Dynamic Optimization Method for Urban Multi-Level Low-Altitude Airspace Functional Zones Enhanced by Spatiotemporal Knowledge

Abstract

An embodiment of the present invention provides a dynamic optimization method for urban multi-level low-altitude airspace functional areas enhanced by spatio-temporal knowledge. The method includes obtaining basic low-altitude airspace data; mapping the data into a grid structure composed of a number of voxel units to generate an initial airspace data model; using a knowledge graph framework to structurally describe low-altitude entity nodes and their semantic relationships to obtain entity node semantic information; constructing a low-altitude airspace rule library; dividing the low-altitude airspace according to the low-altitude airspace rule library; using a non-linear voxel dissection algorithm to perform non-linear dissection on the initial airspace data model to obtain a low-altitude airspace grid stratification model, and endowing the voxel units of the low-altitude airspace grid stratification model with entity node semantic information; dynamically collecting real-time data to dynamically optimize the low-altitude airspace grid stratification model. In this way, the integrated optimization of dynamic adjustment of functional areas and real-time update of multi-source data can be achieved.

Description

Technical Field

[0001] The present invention generally relates to the field of low-altitude airspace modeling and dynamic regulation, and more specifically, to a method for dynamically optimizing urban multi-level low-altitude airspace functional areas enhanced with spatio-temporal knowledge. Background Art

[0002] With the rapid development of smart cities, the low-altitude airspace has gradually become an important part of the intelligent transportation system, showing great potential in environmental monitoring, emergency response, resource allocation, etc. However, the low-altitude airspace has complex multi-variable system characteristics, including terrain and landform, building distribution, and dynamic meteorological conditions (such as wind speed, humidity, temperature), etc., and its dynamics and diversity pose severe challenges to traditional modeling techniques.

[0003] Currently, low-altitude airspace modeling usually relies on static terrain data (such as digital elevation model DEM) and building distribution (such as 3D model CityGML), and these methods can provide airspace descriptions at fixed moments.

[0004] However, in complex urban environments, the low-altitude airspace usually needs to be refined into multiple functional zones (such as no-fly zones, buffer zones, and navigation zones), and combined with different altitude levels (such as ground activity layer, low-altitude flight layer, and high-altitude operation layer) for multi-level division. However, existing methods often lack the ability to adapt to real-time environmental changes and are difficult to achieve the integrated optimization of dynamic adjustment of functional zones and real-time update of multi-source data. Summary of the Invention

[0005] According to an embodiment of the present invention, a spatio-temporal knowledge-enhanced collaborative hierarchical modeling scheme for urban multi-level low-altitude airspace is provided. This scheme can perform refined characterization of multi-dimensional information of the low-altitude airspace in complex urban environments, and achieve dynamic optimization and adjustment through knowledge reasoning, providing an intelligent solution for airspace resource management.

[0006] In a first aspect of the present invention, a method for dynamically optimizing urban multi-level low-altitude airspace functional areas enhanced with spatio-temporal knowledge is provided. The method includes:

[0007] Obtain low-altitude airspace basic data, where the low-altitude airspace basic data is terrain data, building data, dynamic environment data, and airspace rule information within the low-altitude airspace;

[0008] Map the low-altitude airspace basic data into a grid structure composed of a number of voxel units to generate an initial airspace data model;

[0009] Define the semantic relationship between low-altitude entity nodes; use the knowledge graph framework to structurally describe the low-altitude entity nodes and their semantic relationships to obtain entity node semantic information;

[0010] Constructing a low-altitude airspace rule base; the low-altitude airspace rule base includes airspace function zoning rules, dynamic environmental impact rules, and risk area adjustment rules; according to the low-altitude airspace rule base, the low-altitude airspace is divided into a low-speed traffic area, a high-speed traffic area, and a buffer no-fly area;

[0011] The initial airspace data model is nonlinearly partitioned by a nonlinear voxel partitioning algorithm to obtain a low-altitude airspace grid hierarchical model, and entity node semantic information is assigned to the voxel unit of the low-altitude airspace grid hierarchical model;

[0012] Dynamically collect real-time data, update the dynamic attribute values of voxel units according to the real-time data, and dynamically optimize the low-altitude airspace grid layered model.

