Large-scale low-altitude flight safety dynamic calculation and parallel scheduling method and device

Abstract

The embodiment of the disclosure provides a large-scale low-altitude flight safety dynamic calculation parallel scheduling method and device, which is applied to the technical field of low-altitude airspace safety management. The method comprises the following steps: generating a geographical space grid by griding a low-altitude airspace based on a geographical coordinate system, and performing grid coding on each grid; dividing each grid into a calculation area and a buffer area; the calculation area is a core operation unit, which is used for real-time dynamic calculation and parallel scheduling of large-scale low-altitude flight safety, and the buffer area is a data interaction hub, which is used for independently loading and caching grid data of adjacent areas; according to the position information and the safety envelope range of the aircraft, the aircraft is matched to the calculation area to which the aircraft belongs, and the aircraft data is routed to the corresponding buffer area. Based on this, the calculation decoupling between the partitions can be realized through the cooperative work of the calculation area and the buffer area, the dynamic calculation efficiency of the low-altitude flight safety is significantly improved, and therefore a real-time and efficient safety calculation solution is provided for a high-density low-altitude flight scene.

Description

Technical Field

[0001] This disclosure relates to the field of data processing technology, and in particular to the field of low-altitude airspace safety management technology, specifically to a method and apparatus for large-scale low-altitude flight safety dynamic calculation and parallel scheduling. Background Technology

[0002] With the rapid development of the low-altitude economy, drone swarms are increasingly used in logistics delivery, urban inspection, and emergency rescue, leading to a growing demand for low-altitude drone flight safety computation. However, the cross-regional nature of low-altitude drone safety envelopes causes a surge in data interaction and computational complexity, especially in large-scale, high-density operation scenarios. The request for partitioned grid data and the computation of drone flight safety face severe challenges. Current partitioned interactive computation models require access to the full grid data of adjacent partitions, which integrate multi-dimensional data such as terrain and airspace information. When large-scale drone swarms operate simultaneously, using full-data interactive computation not only results in unnecessary repeated data reading and storage, putting pressure on memory resources, but also increases the system's computational load, significantly extending computational response time and failing to meet the needs of real-time safety assessment. Furthermore, the lack of dynamic data routing in existing safety computation frameworks causes multiple partitions to repeatedly receive and compute cross-regional drone flight data, resulting in additional computational resource consumption and reduced flight safety computation efficiency.

[0003] In summary, current low-altitude flight computing faces two main problems. First, full-data computation leads to memory waste and response latency. When dealing with large-scale UAV swarm operations and cross-regional safety envelope issues, the full data of adjacent partitions is directly called for computation, resulting in a surge in data interaction, severe waste of system memory resources, and a significant increase in computation response time. Second, cross-regional data duplication occurs. Due to the lack of intelligent routing mechanisms, dynamic information such as the position and speed of UAVs in different regions is often simultaneously allocated to the two airspace grid partitions they occupy. Because of the lack of accurate identification and dynamic scheduling of aircraft information, each partition cannot perceive the redundancy of data, resulting in the same UAV's safety computation being repeatedly executed in adjacent partitions. Moreover, the inefficient data processing mode directly aggravates the consumption of computing resources, which is particularly significant in high-density UAV swarm scenarios.

[0004] Therefore, how to overcome the coupling dilemma of data interaction between different sections of the airspace grid and realize safe parallel computing for large-scale low-altitude UAV flights has become a key technical challenge in the field of low-altitude safety assurance. Summary of the Invention

[0005] This disclosure provides a method, apparatus, device, and storage medium for large-scale low-altitude flight safety dynamic calculation and parallel scheduling.

[0006] According to a first aspect of this disclosure, a parallel scheduling method for large-scale low-altitude flight safety dynamic calculation is provided. The method includes:

[0007] Based on the geographic coordinate system, the low-altitude airspace is gridded to generate a geospatial grid, and each grid is coded.

