Adaptive voxel roughening method and apparatus for uav airspace management

Abstract

The application relates to the technical field of airspace control, and discloses a method and device for controlling unmanned aerial vehicles in airspace by self-adaptive voxel coarsening and refining, which comprises the following steps: determining the space-time attribute data set of all voxels by using the obtained basic data source of airspace, determining the flow state parameter of each voxel, and determining the target control information of each voxel according to the flow state parameter of each voxel, so as to realize the unmanned aerial vehicle control process in the airspace; meanwhile, the voxel is processed in real time during the control process, so as to continuously update the voxel resolution and the voxel flow state. In this way, the airspace capacity quantization and the dynamic voxel resolution adaptation process can be realized, the control reliability and control accuracy of the airspace are improved, the artificial intervention is reduced through the closed-loop autonomous mechanism, the control response speed of the airspace is improved, the communication load in the airspace control process is reduced, and therefore, the resource utilization rate can be improved under the condition of ensuring safety accuracy.

Description

Technical Field

[0001] This invention relates to the field of airspace management technology, and in particular to an adaptive voxel coarsening method and apparatus for UAV airspace management. Background Technology

[0002] With the large-scale deployment of civilian drones in areas such as express delivery, power line inspection, and emergency response, the density and dynamic flow of aircraft in urban low-altitude airspace are experiencing explosive growth. Traditional airspace management systems can no longer meet the demands for safe and efficient management. The existing management model, which is based on static zoning, fixed route planning, and manual scheduling, has significant drawbacks: it cannot accommodate multiple drones operating concurrently in real time, and its response to dynamic scenarios such as sudden weather changes, temporary no-fly zones, and sudden ground risks is relatively slow; furthermore, its reliance on drones reporting precise locations for conflict control increases the load on communication links and introduces privacy risks.

[0003] To address the aforementioned issues, spatial discretization representation based on 3D meshes / voxels has become a hot research topic. This method divides the spatial domain into coded and queryable 3D units, offering advantages such as efficient indexing, adaptability to parallel computing, and ease of integration with rule bases / knowledge graphs for reasoning. However, existing solutions still suffer from key drawbacks: the lack of an effective capacity measurement mechanism and the predominantly fixed mesh resolution, which can lead to wasted computational and communication resources or insufficient control precision; furthermore, insufficient temporal dimension integration makes it difficult to predict future airspace capacity, hindering the advance allocation and proactive control of UAVs. Therefore, providing a method to improve the real-time performance and accuracy of UAV airspace control has become a pressing issue. Summary of the Invention

[0004] This invention provides an adaptive voxel coarsening method and apparatus for UAV airspace management, which can realize airspace capacity quantification and dynamic voxel resolution adaptation, improving the reliability and accuracy of airspace management. At the same time, through a closed-loop autonomous mechanism, it reduces human intervention, thereby improving the response speed of airspace management and reducing the communication load in the airspace management process, thus helping to improve resource utilization while ensuring safety and accuracy.

[0005] To address the aforementioned technical problems, the first aspect of this invention discloses a method for UAV airspace management with adaptive voxel coarsening, the method comprising: The basic data source of the airspace is obtained, and the spatiotemporal attribute datasets of all voxels corresponding to the airspace are determined based on the basic data source. The basic data source of the airspace includes historical UAV flight data, meteorological data, ground environment data and target control data of the airspace. The spatiotemporal attribute dataset of each voxel includes historical traffic time series data, meteorological risk data, ground risk data and control identification data of the voxel under any preset time window. For each voxel, the discrete allowed concurrent drone count of the voxel is determined based on the voxel's temporal attribute dataset, and the flow state parameter of the voxel is determined based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel. Based on the flow status parameters of the voxels, the target control information corresponding to the voxels is determined, and based on the target control information corresponding to all the voxels, UAV control operations are performed on the airspace. During the operation of UAV control over the airspace, for each voxel, a matching voxel processing operation is performed to obtain a processed voxel; the voxel processing operation includes voxel refinement processing operation, voxel coarsening and merging operation, or voxel maintenance processing operation. The voxel is updated according to the processed voxel, and the discrete allowed concurrent drone count of the voxel is updated. The operation of determining the flow status parameter of the voxel based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel is retried and executed.

[0006] As an optional implementation, in the first aspect of the present invention, determining the discrete allowable concurrent UAV number of the voxel based on the temporal attribute dataset of the voxel includes: Obtain the baseline concurrent UAV density parameters, ground obstacle parameters, and no-fly zone parameters of the voxels; Based on the ground obstacle parameters and no-fly zone parameters of the voxel, the volume normalization factor of the voxel is determined, and based on the baseline concurrent UAV density parameters and volume normalization factor of the voxel, the baseline UAV capacity parameters of the voxel are determined. Based on the baseline drone capacity parameters and time-series attribute dataset of the voxel, determine the discrete allowable concurrent drone count of the voxel.

[0007] As an optional implementation, in the first aspect of the present invention, determining the discrete allowable concurrent number of drones for the voxel based on the voxel's baseline drone capacity parameters and time-series attribute dataset includes: For each time window, the historical flow adjustment capacity weight of the voxel is calculated based on the historical flow time series data of the voxel and the preset flow influence coefficient and reference flow parameters, and the meteorological risk adjustment capacity weight of the voxel is calculated based on the meteorological risk data of the voxel and the preset meteorological influence coefficient. For each time window, the ground risk adjustment capacity weight of the voxel is calculated based on the ground risk data of the voxel and the preset ground impact coefficient, and the functional area type adjustment capacity weight of the voxel is determined based on the control identification data of the voxel. The adjusted UAV capacity parameters of the voxel are determined based on the baseline UAV capacity parameters of the voxel, the historical traffic adjustment capacity weight, the meteorological risk adjustment capacity weight, the ground risk adjustment capacity weight, and the functional area type adjustment capacity weight. Based on the adjusted UAV capacity parameters of the voxel, determine the discrete allowable number of concurrent UAVs for the voxel.

[0008] As an optional implementation, in the first aspect of the invention, performing a matching voxel processing operation on the voxel to obtain a processed voxel includes: Based on the flow state parameters of the voxel, determine the target voxel processing type corresponding to the voxel; When the target voxel processing type corresponding to the voxel is voxel refinement processing type, the voxel is subjected to voxel refinement processing operation according to the preset refinement factor parameter to obtain all refined sub-voxels corresponding to the voxel, and all the refined sub-voxels are determined as processed voxels. When the target voxel processing type corresponding to the voxel is voxel coarsening and merging type, voxel coarsening and merging operation is performed on the voxel and the neighboring voxels corresponding to the voxel to obtain the coarsened parent voxel corresponding to the voxel, and the coarsened parent voxel is determined as the processed voxel. When the target voxel processing type corresponding to the voxel is the voxel maintenance processing type, the voxel is directly identified as the processed voxel.

[0009] As an optional implementation, in the first aspect of the present invention, determining the target voxel processing type corresponding to the voxel based on the voxel's flow state parameters includes: Based on the flow state parameters of the voxel, determine the flow trend parameters of the voxel; The drone arrival rate parameter of the voxel is obtained, and the overload probability parameter of the voxel is determined based on the drone arrival rate parameter and the discrete allowed concurrent drone number. Based on the discrete number of concurrent drones allowed for the voxel, the refinement trigger threshold and the coarsening trigger threshold for the voxel are determined; the refinement trigger threshold is greater than the coarsening trigger threshold. Based on the voxel's flow status parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold, the target voxel processing type corresponding to the voxel is determined.

[0010] As an optional implementation, in the first aspect of the present invention, determining the target voxel processing type corresponding to the voxel based on the voxel's flow state parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold includes: When the flow state parameter of the voxel is less than the coarsening trigger threshold, the target voxel parameter of the neighboring voxel associated with the voxel is obtained, and based on the target voxel parameter, it is determined whether the voxel and the neighboring voxel meet the preset voxel merging condition; if yes, the target voxel processing type corresponding to the voxel is determined to be the voxel coarsening merging type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When the flow state parameter of the voxel is greater than or equal to the coarsening trigger threshold and less than the refinement trigger threshold, determine whether the flow trend parameter of the voxel is greater than or equal to the preset flow trend threshold. When it is determined that the flow trend parameter of the voxel is less than the flow trend threshold, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When it is determined that the flow trend parameter of the voxel is greater than or equal to the flow trend threshold, it is determined whether the voxel specification parameter of the voxel is greater than or equal to the preset minimum voxel refinement specification threshold. If yes, the target voxel processing type corresponding to the voxel is determined to be a candidate refinement processing type in the voxel refinement processing type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When the voxel's flow state parameter is greater than or equal to the refinement trigger threshold, it is determined whether the voxel's voxel specification parameter is greater than or equal to the minimum voxel refinement specification threshold. If yes, the target voxel processing type corresponding to the voxel is determined to be the preferred refinement processing type among the voxel refinement processing types; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. The refinement processing priority corresponding to the preferred refinement processing type is higher than the refinement processing priority corresponding to the candidate refinement processing type; or... When the overload probability parameter of the voxel is greater than or equal to the preset overload probability threshold, it is determined whether the voxel specification parameter of the voxel is greater than or equal to the minimum voxel refinement specification threshold; if yes, the target voxel processing type corresponding to the voxel is determined to be the preferred refinement processing type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type.

