A near space grid location coding method and system based on virtual beidou grid unit

Abstract

The application discloses a near-space grid position coding and decoding method and system based on a virtual Beidou grid unit, and the method comprises the following steps: defining a target airspace and a virtual Beidou grid unit, and collecting target airspace range information; dividing the target airspace according to the target airspace range information and a preset spatial precision, to obtain a virtual Beidou grid unit set; gridizing geographical entities of the target airspace according to the virtual Beidou grid unit set, to obtain a geographical entity virtual Beidou grid unit set; obtaining entity codes of all geographical entities according to the geographical entity virtual Beidou grid unit set, and constructing a geographical entity virtual Beidou grid unit grid coding set; inputting a to-be-queried space-time code into a preset geographical entity retrieval algorithm, to obtain a geographical entity set; and decoding and restoring all entities in the geographical entity set, to obtain geographical entity information. The application can guarantee spatial expression precision, reduce grid redundancy and improve query efficiency.

Description

Technical Field

[0001] This invention relates to the fields of geographic information systems and spatial grid coding technology, and in particular to a near-ground airspace grid location coding and decoding method and system based on virtual BeiDou grid units. Background Technology

[0002] With the development of Geographic Information Systems (GIS) and BeiDou Navigation Satellite System (BDS) technology, the demand for unified coding and efficient processing of geographic entities is constantly increasing for applications such as low-altitude economy, UAV operation management, and refined spatial control. Especially in near-Earth airspace environments, spatial objects are diverse in type (such as UAV tracks, airspace areas, and three-dimensional obstacles), and have obvious dynamic and multi-scale characteristics, which places higher demands on spatial representation methods.

[0003] Currently, the gridded representation of geographic entities is mostly based on the GB / T 39409-2020 "BeiDou Grid Location Code" standard. This method divides space into pre-defined levels and uses grid cells of the corresponding levels for location encoding. While this approach offers good standardization in regular scenarios, in practical applications, it is often difficult to directly match a suitable grid granularity when users need to represent non-standard scales (such as those requiring specific precision) or process complex 3D objects.

[0004] In existing methods, to accommodate different scale requirements, a multi-level structure based on quadtrees or octrees is typically introduced to recursively partition the space. While these methods can adapt to different resolutions to some extent, the inconsistent size of spatial cells results from the possibility of using different levels of mesh cells in different regions. During entity analysis or spatial computation, repeated conversions between different levels are often necessary, increasing computational complexity and hindering standardized processing.

[0005] On the other hand, existing encoding methods are mostly based on spatial location, with relatively limited support for temporal and attribute information. When dealing with dynamic targets such as drones, it is often necessary to combine time and attribute conditions for querying. However, existing methods lack a unified encoding organization and a corresponding indexing mechanism, resulting in low query efficiency, especially when the data scale is large.

[0006] In summary, in near-Earth airspace applications characterized by high dynamism, large scale variations, and complex object types, existing grid coding methods based on fixed-level or multi-level tree structures still have limitations in terms of accuracy adaptation, computational complexity, and multi-dimensional querying. Therefore, it is necessary to design a grid location encoding and decoding method that can flexibly adjust the grid granularity according to actual needs and simultaneously support the organization of spatial, temporal, and attribute information to improve the efficiency of spatial data representation and processing. Summary of the Invention

[0007] To address the challenges of existing grid location coding methods in near-Earth airspace where spatial scales are not fixed and accuracy requirements vary significantly, this invention provides a near-Earth airspace grid location coding and decoding method and system based on Virtual BeiDou Grid Units (VBUs). This invention constructs a Virtual BeiDou Grid Unit (VBU) grid model under airspace and accuracy constraints and generates a corresponding grid system based on user-defined accuracy. Geographic entities are then converted into VBU grid sets, and composite codes are generated based on spatial, temporal, and attribute information. Multi-condition queries are implemented using B+ trees and inverted indexes. Finally, all entities in the geographic entity set are decoded and restored to obtain the geographic entity information. This invention allows for flexible adjustment of grid granularity according to actual needs and simultaneously supports grid location coding and decoding methods that organize spatial, temporal, and attribute information, thereby improving the efficiency of spatial data representation and processing.