[0008] Each grid is divided into a computing area and a buffer zone; the computing area is the core computing unit, used for real-time dynamic computing and parallel scheduling of large-scale low-altitude flight safety; the buffer zone is the data interaction hub, used to independently load and cache grid data from adjacent areas; the computing area and the buffer zone form a data interaction closed loop.

[0009] Based on the aircraft's location information and safety envelope range, the aircraft is matched to its corresponding computing region, and the aircraft data is routed to the corresponding buffer.

[0010] In addition to the aspects described above and any possible implementations, a further implementation is provided in which the mesh encoding of each mesh includes:

[0011] Based on the Earth's ellipsoid, each grid is transformed into a hierarchical grid unit using latitude, longitude, and altitude information, with each grid unit corresponding to a unique coded identifier.

[0012] In addition to the aspects described above and any possible implementation, a further implementation is provided in which the grid data includes the base terrain, airspace information, and dynamic position information of the aircraft near the grid boundary.

[0013] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0014] When the aircraft performs flight calculations in the computing area, it obtains grid data from adjacent areas through the buffer and synchronizes the calculation results to the buffer.

[0015] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0016] When an aircraft flies across regions, it is assigned to a set of computing regions and buffer zones based on its aircraft position information and safety envelope.

[0017] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0018] When an aircraft is in dangerous proximity to another aircraft near the boundary within the neighborhood, the location information of the aircraft near the boundary within the neighborhood is routed to the buffer in real time.

[0019] In addition to the aspects and any possible implementations described above, a further implementation is provided, wherein the method further includes:

[0020] Based on the aircraft's location information and safety envelope range, the aircraft are divided into conventional aircraft and near-boundary aircraft. The conventional aircraft are matched to the calculation area, and the near-boundary aircraft are matched to the buffer of the adjacent area.

[0021] According to a second aspect of this disclosure, a parallel scheduling device for large-scale low-altitude flight safety dynamic calculation is provided. The device includes:

[0022] The encoding module is used to generate a geospatial grid by gridding the low-altitude airspace based on the geographic coordinate system, and to perform grid encoding on each grid.

[0023] The partitioning module is used to divide each grid into a computing area and a buffer. The computing area is the core computing unit, used for real-time dynamic computing and parallel scheduling of large-scale low-altitude flight safety. The buffer is the data interaction hub, used to independently load and cache grid data from adjacent areas. The computing area and the buffer form a data interaction closed loop.

[0024] The matching module is used to match the aircraft to its corresponding computing area based on the aircraft's location information and safety envelope range, and to route the aircraft data to the corresponding buffer.

[0025] According to a third aspect of this disclosure, an electronic device is provided. The electronic device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the program to implement the method described above.

[0026] According to a fourth aspect of this disclosure, a computer-readable storage medium is provided having a computer program stored thereon that, when executed by a processor, implements the method described above.

[0027] This application provides a parallel scheduling method for large-scale low-altitude flight safety dynamic computation. It generates a geospatial grid by gridding the low-altitude airspace based on a geographic coordinate system and encodes each grid. Each grid is then divided into a computation area and a buffer zone. The computation area serves as the core computing unit for real-time dynamic computation and parallel scheduling of large-scale low-altitude flight safety. The buffer zone acts as a data interaction hub, independently loading and caching grid data from adjacent areas. The computation area and buffer zone form a closed-loop data interaction. Based on the aircraft's position information and safety envelope, the aircraft is matched to its corresponding computation area, and its data is routed to the corresponding buffer zone. This provides a large-scale flight safety computation parallel scheduling framework based on a low-altitude airspace grid. The airspace is divided into multiple computation areas (geospatial grids) based on geographic space, further subdivided into buffer zones and computation areas. The buffer zone pre-stores some neighboring grid data to reduce data requests and stores the dynamic position information of other aircraft near the boundary in real time for flight conflict calculation. By introducing the buffer zone, inter-zone computation decoupling is achieved, thereby supporting large-scale low-altitude flight safety dynamic computation and meeting the future safety computation needs of dense aircraft flights.