[0013] Furthermore, it also includes:

[0014] After obtaining the basic data of low-altitude airspace, outliers in the dynamic environment data are eliminated, and missing information of terrain data and building data is supplemented.

[0015] Furthermore, the initial airspace data model includes a plurality of voxel units, each voxel unit is a cube, and the voxel unit is used to correspond to a position in the low-altitude airspace and carry attributes of the position.

[0016] Further, the low-altitude entity node includes static attributes and dynamic attributes;

[0017] The semantic relationship between the low-altitude entity nodes includes: proximity relationship, influence relationship and inclusion relationship.

[0018] Furthermore, the nonlinear voxel partitioning algorithm is used to perform nonlinear partitioning on the initial airspace data model to obtain a low-altitude airspace grid layered model, including:

[0019] Determine a nonlinear partitioning basis according to the low-altitude airspace rule base;

[0020] Adjusting the voxel side length according to the nonlinear segmentation basis, segmenting the corresponding area of the initial spatial domain data model according to the voxel side length to obtain a segmentation result;

[0021] The segmentation results are layered according to the low-altitude airspace rule base to obtain a low-altitude airspace grid layered model.

[0022] Furthermore, it also includes:

[0023] Morton coding is used to convert the spatial position of each voxel unit into a one-dimensional code.

[0024] Further, the updating of the dynamic attribute value of the voxel unit according to the real-time data includes:

[0025] Update the low-altitude entity nodes and their semantic relationships in the semantic information of entity nodes through real-time data, and perform logical reasoning according to the low-altitude airspace rule base to obtain the reasoning results of the spatio-temporal knowledge graph;

[0026] Assign the dynamic attributes of the reasoning results to the voxel units.

[0027] In the second aspect of the present invention, there is provided a spatio-temporal knowledge-enhanced collaborative hierarchical modeling device for urban multi-level low-altitude airspace networks. The device includes:

[0028] A data acquisition module for acquiring basic low-altitude airspace data, where the basic low-altitude airspace data is terrain data, building data, dynamic environment data, and airspace rule information within the low-altitude airspace;

[0029] A data model generation module for mapping the basic low-altitude airspace data into a grid structure composed of a number of voxel units to generate an initial airspace data model;

[0030] A semantic acquisition module for defining the semantic relationships between low-altitude entity nodes; using the knowledge graph framework to structurally describe the low-altitude entity nodes and their semantic relationships to obtain entity node semantic information;

[0031] A rule base construction module for constructing a low-altitude airspace rule base; the low-altitude airspace rule base includes airspace function partition rules, dynamic environment impact rules, and risk area adjustment rules; according to the low-altitude airspace rule base, the low-altitude airspace is divided into a low-speed traffic area, a high-speed traffic area, and a buffer no-fly area;

[0032] A semantic assignment module for performing non-linear dissection on the initial airspace data model using a non-linear voxel dissection algorithm to obtain a low-altitude airspace grid hierarchical model, and assigning entity node semantic information to the voxel units of the low-altitude airspace grid hierarchical model;

[0033] An optimization module for dynamically collecting real-time data, updating the dynamic attribute values of the voxel units according to the real-time data, and dynamically optimizing the low-altitude airspace grid hierarchical model.

[0034] In the third aspect of the present invention, there is provided an electronic device. The electronic device has at least one processor; and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method of the first aspect of the present invention.

[0035] It should be understood that the content described in the Summary of the Invention section is not intended to limit the key or important features of the embodiments of the present invention, nor to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In conjunction with the accompanying drawings and with reference to the following detailed description, the above and other features, advantages, and aspects of the embodiments of the present invention will become more apparent. In the drawings, the same or similar reference numerals denote the same or similar elements, where:

[0037] Figure 1 FIG. shows a flowchart of a method for dynamically optimizing the urban multi-level low-altitude airspace functional area with spatio-temporal knowledge enhancement according to an embodiment of the present invention;

[0038] Figure 2 FIG. shows a flowchart of a low-altitude airspace grid layering model according to an embodiment of the present invention;

[0039] Figure 3 FIG. shows a block diagram of a spatio-temporal knowledge-enhanced urban multi-level low-altitude airspace network collaborative layering modeling device according to an embodiment of the present invention;

[0040] Figure 4 FIG. shows a block diagram of an exemplary electronic device capable of implementing the embodiments of the present invention.