[0008] To achieve the above objectives, the technical solution of the present invention is as follows:

[0009] The first aspect of this invention proposes a near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells, comprising:

[0010] Step 1: Define the target airspace and virtual BeiDou grid cells, and collect the range information of the target airspace to facilitate a unified grid representation of the near-Earth airspace under arbitrary accuracy requirements;

[0011] Step 2: Divide the target airspace according to the range information and preset spatial accuracy to obtain a virtual BeiDou grid unit set, which facilitates the creation of a virtual grid that can flexibly adjust the grid granularity according to actual needs;

[0012] Step 3: Grid the geographic entities in the target airspace according to the virtual BeiDou grid unit set to obtain the virtual BeiDou grid unit set of geographic entities. This makes it easier to map the actual geographic entities in the near-ground airspace to the grid structure, realizing the expression conversion from continuous space to discrete grid space.

[0013] Step 4: Obtain the entity code of all geographic entities based on the set of virtual BeiDou grid units of geographic entities, and construct the set of grid codes of virtual BeiDou grid units of geographic entities based on the entity codes of all geographic entities to facilitate unified organization and subsequent retrieval;

[0014] Step 5: Use the set of virtual Beidou grid cell grid codes of geographic entities as the index data source, input the spatiotemporal codes to be queried into the preset geographic entity retrieval algorithm to obtain the set of geographic entities, which facilitates efficient querying and management of geographic entities;

[0015] Step 6: Decode and restore all entities in the geographic entity set to obtain geographic entity information; the geographic entity information includes three-dimensional coordinate range, time code and attribute code, which facilitates the restoration of geographic entity information.

[0016] Furthermore, the target airspace is represented by the following formula:

[0017]

[0018] in, For the target airspace, P represents the spatial range of the target airspace, G represents the set spatial accuracy requirement, E represents the grid structure built based on virtual BeiDou grid units, I represents the set of gridded geographic entities within the target airspace, and I represents the index structure that supports queries.

[0019] Furthermore, the virtual BeiDou grid unit is represented by the following formula:

[0020]

[0021] in, For virtual BeiDou grid units, For the entry grid encoding of the virtual BeiDou grid unit, It is the three-dimensional minimum bounding rectangle of the virtual BeiDou grid unit.

[0022] Furthermore, the step of dividing the target airspace according to its range and a preset spatial precision to obtain a virtual BeiDou grid unit set specifically includes:

[0023] A virtual grid parameter set is obtained based on the target airspace range and a preset spatial accuracy. The virtual grid parameter set includes a reference level, virtual grid scale parameters, and the BeiDou grid boundary range of the target airspace. The reference level is the level with the smallest level number among all BeiDou grid levels with accuracy values ​​less than or equal to the preset accuracy value. The virtual grid scale parameters include the number of reference level grids contained in a single virtual grid unit in the longitude, latitude, and altitude directions. The BeiDou grid boundary range of the target airspace includes the BeiDou grid codes of the reference level corresponding to the maximum and minimum three-dimensional coordinates of the target airspace.

[0024] Along the longitude, latitude, and altitude directions, all positions are traversed with virtual grid scale parameters as the step size to obtain multiple starting point 3D coordinates. For each starting point 3D coordinate, it is converted into a reference-level BeiDou grid code. The ending point 3D coordinate is determined according to the grid span corresponding to the virtual grid scale parameters. The minimum bounding rectangle of the virtual BeiDou grid unit is constructed based on the starting point 3D coordinate and the ending point 3D coordinate. The virtual BeiDou grid unit is obtained based on the reference parameter set and the minimum bounding rectangle of the virtual BeiDou grid unit.

[0025] A virtual BeiDou grid cell set is constructed based on all virtual BeiDou grid cells.

[0026] Furthermore, step three specifically includes:

[0027] Collect geographic entities in the target airspace and construct a set of geographic entities;

[0028] Iterate through each entity in the set of geographic entities. For each entity, construct the minimum bounding rectangle of the entity and convert the minimum and maximum 3D coordinates of the minimum bounding rectangle of the entity into minimum and maximum grid codes. Along the longitude, latitude and altitude directions, traverse all positions between the minimum and maximum 3D coordinates with the virtual grid scale parameter as the step size to obtain multiple 3D grid coordinates.