[0028] It should be understood that the description in the Summary of the Invention section is not intended to limit the key or essential features of the embodiments of this disclosure, nor is it intended to restrict the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0029] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. The drawings are provided for a better understanding of the invention and are not intended to limit the scope of this disclosure. In the drawings, the same or similar reference numerals denote the same or similar elements, wherein:

[0030] Figure 1 A flowchart is shown for a parallel scheduling method for large-scale low-altitude flight safety dynamic calculation according to an embodiment of the present disclosure;

[0031] Figure 2 A schematic diagram of geospatial grid calculation area division according to an embodiment of the present disclosure is shown;

[0032] Figure 3 A schematic diagram of ground-to-air vehicle information routing according to an embodiment of the present disclosure is shown;

[0033] Figure 4 A block diagram of a large-scale low-altitude flight safety dynamic calculation parallel scheduling apparatus according to an embodiment of the present disclosure is shown;

[0034] Figure 5 A block diagram of an exemplary electronic device capable of implementing embodiments of the present disclosure is shown. Detailed Implementation

[0035] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0036] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article generally indicates that the preceding and following related objects have an "or" relationship.

[0037] This disclosure provides a parallel scheduling framework for large-scale flight safety computation based on a low-altitude airspace grid. The airspace is divided into multiple computation regions, i.e., geospatial grids, based on geospatial data. These grids are further subdivided into buffer zones and computation zones. The buffer zones pre-store some neighboring grid data to reduce data requests and store the dynamic position information of other aircraft near the boundary in real time for flight conflict calculation. By introducing the buffer zones, the computation between the zones is decoupled, thereby supporting large-scale low-altitude flight safety dynamic computation and meeting the safety computation needs of dense future aircraft flights.

[0038] Figure 1 A flowchart of a large-scale low-altitude flight safety dynamic calculation parallel scheduling method 100 according to an embodiment of the present disclosure is shown.

[0039] In box 110, based on the geographic coordinate system, the low-altitude airspace is gridded to generate a geospatial grid, and each grid is grid-coded.

[0040] In some embodiments, the low-altitude airspace can be divided into multi-level grid units, i.e., geospatial grids, based on the Earth's geographic coordinate system, so that a unique grid code can be assigned to each grid unit, thereby accurately obtaining the latitude, longitude, and altitude range of the grid through the grid code.

[0041] In some embodiments, grid coding can be a spatial coding system that divides two-dimensional planes and three-dimensional spaces into multiple levels based on geographic coordinate systems.

[0042] In some embodiments, the above-described mesh encoding of each mesh includes:

[0043] Based on the Earth's ellipsoid, each grid is transformed into a hierarchical grid unit using latitude, longitude, and altitude information, with each grid unit corresponding to a unique coded identifier.

[0044] In some embodiments, grid coding can be based on the Earth's ellipsoid and use latitude, longitude, and altitude information to transform geospatial space into hierarchical grid cells, each cell corresponding to a unique coded identifier, in order to support rapid indexing and querying of spatial locations.

[0045] In box 120, each grid is divided into a computing area and a buffer. The computing area is the core computing unit, used for real-time dynamic computing and parallel scheduling of large-scale low-altitude flight safety. The buffer is the data interaction hub, used to independently load and cache grid data from adjacent areas. The computing area and the buffer form a closed loop of data interaction.

[0046] In some embodiments, a geospatial grid can be used as a computational region, which is further divided into a computational area and a buffer zone. The computational area serves as the core computing unit responsible for real-time safety calculations of the low-altitude aircraft, while the buffer zone stores grid data from adjacent areas and the aircraft's real-time position information. Figure 2 As shown, in the geospatial grid computing region division, the computing regions are closely adjacent, and each computing region expands outward as a buffer zone.