[0011] As an optional implementation, in the first aspect of the present invention, the target voxel parameters of the neighborhood voxel include the voxel specification parameters, flow status parameters, overload probability parameters, current control status, functional attribute parameters, and risk level parameters of the neighborhood voxel. The step of determining whether the voxel and the neighboring voxels satisfy a preset voxel merging condition based on the target voxel parameters includes: Based on the voxel specification parameters of the voxel and the voxel specification parameters of the neighboring voxels, determine the merged voxel specification parameters corresponding to the voxel and the neighboring voxels, and determine whether the merged voxel specification parameters are less than or equal to the preset maximum voxel merging specification threshold. When it is determined that the merged voxel specification parameter is greater than the maximum voxel merging specification threshold, it is determined that the voxel and the neighboring voxels do not meet the preset voxel merging conditions. When it is determined that the merged voxel specification parameter is less than or equal to the maximum voxel merging specification threshold, it is determined whether the flow status parameter of the neighboring voxel is less than the target coarsening trigger threshold of the neighboring voxel, whether the overload probability parameter of the neighboring voxel is less than the target overload probability threshold of the neighboring voxel, whether the current control status of the neighboring voxel is in the free entry and exit control status, whether the functional attribute parameter of the neighboring voxel matches the functional attribute parameter of the voxel, and whether the risk level parameter of the neighboring voxel matches the risk level parameter of the voxel. When all judgment results are yes, it is determined that the voxel and the neighboring voxel satisfy the voxel merging condition; If any judgment result is negative, it is determined that the voxel and the neighboring voxels do not satisfy the voxel merging condition.

[0012] A second aspect of this invention discloses an adaptive voxel coarsening unmanned aerial vehicle (UAV) airspace management device, the device comprising: The acquisition module is used to acquire the basic data source of the airspace; The determination module is used to determine the spatiotemporal attribute datasets of all voxels corresponding to the airspace based on the basic data source. The basic data source of the airspace includes historical UAV flight data, meteorological data, ground environment data, and target control data of the airspace. The spatiotemporal attribute dataset of each voxel includes historical traffic time series data, meteorological risk data, ground risk data, and control identification data of the voxel under any preset time window. For each voxel, the discrete allowed concurrent UAV count of the voxel is determined based on the time series attribute dataset of the voxel, and the traffic status parameters of the voxel are determined based on the discrete allowed concurrent UAV count of the voxel and the current UAV count of the voxel. The target control information corresponding to the voxel is determined based on the traffic status parameters of the voxel. The control module is used to perform UAV control operations on the airspace based on the target control information corresponding to all the voxels. The voxel processing module is used to perform a matching voxel processing operation on each voxel during the process of the control module performing UAV control operations on the airspace, so as to obtain a processed voxel; the voxel processing operation includes voxel refinement processing operation, voxel coarsening and merging operation, or voxel maintenance processing operation. The update module is used to update the voxel based on the processed voxel, update the discrete allowed concurrent drone count of the voxel, and re-trigger the determination module to perform the operation of determining the flow status parameter of the voxel based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel.

[0013] As an optional implementation, in a second aspect of the invention, the method by which the determining module determines the discrete allowable concurrent UAV number of the voxel based on the voxel's temporal attribute dataset specifically includes: Obtain the baseline concurrent UAV density parameters, ground obstacle parameters, and no-fly zone parameters of the voxels; Based on the ground obstacle parameters and no-fly zone parameters of the voxel, the volume normalization factor of the voxel is determined, and based on the baseline concurrent UAV density parameters and volume normalization factor of the voxel, the baseline UAV capacity parameters of the voxel are determined. Based on the baseline drone capacity parameters and time-series attribute dataset of the voxel, determine the discrete allowable concurrent drone count of the voxel.

[0014] As an optional implementation, in a second aspect of the present invention, the method by which the determining module determines the discrete allowable concurrent number of drones for the voxel based on the voxel's baseline drone capacity parameters and the time-series attribute dataset specifically includes: For each time window, the historical flow adjustment capacity weight of the voxel is calculated based on the historical flow time series data of the voxel and the preset flow influence coefficient and reference flow parameters, and the meteorological risk adjustment capacity weight of the voxel is calculated based on the meteorological risk data of the voxel and the preset meteorological influence coefficient. For each time window, the ground risk adjustment capacity weight of the voxel is calculated based on the ground risk data of the voxel and the preset ground impact coefficient, and the functional area type adjustment capacity weight of the voxel is determined based on the control identification data of the voxel. The adjusted UAV capacity parameters of the voxel are determined based on the baseline UAV capacity parameters of the voxel, the historical traffic adjustment capacity weight, the meteorological risk adjustment capacity weight, the ground risk adjustment capacity weight, and the functional area type adjustment capacity weight. Based on the adjusted UAV capacity parameters of the voxel, determine the discrete allowable number of concurrent UAVs for the voxel.

[0015] As an optional implementation, in a second aspect of the invention, the voxel processing module performs a matching voxel processing operation on the voxel to obtain the processed voxel, specifically including: Based on the flow state parameters of the voxel, determine the target voxel processing type corresponding to the voxel; When the target voxel processing type corresponding to the voxel is voxel refinement processing type, the voxel is subjected to voxel refinement processing operation according to the preset refinement factor parameter to obtain all refined sub-voxels corresponding to the voxel, and all the refined sub-voxels are determined as processed voxels. When the target voxel processing type corresponding to the voxel is voxel coarsening and merging type, voxel coarsening and merging operation is performed on the voxel and the neighboring voxels corresponding to the voxel to obtain the coarsened parent voxel corresponding to the voxel, and the coarsened parent voxel is determined as the processed voxel. When the target voxel processing type corresponding to the voxel is the voxel maintenance processing type, the voxel is directly identified as the processed voxel.

[0016] As an optional implementation, in the second aspect of the present invention, the method by which the voxel processing module determines the target voxel processing type corresponding to the voxel based on the voxel's flow state parameters specifically includes: Based on the flow state parameters of the voxel, determine the flow trend parameters of the voxel; The drone arrival rate parameter of the voxel is obtained, and the overload probability parameter of the voxel is determined based on the drone arrival rate parameter and the discrete allowed concurrent drone number. Based on the discrete number of concurrent drones allowed for the voxel, the refinement trigger threshold and the coarsening trigger threshold for the voxel are determined; the refinement trigger threshold is greater than the coarsening trigger threshold. Based on the voxel's flow status parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold, the target voxel processing type corresponding to the voxel is determined.

[0017] As an optional implementation, in the second aspect of the present invention, the method by which the voxel processing module determines the target voxel processing type corresponding to the voxel based on the voxel's flow status parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold specifically includes: When the flow state parameter of the voxel is less than the coarsening trigger threshold, the target voxel parameter of the neighboring voxel associated with the voxel is obtained, and based on the target voxel parameter, it is determined whether the voxel and the neighboring voxel meet the preset voxel merging condition; if yes, the target voxel processing type corresponding to the voxel is determined to be the voxel coarsening merging type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When the flow state parameter of the voxel is greater than or equal to the coarsening trigger threshold and less than the refinement trigger threshold, determine whether the flow trend parameter of the voxel is greater than or equal to the preset flow trend threshold. When it is determined that the flow trend parameter of the voxel is less than the flow trend threshold, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When it is determined that the flow trend parameter of the voxel is greater than or equal to the flow trend threshold, it is determined whether the voxel specification parameter of the voxel is greater than or equal to the preset minimum voxel refinement specification threshold. If yes, the target voxel processing type corresponding to the voxel is determined to be a candidate refinement processing type in the voxel refinement processing type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When the voxel's flow state parameter is greater than or equal to the refinement trigger threshold, it is determined whether the voxel's voxel specification parameter is greater than or equal to the minimum voxel refinement specification threshold. If yes, the target voxel processing type corresponding to the voxel is determined to be the preferred refinement processing type among the voxel refinement processing types; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. The refinement processing priority corresponding to the preferred refinement processing type is higher than the refinement processing priority corresponding to the candidate refinement processing type; or... When the overload probability parameter of the voxel is greater than or equal to the preset overload probability threshold, it is determined whether the voxel specification parameter of the voxel is greater than or equal to the minimum voxel refinement specification threshold; if yes, the target voxel processing type corresponding to the voxel is determined to be the preferred refinement processing type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type.

[0018] As an optional implementation, in a second aspect of the present invention, the target voxel parameters of the neighborhood voxel include the voxel specification parameters, flow status parameters, overload probability parameters, current control status, functional attribute parameters, and risk level parameters of the neighborhood voxel. Specifically, the method by which the voxel processing module determines whether the target voxel and its neighboring voxels meet the preset voxel merging conditions based on the target voxel parameters includes: Based on the voxel specification parameters of the voxel and the voxel specification parameters of the neighboring voxels, determine the merged voxel specification parameters corresponding to the voxel and the neighboring voxels, and determine whether the merged voxel specification parameters are less than or equal to the preset maximum voxel merging specification threshold. When it is determined that the merged voxel specification parameter is greater than the maximum voxel merging specification threshold, it is determined that the voxel and the neighboring voxels do not meet the preset voxel merging conditions. When it is determined that the merged voxel specification parameter is less than or equal to the maximum voxel merging specification threshold, it is determined whether the flow status parameter of the neighboring voxel is less than the target coarsening trigger threshold of the neighboring voxel, whether the overload probability parameter of the neighboring voxel is less than the target overload probability threshold of the neighboring voxel, whether the current control status of the neighboring voxel is in the free entry and exit control status, whether the functional attribute parameter of the neighboring voxel matches the functional attribute parameter of the voxel, and whether the risk level parameter of the neighboring voxel matches the risk level parameter of the voxel. When all judgment results are yes, it is determined that the voxel and the neighboring voxel satisfy the voxel merging condition; If any judgment result is negative, it is determined that the voxel and the neighboring voxels do not satisfy the voxel merging condition.