[0029] Traverse the three-dimensional grid coordinates. For each three-dimensional grid coordinate, obtain its corresponding BeiDou grid code. Use the BeiDou grid code as the entry grid code of the current virtual BeiDou grid unit. With the entry grid code as the starting point and the virtual grid scale parameter as the step size, obtain the endpoint BeiDou grid code. Based on the entry grid code and the endpoint BeiDou grid code, obtain the three-dimensional minimum bounding rectangle of the current virtual BeiDou grid unit.

[0030] Determine whether there is an intersection between the current virtual BeiDou grid cell's minimum 3D bounding rectangle and the actual geometry of the entity. If there is an intersection, add the current virtual BeiDou grid cell's entry grid code to the preset set of virtual BeiDou grid cell codes corresponding to the entity. After traversing all 3D grids, select the BeiDou grid code corresponding to the minimum 3D coordinates of the entity's minimum bounding rectangle from the set of virtual BeiDou grid cell codes corresponding to the entity as the entity's entry grid code. Then, add the set of virtual BeiDou grid cell codes corresponding to the entity and the entity with the entry grid code to the preset set of virtual BeiDou grid cells for geographic entities. The preset set of virtual BeiDou grid cell codes corresponding to the entity and the preset set of virtual BeiDou grid cells for geographic entities are initially empty sets.

[0031] Furthermore, the entity code is represented by the following formula:

[0032]

[0033] In the formula,

[0034]

[0035]

[0036] in, For entity encoding, As a unique identifier for an entity, For spatial codes, The overall spatial extent of the entity; Encode the entry grid for the entity; This is the set of virtual BeiDou grid unit grid codes corresponding to the entity. For timecode, For attribute codes, This represents the total number of attribute key-value pairs contained in the entity. For a specific sequence of key-value pairs.

[0037] Furthermore, the preset geographic entity retrieval algorithm includes a spatial index, a time index, and an attribute inverted index; the geographic entity retrieval algorithm calls the corresponding spatial index, time index, or attribute inverted index according to the type of the query condition to obtain one or more retrieval results; when multiple retrieval results are obtained, the intersection operation of the multiple retrieval results is performed to obtain a set of geographic entities; both the spatial index and the time index include an m-order B+ tree.

[0038] Furthermore, the initialization process of the spatial index includes:

[0039] Initialize an m-order B+ tree as a spatial index;

[0040] Initialize the fixed-length entity set, traverse all entities in the set of virtual Beidou grid unit grid codes of geographic entities, and for each entity, extract the entry grid code from the entity spatial code, use the entry grid code as the index key, and insert the association between the index key, the entity's unique identifier, and the set of virtual Beidou grid unit grid codes corresponding to the entity into the spatial index;

[0041] The minimum bounding rectangle and entrance mesh of the entity are encoded as fixed-length spatial codes. The fixed-length spatial codes replace the original spatial codes of the entity, and the processed entity is added to the fixed-length entity set.

[0042] After traversing all entities, we obtain the constructed spatial index and the fixed-length entity set.

[0043] Furthermore, step six specifically includes:

[0044] Traverse all entities in the geographic entity set. For each entity, obtain the entry grid code from the spatial code corresponding to the entity, input the entry grid code into the spatial index, and obtain the set of virtual Beidou grid unit grid codes corresponding to the entity.

[0045] Traverse each BeiDou grid code in the set of virtual BeiDou grid unit grid codes corresponding to the entity, and convert the BeiDou grid code into a three-dimensional coordinate range according to the reference level;

[0046] Based on the entity, obtain the corresponding entity code, and based on the entity code, obtain the time code and attribute code.

[0047] A second aspect of this invention proposes a near-Earth airspace grid location encoding and decoding system based on virtual BeiDou grid cells, comprising:

[0048] The definition module is used to define the target airspace and virtual BeiDou grid cells, and collect the range information of the target airspace to facilitate the unified grid expression of the near-Earth airspace under arbitrary accuracy requirements.