[0047] In some embodiments, the aircraft may be a drone.

[0048] In some embodiments, the buffer is an auxiliary data storage unit constructed in conjunction with the computing area when the computing area is divided based on geographic location, and its core function is to store grid data of adjacent areas.

[0049] In some embodiments, the aforementioned grid data includes multi-dimensional data such as base terrain and airspace information, as well as dynamic position information of the aircraft near the grid boundary. The buffer acts as a data interaction hub, independently loading and caching grid data at adjacent partition boundaries, avoiding full data calls during cross-region computation, and achieving decoupling of partition computation.

[0050] Furthermore, by dynamically caching the positions of neighboring aircraft, flight safety calculations can be performed between aircraft that are geographically close but belong to different grid partitions. This design reduces redundant computation and network transmission, optimizes flight conflict detection and resolution, and theoretically supports the unlimited expansion and dynamic hot-scaling of the computational area, providing data interaction support for real-time calculations of large-scale low-altitude flight safety.

[0051] In some embodiments, the computing area is a core computing unit divided based on geospatial location, forming a collaborative working architecture with the buffer zone. Its core function is to carry out real-time computing tasks for aircraft flight safety.

[0052] In some embodiments, the computing area and the buffer form a closed loop for data interaction.

[0053] It should be noted that, unlike the current technology which divides regions based on spatial location, the computational region is divided based on computational logic. Furthermore, the grid to which the computational region and buffer zone belong not only represents spatial location, but can also represent information of different dimensions through associated data attributes.

[0054] In box 130, the aircraft is matched to its computing region based on the aircraft's location information and the safety envelope range, and the aircraft data is routed to the corresponding buffer.

[0055] In some embodiments, a message conversion middleware can be used as the core hub for data scheduling to retrieve flight status information such as the aircraft's position, speed, and heading in real time. Based on a grid coding system, the aircraft's coordinates are quickly matched to its corresponding computing region according to the aircraft's position information and safety envelope range. The aircraft data is then accurately routed to that region, so as to match the aircraft to its corresponding computing region for real-time dynamic calculation and parallel scheduling of low-altitude flight safety. The aircraft data is also routed to the corresponding buffer to provide data support for flight safety calculation.

[0056] In some embodiments, the message conversion middleware can be an intelligent scheduling hub that connects the real-time data of low-altitude aircraft with the computing area. Its core function is to route information such as the position and speed of low-altitude aircraft to appropriate partitions for computation within those partitions.

[0057] In some embodiments, the message conversion middleware constructs an efficient channel for data transmission and computation task distribution based on geospatial partitioning rules. It can analyze dynamic information such as the aircraft's position, speed, and heading in real time, and accurately route data to the corresponding computation area according to grid coding and computation area boundaries, ensuring the timeliness and accuracy of data flow between the buffer zone and the computation area.

[0058] In some embodiments, this paper addresses the technical problem of parallel computing for low-altitude aircraft safety in low-altitude airspace safety management. This aims to meet the flight safety computing needs of a large number of low-altitude aircraft, achieve real-time monitoring of regional low-altitude flight safety, and address the issue of large numbers of low-altitude drones operating in high-density scenarios, where the drone safety envelope spans multiple grid partitions. If the current partitioned interactive computing mode is still used for flight safety assessment, the computational load will increase exponentially, operational efficiency will decrease significantly, and system performance will severely degrade, failing to meet the timely response requirements for low-altitude drone flight safety computing and severely restricting the operational efficiency of large-scale drone swarms. Therefore, to solve the above problems, the airspace is divided into multiple computing regions based on geographic location, and these regions are further divided into buffer zones and computing areas. The buffer is used to store grid data of adjacent areas, and the computing area is used to support real-time flight calculations. Through message conversion middleware, the position, speed and other information of the aircraft are routed to the appropriate partition for calculation. At the same time, by introducing the buffer, the calculation between partitions is decoupled, which theoretically enables the infinite expansion of the computing area and dynamic hot scaling according to the number of aircraft, thereby supporting dynamic calculations for large-scale low-altitude flight safety and meeting the safety calculation needs of dense low-altitude UAV flights in the future.