[0019] A third aspect of the present invention discloses another adaptive voxel coarsening unmanned aerial vehicle (UAV) airspace management device, the device comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the adaptive voxel coarsening UAV airspace management method disclosed in the first aspect of the present invention.

[0020] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the adaptive voxel coarsening UAV airspace management method disclosed in the first aspect of the present invention.

[0021] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: In this embodiment of the invention, by acquiring the basic data source of the airspace, the spatiotemporal attribute dataset of all voxels is determined to identify the flow state parameters of each voxel. Based on these parameters, the target control information for each voxel is determined, thereby realizing the UAV control process in the airspace. Simultaneously, during the control process, voxels are processed in real time to continuously update their size and flow state. This enables airspace capacity quantification and dynamic voxel resolution adaptation, improving the reliability and accuracy of airspace control. Furthermore, the closed-loop autonomous mechanism reduces human intervention, thereby improving the response speed of airspace control and reducing the communication load during airspace control, thus facilitating resource utilization while ensuring safety and accuracy. Attached Figure Description

[0022] 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.

[0023] Figure 1 This is a flowchart illustrating an adaptive voxel coarsening method for UAV airspace management disclosed in an embodiment of the present invention. Figure 2 This is a flowchart illustrating another adaptive voxel coarsening method for UAV airspace management disclosed in an embodiment of the present invention. Figure 3 This is a schematic diagram of the structure of an adaptive voxel coarsening unmanned aerial vehicle (UAV) airspace management device disclosed in an embodiment of the present invention; Figure 4 This is a schematic diagram of another adaptive voxel coarsening UAV airspace management device disclosed in an embodiment of the present invention. Detailed Implementation

[0024] 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 are within the scope of protection of the present invention.

[0025] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0026] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0027] This invention discloses an adaptive voxel coarsening method and apparatus for UAV airspace management, which can realize airspace capacity quantification and dynamic voxel resolution adaptation, improving the reliability and accuracy of airspace management. At the same time, through a closed-loop autonomous mechanism, it reduces human intervention, thereby improving the response speed of airspace management and reducing the communication load in the airspace management process, thus helping to improve resource utilization while ensuring safety and accuracy.

[0028] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating an adaptive voxel coarsening method for UAV airspace management disclosed in an embodiment of the present invention. Optionally, this method can be implemented by a UAV airspace management device, which can be integrated into a UAV swarm scheduling system, a regional edge gateway of a low-altitude airspace management platform, or a smart transportation integrated management terminal. It can also be a local server or cloud server used to process the UAV airspace management process, etc., and the embodiments of the present invention do not impose limitations. Figure 1 As shown, this adaptive voxel coarsening UAV airspace management method may include the following operations: 101. Obtain the basic data source for the spatial domain, and based on the basic data source, determine the spatiotemporal attribute dataset of all voxels corresponding to the spatial domain.

[0029] In this embodiment of the invention, the basic data source of the airspace includes historical UAV flight data (such as historical UAV traffic patterns, flight track records, etc.), meteorological data, ground environment data, and target control data (such as temporary control information, no-fly zone information, etc.); and the spatiotemporal attribute dataset for each voxel includes historical traffic time series data, meteorological risk data, ground risk data, and control identification data for that voxel under any preset time window, and may further include recent scheduling / activity identification data. Optionally, the ground environment data of the airspace may include at least one of the following: terrain data, elevation data, ground building data (such as 3D models of ground buildings, distribution, usage, etc.), and ground activity data (such as ground activity crowd density, activity time, activity type, etc.).

[0030] Optionally, for each type of data in the basic data source of the spatial domain, further data processing operations such as outlier removal, data completion, and format conversion can be performed, and then the spatiotemporal attribute dataset of all voxels corresponding to the spatial domain can be determined based on the processed data.

[0031] Furthermore, the spatiotemporal attribute dataset for each voxel can be understood as follows: First, a GeoSOT-3D voxel grid is generated in the region of interest according to a preset initial spatial boundary length, and each voxel is assigned static attributes (terrain height, building density, proximity facility level, etc.) and initial dynamic attributes (historical flow statistics summary, default risk coefficient). Then, from the time dimension, historical flow time series data (e.g., time-series flow in the most recent T=24 hours) and short-term forecast inputs (e.g., 0–6 hour weather forecasts, known event plans) within a sliding window are retained for each voxel. During this process, the time series data can be normalized and time-delay weighted for use in capacity modeling. In short, the spatiotemporal attribute dataset for each voxel in this invention refers to a continuous record in the time dimension including information such as historical flow, meteorological factors, ground risk, and temporary control. The system forms a complete time series dataset by establishing a sliding time window (e.g., the most recent 24 hours) for each voxel. Unlike existing technologies that only store the instantaneous state of voxels or simple average values, this time-series data will be used as an input variable for probabilistic capacity model calculation and future capacity prediction, ultimately achieving adaptive control of the dynamic evolution of spatial capacity over time.

[0032] Furthermore, the spatiotemporal attribute dataset for each voxel can be expressed as: , where H i For historical traffic time series data, W i For meteorological risk data, Rg iFor ground risk data, F i For regulatory identification data and S i This is recent scheduling / activity identification data.

[0033] 102. For each voxel, determine the discrete allowed concurrent drone count of the voxel based on the voxel's temporal attribute dataset, and determine the voxel's traffic state parameters based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel.

[0034] In this embodiment of the invention, for each voxel, the historical flow adjustment capacity weight, meteorological risk adjustment capacity weight, ground risk adjustment capacity weight, and functional area type adjustment capacity weight of the voxel can be determined first based on the voxel's historical flow time series data, meteorological risk data, ground risk data, and control identification data, and then the discrete allowable concurrent number of UAVs for the voxel can be determined.

[0035] The flow state parameter of the voxel is: ρ = Ni / Ni represents the current number of voxels. The number of concurrent drones allowed for the discrete voxel representation.

[0036] 103. Based on the flow state parameters of the voxels, determine the target control information corresponding to the voxels, and perform UAV control operations on the airspace based on the target control information corresponding to all voxels.

[0037] In this embodiment of the invention, the target control information corresponding to each voxel includes one or more of the following: free entry and exit, restricted entry permission, priority queuing, route reallocation (e.g., bypassing high-density voxels), time slot allocation (e.g., entering in batches according to time windows), and exit-only.

[0038] 104. During the operation of UAV control in airspace, for each voxel, perform a matching voxel processing operation to obtain the processed voxel.

[0039] In this embodiment of the invention, the voxel processing operation includes a voxel refinement operation, a voxel coarsening and merging operation, or a voxel maintenance operation.

[0040] 105. Update the voxel based on the processed voxel, and update the discrete allowed concurrent drone count of the voxel, and re-trigger the operation in step 102 of determining the voxel's flow status parameters based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel obtained.

[0041] In this embodiment of the invention, during the process of managing and controlling UAVs in the airspace, the voxel resolution and the number of discrete concurrent UAVs allowed after the voxel update are updated in real time (the number of discrete concurrent UAVs allowed can be updated by the basic data source of the airspace obtained in real time), so as to respond in real time to the voxel management process under different resolutions and different time windows.

[0042] Optionally, if a voxel is frequently refined / coarsened within K consecutive periods, the system can automatically enter a "frozen state," temporarily locking the voxel level and extending the sampling period to prevent overcomputation due to short-term fluctuations. If the confidence level of the coarsening / refinement decision model is too low or the data source is abnormal (e.g., missing weather reports), the system can automatically revert to the previous voxel level. It should be noted that using temporary locking during critical reconstruction or path reassignment can reduce conflicts.

[0043] It is evident that implementing the embodiments of the present invention enables the quantization of airspace capacity and the dynamic voxel resolution adaptation process, thereby improving the reliability and accuracy of airspace management and control. At the same time, through the closed-loop autonomous mechanism, manual intervention is reduced, which in turn improves the response speed of airspace management and control and reduces the communication load in the airspace management and control process, thus helping to improve resource utilization while ensuring safety and accuracy.

[0044] In an optional embodiment, step 102 above, determining the discrete allowable concurrent drone count for a voxel based on the voxel's temporal attribute dataset, includes: Acquire baseline concurrent UAV density parameters, ground obstacle parameters, and no-fly zone parameters for voxels; Based on the ground obstacle parameters and no-fly zone parameters of the voxels, the volume normalization factor of the voxels is determined, and based on the baseline concurrent UAV density parameters and volume normalization factor of the voxels, the baseline UAV capacity parameters of the voxels are determined. Based on the baseline drone capacity parameters of the voxels and the temporal attribute dataset, determine the discrete allowable number of concurrent drones for each voxel.

[0045] In this optional embodiment, the ground obstacle parameters may include ground obstacle type parameters, ground obstacle location parameters, ground obstacle specification parameters, etc., and the no-fly zone parameters may include no-fly zone type parameters, no-fly zone location parameters, no-fly zone size parameters, etc.