[0049] The virtual grid division module is used to divide the target airspace according to the range information and preset spatial accuracy to obtain a virtual BeiDou grid unit set, which facilitates the creation of a virtual grid that can flexibly adjust the grid granularity according to actual needs.

[0050] The geographic entity gridding module is used to grid geographic entities in the target airspace according to the virtual BeiDou grid unit set, and obtain the geographic entity virtual BeiDou grid unit set. This makes it easier to map the actual geographic entities in the near-ground airspace to the grid structure, realizing the expression conversion from continuous space to discrete grid space.

[0051] The entity coding module is used to obtain the entity codes of all geographic entities based on the set of virtual BeiDou grid units of geographic entities, and to construct a set of grid codes of virtual BeiDou grid units of geographic entities based on the entity codes of all geographic entities, so as to facilitate unified organization and subsequent retrieval.

[0052] The index module is used to take the set of virtual Beidou grid unit grid codes of geographic entities as the index data source, input the spatiotemporal codes to be queried into the preset geographic entity retrieval algorithm, and obtain the set of geographic entities, which facilitates the efficient query and management of geographic entities.

[0053] The decoding and restoration module is used to decode and restore all entities in the geographic entity set to obtain geographic entity information; the geographic entity information includes three-dimensional coordinate range, time code and attribute code, which facilitates the restoration of geographic entity information.

[0054] The beneficial effects of this invention are:

[0055] This invention proposes a VBU grid system construction method based on user-defined spatial precision. By automatically inferring the baseline BeiDou grid hierarchy and calculating virtual grid scale parameters, it achieves adaptive mapping from any spatial scale to the standard BeiDou grid system. This method can convert user-defined precision into an underlying standard BeiDou grid combination while ensuring spatial coverage integrity, solving the problems of fixed spatial scale and difficulty in adapting to different application requirements in traditional grid coding methods.

[0056] This invention constructs a three-dimensional spatial grid system based on VBU virtual mesh units and proposes a unified meshing method applicable to various types of geographic entities such as points, lines, surfaces, and volumes, achieving a unified spatial representation of two-dimensional and three-dimensional geographic entities. By filling the spatial domain with a three-dimensional VBU mesh, complex spatial objects can be represented in a unified mesh set form, improving the organization efficiency and representation consistency of near-Earth spatial data.

[0057] This invention proposes a geographic entity VBU grid encoding structure consisting of spatial codes, temporal codes, and attribute codes. The spatial code comprises the entity minimum bounding box (MBR) and a VBU grid sequence; the temporal code uses a fixed-time encoding; and the attribute code employs an extensible attribute description structure. This encoding structure is clear and well-defined, capable of simultaneously describing the spatial location, temporal information, and attribute characteristics of geographic entities, and exhibits good scalability and readability.

[0058] This invention constructs a multi-dimensional indexing system based on VBU grid coding, including a spatial index, a temporal index, and an attribute inverted index. The spatial index adopts a B+Tree structure, using the entry VBU grid code as the index key; the temporal index also uses a B+Tree structure to support efficient time range queries; and the attribute index uses an inverted index structure to support attribute-based retrieval. By performing aggregate operations on the results of various indexes using a geographic entity retrieval algorithm, spatial, temporal, and multi-attribute combined queries of geographic entities can be achieved, improving the efficiency of spatiotemporal data retrieval. Attached Figure Description

[0059] Figure 1 The flowchart illustrates a near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid units, as provided in this embodiment of the invention.

[0060] Figure 2 This is an architecture diagram of a near-ground airspace grid location encoding and decoding system based on virtual BeiDou grid units, provided for an embodiment of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of the embodiments of this invention will be clearly described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0062] Example 1

[0063] like Figure 1 As shown, this invention proposes a near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells, comprising:

[0064] S101: Define the target airspace and virtual BeiDou grid cells, and collect range information of the target airspace;

[0065] S102: Divide the target airspace according to the range information and preset spatial accuracy to obtain a virtual BeiDou grid unit set;

[0066] S103: Grid the geographic entities in the target airspace according to the virtual BeiDou grid unit set to obtain the virtual BeiDou grid unit set of geographic entities;

[0067] S104: Obtain the entity code of all geographic entities based on the set of virtual BeiDou grid units of geographic entities, and construct the set of grid codes of virtual BeiDou grid units of geographic entities based on the entity codes of all geographic entities.

