A geosot airspace graph data dynamic updating method and system

Abstract

The present application relates to airspace state update, in particular to a kind of GeoSOT airspace graph data dynamic updating method and system, real-time access multi-source heterogeneous data, by mapping data to GeoSOT-3D grid uniform space-time reference, eliminate data difference;For each GeoSOT-3D grid is endowed with dynamic attribute, and support local update and version management, realize the real-time representation and historical trace of airspace state;Through event triggering and edge computing, realize the low delay update of grid state, and combine AI prediction to optimize airspace resource allocation;The technical scheme provided by the present application can effectively overcome the defects that real-time representation and historical trace of airspace state are difficult to realize and airspace state update delay is higher in prior art.

Description

TECHNICAL FIELD

[0001] The present application relates to airspace state update, in particular to a GeoSOT airspace graph data dynamic update method and system. BACKGROUND

[0002] With the increasing frequency of air transportation, low-altitude flight activities, etc., the airspace state presents a high degree of dynamicity and complexity. Accurate and real-time mastery of airspace state information and effective management thereof to achieve optimal allocation of airspace resources are the key to ensuring flight safety and improving airspace utilization.

[0003] However, current airspace data sources are extensive, covering satellite remote sensing images, ground sensor detection data, aircraft ADS-B messages and other multi-source heterogeneous data. These data have significant differences in format, spatiotemporal reference, etc., and are difficult to directly fuse and uniformly process, posing great challenges to accurate characterization of airspace state.

[0004] At the same time, traditional airspace graph update methods often lack flexibility and real-time performance, making it difficult to quickly respond to dynamic changes in airspace state. For airspace state update, a global update mode is usually adopted, which consumes a large amount of computing resources and time, and cannot meet the demand for low-latency update of airspace state. In addition, in the face of sudden airspace events, there is a lack of effective mechanism to quickly generate reasonable avoidance tracks to optimize airspace resource allocation and ensure flight safety. SUMMARY

[0005] (I) Technical problems solved

[0006] In view of the above-mentioned shortcomings of the prior art, the present application provides a GeoSOT airspace graph data dynamic update method and system, which can effectively overcome the defects of the prior art that it is difficult to realize real-time characterization and historical tracing of airspace state, and the update of airspace state is delayed.

[0007] (II) Technical solutions

[0008] To achieve the above purpose, the present application is realized by the following technical solutions:

[0009] A GeoSOT airspace graph data dynamic update method, comprising the following steps:

[0010] S1, real-time access to multi-source heterogeneous data, eliminate data differences by mapping the data to a GeoSOT-3D grid unified spatiotemporal reference;

[0011] S2, give each GeoSOT-3D grid a dynamic attribute, and support local update and version management, realize real-time characterization and historical tracing of airspace state;

[0012] S3, low-latency update of grid state is realized through event triggering and edge computing, and airspace resource allocation is optimized in combination with AI prediction;

[0013] In step S3, the grid state update is automatically triggered based on the airspace event. When an event trigger occurs, the AI prediction model deployed on the edge node is called to generate an avoidance track for the GeoSOT-3D grid that needs to update the grid state, and a cloud-edge node collaborative mechanism is established. The cloud manages the global grid model, and the edge node performs local real-time calculation to ensure low-latency response.

[0014] Preferably, in S1, real-time access to multi-source heterogeneous data is realized by mapping the data to the GeoSOT-3D grid unified time and space reference to eliminate data differences, including:

[0015] S11, real-time access to real-time stream data including satellite remote sensing images, ground sensor detection data and aircraft ADS-B messages is realized through a distributed stream processing architecture, and a data cleaning algorithm is used to filter noise data and retain valid airspace state information;

[0016] S12, based on the GeoSOT-3D grid subdivision standard, the data is mapped to the GeoSOT-3D grid to unify the time and space reference and eliminate data differences.