[0046] The baseline UAV capacity parameters for voxels are as follows: C base V is the baseline concurrent drone density parameter for voxels. i The volume normalization factor for voxels (related to obstacles and no-fly zones), this CO i Used to represent the effective flight volume V of this voxel iThe theoretical maximum number of drones is estimated based on a uniform safety distance.

[0047] Furthermore, based on the baseline drone capacity parameters of the voxels and the temporal attribute dataset, the discrete allowable number of concurrent drones per voxel is determined, including: For each time window, the historical flow adjustment capacity weight of the voxel is calculated based on the historical flow time series data of the voxel and the preset flow influence coefficient and reference flow parameters. The meteorological risk adjustment capacity weight of the voxel is also calculated based on the meteorological risk data of the voxel and the preset meteorological influence coefficient. For each time window, the ground risk adjustment capacity weight of the voxel is calculated based on the voxel's ground risk data and the preset ground impact coefficient, and the functional area type adjustment capacity weight of the voxel is determined based on the voxel's control identification data. The adjusted UAV capacity parameters of the voxels are determined based on the baseline UAV capacity parameters of the voxels, the capacity weight adjusted by historical traffic, the capacity weight adjusted by meteorological risk, the capacity weight adjusted by ground risk, and the capacity weight adjusted by functional area type. Based on the adjusted drone capacity parameters of the voxels, determine the discrete allowable number of concurrent drones for each voxel.

[0048] Specifically, the historical flow adjustment capacity weight for this voxel is: λ represents a coefficient for adjusting capacity based on historical flow. That is, when historical flow increases, the denominator increases, and the capacity decreases, reflecting the historical penalty effect of congestion. H E[H] is the flow influence coefficient. i This represents the historical average flow rate, reflecting the time-averaged or weighted average of historical flow rates. ef The reference flow rate parameter represents the normal baseline flow rate level set by the system. The meteorological risk adjustment capacity weight for this voxel is: It represents a coefficient for adjusting capacity based on meteorological risk; for example, the stronger the wind, the smaller the capacity. w W is the meteorological impact coefficient. i Meteorological risk data is used to map wind speed, wind direction instability, etc., into risk values ​​(0-1). The ground risk-adjusted capacity weight for this voxel is: It represents a coefficient for adjusting capacity based on ground risk; for example, high ground risk significantly reduces capacity. R Rg is the ground influence coefficient. i This is a ground risk indicator, with a value of (0-1), where 1 represents a high-risk area; The functional area type of this voxel adjusts the capacity weight. This coefficient represents the adjustment of capacity based on the functional zone type. 0 indicates a complete no-fly zone, while restricted flight zones use a value between 0 and 1.

[0049] in, It can be a normalization adjustment term, where each weight less than 1 indicates a reduction in capacity, and greater than 1 indicates an increase in capacity.

[0050] Furthermore, the adjusted drone capacity parameters based on this voxel can be: .

[0051] Furthermore, the discreteness of this voxel allows for the following number of concurrent drones: .

[0052] Furthermore, the actual execution results (whether the load was successfully released) can be used as training samples for the model to correct the parameter λ. H , λ R etc., to update the spatiotemporal attribute set A of the voxels. i (t) and notify the prediction module.

[0053] As can be seen, this optional embodiment first determines the volume normalization factor of the voxel based on the ground obstacle parameters and no-fly zone parameters. Then, combined with the voxel's baseline concurrent UAV density parameters, it determines the baseline UAV capacity parameters of the voxel. Based on the baseline UAV capacity parameters and the historical traffic adjustment capacity weights, meteorological risk adjustment capacity weights, ground risk adjustment capacity weights, and functional zone type adjustment capacity weights calculated from the voxel's time-series attribute dataset, it determines the adjusted UAV capacity parameters of the voxel, thus determining the discrete allowable number of concurrent UAVs for the voxel. In this way, by integrating multi-dimensional weight adjustments based on historical traffic, meteorology, ground risk, and functional zone type, the reliability and accuracy of determining the adjusted UAV capacity parameters of the voxel are improved, enabling dynamic and precise capacity adaptation. This shifts the decision-making basis from "rules / experience" to "quantifiable capacity and overload probability," providing reliable data support for subsequent voxel coarsening and control strategies, facilitating supervision, auditing, and parameter optimization, thereby improving airspace utilization and operational safety.

[0054] Example 2 Please see Figure 2 , Figure 2 This is a flowchart illustrating another adaptive voxel coarsening method for UAV airspace management disclosed in an embodiment of the present invention. Optionally, this method can be implemented by a UAV airspace management device, which can be integrated into a UAV swarm scheduling system, a regional edge gateway of a low-altitude airspace management platform, or a smart transportation integrated management terminal. It can also be a local server or cloud server used to process the UAV airspace management process, etc., and the embodiments of the present invention do not impose limitations. Figure 2As shown, this adaptive voxel coarsening UAV airspace management method may include the following operations: 201. Obtain the basic data source for the spatial domain, and based on the basic data source, determine the spatiotemporal attribute dataset of all voxels corresponding to the spatial domain.

[0055] 202. For each voxel, determine the discrete allowed concurrent drone count of the voxel based on the voxel's temporal attribute dataset, and determine the voxel's traffic state parameters based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel.

[0056] 203. Based on the flow state parameters of the voxels, determine the target control information corresponding to the voxels, and perform UAV control operations on the airspace based on the target control information corresponding to all voxels.

[0057] 204. During the operation of UAV control in airspace, for each voxel, the target voxel processing type corresponding to the voxel is determined based on the voxel's flow state parameters.

[0058] 205. When the target voxel processing type corresponding to the voxel is voxel refinement processing type, voxel refinement processing operation is performed on the voxel according to the preset refinement factor parameter to obtain all refined sub-voxels corresponding to the voxel, and all refined sub-voxels are determined as processed voxels.

[0059] In this embodiment of the invention, the voxel refinement process can be understood as follows: if a refinement event for a voxel is received, and the voxel has not been refined to the minimum level, the voxel is split into multiple refined sub-voxels, and each refined sub-voxel inherits its corresponding static attributes, such as terrain, altitude, risk level, etc., and the corresponding adjusted UAV capacity parameters are recalculated for each refined sub-voxel, and the voxel encoding is updated according to the GeoSOT encoding rules.

[0060] 206. When the target voxel processing type is voxel coarsening and merging, voxel coarsening and merging operation is performed on the voxel and its neighboring voxels to obtain the coarsened parent voxel and the coarsened parent voxel is determined as the processed voxel.

[0061] In this embodiment of the invention, the voxel coarsening and merging process can be understood as follows: if a coarsening event of a voxel is received, and the voxel has not been merged to the maximum level and the neighboring voxels also meet the corresponding merging conditions, then the voxel is merged with the neighboring voxels, and the capacity and risk weighting value of the coarsened parent voxel are recalculated; at the same time, the voxel and the neighboring voxels before merging are de-coded, and the coarsened parent voxel is marked as an "active voxel".

[0062] 207. When the target voxel processing type corresponding to the voxel is the voxel maintenance processing type, the voxel is directly determined as the processed voxel.

[0063] In this embodiment of the invention, the voxel maintenance process can be understood as: maintaining the current grid granularity of the voxel unchanged, and continuing to periodically monitor the voxel in order to determine the voxel flow status and target voxel processing type in the next cycle.

[0064] 208. Update the voxel based on the processed voxel, and update the discrete allowed concurrent drone count of the voxel, and re-trigger the operation in step 202 to determine the voxel's flow status parameters based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel.

[0065] In this embodiment of the invention, for other descriptions of steps 201-203 and 208, please refer to the detailed description of steps 101-103 and 105 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.

[0066] As can be seen, implementing the embodiments of the present invention can determine the target voxel processing type corresponding to the voxel based on the voxel's flow state parameters, so as to perform refinement, coarsening, or maintenance operations on the voxel. Then, after updating the voxel and its adjusted UAV capacity parameters, the flow state parameter calculation is triggered cyclically, forming a closed-loop airspace management process. In this way, dynamic quantification and adaptive coarsening of airspace capacity are realized—high-density / high-risk areas are instantly finely divided to improve resolution and security, and low-density areas are merged to save computing and communication resources, thereby effectively improving the accuracy and resource efficiency of airspace management. At the same time, through the closed-loop management mechanism, manual intervention is reduced, and the response speed and robustness of airspace management are improved. In addition, through the voxel encoding update and merging cancellation specifications, the continuity of management is ensured, and the communication load is reduced and the location privacy risk is enhanced (the UAV only reports the voxel encoding (GeoSOT-3D) and necessary summary information, without needing to upload precise latitude and longitude).

[0067] In an optional embodiment, step 204 above, determining the target voxel processing type based on the voxel's flow state parameters, includes: Based on the voxel's flow state parameters, determine the voxel's flow trend parameters; The voxel-based drone arrival rate parameter is obtained, and the overload probability parameter of the voxel is determined based on the drone arrival rate parameter and the discrete allowed number of concurrent drones. Based on the number of concurrent drones allowed for the discreteness of voxels, determine the refinement trigger threshold and the coarsening trigger threshold for voxels; the refinement trigger threshold is greater than the coarsening trigger threshold. Based on the voxel's flow state parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold, determine the target voxel processing type corresponding to the voxel.