[0059] Therefore, by working together in the computation area and the buffer zone, computational decoupling between partitions is achieved, effectively avoiding the performance bottleneck of current partition interaction computation, and significantly improving the dynamic computation efficiency of low-altitude flight safety, thus providing a real-time and efficient safety computation solution for high-density low-altitude flight scenarios.

[0060] According to the embodiments of this disclosure, the following technical effects are achieved:

[0061] This system can generate a geospatial grid by gridding low-altitude airspace based on a geographic coordinate system and encoding each grid. Each grid is then divided into computational zones and buffer zones. The computational zone serves as the core computing unit, used for real-time dynamic computation and parallel scheduling of large-scale low-altitude flight safety. The buffer zone acts as a data interaction hub, independently loading and caching grid data from adjacent areas. The computational zone and buffer zone form a closed-loop data interaction. Based on the aircraft's position information and safety envelope, the aircraft is matched to its corresponding computational zone, and its data is routed to the corresponding buffer zone. Based on this, a large-scale flight safety computation parallel scheduling framework based on a low-altitude airspace grid can be provided. The airspace is divided into multiple computational regions, i.e., geospatial grids, based on geographic space, and further subdivided into buffer zones and computational zones. The buffer zone pre-stores some neighboring grid data to reduce data requests and stores the dynamic position information of other aircraft near the boundary in real time for flight conflict calculation. By introducing the buffer zone, inter-zone computation decoupling is achieved, thereby supporting large-scale low-altitude flight safety dynamic computation and meeting the future safety computation needs of dense aircraft flights.

[0062] In some embodiments, the above method further includes:

[0063] When the aircraft performs flight calculations in the computing area, it obtains grid data from adjacent areas through the buffer and synchronizes the calculation results to the buffer.

[0064] In some embodiments, when the aircraft performs flight calculations within the computing area (i.e., when the computing area is performing flight calculations), boundary grid data of adjacent areas is obtained through a buffer to reduce data access. Simultaneously, calculation results can be synchronized to the buffer as needed to provide data support for surrounding computing areas.

[0065] According to the embodiments of this disclosure, it can be seen that the above design achieves spatial decoupling of computing tasks, which can not only meet the fine computing needs of a single area, but also cope with large-scale cluster operation scenarios through the collaboration of multiple computing areas, thus solving the performance bottleneck problem of traditional full-partition interactive computing from the architectural level.

[0066] In some embodiments, the above method further includes:

[0067] When an aircraft flies across regions, it is assigned to a set of computing regions and buffer zones based on its aircraft position information and safety envelope.

[0068] In some embodiments, to address the problems of redundant cross-regional data processing and wasted computing resources in current flight safety calculations, a partitioned computing decoupling technique is proposed. This technique further divides the computing area into computing zones and buffer zones, and establishes a collaborative mechanism to achieve efficient execution of flight safety computing tasks. In large-scale aircraft cluster operation scenarios, the real-time position, speed, and other information of the aircraft are passed to a message conversion middleware for partition matching and data routing, and low-altitude UAV flight safety calculation tasks are performed locally within each partition. When an aircraft flies across regions, the message conversion middleware intelligently allocates the aircraft to the most suitable set of computing zones and buffer zones based on its position and safety envelope range, avoiding redundant processing of the same data in multiple partitions and reducing unnecessary computational load.

[0069] Furthermore, since the buffer has pre-stored some grid data from adjacent regions, when the aircraft performs cross-domain flight, the message conversion middleware calculates the safety envelope range in real time and directly calls the information in the buffer to perform calculations in the routing partition, without needing to call the full data of adjacent partitions. This avoids the memory and computing resource consumption caused by repeated calculations in multiple partitions, thus achieving decoupling of partitioned flight safety calculations under large-scale UAV operations.

