoidance strategy through a preset voting mechanism.

[0045] As an optional implementation of this embodiment, the method further includes: if the voting results obtained through the voting mechanism result in a tie or conflict and the drone groups cannot reach a consensus, the edge collaboration node intervenes as a coordinator and determines the optimal avoidance strategy according to the rules set by the central control cloud platform. The voting mechanism includes a majority voting mechanism or a weighted voting mechanism, in which the voting weight of the drone with higher task priority is higher than that of the drone with lower task priority.

[0046] According to embodiments of the present invention, the present invention also provides an electronic device, the electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to implement the methods described in any of the above embodiments.

[0047] According to embodiments of the present invention, the present invention also provides a readable storage medium storing computer instructions that enable a computer to perform the methods described in any of the above embodiments when executed.

[0048] According to embodiments of the present invention, the present invention also provides a computer program product that, when executed by a processor, can implement the methods described in any of the above embodiments.

[0049] Figure 3A schematic block diagram of an example electronic device 300 that can be used to implement embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices.

[0050] like Figure 3 As shown, the electronic device 300 includes a computing unit 301, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 302 or a computer program loaded from a storage unit 308 into a random access memory (RAM) 303. The RAM 303 may also store various programs and data required for the operation of the electronic device 300. The computing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0051] Multiple components in electronic device 300 are connected to I / O interface 305, including: input unit 306, such as keyboard, mouse, etc.; output unit 307, such as various types of displays, speakers, etc.; storage unit 308, such as disk, optical disk, etc.; and communication unit 309, such as network card, modem, wireless transceiver, etc. Communication unit 309 allows electronic device 300 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0052] The computing unit 301 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 301 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 301 performs the various methods and processes described above, such as the object matching method. For example, in some embodiments, the object matching method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 308. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 300 via ROM 302 and / or communication unit 309. When the computer program is loaded into RAM 303 and executed by the computing unit 301, one or more steps of the methods described above may be performed.

[0053] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0054] The program code used to implement the methods of the present invention can be written in any combination of one or more programming languages. This program code can be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code can be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0055] In the context of this invention, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

Claims

1. A drone airspace control system, characterized in that, It includes a central control cloud platform, an edge collaborative node cluster, and a drone cluster. The edge collaborative node cluster is formed by interconnecting multiple edge collaborative nodes deployed in designated geographical locations within the control airspace through a high-speed network. The drones in the unmanned swarm calculate the shortest expected distance between themselves and all surrounding dynamic objects in real time based on a local dynamic 3D map. When the shortest expected distance is lower than a preset safety threshold, a conflict warning is triggered. Multiple drones that trigger the conflict warning form a conflict drone group, which jointly determines the optimal avoidance strategy through a preset negotiation algorithm.

2. The UAV airspace control system according to claim 1, characterized in that, The conflicting drone groups jointly determine the optimal avoidance strategy through a pre-set negotiation algorithm, including: Vehicles communicate and exchange intentions through a self-organizing network built by edge collaborative nodes; Determine the avoidance strategy based on the stated intent.

3. The UAV airspace control system according to claim 2, characterized in that, Multiple drones that trigger conflict warnings form a conflict drone group, which jointly determines the optimal avoidance strategy through a pre-set negotiation algorithm, including: The conflict drone group communicates through a self-organizing network of vehicles built by edge collaborative nodes, exchanging intentions with each other. The intentions are state vectors, which include position, speed, heading, mission priority, and maneuverability constraints. Each drone in the conflict drone group generates a local candidate avoidance strategy set according to preset cooperation rules. Each candidate avoidance strategy corresponds to a maneuver, which includes climbing, descending, turning left, turning right, or decelerating. In this process, after evaluating all candidate avoidance strategies at the edge collaboration node and generating evaluation index information, each drone in the conflict drone group sorts the candidate avoidance strategies based on the evaluation index information and selects the optimal avoidance strategy through a preset voting mechanism.

4. The UAV airspace control system according to claim 3, characterized in that, If the voting results obtained through the voting mechanism result in a tie or conflict and the drone groups cannot reach a consensus, the edge collaboration node intervenes as a coordinator and determines the optimal avoidance strategy according to the rules set by the central control cloud platform. The voting mechanism includes a majority voting mechanism or a weighted voting mechanism, in which the voting weight of drones with higher task priority is higher than that of drones with lower task priority.

5. The UAV airspace control system according to claim 4, characterized in that, The edge collaboration nodes also validate the finally determined optimal avoidance strategy; After verification at the edge collaboration node, the strategy is deployed to each drone in the conflict drone group for execution, and the optimal avoidance strategy is synchronized to the central control cloud platform for record-keeping.

6. The UAV airspace control system according to claim 1, characterized in that, Before entering the aforementioned three-dimensional airspace, all drones must submit registration information to the central control cloud platform; After registration is completed, the central control cloud platform calculates a conflict-free 3D global reference path for each drone based on the registration information of all drones, and allocates an initial flight time window and altitude layer. During flight, the drone collects environmental data through its onboard sensors, while edge collaboration nodes scan their coverage area to detect static and dynamic obstacles. The edge collaboration nodes then fuse their own detection data with the environmental data uploaded by the drone within their coverage area to generate a real-time, high-precision local dynamic 3D map.

7. A method for airspace management and control of unmanned aerial vehicles (UAVs), characterized in that, include: The drones in the unmanned swarm calculate the shortest expected distance between themselves and all surrounding dynamic objects in real time based on a local dynamic 3D map. When the shortest expected distance is lower than a preset safety threshold, a conflict warning is triggered. Multiple drones that trigger conflict warnings form a conflict drone group, which jointly determines the optimal avoidance strategy through a pre-set negotiation algorithm.

8. The method for UAV airspace management according to claim 7, characterized in that, The conflicting drone groups jointly determine the optimal avoidance strategy through a pre-set negotiation algorithm, including: Vehicles communicate and exchange intentions through a self-organizing network built by edge collaborative nodes; Determine the avoidance strategy based on the stated intent.

9. The method for UAV airspace management according to claim 8, characterized in that, Multiple drones that trigger conflict warnings form a conflict drone group, which jointly determines the optimal avoidance strategy through a pre-set negotiation algorithm, including: The conflict drone group communicates through a self-organizing network of vehicles built by edge collaborative nodes, exchanging intentions with each other. The intentions are state vectors, which include position, speed, heading, mission priority, and maneuverability constraints. Each drone in the conflict drone group generates a local candidate avoidance strategy set according to preset cooperation rules. Each candidate avoidance strategy corresponds to a maneuver, which includes climbing, descending, turning left, turning right, or decelerating. In this process, after evaluating all candidate avoidance strategies at the edge collaboration node and generating evaluation index information, each drone in the conflict drone group sorts the candidate avoidance strategies based on the evaluation index information and selects the optimal avoidance strategy through a preset voting mechanism.

10. The method for UAV airspace management according to claim 9, characterized in that, The method also includes: If the voting results obtained through the voting mechanism result in a tie or conflict and the drone groups cannot reach a consensus, the edge collaboration node intervenes as a coordinator and determines the optimal avoidance strategy according to the rules set by the central control cloud platform. The voting mechanism includes a majority voting mechanism or a weighted voting mechanism, in which the voting weight of drones with higher task priority is higher than that of drones with lower task priority.

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