[0068] In this optional embodiment, specifically, the flow trend parameter of the voxel can be: dρ / dt, where ρ is the flow state parameter of the voxel; the overload probability parameter of the voxel (i.e., the estimated overload risk corresponding to the voxel) can be: , where λ i λ represents the arrival rate of the UAV (UAV / s) of voxel i at time t. i △t represents the expected number of drones arriving within the time window. Furthermore, the refined trigger threshold and the coarse trigger threshold can be expressed as follows: , Where y1 and y2 are both values ​​between 0 and 1, and y1 > y2 (both can be empirical coefficients). It can be seen that the refined trigger threshold, coarsened trigger threshold, and voxel overload probability parameters mentioned above are not fixed values, but rather dynamic proportional thresholds that change with the spatiotemporal attributes of the voxels. For example, worsening weather, increased ground risk, and a surge in historical flow will all reduce these thresholds through the model. This automatically lowers the corresponding thresholds and parameters, allowing the system to enter a refined or flow-limiting state earlier, thus enabling timely airspace control.

[0069] As can be seen, this optional embodiment can adaptively determine the target voxel processing type corresponding to a voxel based on the voxel's flow status parameters, flow trend parameters, overload probability parameters, refined trigger threshold, and coarsened trigger threshold. This achieves reliable and accurate determination of the target voxel processing type, thereby improving the response efficiency and timeliness of airspace control. It can accurately match airspace flow changes and risk levels to ensure airspace utilization and operational safety.

[0070] In another optional embodiment, the step of determining the target voxel processing type based on the voxel's flow state parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold includes: When the flow state parameter of a voxel is less than the coarsening trigger threshold, the target voxel parameter of the neighboring voxels associated with the voxel is obtained, and based on the target voxel parameter, it is determined whether the voxel and the neighboring voxels meet the preset voxel merging conditions; if yes, the target voxel processing type corresponding to the voxel is determined to be the voxel coarsening merging type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When the voxel's flow state parameter is greater than or equal to the coarsening trigger threshold and less than the refinement trigger threshold, determine whether the voxel's flow trend parameter is greater than or equal to the preset flow trend threshold. When it is determined that the flow trend parameter of a voxel is less than the flow trend threshold, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When it is determined that the flow trend parameter of a voxel is greater than or equal to the flow trend threshold, it is determined whether the voxel specification parameter of the voxel is greater than or equal to the preset minimum voxel refinement specification threshold. If so, the target voxel processing type corresponding to the voxel is determined to be a candidate refinement processing type in the voxel refinement processing type; if not, the target voxel processing type corresponding to the voxel is determined to be a voxel maintenance processing type. When the voxel's flow state parameter is greater than or equal to the refinement trigger threshold, determine whether the voxel's voxel specification parameter is greater than or equal to the minimum voxel refinement specification threshold. If yes, determine that the target voxel processing type is the preferred refinement processing type among the voxel refinement processing types; otherwise, determine that the target voxel processing type is the voxel maintenance processing type. The refinement processing priority corresponding to the preferred refinement processing type is higher than the refinement processing priority corresponding to the candidate refinement processing type; or... When the overload probability parameter of a voxel is greater than or equal to the preset overload probability threshold, determine whether the voxel specification parameter of the voxel is greater than or equal to the minimum voxel refinement specification threshold; if yes, determine that the target voxel processing type corresponding to the voxel is the priority refinement processing type; if no, determine that the target voxel processing type corresponding to the voxel is the voxel maintenance processing type.

[0071] In this optional embodiment, the target voxel parameters of the neighboring voxels include the voxel specification parameters, flow status parameters, overload probability parameters, current control status, functional attribute parameters, and risk level parameters of the neighboring voxels.

[0072] It should be noted that this embodiment can be understood as: (1) when ρ < T coarse If a voxel and its neighboring voxels meet the preset voxel merging conditions, then it is determined that the voxel and its neighboring voxels need to be merged (that is, during real-time monitoring, as long as the drone traffic of the voxel falls back to T in the current period). coarse (2) When T coarse <ρ<T refine When dρ / dt>0, it indicates that the drone traffic of this voxel continues to increase and approaches T. refine If a voxel is not refined to the smallest voxel granularity, it enters the candidate refinement queue (if the voxel is already at the smallest voxel granularity, it is placed in a buffer monitoring state). For voxels in the candidate refinement queue, their refinement priority can be calculated based on their load level, load growth trend, impact range, risk weight, and system resource usage, and refinement operations are performed in priority order; when T... coarse<ρ<T refine When dρ / dt < 0, the voxel is placed in a buffered monitoring state. Furthermore, when T... coarse <ρ<T refine When dρ / dt < 0 and the downward trend remains stable within the preset time window, and the neighboring voxels meet the above voxel merging conditions, voxel merging can be triggered to reduce frequent grid jitter and ensure the stability of capacity assessment and spatial security; (3) when ρ ≥ T refine or If the voxel is not refined to the smallest voxel granularity (based on the preset overload probability threshold), then voxel refinement is triggered directly (i.e., priority refinement processing type). At this time, the voxel has entered a high-load state and can be directly triggered for voxel refinement processing without queuing.

[0073] Furthermore, the step 203 above, which determines the target control information corresponding to the voxel based on the voxel's flow state parameters, can be understood as: when ρ < T coarse At that time, the target control information corresponding to this voxel includes free entry and exit; when T coarse <ρ<T refine ρ≥T refine or At that time, the target control information corresponding to this voxel includes one or more of the following: restricted entry permission, priority queuing, route reallocation (such as bypassing high-density voxels), time slot allocation (such as entering in batches according to time windows), and exit-only (if the flow status of this voxel subsequently drops back to T). coarse When the voxel is at its smallest voxel size and ρ ≥ T, free entry and exit can be restored. min At that time, the target control information corresponding to that voxel includes either an exit-only or restricted entry permit. Among them, T min = y3 is the preset minimum voxel allowable capacity threshold, which represents the minimum capacity threshold that the smallest voxel can accommodate for a drone.

[0074] As can be seen, this optional embodiment can accurately trigger voxel coarsening and merging based on flow state parameters and merging conditions of neighboring voxels, realize voxel candidate and priority refinement based on voxel flow trends and granularity thresholds, and directly start priority refinement when the voxel overload probability reaches the standard, forming an automatic voxel refinement / coarsening closed loop, ensuring timely response to voxel control and enhancing the system's adaptability; at the same time, the target control information can be dynamically adapted with the flow state, allowing free entry and exit under low load, enabling multi-dimensional flow limiting strategies under high load, and executing out-only and no-entry when the smallest granularity is overloaded, effectively reducing frequent grid jitter, thereby ensuring airspace safety.

[0075] In another optional embodiment, the step of determining whether a voxel and its neighboring voxels meet preset voxel merging conditions based on the target voxel parameters includes: Based on the voxel specification parameters of the voxel and the voxel specification parameters of the neighboring voxels, determine the merged voxel specification parameters corresponding to the voxel and the neighboring voxels, and determine whether the merged voxel specification parameters are less than or equal to the preset maximum voxel merging specification threshold. When it is determined that the voxel specification parameters after merging are greater than the maximum voxel merging specification threshold, it is determined that the voxel and its neighboring voxels do not meet the preset voxel merging conditions. When it is determined that the merged voxel specification parameter is less than or equal to the maximum voxel merge specification threshold, it is determined whether the flow status parameter of the neighboring voxel is less than the target coarsening trigger threshold of the neighboring voxel, whether the overload probability parameter of the neighboring voxel is less than the target overload probability threshold of the neighboring voxel, whether the current control status of the neighboring voxel is in the free entry and exit control status, whether the functional attribute parameter of the neighboring voxel matches the functional attribute parameter of the voxel, and whether the risk level parameter of the neighboring voxel matches the risk level parameter of the voxel. When all judgment results are yes, it is determined that the voxel and its neighboring voxels satisfy the voxel merging condition. If any judgment result is negative, it is determined that the voxel and its neighboring voxels do not meet the voxel merging condition.

[0076] In this optional embodiment, the voxel merging condition determination process can be understood as: when the flow state ρ of the voxel is lower than a preset coarsening threshold T coarse When this happens, the system can mark the voxel as a "candidate voxel for coarsening". Subsequently, if the voxel specification parameter after merging the voxel with its neighboring voxels (which can be voxels spatially adjacent to the voxel and at the same level) is less than the maximum voxel merging specification threshold, the system further determines whether the voxel merging condition is met with the neighboring voxels. The voxel merging condition includes at least the following: the flow state ρ of the neighboring voxel is lower than the preset coarsening threshold T. coarse The following conditions must be met for merging a voxel: 1) the overload probability parameter of a neighboring voxel is less than its corresponding overload probability threshold; 2) the neighboring voxel is not in a restricted or locked state; and 3) the functional attributes / risk levels of both the neighboring voxel and the voxel meet the consistency requirements. If a neighboring voxel does not meet any of the above merging conditions, the voxel maintains its current mesh granularity, and periodic monitoring and threshold judgment are continued for the voxel and its neighboring voxels without performing coarsening operations. Otherwise, the two voxels are merged.