[0041] Among them, 400 is an electronic device, 401 is a computing unit, 402 is a ROM, 403 is a RAM, 404 is a bus, 405 is an I / O interface, 406 is an input unit, 407 is an output unit, 408 is a storage unit, and 409 is a communication unit. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without creative efforts based on the embodiments of the present invention fall within the scope of the present invention.

[0043] In addition, the term "and / or" in this document is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after.

[0044] In the present invention, an initial airspace data model is generated from the basic low-altitude airspace data; a knowledge graph framework is used to structurally describe low-altitude entity nodes and their semantic relationships to obtain entity node semantic information; the low-altitude airspace is divided according to a low-altitude airspace rule base; a non-linear voxel dissection algorithm is used to perform non-linear dissection on the initial airspace data model to obtain a low-altitude airspace grid hierarchical model, and the entity node semantic information is assigned to the voxel units of the low-altitude airspace grid hierarchical model. In this way, the integrated optimization of the dynamic adjustment of the functional area and the real-time update of multi-source data is realized.

[0045] Figure 1 FIG. 4 shows a flowchart of a method for dynamically optimizing a multi-level low-altitude airspace functional area with spatio-temporal knowledge enhancement according to an embodiment of the present invention.

[0046] The method includes:

[0047] S101. Obtain basic low-altitude airspace data, where the basic low-altitude airspace data is terrain data, building data, dynamic environment data, and airspace rule information within the low-altitude airspace.

[0048] In this embodiment, the terrain data, building data, dynamic environment data, and airspace rule information are unified into the World Geodetic System - 1984 Coordinate System (WGS84) to ensure the consistency of the data spatial reference.

[0049] Specifically, the static data includes terrain data and building data. The terrain data includes a Digital Elevation Model (DEM) and a Digital Terrain Model (DTM). The building data includes 3D building models (CityGML (Common Data Model for City Templates, City Geography Markup Language), BIM (Building Information Modeling)) for recording the height, use, and geometric boundaries of buildings. The dynamic environment data includes low-altitude aircraft sensor data such as wind speed, humidity, and temperature. The airspace rule information includes polygon boundary information of low-speed traffic areas, high-speed traffic areas, and buffer no-fly zones.

[0050] In this embodiment, it further includes:

[0051] After obtaining the basic low-altitude airspace data, outliers in the dynamic environment data are removed; and missing information in the terrain data and building data is supplemented, such as attribute values of the terrain data or buildings (such as height information of some buildings).

[0052] Specifically, removing outliers from dynamic environment data means defining outliers as abnormal data points that deviate from the normal range or pattern. In this embodiment, based on statistical analysis methods, they are quantified as data points that deviate from the average value by more than 3 standard deviations. At this time, the outliers are replaced by linear interpolation methods. For terrain data, IDW (Inverse Distance Weight) is used for spatial interpolation, and for building information, the adjacent area speculation method is used to complete the information.

[0053] Among them, the normal range refers to the range within which data values should be located at a certain moment or in a certain area based on historical data, industry standards, or physical limitations. For example, assuming that in the low-altitude airspace of a certain city, the normal wind speed range is 0 - 20 m / s. If a data point with a wind speed of 50 m / s appears, it indicates that this data point may be abnormal. Regular errors refer to deviations caused by systematic problems in measurement equipment, transmission processes, or calculation methods.

[0054] Removing outliers from dynamic environment data and completing the missing information of terrain data and building data helps to improve data quality, optimize modeling and prediction, enhance the reliability of decision support, and ensure the accuracy of subsequent analysis.

[0055] S102. Map the low-altitude airspace basic data into a grid structure composed of several voxel units to generate an initial airspace data model.

[0056] Specifically, use spatial overlay analysis technology to map terrain data, building data, and dynamic environment data into a unified grid structure.

[0057] In this embodiment, the initial airspace data model includes several voxel units, each voxel unit is a cube, and the voxel unit is used to correspond to a position in the low-altitude airspace and carry the attributes of this position.

[0058] Among them, the voxel unit is a three-dimensional voxel unit: the entire target airspace is divided into regular three-dimensional voxel units, where the horizontal is in units of longitude and latitude, and the vertical is in units of 10 meters.