[0070] In some embodiments, the above method further includes:

[0071] When an aircraft is in dangerous proximity to another aircraft near the boundary within the neighborhood, the location information of the aircraft near the boundary within the neighborhood is routed to the buffer in real time.

[0072] In some embodiments, when an aircraft is in dangerous proximity to another aircraft near the boundary in the neighborhood, the message conversion middleware routes the location information of the neighboring aircraft to the buffer in real time, providing data support for flight safety calculations of both aircraft and ensuring timely identification and resolution of potential flight conflicts.

[0073] Among them, the aircraft is in dangerous proximity to another aircraft in the neighborhood that is close to the boundary. That is, the distance between the aircraft and the other aircraft in the neighborhood that is close to the boundary is within the preset dangerous distance threshold. The preset dangerous distance threshold can be set according to the user's actual needs.

[0074] In some embodiments, the above method further includes:

[0075] Based on the aircraft's location information and safety envelope range, the aircraft are divided into conventional aircraft and near-boundary aircraft. The conventional aircraft are matched to the computing area, and the near-boundary aircraft are matched to the buffer zone of the adjacent area. This can further help to achieve efficient decoupling of low-altitude UAV flight safety calculation, enabling the computing area to expand dynamically and ensuring the real-time performance and stability of low-altitude flight safety calculation.

[0076] like Figure 3 As shown, taking drones as an example, after receiving a batch of drone information, the message conversion middleware divides the drones into regular drones and drones near the boundary according to the drone location information and the safety envelope range. It then matches the regular drones to the computing area of ​​the computing region and the drones near the boundary to the buffer area of ​​the computing region, thereby performing ground-to-air vehicle information routing.

[0077] In summary, the large-scale low-altitude flight safety dynamic computing parallel scheduling method proposed in this disclosure can realize geospatial computing area partitioning, buffer-based partition computing decoupling, and dynamic data routing based on message conversion middleware.

[0078] Specifically, regarding the geospatial-based computational region division, this disclosure further divides the computational region into two functional areas based on a geographic coordinate system: a computational region and a buffer zone. The computational region, as the core computing unit, is responsible for executing UAV flight safety-related computational tasks within the grid cells. The buffer zone is set around the boundary of the computational region and pre-stores grid data from adjacent areas. During flight, the buffer zone receives real-time position information from UAVs approaching the grid boundary for collision detection and flight safety computation. This technology significantly optimizes the management and retrieval efficiency of grid data, reduces redundant data interactions, lowers data request latency, and improves the accuracy of potential collision identification, providing efficient and stable architectural support for dynamic flight safety computation of large-scale low-altitude UAV swarms.

[0079] To address the decoupling of partitioned computation based on buffers, this disclosure introduces a buffer, allowing the computation area to directly retrieve the required neighboring grid data from the buffer without initiating full data requests to adjacent partitions. This technology achieves decoupling of grid partitioned computation, significantly improving the response speed of flight safety calculations while avoiding redundant full data interaction during cross-partition computations, thus reducing memory pressure. Theoretically, this invention can achieve unlimited expansion of the computation area and dynamically hot-scale scaling based on the number of aircraft, thereby supporting large-scale dynamic calculations for low-altitude flight safety.

[0080] For dynamic data routing based on message conversion middleware, this disclosure uses real-time aircraft position, speed, heading, and other status information, and quickly matches them to their respective computational regions using grid coding rules, routing the flight data to the partitions. When an aircraft flies across regions, the computational region is intelligently assigned by calculating the positional relationship between the safety envelope and the partition boundary line, effectively avoiding resource waste caused by redundant calculations across multiple partitions. When an aircraft makes a dangerous approach to another aircraft within its neighborhood that is close to the grid boundary, the message conversion middleware routes the position information of the neighboring UAV to a buffer for storage, which is then used for flight safety calculations. This technology effectively optimizes data transmission efficiency, ensuring that the computational region only accepts and processes data from aircraft within its own partition, reducing invalid data interactions, and providing stable and effective data support for decoupling partitioned computations.