[0077] As can be seen, this optional embodiment can achieve multi-level and multi-dimensional voxel merging condition verification. Thus, limiting the size threshold after merging can reduce the decrease in control accuracy caused by excessively large voxels. Furthermore, verifying the consistency of flow, risk, control status, and attributes can reduce control logic conflicts and safety hazards after voxel merging. Simultaneously, maintaining the status quo and continuous monitoring when conditions are not met can reduce frequent grid fluctuations, ensuring the stability and continuity of airspace control, thereby ensuring resource utilization efficiency and flight safety.

[0078] In summary, the following examples further illustrate this adaptive voxel coarsening UAV airspace management scenario: 1. Reporting Information: Each UAV only needs to periodically report the following compressed information packets during flight: (a) GeoSOT-3D voxel code: uniquely identifies the current voxel grid of the UAV (including the hierarchical layer number and voxel number), excluding latitude and longitude coordinates; (b) Heading and speed summary: including heading angle, speed magnitude and trend of change (acceleration or constant speed indicator), which can be represented by low-precision quantization; (c) Task priority label: indicating the task level (normal, supervision, emergency, emergency exemption, etc.); 2. Reporting Information Reception and Parsing: After receiving the reported information from UAVs, the regional edge nodes or central control platform do not perform coordinate inversion, but only establish a voxel-UAV index table based on voxel encoding. The system updates the number of UAVs for each voxel in real time, which is then input into the probabilistic capacity model as a traffic observation value. Specifically, the heading / speed summary is used to estimate the set of voxels that UAVs may be heading to in the next cycle (i.e., predicting the "migration probability" of traffic to neighboring voxels), forming a voxel migration matrix; the task priority is used to calculate priority weights in subsequent control information allocation strategies, affecting capacity allocation and exception permissions under the "outbound only" mode. 3. Data fusion and temporal caching: Each edge node maintains a temporal cache unit for the voxels within its jurisdiction, recording the basic data sources for the most recent few periods. This data becomes the support for subsequent calculations, such as: the latest part of the short-term historical traffic sequence, the real-time input of the capacity model, the training and validation samples of the prediction module (used to predict traffic trends and overload probability in future time periods), etc. 4. Integration of Reported Data and Prediction Module: Based on the reported data, the prediction module generates local predicted traffic flow in real time. Based on the migration matrix and current velocity direction, it predicts the expected occupancy of each voxel within a future time period. Integrating factors such as the current discrete allowable concurrent UAV count, predicted traffic flow status parameters, and weather forecasts, the module outputs the future discrete allowable concurrent UAV count after model correction, and sets traffic flow status warnings and overload probability warnings (if subsequent traffic flow status parameters or overload probability parameters exceed the corresponding thresholds, a refinement or allocation strategy is triggered in advance). It should be noted that although the reported data only contains voxel numbers and a small amount of summary, through voxel encoding, the system knows the static attributes of the space where the UAV is located (ground risk, building density, no-fly zone, etc.), thus enabling capacity inference without disclosing specific latitude and longitude. For example, the changing trends of heading and speed determine the future cross-voxel migration probability, which is the core input supporting the prediction; priority labels determine the exception handling logic of the control strategy in allocation or "outbound only" states. This mechanism enables the system to maintain a complete dynamic prediction and control closed loop under the conditions of minimal information and privacy protection: information reporting → traffic / migration update → probability capacity update → threshold determination → refinement / coarsening or allocation adjustment → feedback to UAV; at the same time, it achieves adaptive reporting frequency: that is, a low reporting frequency is used in low-resolution voxels; when refined to high-resolution voxels or in a conflict period, the reporting frequency is increased. This mechanism significantly reduces the overall reporting traffic and improves the privacy protection effect.

[0079] Specifically, the above process can be achieved through layered deployment, where real-time grid monitoring and preliminary capacity assessment capabilities are placed on edge service nodes (regional controllers), while the central policy engine is responsible for network-wide optimization and cross-regional coordination. This distributed approach improves scalability and fault tolerance. Furthermore, online updates via edge aggregation (edge ​​nodes retain their local voxel models and short-term predictions) can be used to maintain local autonomy and ensure safe backoff during network anomalies. In addition, the state evolution of all voxels, refinement / coarsening decisions, allocation strategies, and UAV reporting are recorded for post-event auditing and model training, and a visualization interface supports multi-level zooming (by voxel / sub-voxel) to view current capacity and historical evolution.

[0080] Example 3 Please see Figure 3 , Figure 3 This is a schematic diagram of the structure of an adaptive voxel coarsening unmanned aerial vehicle (UAV) airspace management device disclosed in an embodiment of the present invention. Figure 3 As shown, the adaptive voxel coarsening UAV airspace management device may include: Module 301 is used to obtain the basic data source for the airspace; The determination module 302 is used to determine the spatiotemporal attribute dataset of all voxels corresponding to the airspace based on the basic data source; for each voxel, the discrete allowed concurrent UAV number of the voxel is determined based on the voxel's temporal attribute dataset, and the traffic status parameters of the voxel are determined based on the discrete allowed concurrent UAV number of the voxel and the current number of UAVs of the voxel; and the target control information corresponding to the voxel is determined based on the voxel's traffic status parameters. The control module 303 is used to perform UAV control operations on the airspace based on the target control information corresponding to all voxels; The voxel processing module 304 is used to perform a matching voxel processing operation on each voxel during the process of the control module 303 performing UAV control operations on the airspace, so as to obtain the processed voxel. The update module 305 is used to update the voxel based on the processed voxel, and update the discrete allowed concurrent drone count of the voxel, and re-trigger the operation performed by the determination module 302 to determine the voxel's flow status parameters based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel obtained.

[0081] In this embodiment of the invention, the basic data source of the airspace includes historical UAV flight data, meteorological data, ground environment data and target control data of the airspace. The spatiotemporal attribute dataset of each voxel includes historical traffic time series data, meteorological risk data, ground risk data and control identification data of the voxel under any preset time window. The voxel processing operation includes voxel refinement processing operation, voxel coarsening and merging operation or voxel maintenance processing operation.

[0082] It is evident that implementation Figure 3 The described adaptive voxel coarsening UAV airspace management device can realize airspace capacity quantification and dynamic voxel resolution adaptation, improving the reliability and accuracy of airspace management. At the same time, through a closed-loop autonomous mechanism, it reduces human intervention, thereby improving the response speed of airspace management and reducing the communication load in the airspace management process, which is conducive to improving resource utilization while ensuring safety and accuracy.

[0083] In an optional embodiment, the method by which the determining module 302 determines the discrete allowable number of concurrent drones for a voxel based on the voxel's temporal attribute dataset specifically includes: Acquire baseline concurrent UAV density parameters, ground obstacle parameters, and no-fly zone parameters for voxels; Based on the ground obstacle parameters and no-fly zone parameters of the voxels, the volume normalization factor of the voxels is determined, and based on the baseline concurrent UAV density parameters and volume normalization factor of the voxels, the baseline UAV capacity parameters of the voxels are determined. Based on the baseline drone capacity parameters of the voxels and the temporal attribute dataset, determine the discrete allowable number of concurrent drones for each voxel.

[0084] In this optional embodiment, the determining module 302 further determines the discrete allowable concurrent number of drones for a voxel based on the voxel's baseline drone capacity parameters and the temporal attribute dataset, specifically including: For each time window, the historical flow adjustment capacity weight of the voxel is calculated based on the historical flow time series data of the voxel and the preset flow influence coefficient and reference flow parameters. The meteorological risk adjustment capacity weight of the voxel is also calculated based on the meteorological risk data of the voxel and the preset meteorological influence coefficient. For each time window, the ground risk adjustment capacity weight of the voxel is calculated based on the voxel's ground risk data and the preset ground impact coefficient, and the functional area type adjustment capacity weight of the voxel is determined based on the voxel's control identification data. The adjusted UAV capacity parameters of the voxels are determined based on the baseline UAV capacity parameters of the voxels, the capacity weight adjusted by historical traffic, the capacity weight adjusted by meteorological risk, the capacity weight adjusted by ground risk, and the capacity weight adjusted by functional area type. Based on the adjusted drone capacity parameters of the voxels, determine the discrete allowable number of concurrent drones for each voxel.

[0085] It is evident that implementation Figure 3 The described adaptive voxel coarsening UAV airspace management device first determines the volume normalization factor of a voxel based on its ground obstacle parameters and no-fly zone parameters. Then, combining this with the voxel's baseline concurrent UAV density parameters, it determines the voxel's baseline UAV capacity parameters. Finally, based on these baseline UAV capacity parameters and historical traffic adjustment weights, meteorological risk adjustment weights, ground risk adjustment weights, and functional zone type adjustment weights calculated from the voxel's time-series attribute dataset, it determines the adjusted UAV capacity parameters for that voxel, thus determining the discrete allowable number of concurrent UAVs for that voxel. By integrating multi-dimensional weight adjustments based on historical traffic, meteorology, ground risk, and functional zone type, the reliability and accuracy of determining the adjusted UAV capacity parameters for voxels are improved, enabling dynamic and precise capacity adaptation. This shifts the decision-making basis from "rules / experience" to "quantifiable capacity and overload probability," providing reliable data support for subsequent voxel coarsening and management strategies. It also facilitates supervision, auditing, and parameter optimization, thereby improving airspace utilization and operational safety.