[0068] S105: Use the set of virtual Beidou grid cell grid codes of geographic entities as the index data source, input the spatiotemporal codes to be queried into the preset geographic entity retrieval algorithm, and obtain the set of geographic entities;

[0069] S106: Decode and restore all entities in the geographic entity set to obtain geographic entity information; the geographic entity information includes three-dimensional coordinate range, time code and attribute code.

[0070] This invention first defines a target airspace and virtual BeiDou grid units, and then divides the target airspace into a set of virtual BeiDou grid units based on the target airspace's range information and preset spatial precision. Next, geographic entities in the target airspace are gridded to obtain a set of geographic entity virtual BeiDou grid units. Based on this set, the entity codes of all geographic entities are obtained, and a set of geographic entity virtual BeiDou grid unit grid codes is constructed. The spatiotemporal code to be queried is input into a preset geographic entity retrieval algorithm to obtain a set of geographic entities. Finally, all entities in the geographic entity set are decoded and restored to obtain the geographic entity information. This invention designs a grid location encoding and decoding method that can flexibly adjust the grid granularity according to actual needs and simultaneously support the organization of spatial, temporal, and attribute information, thereby improving the efficiency of spatial data representation and processing. The VBU grid encoding and indexing method proposed in this invention can be widely applied to scenarios such as low-altitude airspace management, UAV traffic management, emergency command, and urban three-dimensional spatial management. For example, in low-altitude airspace management, it can enable rapid coding and spatial retrieval of flight activity areas; in emergency command scenarios, it can enable rapid location of geographical entities in disaster areas and resource scheduling; in urban 3D management, it can support the unified expression and analysis of multi-scale spatial data, and has good engineering application value and promotion prospects.

[0071] Example 2

[0072] Based on the above embodiments, this invention proposes a near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells, which can be implemented within the framework of the GB / T 39409-2020 "BeiDou Grid Location Code" standard, including:

[0073] S201: Define the target airspace and virtual BeiDou grid cells, and collect range information of the target airspace.

[0074] Specifically, to achieve a unified grid representation of near-Earth airspace under arbitrary accuracy requirements, the target airspace and its grid structure are first defined. Let... The near-Earth airspace to be processed, consisting of spatial extent, precision constraints, mesh structure, and related objects, can be represented as:

[0075]

[0076] In the formula,

[0077]

[0078] in, For the target airspace, The spatial range of the target airspace. Latitude Longitude For height, To meet the set spatial accuracy requirements, For longitude, single-cell precision. For latitude, single-cell precision. For height, G represents the single-cell accuracy; G is the grid structure constructed based on virtual BeiDou grid cells. A gridded collection of geographic entities within the target airspace, such as drones and buildings. I represents the VBU grid composite encoding after mapping geographic entities, and I represents the index structure that supports queries.

[0079] To address the issue that traditional BeiDou grids rely on fixed levels and struggle to directly match arbitrary precision, this invention introduces a "Virtual BeiDou Grid Unit (VBU)" as the basic partitioning unit. Each VBU unit is determined by a basic grid code and its corresponding spatial range, and can be represented as:

[0080]

[0081] in, For virtual BeiDou grid units, The entry grid code for the virtual BeiDou grid unit serves as the unique starting point for the spatial fixed-length index. The three-dimensional minimum bounding rectangle of the virtual BeiDou grid unit is defined by the three-dimensional point coordinates of its lower left and upper right corners, which define the entity boundary.

[0082] Based on this, the spatial VBU mesh attribute set G is further normalized and expanded, and can be expressed as:

[0083]

[0084] in, This refers to the virtual grid scale parameter, specifically the number of basic BeiDou grid cells contained in a single VBU cell in the three directions of longitude, latitude, and altitude. , For airspace The boundary range of the BeiDou grid is represented by the reference BeiDou grid code at the lower left and upper right corners of the target airspace. : Represents the set of VBU meshes covering the entire airspace. As a baseline level, its spatial dimensions shall not exceed the user-defined precision. The specific division method and corresponding spatial precision of this level are shown in Table 1.