[0017] Preferably, in S12, based on the GeoSOT-3D grid subdivision standard, the data is mapped to the GeoSOT-3D grid to unify the time and space reference and eliminate data differences, including:

[0018] In the process of mapping the data to the GeoSOT-3D grid, the grid precision is dynamically adjusted with the grid level, and the following formula is used for encoding:

[0019] ;

[0020] Where E is the encoding result of the data point after GeoSOT based on geographical space grid division, (x, y, z) is the three-dimensional coordinates of the data point, x is the longitude, y is the latitude, z is the height, L is the grid level, which is used to control the spatial resolution and grid precision, the range is 1~30 levels, L=1 is the lowest grid precision, the edge length of a single GeoSOT-3D grid is 1000km, L=30 is the highest grid precision, the edge length of a single GeoSOT-3D grid is 1cm, and t is the current time.

[0021] Preferably, the grid level is dynamically adjusted according to the ground object type:

[0022] For buildings, the grid level is dynamically adjusted according to the building height, and the grid resources are allocated as needed:

[0023] ;

[0024] wherein, L building is the grid level of the building, h is the building height, h 0 is the reference height, denotes rounding down, L 0 is the reference grid level;

[0025] For complex terrain, irregular body grid coding is combined with Delaunay triangulation to break through the limitation of traditional rectangular grid.

[0026] Preferably, S2 gives each GeoSOT-3D grid a dynamic attribute and supports local update and version management, realizing real-time representation and historical tracing of airspace state, including:

[0027] Each GeoSOT-3D grid adopts binary coding to identify dynamic attributes including traffic state, risk level and occupancy time limit, and is associated with time-varying parameters including weather and electromagnetic interference:

[0028] ;

[0029] wherein b1b2b3 is the traffic state code, 001 means passable, 010 means temporarily restricted, and 100 means prohibited; b4b5b6 is the risk level code, 001 means low risk, 010 means medium risk, and 100 means high risk; b7b8 is the occupancy time limit code, 00 means permanent occupation, 01 means temporary occupation, and 10 means periodic occupation; Metadata is the time-varying parameter code, used to associate time-varying parameters including weather and electromagnetic interference, supporting flexible extension in JSON format to avoid fixed field restrictions.

[0030] Preferably, S2 gives each GeoSOT-3D grid a dynamic attribute and supports local update and version management, realizing real-time representation and historical tracing of airspace state, including:

[0031] A local update mechanism is established to trigger recalculation only for grids with state changes, reducing the overall reconstruction calculation overhead, while avoiding the delay problem of fixed time interval polling through event-driven:

[0032] ;

[0033] wherein, is the grid set to be updated, g is a single GeoSOT-3D grid, and G is the set of all GeoSOT-3D grids in the airspace, , GeoSOT-3D grid g at time t, of state, is a time interval.

[0034] Preferably, in S2, dynamic attributes are given to each GeoSOT-3D grid, and local updates and version management are supported, realizing real-time representation and historical tracing of airspace state, including:

[0035] The grid state history version is stored through the time axis database, supporting version retrieval at any time, ensuring the accuracy of future airspace plan deduction and accident analysis, and version backtracking is expressed by the following formula:

[0036] ;

[0037] where, is the state of all GeoSOT-3D grid set G in airspace at time , and represents the version retrieval function, and Version ID is the version identifier.

[0038] Preferably, in S3, low-latency updates of grid state are realized through event triggering and edge computing, and AI prediction is combined to optimize airspace resource allocation, including:

[0039] Based on airspace events, automatic triggering of grid state updates improves response speed:

[0040] ;

[0041] where, is the event triggering flag of GeoSOT-3D grid g at time t, 1 represents event triggering, which requires updating the grid state, and 0 represents no event triggering, which does not require updating the grid state, represents the state of GeoSOT-3D grid g at time t violates the preset rules of GeoSOT-3D grid g ;

[0042] When an event trigger occurs, for the GeoSOT-3D grid that needs to update the grid state, an AI prediction model deployed by the edge node is called to generate an evasive flight path:

[0043] ;

[0044] where, T is the optimal evasive flight path, A3C represents the asynchronous advantage actor-critic algorithm, is the state of all GeoSOT-3D grid set G in airspace at time t, Obstacles is the obstacle set, and Constraints is the constraint condition set.