[0086] In another optional embodiment, the voxel processing module 304 performs matching voxel processing operations on the voxels, and the specific methods for obtaining the processed voxels include: Based on the voxel's flow state parameters, determine the target voxel processing type corresponding to the voxel; When the target voxel processing type corresponding to the voxel is voxel refinement processing type, voxel refinement processing operation is performed on the voxel according to the preset refinement factor parameter to obtain all refined sub-voxels corresponding to the voxel, and all refined sub-voxels are determined as processed voxels. When the target voxel processing type corresponding to the voxel is voxel coarsening and merging type, voxel coarsening and merging operation is performed on the voxel and its corresponding neighboring voxels to obtain the coarsened parent voxel, and the coarsened parent voxel is determined as the processed voxel. When the target voxel processing type corresponding to the voxel is the voxel maintenance processing type, the voxel is directly identified as the processed voxel.

[0087] It is evident that implementation Figure 3 The described adaptive voxel coarsening UAV airspace management device can determine the target voxel processing type based on the voxel's flow state parameters, performing refinement, coarsening, or maintenance operations on the voxel. After updating the voxel and its adjusted UAV capacity parameters, it cyclically triggers flow state parameter calculations, forming a closed-loop airspace management process. This achieves dynamic quantification and adaptive coarsening of airspace capacity—real-time fine-grained division of high-density / high-risk areas to improve resolution and security, and merging of low-density areas to save computing and communication resources, effectively improving airspace management accuracy and resource efficiency. Simultaneously, the closed-loop management mechanism reduces manual intervention, improving the response speed and robustness of airspace management. Furthermore, the voxel encoding update and merging / cancellation specifications ensure continuity of management, reduce communication load, and enhance location privacy risks (UAVs only report voxel codes (GeoSOT-3D) and necessary summary information, without needing to upload precise latitude and longitude).

[0088] In yet another optional embodiment, the voxel processing module 304 determines the target voxel processing type corresponding to the voxel based on the voxel's flow status parameters in the following specific ways: Based on the voxel's flow state parameters, determine the voxel's flow trend parameters; The voxel-based drone arrival rate parameter is obtained, and the overload probability parameter of the voxel is determined based on the drone arrival rate parameter and the discrete allowed number of concurrent drones. Based on the number of concurrent drones allowed by the discreteness of voxels, determine the refinement trigger threshold and the coarsening trigger threshold of the voxels. Based on the voxel's flow state parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold, determine the target voxel processing type corresponding to the voxel.

[0089] In this optional embodiment, the refined trigger threshold is greater than the coarse trigger threshold.

[0090] It is evident that implementation Figure 3 The described adaptive voxel coarsening UAV airspace management device can adaptively determine the target voxel processing type corresponding to a voxel based on voxel traffic status parameters, traffic trend parameters, overload probability parameters, refinement trigger thresholds, and coarsening trigger thresholds. This achieves reliable and accurate determination of the target voxel processing type, thereby improving the response efficiency and timeliness of airspace management. It can accurately match airspace traffic changes and risk levels to ensure airspace utilization and operational safety.

[0091] In another optional embodiment, the voxel processing module 304 determines the target voxel processing type based on the voxel's flow status parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold, specifically including: When the flow state parameter of a voxel is less than the coarsening trigger threshold, the target voxel parameter of the neighboring voxels associated with the voxel is obtained, and based on the target voxel parameter, it is determined whether the voxel and the neighboring voxels meet the preset voxel merging conditions; if yes, the target voxel processing type corresponding to the voxel is determined to be the voxel coarsening merging type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When the voxel's flow state parameter is greater than or equal to the coarsening trigger threshold and less than the refinement trigger threshold, determine whether the voxel's flow trend parameter is greater than or equal to the preset flow trend threshold. When it is determined that the flow trend parameter of a voxel is less than the flow trend threshold, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When it is determined that the flow trend parameter of a voxel is greater than or equal to the flow trend threshold, it is determined whether the voxel specification parameter of the voxel is greater than or equal to the preset minimum voxel refinement specification threshold. If so, the target voxel processing type corresponding to the voxel is determined to be a candidate refinement processing type in the voxel refinement processing type; if not, the target voxel processing type corresponding to the voxel is determined to be a voxel maintenance processing type. When the voxel's flow state parameter is greater than or equal to the refinement trigger threshold, determine whether the voxel's voxel specification parameter is greater than or equal to the minimum voxel refinement specification threshold. If yes, determine that the target voxel processing type is the preferred refinement processing type among the voxel refinement processing types; otherwise, determine that the target voxel processing type is the voxel maintenance processing type. The refinement processing priority corresponding to the preferred refinement processing type is higher than the refinement processing priority corresponding to the candidate refinement processing type; or... When the overload probability parameter of a voxel is greater than or equal to the preset overload probability threshold, determine whether the voxel specification parameter of the voxel is greater than or equal to the minimum voxel refinement specification threshold; if yes, determine that the target voxel processing type corresponding to the voxel is the priority refinement processing type; if no, determine that the target voxel processing type corresponding to the voxel is the voxel maintenance processing type.

[0092] It is evident that implementation Figure 3 The described adaptive voxel coarsening UAV airspace management device can accurately trigger voxel coarsening and merging based on traffic state parameters and neighboring voxel merging criteria, realize voxel candidate and priority refinement based on voxel traffic trends and granularity thresholds, and directly initiate priority refinement when the voxel overload probability reaches the standard, forming an automatic voxel refinement / coarsening closed loop. This ensures timely response to voxel management and enhances the system's adaptability. At the same time, target management information can be dynamically adapted to traffic state. Under low load, free entry and exit are allowed; under high load, a multi-dimensional flow limiting strategy is activated; and under minimum granularity overload, only exit is allowed, effectively reducing frequent grid jitter and thus ensuring airspace safety.

[0093] In another optional embodiment, the target voxel parameters of the neighborhood voxel include the voxel specification parameters, flow status parameters, overload probability parameters, current control status, functional attribute parameters, and risk level parameters of the neighborhood voxel. The voxel processing module 304 determines whether a voxel and its neighboring voxels meet preset voxel merging conditions based on the target voxel parameters in the following ways: Based on the voxel specification parameters of the voxel and the voxel specification parameters of the neighboring voxels, determine the merged voxel specification parameters corresponding to the voxel and the neighboring voxels, and determine whether the merged voxel specification parameters are less than or equal to the preset maximum voxel merging specification threshold. When it is determined that the voxel specification parameters after merging are greater than the maximum voxel merging specification threshold, it is determined that the voxel and its neighboring voxels do not meet the preset voxel merging conditions. When it is determined that the merged voxel specification parameter is less than or equal to the maximum voxel merge specification threshold, it is determined whether the flow status parameter of the neighboring voxel is less than the target coarsening trigger threshold of the neighboring voxel, whether the overload probability parameter of the neighboring voxel is less than the target overload probability threshold of the neighboring voxel, whether the current control status of the neighboring voxel is in the free entry and exit control status, whether the functional attribute parameter of the neighboring voxel matches the functional attribute parameter of the voxel, and whether the risk level parameter of the neighboring voxel matches the risk level parameter of the voxel. When all judgment results are yes, it is determined that the voxel and its neighboring voxels satisfy the voxel merging condition. If any judgment result is negative, it is determined that the voxel and its neighboring voxels do not meet the voxel merging condition.

[0094] It is evident that implementation Figure 3 The described adaptive voxel coarsening UAV airspace management device can achieve multi-level and multi-dimensional voxel merging condition verification. This reduces the possibility of decreased management accuracy due to excessively large voxels by limiting the size of the merged voxels, and reduces management logic conflicts and safety hazards after voxel merging by verifying the consistency of traffic, risk, management status, and attributes. Simultaneously, maintaining the status quo and continuous monitoring when conditions are not met reduces frequent grid fluctuations, ensuring the stability and continuity of airspace management, thereby ensuring resource utilization efficiency and flight safety.

[0095] Example 4 Please see Figure 4 , Figure 4 This is a schematic diagram of another adaptive voxel coarsening UAV airspace management device disclosed in an embodiment of the present invention. Figure 4 As shown, the adaptive voxel coarsening UAV airspace management device may include: Memory 401 storing executable program code; Processor 402 coupled to memory 401; The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the adaptive voxel coarsening UAV airspace management method described in Embodiment 1 or Embodiment 2 of the present invention.

[0096] Example 5 This invention discloses a computer storage medium storing computer instructions. When these computer instructions are invoked, they are used to execute the steps in the adaptive voxel coarsening UAV airspace management method described in Embodiment 1 or Embodiment 2 of this invention.

[0097] Example 6 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps in the adaptive voxel coarsening UAV airspace management method described in Embodiment 1 or Embodiment 2.

[0098] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0099] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0100] Finally, it should be noted that the adaptive voxel coarsening UAV airspace management method and apparatus disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptive voxel coarsening of UAV airspace management, characterized in that, The method includes: The basic data source of the airspace is obtained, and the spatiotemporal attribute datasets of all voxels corresponding to the airspace are determined based on the basic data source. The basic data source of the airspace includes historical UAV flight data, meteorological data, ground environment data and target control data of the airspace. The spatiotemporal attribute dataset of each voxel includes historical traffic time series data, meteorological risk data, ground risk data and control identification data of the voxel under any preset time window. For each voxel, the discrete allowed concurrent drone count of the voxel is determined based on the voxel's temporal attribute dataset, and the flow state parameter of the voxel is determined based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel. Based on the flow status parameters of the voxels, the target control information corresponding to the voxels is determined, and based on the target control information corresponding to all the voxels, UAV control operations are performed on the airspace. During the operation of UAV control over the airspace, for each voxel, a matching voxel processing operation is performed to obtain a processed voxel; the voxel processing operation includes voxel refinement processing operation, voxel coarsening and merging operation, or voxel maintenance processing operation. The voxel is updated according to the processed voxel, and the discrete allowed concurrent drone count of the voxel is updated. The operation of determining the flow status parameter of the voxel based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel is retried and executed.