[0059] Specifically, mapping the low-altitude airspace basic data into a grid structure composed of several voxel units means matching terrain data and building data to voxel units through unified geographical coordinates, and then mapping dynamic environment data and airspace control rules to voxel units through IDW point interpolation methods. Among them, dynamic environment data and airspace control rules are usually point data.

[0060] Among them, the specific process of matching terrain data and building data to voxel units and then mapping dynamic environment data and airspace control rules to voxel units through IDW point interpolation methods is as follows:

[0061] (1) Fill the ground height information of each voxel unit into the corresponding voxel unit according to the terrain data.

[0062] (2) Map the building information (such as height) in each voxel unit to the corresponding voxel according to the building data;

[0063] (3) For dynamic environment data (such as wind speed), use the IDW interpolation method to interpolate these point data into the voxel units of the airspace grid;

[0064] (4) Use the IDW interpolation method to map the airspace control rules (such as low-speed traffic areas and buffer no-fly zones) to the voxel units.

[0065] The initial airspace data model provides a detailed spatial representation for the low-altitude airspace, covering information such as terrain, buildings, and control areas, and provides a basis for subsequent airspace management and risk assessment.

[0066] S103. Define the semantic relationships between low-altitude entity nodes; use the knowledge graph framework to structurally describe the low-altitude entity nodes and their semantic relationships to obtain the semantic information of the entity nodes.

[0067] Specifically, the low-altitude entity nodes include terrain nodes (such as digital elevation model nodes), building nodes (such as building height nodes), and dynamic environment nodes (such as wind speed nodes). The structural description means structurally describing the above low-altitude entity nodes and their semantic relationships through a knowledge graph framework (such as RDF (Resource Description Framework) / OWL (Web Ontology Language)) to form triples <low-altitude entity, relationship, low-altitude entity>, for example, "<Building A, contained in, Buffer No-Fly Zone B>".

[0068] This embodiment realizes the semantic description and dynamic reasoning of airspace stratification through low-altitude entity nodes and their semantic relationships. In addition, the structural description of low-altitude entity nodes and their semantic relationships through the knowledge graph realizes semantic data integration, enabling airspace stratification to not only rely on geometric models but also have semantic expression capabilities.

[0069] In this embodiment, the low-altitude entity nodes include static attributes and dynamic attributes; the semantic relationships between the low-altitude entity nodes include: proximity relationship, influence relationship, and inclusion relationship.

[0070] Specifically, the static attributes include position and geometric shape; the dynamic attributes include wind speed and risk level; the static attributes and dynamic attributes are used to express the basic elements and their dynamic characteristics in the airspace.

[0071] In this embodiment, the semantic relationships between low-altitude entity nodes include:

[0072] Proximity relationship. For example, the distance between a building and the boundary of a flight area is between 50 meters and 200 meters, indicating that the building is spatially adjacent to the flight area.

[0073] Influence relationship. For example, the influence of the wind speed node on the risk level of the top area of a building.

[0074] Containment relationship. For example, a specific area is subject to the rule constraints of a buffer no-fly zone.

[0075] The knowledge graph defines the static and dynamic attributes of low-altitude entity nodes, establishes a semantic entity relationship network, and combines a rule engine to reason and update dynamic environment data. For example, it dynamically adjusts the area risk level or functional partition according to the wind speed change.

[0076] S104. Construct a low-altitude airspace rule library; the low-altitude airspace rule library includes airspace functional partition rules, dynamic environment influence rules, and risk area adjustment rules; according to the low-altitude airspace rule library, the low-altitude airspace is divided into a low-speed traffic area, a high-speed traffic area, and a buffer no-fly area.

[0077] In this embodiment, the low-altitude airspace is divided under the guidance of the knowledge graph, which can be used for hierarchical optimization of the initial airspace data model.

[0078] Specifically, the low-altitude airspace rule library includes:

[0079] (1) Airspace functional partition rules: Describe the functional partitions of the airspace (such as low-speed traffic areas, high-speed traffic areas, and buffer no-fly zones) and their dynamic adjustment conditions. For example, if the wind speed in the buffer area exceeds the threshold (generally 10 m / s), it is adjusted to a buffer no-fly zone; if the distance from a sensitive building is <500 m, it is adjusted to a buffer no-fly zone.