[0045] Preferably, the low-delay update of the grid state in S3 is realized by event triggering and edge computing, and the airspace resource allocation is optimized in combination with AI prediction, including:

[0046] A cloud-edge node cooperation mechanism is established, the cloud manages the global grid model, the edge node performs local real-time calculation, low-delay response is ensured, and the task allocation is represented by the following formula:

[0047] ;

[0048] Wherein, is the task allocation execution location of the GeoSOT-3D grid g, Edge and Cloud are edge node and cloud respectively, is the update delay of the GeoSOT-3D grid g, and t' is the delay threshold.

[0049] A GeoSOT airspace graph data dynamic updating system for executing the above-mentioned GeoSOT airspace graph data dynamic updating method, comprising:

[0050] A data access module for real-time access to multi-source heterogeneous data;

[0051] A data mapping module for eliminating data differences by mapping data to a GeoSOT-3D grid unified space-time reference;

[0052] A grid management module for assigning dynamic attributes to each GeoSOT-3D grid and supporting local update and version management, realizing real-time representation and historical tracing of airspace state;

[0053] A grid update module for realizing low-delay update of the grid state by event triggering and edge computing, and optimizing airspace resource allocation in combination with AI prediction;

[0054] In the grid update module, the grid state update is automatically triggered based on airspace events, when an event trigger occurs, for the GeoSOT-3D grid that needs to update the grid state, an AI prediction model deployed by the edge node is called to generate an avoidance track, and a cloud-edge node cooperation mechanism is established, the cloud manages the global grid model, the edge node performs local real-time calculation, and low-delay response is ensured.

[0055] (Three) beneficial effects

[0056] Compared with the prior art, the GeoSOT airspace graph data dynamic updating method and system provided by the present application has the following beneficial effects:

[0057] 1) Eliminate data differences and realize efficient fusion

[0058] By accessing multi-source heterogeneous data in real time and mapping them to the GeoSOT-3D grid unified time and space reference, the differences in format, time and space reference, etc. of data from different sources are effectively eliminated, which breaks the data barriers and enables seamless integration of various airspace data, providing a comprehensive, accurate and unified data basis for subsequent analysis and processing of airspace status, avoiding analysis errors and decision-making errors caused by inconsistent data, and greatly improving data processing efficiency and accuracy.

[0059] 2) Support dynamic update to ensure real-time accuracy

[0060] Each GeoSOT-3D grid is given dynamic attributes, and local update and version management are supported, which can represent the airspace status in real time and realize historical tracing. In the complex environment where the airspace status is constantly changing, this dynamic update mechanism can capture subtle changes in the airspace in a timely manner, ensuring that the airspace map always reflects the current actual situation. At the same time, the version management function facilitates the tracing and analysis of the change process of the airspace status, which helps to summarize experience, find problems, and provides strong historical data support for airspace management decision-making, improving the real-time and accuracy of airspace management;

[0061] 3) Combined with intelligent prediction, optimize resource allocation

[0062] With the help of event triggering and edge computing, the grid state is updated with low delay, and AI prediction is combined to optimize airspace resource allocation. Based on airspace events, the grid state is automatically updated to quickly respond to various emergencies in the airspace. When an event trigger occurs, the AI prediction model deployed on the edge node is called to generate an avoidance trajectory, providing a reasonable flight path planning for aircraft to avoid conflicts and congestion. At the same time, a cloud-edge node collaborative mechanism is established, the cloud manages the global grid model, and the edge node performs local real-time calculation, which ensures overall planning management and realizes low-delay response, improving the real-time of airspace status update and flight safety. BRIEF DESCRIPTION OF DRAWINGS

[0063] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0064] Figure 1 The flowchart of the present application;

[0065] Figure 2 The system diagram of the present application. DETAILED DESCRIPTION

[0066] In order to make the purposes, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0067] The specific process of the GeoSOT airspace graph data dynamic updating method provided by the present application will be introduced below in combination with specific examples (as shown in Figure 1 The technical effects are as follows.