2. The adaptive voxel coarsening UAV airspace management method according to claim 1, characterized in that, The step of determining the discrete allowable concurrent drone count for each voxel based on its temporal attribute dataset includes: Obtain the baseline concurrent UAV density parameters, ground obstacle parameters, and no-fly zone parameters of the voxels; Based on the ground obstacle parameters and no-fly zone parameters of the voxel, the volume normalization factor of the voxel is determined, and based on the benchmark concurrent UAV density parameters and volume normalization factor of the voxel, the benchmark UAV capacity parameters of the voxel are determined. Based on the baseline drone capacity parameters and time-series attribute dataset of the voxel, determine the discrete allowable concurrent drone count of the voxel.

3. The adaptive voxel coarsening UAV airspace management method according to claim 2, characterized in that, The step of determining the discrete allowable concurrent number of drones for a voxel based on the baseline drone capacity parameters and time-series attribute dataset of the voxel includes: For each time window, the historical flow adjustment capacity weight of the voxel is calculated based on the historical flow time series data of the voxel and the preset flow influence coefficient and reference flow parameters, and the meteorological risk adjustment capacity weight of the voxel is calculated based on the meteorological risk data of the voxel and the preset meteorological influence coefficient. For each time window, the ground risk adjustment capacity weight of the voxel is calculated based on the ground risk data of the voxel and the preset ground impact coefficient, and the functional area type adjustment capacity weight of the voxel is determined based on the control identification data of the voxel. The adjusted UAV capacity parameters of the voxel are determined based on the baseline UAV capacity parameters of the voxel, the historical traffic adjustment capacity weight, the meteorological risk adjustment capacity weight, the ground risk adjustment capacity weight, and the functional area type adjustment capacity weight. Based on the adjusted UAV capacity parameters of the voxel, determine the discrete allowable number of concurrent UAVs for the voxel.

4. The adaptive voxel coarsening UAV airspace management method according to any one of claims 1-3, characterized in that, The step of performing a matching voxel processing operation on the voxel to obtain the processed voxel includes: Based on the flow state parameters of the voxel, determine the target voxel processing type corresponding to the voxel; When the target voxel processing type corresponding to the voxel is voxel refinement processing type, the voxel is subjected to voxel refinement processing operation according to the preset refinement factor parameter to obtain all refined sub-voxels corresponding to the voxel, and all the refined sub-voxels are determined as processed voxels. When the target voxel processing type corresponding to the voxel is voxel coarsening and merging type, voxel coarsening and merging operation is performed on the voxel and the neighboring voxels corresponding to the voxel to obtain the coarsened parent voxel corresponding to the voxel, and the coarsened parent voxel is determined as the processed voxel. When the target voxel processing type corresponding to the voxel is the voxel maintenance processing type, the voxel is directly identified as the processed voxel.

5. The adaptive voxel coarsening UAV airspace management method according to claim 4, characterized in that, The step of determining the target voxel processing type corresponding to the voxel based on the voxel's flow state parameters includes: Based on the flow state parameters of the voxel, determine the flow trend parameters of the voxel; The drone arrival rate parameter of the voxel is obtained, and the overload probability parameter of the voxel is determined based on the drone arrival rate parameter and the discrete allowed concurrent drone number. Based on the discrete number of concurrent drones allowed for the voxel, the refinement trigger threshold and the coarsening trigger threshold for the voxel are determined; the refinement trigger threshold is greater than the coarsening trigger threshold. Based on the voxel's flow status parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold, the target voxel processing type corresponding to the voxel is determined.

6. The adaptive voxel coarsening UAV airspace management method according to claim 5, characterized in that, The step of determining the target voxel processing type corresponding to the voxel based on the voxel's flow state parameters, flow trend parameters, overload probability parameters, refinement trigger threshold, and coarsening trigger threshold includes: When the flow state parameter of the voxel is less than the coarsening trigger threshold, the target voxel parameter of the neighboring voxel associated with the voxel is obtained, and based on the target voxel parameter, it is determined whether the voxel and the neighboring voxel meet the preset voxel merging condition; if yes, the target voxel processing type corresponding to the voxel is determined to be the voxel coarsening merging type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When the flow state parameter of the voxel is greater than or equal to the coarsening trigger threshold and less than the refinement trigger threshold, determine whether the flow trend parameter of the voxel is greater than or equal to the preset flow trend threshold. When it is determined that the flow trend parameter of the voxel is less than the flow trend threshold, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When it is determined that the flow trend parameter of the voxel is greater than or equal to the flow trend threshold, it is determined whether the voxel specification parameter of the voxel is greater than or equal to the preset minimum voxel refinement specification threshold. If yes, the target voxel processing type corresponding to the voxel is determined to be a candidate refinement processing type in the voxel refinement processing type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. When the voxel's flow state parameter is greater than or equal to the refinement trigger threshold, it is determined whether the voxel's voxel specification parameter is greater than or equal to the minimum voxel refinement specification threshold. If yes, the target voxel processing type corresponding to the voxel is determined to be the preferred refinement processing type among the voxel refinement processing types; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type. The refinement processing priority corresponding to the preferred refinement processing type is higher than the refinement processing priority corresponding to the candidate refinement processing type; or... When the overload probability parameter of the voxel is greater than or equal to the preset overload probability threshold, it is determined whether the voxel specification parameter of the voxel is greater than or equal to the minimum voxel refinement specification threshold; if yes, the target voxel processing type corresponding to the voxel is determined to be the preferred refinement processing type; if no, the target voxel processing type corresponding to the voxel is determined to be the voxel maintenance processing type.

7. The adaptive voxel coarsening UAV airspace management method according to claim 6, characterized in that, The target voxel parameters of the neighborhood voxel include the voxel specification parameters, flow status parameters, overload probability parameters, current control status, functional attribute parameters, and risk level parameters of the neighborhood voxel. The step of determining whether the voxel and the neighboring voxels satisfy a preset voxel merging condition based on the target voxel parameters includes: Based on the voxel specification parameters of the voxel and the voxel specification parameters of the neighboring voxels, determine the merged voxel specification parameters corresponding to the voxel and the neighboring voxels, and determine whether the merged voxel specification parameters are less than or equal to the preset maximum voxel merging specification threshold. When it is determined that the merged voxel specification parameter is greater than the maximum voxel merging specification threshold, it is determined that the voxel and the neighboring voxels do not meet the preset voxel merging conditions. When it is determined that the merged voxel specification parameter is less than or equal to the maximum voxel merging specification threshold, it is determined whether the flow status parameter of the neighboring voxel is less than the target coarsening trigger threshold of the neighboring voxel, whether the overload probability parameter of the neighboring voxel is less than the target overload probability threshold of the neighboring voxel, whether the current control status of the neighboring voxel is in the free entry and exit control status, whether the functional attribute parameter of the neighboring voxel matches the functional attribute parameter of the voxel, and whether the risk level parameter of the neighboring voxel matches the risk level parameter of the voxel. When all judgment results are yes, it is determined that the voxel and the neighboring voxel satisfy the voxel merging condition; If any judgment result is negative, it is determined that the voxel and the neighboring voxels do not satisfy the voxel merging condition.

8. An adaptive voxel coarsening UAV airspace management device, characterized in that, The device includes: The acquisition module is used to acquire the basic data source of the airspace; The determination module is used to determine the spatiotemporal attribute datasets of all voxels corresponding to the airspace based on the basic data source. The basic data source of the airspace includes historical UAV flight data, meteorological data, ground environment data, and target control data of the airspace. The spatiotemporal attribute dataset of each voxel includes historical traffic time series data, meteorological risk data, ground risk data, and control identification data of the voxel under any preset time window. For each voxel, the discrete allowed concurrent UAV count of the voxel is determined based on the time series attribute dataset of the voxel, and the traffic status parameters of the voxel are determined based on the discrete allowed concurrent UAV count of the voxel and the current UAV count of the voxel. The target control information corresponding to the voxel is determined based on the traffic status parameters of the voxel. The control module is used to perform UAV control operations on the airspace based on the target control information corresponding to all the voxels. The voxel processing module is used to perform a matching voxel processing operation on each voxel during the process of the control module performing UAV control operations on the airspace, so as to obtain a processed voxel; the voxel processing operation includes voxel refinement processing operation, voxel coarsening and merging operation, or voxel maintenance processing operation. The update module is used to update the voxel based on the processed voxel, update the discrete allowed concurrent drone count of the voxel, and re-trigger the determination module to perform the operation of determining the flow status parameter of the voxel based on the discrete allowed concurrent drone count of the voxel and the current drone count of the voxel.

9. An adaptive voxel coarsening UAV airspace management device, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the adaptive voxel coarsening UAV airspace management method as described in any one of claims 1-7.

10. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the adaptive voxel coarsening UAV airspace management method as described in any one of claims 1-7.

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