[0080] (2) Dynamic environment influence rules: Reflect the influence of the dynamic environment (such as wind speed, humidity) on the airspace risk. For example, if the wind speed > 10 m / s, and the humidity > 90% or adjacent to the buffer area, the area risk level is upgraded to "high risk".

[0081] (3) Risk area adjustment rules, which are used to dynamically evaluate the area risk and adjust the partition function according to the risk level. For example, if the area risk level reaches "high risk", access is restricted or the buffer range is expanded.

[0082] In this embodiment, the division of the low-altitude airspace includes:

[0083] (1) Low-speed traffic area, for example: , and the distance from the building is <50 meters.

[0084] (2) High-speed traffic areas, such as: , and the building density < 10 buildings / km 2 .

[0085] (3) Buffer no-fly areas, such as: 50m < distance from the no-fly zone boundary < 200m, and wind speed > 15m / s.

[0086] In this embodiment, the structured representation of the rule base is to represent the rules using a knowledge graph framework (such as RDF / OWL), and the constructed rule base includes but is not limited to Table 1 shown below:

[0087] Table 1

[0088] Rule description Condition Execution No-fly zone setting Less than 500 meters away from sensitive buildings The area is marked as "buffer no-fly zone" Wind speed impact If the wind speed > 10m / s and adjacent to the buffer zone Raise the area risk level to "high risk" Low-speed traffic area division The height range is between 20 meters and 50 meters; the distance from the building is < 50 meters The area is marked as "low-speed traffic area" High-speed traffic area division <![CDATA[The height range is between 50 meters and 150 meters; the building density < 10 buildings / km 2 > The area is marked as "high-speed traffic area" Buffer no-fly area division Between 50 meters and 200 meters from the no-fly zone boundary; wind speed > 15m / s The area is marked as "buffer no-fly area" Humidity impact Humidity > 90% Reduce the area passage priority Risk diffusion High risk duration > 10 minutes Expand the buffer zone range Priority adjustment The area is designated as an emergency mission area Raise the area passage priority to the "priority mission" level Building density impact <![CDATA[Building density > 50 buildings / km 2 and building height > 30 m]]> The area is marked as "complex building area" and the area passage priority is reduced Adjacent facility protection Less than 300 meters from critical facilities (such as airports, bridges) Marked as "sensitive area" and raise the area passage priority to the "priority protection" level

[0089] S105. Use a non-linear voxel dissection algorithm to perform non-linear dissection on the initial airspace data model, obtain a low-altitude airspace grid layering model, and assign entity node semantic information to the voxel units of the low-altitude airspace grid layering model.

[0090] Specifically, use a non-linear voxel dissection algorithm to perform non-linear dissection and digital encoding on the initial airspace, generate refined voxel grids, achieve high-precision numerical representation at any position, and then assign semantic information (such as the functional partition of the airspace, the regional risk level) to the voxel units through dynamic reasoning of the knowledge graph, optimize the attributes and structure of the layering model, and finally obtain a low-altitude airspace grid layering model.

[0091] In this embodiment, as Figure 2 shown, the use of a non-linear voxel dissection algorithm to perform non-linear dissection on the initial airspace data model to obtain a low-altitude airspace grid layering model includes:

[0092] S201. Determine the basis for non-linear dissection according to the low-altitude airspace rule base.

[0093] In this embodiment, non-linear dissection means dividing the continuous three-dimensional airspace into voxel grids composed of cube units. Voxel units are the smallest spatial representation units, and each voxel carries static and dynamic attributes.

[0094] Specifically, the basis for non-linear dissection is: through an adaptive dissection strategy, dynamically adjust the resolution of the grid cells according to the spatial complexity (such as terrain undulation, building density), that is, in areas with lower spatio-temporal complexity such as low-speed traffic areas and buffer no-fly areas, generate larger voxel units to reduce the calculation and model complexity; in areas with higher spatio-temporal complexity such as high-speed traffic areas, generate smaller voxel units to improve the accuracy, that is, the dissection accuracy of the high-speed traffic area is finer.