[0068] I. Real-time access to multi-source heterogeneous data, eliminate data differences by mapping data to GeoSOT-3D grid unified space-time reference.

[0069] 1) Real-time access to real-time stream data including satellite remote sensing images, ground sensor detection data and aircraft ADS-B messages through a distributed stream processing architecture, and filter noise data by using a data cleaning algorithm to retain valid airspace state information;

[0070] 2) Based on the GeoSOT-3D grid subdivision standard, map the data to the GeoSOT-3D grid, unify the space-time reference, eliminate data differences, including:

[0071] In the process of mapping data to the GeoSOT-3D grid, the grid precision is dynamically adjusted according to the grid level, and the following formula is used for encoding:

[0072] ;

[0073] Wherein, E is the encoding result of the data point after the encoding system based on the geographical space grid division GeoSOT, (x, y, z) is the three-dimensional coordinates of the data point, x is the longitude, y is the latitude, z is the height, L is the grid level, used to control the spatial resolution and grid precision, the range is 1~30 levels, L=1 is the lowest grid precision, the edge length of a single GeoSOT-3D grid is 1000km, L=30 is the highest grid precision, the edge length of a single GeoSOT-3D grid is 1cm, t is the current time.

[0074] Specifically, the grid level is dynamically adjusted according to the ground object type (such as cuboid building, cylindrical facility, etc.):

[0075] For buildings, dynamically adjust the grid level according to the building height, and allocate grid resources as needed:

[0076] ;

[0077] wherein, L building is the grid level of the building, h is the building height, h 0 is the reference height, denotes rounding down, L 0 is the reference grid level;

[0078] For complex terrain, irregular body grid coding is combined with Delaunay triangulation to break through the limitation of traditional rectangular grid.

[0079] The above technical scheme effectively eliminates the differences in format, space-time reference, etc. of different sources of data by real-time access to multi-source heterogeneous data and mapping them to the GeoSOT-3D grid unified space-time reference. This initiative breaks down data barriers, enabling seamless integration of various airspace data, providing a comprehensive, accurate and unified data foundation for subsequent airspace state analysis and processing, avoiding analysis errors and decision-making errors caused by inconsistent data, and greatly improving data processing efficiency and accuracy.

[0080] II. Each GeoSOT-3D grid is given dynamic attributes and supports local update and version management, realizing real-time representation and historical tracing of airspace state.

[0081] 1) Each GeoSOT-3D grid uses binary encoding to identify dynamic attributes including traffic state, risk level and occupancy time, and is associated with time-varying parameters including weather and electromagnetic interference:

[0082] ;

[0083] wherein b1b2b3 is the traffic state code, 001 means passable, 010 means temporarily restricted, and 100 means prohibited; b4b5b6 is the risk level code, 001 means low risk, 010 means medium risk, and 100 means high risk; b7b8 is the occupancy time code, 00 means permanent occupation, 01 means temporary occupation, and 10 means periodic occupation; Metadata is the time-varying parameter code, used to associate time-varying parameters including weather and electromagnetic interference, supporting flexible extension in JSON format to avoid fixed field restrictions.

[0084] 2) Establish a local update mechanism, only trigger recalculation for grids with state changes (such as new obstacles, temporary airspace restrictions, etc.), reduce the computational overhead of global reconstruction, and avoid the delay problem of fixed time interval polling through event-driven:

[0085] ;

[0086] wherein, is the state of the GeoSOT-3D grid g at time t, 、 is the state of the GeoSOT-3D grid g at time t, is the time interval.