[0081] More notably, regarding the optimization of data requests based on computational region partitioning, current flight safety calculations use a single grid as the computational unit, lacking pre-loading and pre-storage of adjacent grid data. Cross-regional calculations require full access to neighboring data, resulting in unnecessary data requests and directly causing computational response delays. This disclosure introduces a buffer to pre-store static grid data and the real-time location of the UAV around the computational region boundary, thereby avoiding invalid data transmission, significantly reducing the amount of data requests, and optimizing the collision detection and resolution of UAV pairs belonging to different partitions but located close to each other. When calling neighboring data for computation, this partitioned optimization structure enables rapid location and extraction of valid data, reducing data request time and computational overhead, and significantly improving the timeliness and accuracy of flight collision early warning.

[0082] In addition, this disclosure can decouple partitioned computation, reducing computational load. In current technologies, each grid acts as an independent computational unit, frequently requiring the access of grid data from adjacent partitions. This not only results in a large total computational load but also leads to problems such as a large volume of data accesses and the need to filter out invalid data. This disclosure decouples partitioned computation by introducing a buffer, avoiding multi-partition interactive computation. This technology ensures that flight safety calculations can be completed within a single computational region, effectively optimizing memory resource usage, improving computational efficiency, and shortening the response time for large-scale low-altitude UAV flight safety calculations.

[0083] Furthermore, this disclosure improves data transmission efficiency and avoids redundant calculations. In existing low-altitude flight safety calculation frameworks, data transmission lacks a dynamic routing mechanism, and information from drones flying across regions is often repeatedly received and calculated by multiple partitions. This disclosure addresses this by deploying a data scheduling message conversion middleware that matches drone positions in real-time based on grid coding and routes their flight status information to the corresponding partition. For drones flying across regions, intelligent assignment of flight safety calculation tasks is achieved by calculating the positional relationship between the safety envelope and grid boundaries, and data is routed to appropriate partitions. For drones flying close to grid boundaries, they are routed to the buffer zone of the grid where the dangerously close drone is located, optimizing conflict detection and effectively avoiding resource waste caused by redundant calculations in adjacent partitions. This ensures accurate allocation of computing resources in densely populated scenarios and significantly improves the real-time performance and reliability of large-scale low-altitude flight safety calculations.

[0084] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this disclosure is not limited to the described order of actions, because according to this disclosure, some steps can be performed in other orders or simultaneously. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this disclosure.

[0085] The above is an introduction to the method embodiments. The following describes the solution described in this disclosure further through device embodiments.

[0086] Figure 4 A block diagram of a large-scale low-altitude flight safety dynamic calculation parallel scheduling apparatus 400 according to an embodiment of the present disclosure is shown. Figure 4 As shown, the device 400 includes:

[0087] The encoding module 410 is used to generate a geospatial grid by gridding the low-altitude airspace based on the geographic coordinate system, and to perform grid encoding on each grid.

[0088] The partitioning module 420 is used to divide each grid into a computing area and a buffer. The computing area is the core computing unit, used for real-time dynamic computing and parallel scheduling of large-scale low-altitude flight safety. The buffer is the data interaction hub, used to independently load and cache grid data of adjacent areas. The computing area and the buffer form a data interaction closed loop.

[0089] The matching module 430 is used to match the aircraft to its corresponding computing area based on the aircraft's position information and safety envelope range, and to route the aircraft data to the corresponding buffer.

[0090] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the described module can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0091] The acquisition, storage, and application of user personal information involved in the technical solution disclosed herein comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0092] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0093] Figure 5 A block diagram of an exemplary electronic device 500 capable of implementing embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0094] Electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in ROM 502 or a computer program loaded into RAM 503 from storage unit 508. RAM 503 can also store various programs and data required for the operation of electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via bus 504. I / O interface 505 is also connected to bus 504.