[0095] S202. Traverse each voxel unit of the initial airspace data model, adjust the voxel side length according to the non-linear dissection basis, and dissect the corresponding area of the initial airspace data model according to the voxel side length to obtain a dissection result.

[0096] Specifically, the voxel side length is:

[0097] ;

[0098] where is the low-resolution voxel side length (such as 10 meters), is the high-resolution voxel side length (such as 1 meter).

[0099] S203. Stratify the dissection result according to the low-altitude airspace rule library to obtain a low-altitude airspace grid stratification model.

[0100] Specifically, stratify the dissection result according to the division heights of the low-speed traffic area, high-speed traffic area, and buffer no-fly area determined by the low-altitude airspace rule library to obtain a low-altitude airspace grid stratification model.

[0101] In this embodiment, the entity node semantic information includes: functional partitions (low-speed traffic area, high-speed traffic area, and buffer no-fly area), risk information (risk level, risk source, risk duration), dynamic environment attributes (wind speed, wind direction, humidity, temperature, visibility), static attributes (terrain height, building height, building density, infrastructure proximity), etc.

[0102] Specifically, the voxel unit further includes: converting the spatial position of each voxel unit into a one-dimensional code using Morton coding for rapid retrieval, update, and query of voxel information.

[0103] By using a non-linear voxel dissection algorithm to dissect the initial airspace data model, the low-altitude airspace grid model can provide a fine airspace representation according to the actual characteristics and dynamic changes of the airspace.

[0104] S106. Dynamically collect real-time data, update the dynamic attribute values of voxel units according to the real-time data, and perform dynamic optimization on the low-altitude airspace grid stratification model.

[0105] Specifically, performing dynamic optimization on the low-altitude airspace grid stratification model includes: performing dynamic optimization on each functional area in the low-altitude airspace grid stratification model, which includes expanding or contracting the functional area boundary, merging or refining the partition, to obtain a dynamically optimized low-altitude airspace network model.

[0106] In this embodiment, updating the dynamic attribute values of voxel units according to the real-time data includes:

[0107] Update the low-altitude entity nodes and their semantic relationships in the semantic information of entity nodes through real-time data, and perform logical reasoning according to the low-altitude airspace rule base to obtain the reasoning results of the spatio-temporal knowledge graph;

[0108] Assign the dynamic attributes of the reasoning results to the voxel units.

[0109] Specifically, the real-time data includes real-time environmental data and real-time flight sensor data. The dynamic attributes of the reasoning results include functional zoning, risk level, etc.

[0110] The update of dynamic attributes enables airspace management to be more flexible and responsive. With the change of environmental conditions (such as weather changes, adjustment of airspace control areas, etc.), real-time updating of the dynamic attribute values of voxel units can enable the airspace management system to make timely adjustments to meet the needs of aircraft and airspace resources.

[0111] According to the embodiments of the present invention, compared with the prior art, it has the following advantages:

[0112] By constructing a spatio-temporal knowledge graph, systematically describing spatial entities and their semantic relationships, realizing the dynamic reasoning and adjustment of airspace functional zoning, enabling airspace functional zoning to respond in real time to environmental changes, flight demand changes and emergencies, thereby improving the flexibility and adaptability of the airspace management system.

[0113] Introduce the non-linear voxel technology, discretize the airspace into refined multi-level grid units, and combine dynamic environmental data to achieve multi-scale numerical representation of the airspace. Through multi-scale numerical representation, it is possible to perform refined processing according to the actual complexity of the airspace and use grids with different precisions for risk assessment in different airspace regions. The refined grids can accurately represent regional features such as urban dense areas, buildings, and obstacles, while the coarsened grids can be used for extensive open areas. Fine grids can accurately identify risk factors in a small range, such as buildings, wind speed, etc.; in relatively simple areas, using coarser grids can reduce the computational complexity. In this way, it can ensure that airspace features at different scales can be appropriately represented, improving the accuracy of the airspace model.

[0114] (3) Adopt the way of deep integration of the knowledge graph and the voxel model, so that the present invention not only has the ability to accurately represent static geographical information, but also can respond to dynamic environmental data in real time, providing a new path for the refined management and intelligent control of the airspace.

[0115] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0116] The above is the introduction of the method embodiments. The following is a further description of the solution of the present invention through device embodiments having the same inventive concept as the methods in the foregoing embodiments.