[0087] 3) Store the grid state history version through the time axis database, support version retrieval at any time, ensure the accuracy of future airspace plan deduction and accident analysis, and the version backtracking is expressed by the following formula:

[0088] ;

[0089] wherein, is the state of the GeoSOT-3D grid g at time t, represents the version retrieval function, and Version ID is the version identifier.

[0090] The above technical solution gives each GeoSOT-3D grid dynamic attributes, supports local update and version management, can represent airspace state in real time and realize history backtracking, in the complex environment of changing airspace state, this dynamic update mechanism can capture the subtle changes of airspace in time, ensure that the airspace map always reflects the current actual situation; at the same time, the version management function facilitates the backtracking and analysis of the change process of airspace state, helps to summarize experience and find problems, provides strong historical data support for airspace management decision, and improves the real-time and accuracy of airspace management.

[0091] Three, realize low-delay update of grid state through event triggering and edge computing, and optimize airspace resource allocation combined with AI prediction.

[0092] In the above steps, the grid state update is automatically triggered based on airspace events, when an event trigger occurs, for the GeoSOT-3D grid that needs to update the grid state, the AI prediction model deployed by the edge node is called to generate an avoidance track, and a cloud-edge node collaborative mechanism is established, the cloud manages the global grid model, and the edge node performs local real-time calculation, ensuring low-delay response.

[0093] 1) Automatically trigger grid state update based on airspace events (such as unmanned aerial vehicle intrusion, sudden change of weather conditions, etc.), improve response speed:

[0094] ;

[0095] wherein, ​​is the event trigger flag of GeoSOT-3D grid g at time t, 1 represents event trigger, and the grid state needs to be updated, 0 represents no event trigger, and the grid state does not need to be updated, represents the state of GeoSOT-3D grid g at time t violates the preset rules of GeoSOT-3D grid g ;

[0096] When an event trigger occurs, for the GeoSOT-3D grid that needs to update the grid state, an AI prediction model deployed by the edge node is called to generate an evasive flight path:

[0097] ;

[0098] wherein T is the optimal evasive flight path, A3C represents an asynchronous advantage actor-critic algorithm, is the state of all GeoSOT-3D grid sets G in the airspace at time t, Obstacles is the obstacle set, and Constraints is the constraint condition set.

[0099] 2) A cloud-edge node cooperation mechanism is established, the cloud manages the global grid model, the edge node (such as a take-off and landing platform) performs local real-time calculation, low-latency response is ensured, and the task allocation is represented by the following formula:

[0100] ;

[0101] wherein, is the task allocation execution position of GeoSOT-3D grid g, Edge and Cloud are edge node and cloud respectively, is the update delay of GeoSOT-3D grid g, and t' is the delay threshold.

[0102] The above technical solution realizes low-latency update of the grid state with the help of event triggering and edge computing, optimizes airspace resource allocation in combination with AI prediction, automatically triggers grid state update based on airspace events, can quickly respond to various sudden situations in the airspace, calls an AI prediction model deployed by the edge node to generate an evasive flight path when an event trigger occurs, provides a reasonable flight path planning for the aircraft, avoids conflicts and congestion, and at the same time, establishes a cloud-edge node cooperation mechanism, the cloud manages the global grid model, and the edge node performs local real-time calculation, which ensures global overall management and realizes low-latency response, and improves the real-time performance of airspace state update and flight safety.