[0095] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0096] Computing unit 501 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Computing unit 501 performs the various methods and processes described above, such as method 100. For example, in some embodiments, method 100 may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508.

[0097] In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by computing unit 501, one or more steps of method 100 described above may be performed. Alternatively, in other embodiments, computing unit 501 may be configured to perform method 100 by any other suitable means (e.g., by means of firmware).

[0098] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0099] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0100] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0101] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including voice input, speech input, or tactile input).

[0102] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0103] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0104] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0105] The specific embodiments described above do not constitute a limitation on the scope of protection of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A parallel scheduling method for large-scale low-altitude flight safety dynamic calculation, characterized in that, include: Based on the geographic coordinate system, the low-altitude airspace is gridded to generate a geospatial grid, and each grid is coded. Each grid is divided into a computing area and a buffer zone; the computing area is the core computing unit, used for real-time dynamic computing and parallel scheduling of large-scale low-altitude flight safety; the buffer zone is the data interaction hub, used to independently load and cache grid data from adjacent areas; the computing area and the buffer zone form a data interaction closed loop. Based on the aircraft's location information and safety envelope range, the aircraft is matched to its corresponding computing area, and the aircraft data is routed to the corresponding buffer. When the aircraft performs flight calculations in the computing area, it obtains grid data from adjacent areas through the buffer and synchronizes the calculation results to the buffer. When an aircraft is in dangerous proximity to another aircraft near the boundary in the neighborhood, the location information of the aircraft near the boundary in the neighborhood is routed to the buffer in real time. Based on the aircraft's location information and safety envelope range, the aircraft are divided into conventional aircraft and near-boundary aircraft. The conventional aircraft are matched to the calculation area, and the near-boundary aircraft are matched to the buffer of the adjacent area.

2. The method according to claim 1, characterized in that, The grid encoding of each grid includes: Based on the Earth's ellipsoid, each grid is transformed into a hierarchical grid unit using latitude, longitude, and altitude information, with each grid unit corresponding to a unique coded identifier.

3. The method according to claim 1, characterized in that, The grid data includes the base terrain, airspace information, and the dynamic position information of aircraft near the grid boundary.

4. The method according to claim 1, characterized in that, The method further includes: When an aircraft flies across regions, it is assigned to a set of computing regions and buffer zones based on its aircraft position information and safety envelope.

5. A large-scale low-altitude flight safety dynamic calculation parallel scheduling device, characterized in that, include: The encoding module is used to generate a geospatial grid by gridding the low-altitude airspace based on the geographic coordinate system, and to perform grid encoding on each grid. The partitioning module is used to divide each grid into a computing area and a buffer. The computing area is the core computing unit, used for real-time dynamic computing and parallel scheduling of large-scale low-altitude flight safety. The buffer is the data interaction hub, used to independently load and cache grid data from adjacent areas. The computing area and the buffer form a data interaction closed loop. The matching module is used to match an aircraft to its corresponding computing area based on its location information and safety envelope range, and to route the aircraft data to the corresponding buffer. It is also used to acquire grid data from adjacent areas through the buffer when the aircraft is performing flight calculations in the computing area, and to synchronize the calculation results to the buffer. Furthermore, when an aircraft is in dangerous proximity to another aircraft near the boundary in its neighborhood, it routes the location information of the aircraft near the boundary in the neighborhood to the buffer in real time. Additionally, it is used to classify aircraft into regular aircraft and aircraft near the boundary based on their location information and safety envelope range, matching the regular aircraft to the computing area and the aircraft near the boundary to the buffer of the adjacent area.

6. An electronic device, characterized in that, include: At least one processor; as well as A memory that is communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the method described in any one of claims 1-4.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-4.

Images