[0117] As Figure 3 shown, the device 300 includes:

[0118] A data acquisition module 310, configured to acquire basic low-altitude airspace data, where the basic low-altitude airspace data is terrain data, building data, dynamic environment data, and airspace rule information in the low-altitude airspace.

[0119] A data model generation module 320, configured to map the basic low-altitude airspace data into a grid structure composed of a plurality of voxel units to generate an initial airspace data model.

[0120] A semantic acquisition module 330, configured to define semantic relationships between low-altitude entity nodes; use a knowledge graph framework to structurally describe low-altitude entity nodes and their semantic relationships to obtain entity node semantic information.

[0121] A rule library construction module 340, configured to construct a low-altitude airspace rule library; the low-altitude airspace rule library includes airspace function partition rules, dynamic environment impact rules, and risk area adjustment rules; according to the low-altitude airspace rule library, divide the low-altitude airspace into a low-speed traffic area, a high-speed traffic area, and a buffer no-fly area.

[0122] A semantic assignment module 350, configured to perform non-linear subdivision on the initial airspace data model using a non-linear voxel subdivision algorithm to obtain a low-altitude airspace grid hierarchical model, and assign entity node semantic information to the voxel units of the low-altitude airspace grid hierarchical model.

[0123] An optimization module 360, configured to dynamically collect real-time data, update the dynamic attribute values of voxel units according to the real-time data, and perform dynamic optimization on the low-altitude airspace grid hierarchical model.

[0124] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the described modules can refer to the corresponding processes in the foregoing method embodiments, and will not be elaborated here.

[0125] In the technical solution of the present invention, the acquisition, storage, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0126] According to an embodiment of the present invention, the present invention also provides an electronic device.

[0127] Figure 4 FIG. shows a schematic block diagram of an electronic device 400 that can be used to implement the embodiments of the present invention. The electronic device is intended to represent various forms of digital computers, such as, for example, laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as, for example, personal digital processors, cellular telephones, smart phones, 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 invention described and / or claimed herein.

[0128] The electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 402 or a computer program loaded from a storage unit 408 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0129] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0130] The computing unit 401 may be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 401 executes the various methods and processes described above, such as methods S101 - S106. For example, in some embodiments, methods S101 - S106 may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program may be loaded and / or installed onto the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 403 and executed by the computing unit 401, one or more steps of methods S101 - S106 described above may be executed. Alternatively, in other embodiments, the computing unit 401 may be configured to execute methods S101 - S106 in any other suitable manner (e.g., by means of firmware).

[0131] Various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASIC), application-specific standard products (ASSP), systems-on-chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a dedicated or general-purpose programmable processor, receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0132] The program code for implementing the methods of the present invention can be written in any combination of one or more programming languages. These program codes can be provided to the processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the program code is executed by the processor or controller, the functions / operations specified in the flowchart and / or block diagram are implemented. The program code can be executed entirely on the machine, partially on the machine, as an independent software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0133] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in the present invention can be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0134] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. 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 principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A dynamic optimization method for urban multi-level low-altitude airspace functional areas enhanced by spatio-temporal knowledge, characterized in that, Including: Obtaining basic low-altitude airspace data, where the basic low-altitude airspace data is terrain data, building data, dynamic environment data, and airspace rule information within the low-altitude airspace; After obtaining the basic low-altitude airspace data, removing outliers in the dynamic environment data; And complementing missing information in the terrain data and building data; Mapping the basic low-altitude airspace data into a grid structure composed of a number of voxel units to generate an initial airspace data model; the initial airspace data model includes a number of voxel units, each voxel unit is a cube, and the voxel unit is used to correspond to a position in the low-altitude airspace and carry the attributes of that position; Defining semantic relationships between low-altitude entity nodes; using a knowledge graph framework to structurally describe low-altitude entity nodes and their semantic relationships to obtain entity node semantic information; Constructing a low-altitude airspace rule library; the low-altitude airspace rule library includes airspace function zoning rules, dynamic environment impact rules, and risk area adjustment rules; according to the low-altitude airspace rule library, dividing the low-altitude airspace into a low-speed traffic area, a high-speed traffic area, and a buffer no-fly area; Performing non-linear voxel dissection on the initial airspace data model using a non-linear voxel dissection algorithm to obtain a low-altitude airspace grid layering model, and assigning entity node semantic information to the voxel units of the low-altitude airspace grid layering model; Dynamically collecting real-time data, updating the dynamic attribute values of voxel units according to the real-time data, and dynamically optimizing the low-altitude airspace grid layering model; The performing non-linear voxel dissection on the initial airspace data model using a non-linear voxel dissection algorithm to obtain a low-altitude airspace grid layering model includes: Determining the non-linear dissection basis according to the low-altitude airspace rule library; Adjusting the voxel side length according to the non-linear dissection basis, and performing dissection on the corresponding area of the initial airspace data model according to the voxel side length to obtain a dissection result; Layering the dissection result according to the low-altitude airspace rule library to obtain a low-altitude airspace grid layering model; The updating the dynamic attribute values of voxel units according to the real-time data includes: Updating the low-altitude entity nodes and their semantic relationships in the entity node semantic information through real-time data, and performing logical reasoning according to the low-altitude airspace rule library to obtain the reasoning result of the spatio-temporal knowledge graph; Assigning the dynamic attributes of the reasoning result to the voxel units.