[0103] On the basis of the above disclosed GeoSOT airspace graph data dynamic updating method, the technical solution of the present application also discloses a GeoSOT airspace graph data dynamic updating system (such as Figure 2As shown), for executing the above-mentioned GeoSOT airspace graph data dynamic updating method, comprising:

[0104] Data access module, real-time access to multi-source heterogeneous data;

[0105] Data mapping module, by mapping the data to the GeoSOT-3D grid unified space-time reference, eliminate data differences;

[0106] Grid management module, for each GeoSOT-3D grid is given dynamic attribute, and supports local update and version management, realize the real-time representation and historical tracing of airspace state;

[0107] Grid update module, through event triggering and edge computing to realize the low delay update of grid state, and combining with AI prediction to optimize airspace resource allocation;

[0108] Among them, in the grid update module, based on the airspace event automatically triggers the grid state update, when the event trigger occurs, for the need to update the grid state of GeoSOT-3D grid, call the AI prediction model deployed by the edge node to generate the avoidance track, and establish the cloud-edge node collaborative mechanism, cloud management global grid model, edge node executes local real-time calculation, guarantee low delay response.

[0109] The above examples are only used to illustrate the technical solutions of the present application, and not to limit it; although the present application has been described in detail with reference to the foregoing examples, those skilled in the art should understand that: it can still modify the technical solutions recorded in the foregoing examples, or make equivalent replacement for part of the technical features; and these modifications or replacements will not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for dynamically updating GeoSOT spatial map data, characterized in that: Includes the following steps: S1. Real-time access to multi-source heterogeneous data, eliminating data differences by mapping the data to a unified spatiotemporal reference on the GeoSOT-3D grid; S2. Assign dynamic attributes to each GeoSOT-3D mesh and support local updates and version management to achieve real-time representation and historical tracking of spatial status, including: Each GeoSOT-3D mesh uses binary encoding to identify dynamic attributes including access status, risk level, and occupancy duration, and is associated with time-varying parameters including meteorological and electromagnetic interference: ； Among them, b1b2b3 are the passage status codes, where 001 indicates passage is permitted, 010 indicates temporary restriction, and 100 indicates passage is prohibited; b4b5b6 are the risk level codes, where 001 indicates low risk, 010 indicates medium risk, and 100 indicates high risk; b7b8 are the occupancy duration codes, where 00 indicates permanent occupancy, 01 indicates temporary occupancy, and 10 indicates periodic occupancy; and Metadata is the time-varying parameter code, used to associate time-varying parameters including meteorological and electromagnetic interference, supporting flexible expansion in JSON format to avoid limitations imposed by fixed fields. S3. Achieve low-latency updates to the grid state through event triggering and edge computing, and optimize airspace resource allocation by combining AI prediction, including: Automatically triggering grid status updates based on spatial events improves response speed: ； in, This is the event trigger flag for the GeoSOT-3D mesh g at time t. 1 indicates an event has been triggered, requiring an update of the mesh state; 0 indicates no event has been triggered, and no update of the mesh state is needed. This represents the state of the GeoSOT-3D mesh g at time t. Violation of GeoSOT-3D mesh g's default rules ; When an event is triggered, for the GeoSOT-3D mesh that needs to update its mesh state, the AI ​​prediction model deployed at the edge nodes is invoked to generate an avoidance trajectory: ； Where T represents the optimal avoidance trajectory, and A3C denotes the asynchronous advantage actor-critic algorithm. Let G be the state of all GeoSOT-3D mesh sets G in the spatial domain at time t, where Obstacles is the set of obstacles and Constraints is the set of constraints. A cloud-edge node collaboration mechanism is established, with the cloud managing the entire grid model and edge nodes performing local real-time calculations to ensure low-latency response. Task allocation is expressed by the following formula: ； in, Assign execution locations to the GeoSOT-3D mesh g, with Edge and Cloud representing the edge node and cloud node, respectively. t' represents the update latency of the GeoSOT-3D mesh g, and t' represents the latency threshold.

2. The method for dynamically updating GeoSOT spatial map data according to claim 1, characterized in that: S1 integrates multi-source heterogeneous data in real time, eliminating data discrepancies by mapping the data to a unified spatiotemporal reference using the GeoSOT-3D mesh, including: S11. Real-time streaming data, including satellite remote sensing images, ground sensor detection data and aircraft ADS-B messages, is accessed in real time through a distributed streaming processing architecture, and data cleaning algorithms are used to filter noisy data and retain effective airspace status information. S12. Based on the GeoSOT-3D mesh partitioning standard, the data is mapped to the GeoSOT-3D mesh to unify the spatiotemporal reference and eliminate data differences.

3. The method for dynamically updating GeoSOT spatial map data according to claim 2, characterized in that: S12 uses the GeoSOT-3D mesh partitioning standard to map data to the GeoSOT-3D mesh, unifying the spatiotemporal reference and eliminating data discrepancies, including: During the process of mapping data to the GeoSOT-3D mesh, the mesh accuracy is dynamically adjusted according to the mesh level, and is encoded using the following formula: ； Where E is the encoding result obtained by the data point after passing through the GeoSOT coding system based on geospatial grid division, (x,y,z) is the three-dimensional coordinate of the data point, x is longitude, y is latitude, z is altitude, L is the grid level, used to control spatial resolution and grid accuracy, ranging from 1 to 30 levels, L=1 is the lowest grid accuracy, the side length of a single GeoSOT-3D grid is 1000km, L=30 is the highest grid accuracy, the side length of a single GeoSOT-3D grid is 1cm, and t is the current time.

4. The method for dynamically updating GeoSOT spatial map data according to claim 3, characterized in that: The grid hierarchy is dynamically adjusted based on the type of land cover: For buildings, the grid hierarchy is dynamically adjusted based on the building height, and grid resources are allocated as needed. ； Among them, L building Here, h represents the grid level of the building, h0 represents the building height, and h0 represents the reference height. This indicates rounding down, with L0 being the baseline grid level; For complex terrain, irregular volumetric mesh encoding is performed by combining Delaunay triangulation, breaking through the limitations of traditional rectangular meshes.

5. The method for dynamically updating GeoSOT spatial map data according to claim 1, characterized in that: S2 assigns dynamic attributes to each GeoSOT-3D mesh and supports local updates and version management, enabling real-time representation and historical tracking of spatial states, including: A local update mechanism is established, triggering recalculation only for meshes whose state has changed, reducing the computational overhead of global reconstruction. At the same time, event-driven mechanisms avoid the latency issues of polling at fixed time intervals. ； in, Let g be the set of meshes to be updated, g be a single GeoSOT-3D mesh, and G be the set of all GeoSOT-3D meshes in the spatial domain. , These represent the GeoSOT-3D mesh g at time t, state, For time intervals.

6. The method for dynamically updating GeoSOT spatial map data according to claim 5, characterized in that: S2 assigns dynamic attributes to each GeoSOT-3D mesh and supports local updates and version management, enabling real-time representation and historical tracking of spatial status, including: The timeline database stores historical versions of the grid status, supporting version retrieval at any given time to ensure the accuracy of future airspace contingency planning and accident analysis. Version backtracking is represented by the following formula: ； in, For the set G of all GeoSOT-3D meshes in the spatial domain at time t, state, This represents the version retrieval function, where Version ID is the version identifier.

7. A GeoSOT spatial map data dynamic update system, used to execute the GeoSOT spatial map data dynamic update method according to claim 1, characterized in that: include: The data access module enables real-time access to multi-source heterogeneous data. The data mapping module eliminates data discrepancies by mapping data to a unified spatiotemporal reference using the GeoSOT-3D mesh. The grid management module assigns dynamic attributes to each GeoSOT-3D grid and supports local updates and version management, enabling real-time representation and historical tracking of the spatial status. The grid update module achieves low-latency updates to the grid status through event triggering and edge computing, and combines AI prediction to optimize airspace resource allocation; In the grid update module, grid status updates are automatically triggered based on airspace events. When an event is triggered, for the GeoSOT-3D grid that needs to update its grid status, the AI ​​prediction model deployed on the edge node is called to generate an avoidance trajectory, and a cloud-edge node collaboration mechanism is established. The cloud manages the entire grid model, and the edge node performs local real-time calculations to ensure low-latency response.

Images