2. The method according to claim 1, wherein The low-altitude entity nodes include static attributes and dynamic attributes; The semantic relationships between the low-altitude entity nodes include: proximity relationship, influence relationship, and inclusion relationship.

3. The method according to claim 1, characterized in that Also including: Converting the spatial position of each voxel unit into a one-dimensional code using Morton coding.

4. A spatio-temporal knowledge enhanced collaborative hierarchical modeling device for urban multi-level low-altitude airspace network, characterized in that Including: A data acquisition module for obtaining basic low-altitude airspace data, where the basic low-altitude airspace data is terrain data, building data, dynamic environment data, and airspace rule information within the low-altitude airspace; After obtaining the basic low-altitude airspace data, removing outliers in the dynamic environment data; and complementing missing information in the terrain data and building data; A data model generation module, configured to map the basic low-altitude airspace data into a grid structure composed of a number of voxel units to generate an initial airspace data model; the initial airspace data model includes a number of voxel units, each voxel unit is a cube, and the voxel unit is used to correspond to a position in the low-altitude airspace and carry the attributes of this position; A semantic acquisition module, configured to define the semantic relationships between low-altitude entity nodes; use the knowledge graph framework to structurally describe the low-altitude entity nodes and their semantic relationships to obtain entity node semantic information; A rule base construction module, configured to construct a low-altitude airspace rule base; the low-altitude airspace rule base includes airspace function partition rules, dynamic environment impact rules, and risk area adjustment rules; according to the low-altitude airspace rule base, the low-altitude airspace is divided into a low-speed traffic area, a high-speed traffic area, and a buffer no-fly area; A semantic assignment module, configured to perform non-linear dissection on the initial airspace data model using a non-linear voxel dissection algorithm to obtain a low-altitude airspace grid hierarchical model, and assign the entity node semantic information to the voxel units of the low-altitude airspace grid hierarchical model; An optimization module, configured to dynamically collect real-time data, update the dynamic attribute values of voxel units according to the real-time data, and perform dynamic optimization on the low-altitude airspace grid hierarchical model; The performing non-linear dissection on the initial airspace data model using a non-linear voxel dissection algorithm to obtain a low-altitude airspace grid hierarchical model includes: Determining the basis for non-linear dissection according to the low-altitude airspace rule base; Adjusting the voxel side length according to the non-linear dissection basis, and performing dissection on the corresponding area of the initial airspace data model according to the voxel side length to obtain a dissection result; Layering the dissection result according to the low-altitude airspace rule base to obtain a low-altitude airspace grid hierarchical model; The updating the dynamic attribute values of voxel units according to the real-time data includes: Updating the low-altitude entity nodes and their semantic relationships in the entity node semantic information through real-time data, and performing logical reasoning according to the low-altitude airspace rule base to obtain the reasoning result of the spatio-temporal knowledge graph; Assigning the dynamic attributes of the reasoning result to the voxel units.

5. An electronic device, comprising at least one processor; and a memory communicatively connected to the at least one processor; characterized in that, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method according to any one of claims 1-3.

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