[0085]

[0086] S202: Divide the target airspace according to the range information and preset spatial accuracy to obtain a virtual BeiDou grid unit set.

[0087] Specifically, based on the aforementioned definition of airspace attributes, this step is based on the target airspace range. and user-defined precision Calculate and construct the virtual grid attribute system for this spatial domain. This step consists of two construction processes: VBU mesh parameter calculation and VBU mesh set generation.

[0088] VBU mesh parameter calculation:

[0089] Specifically, within the target airspace range .size and user-defined spatial precision As input, a set of virtual mesh parameters, including the baseline level, is calculated. Virtual mesh scale parameters and the range of the airspace grid First, based on the spatial precision given by the user... In the BeiDou grid systems specified in GB / T 39409-2020, the smallest level capable of covering the required accuracy is selected as the reference level. This level serves as the foundational unit for all subsequent mesh construction. Based on this, and considering the relationship between user precision and the mesh size of the selected level, the number of underlying meshes contained in a single virtual mesh cell in the longitude, latitude, and altitude directions (i.e., the number of reference-level meshes contained in the user precision in the longitude, latitude, and altitude directions) is calculated, yielding the virtual mesh scale parameters. Subsequently, based on the target airspace range The spatial boundary is defined, its minimum and maximum three-dimensional coordinates are extracted, and then converted into corresponding... The hierarchical BeiDou grid coding is obtained respectively and Together, these two constitute the range representation of the target airspace within the BeiDou grid system, i.e., the MBR. The VBU grid parameters are calculated through the above process, providing a unified scale basis for subsequent VBU grid generation. Based on the above process, the calculation steps can be represented as Algorithm 1, as shown in Table 2.

[0090]

[0091] VBU mesh set generation:

[0092] After calculating the baseline parameters, a corresponding set of virtual grid cells needs to be generated within the target spatial domain for the subsequent discrete representation of geographic entities. Specifically, using the parameters obtained above... Based on this, within the spatial range defined by the MBR, according to the virtual mesh scale parameters The process unfolds gradually in three directions, constructing a VBU mesh set covering the entire airspace. During the construction process, along the longitude, latitude, and altitude directions, with... The corresponding grid span is used as the interval to divide the spatial domain in an orderly manner. For each division location, its starting grid code is determined. This serves as the identifier for the virtual grid unit, and its coverage area is determined by the corresponding spatial span, thus forming the spatial boundary of the grid unit. As the traversal process progresses, all generated virtual mesh cells are added to the set in sequence. This continues until the entire MBR range is covered. The final result is... This refers to the complete set of virtual meshes constructed for this spatial domain. Through the above process, the transformation from parameter description to actual mesh structure is achieved, and the VBU mesh system is completed. The construction of the part. The above process can be represented as Algorithm2, as shown in Table 3.

[0093]

[0094] S203: Grid the geographic entities in the target airspace according to the virtual BeiDou grid unit set to obtain the virtual BeiDou grid unit set of geographic entities.

[0095] Specifically, after obtaining the VBU grid system in step S202, it is necessary to map the actual geographic entities in the near-Earth spatial domain to this grid structure, realizing the representation conversion from continuous space to discrete grid space. Let the set of geographic entities be... Each entity Including identification information, geometric shape, and attribute information, it can be represented as:

[0096]

[0097] in, As a unique identifier for an entity, Encode the entry grid for the entity. The mbr represents the actual spatial form of an entity, and its minimum bounding rectangle represents the spatial extent of the entity. This represents the VBU mesh code set corresponding to the entity. This indicates the entry grid code selected from this set, used for subsequent indexing and fast location. For time, It is an attribute.

[0098] For each geographic entity First, based on its spatial range Determine its position at the baseline level. The corresponding BeiDou grid coding interval is used to limit the grid search range where the entity is located. Within this range, the virtual grid scale determined in step S202 is used. The process involves progressively locating grid cells potentially associated with entities in three directions. For each candidate grid cell, it is determined whether its spatial extent overlaps with the entity's geometry. If spatial intersection exists, the grid cell is considered to participate in the representation of the entity, and its corresponding grid code is recorded. As the selection process progresses, each geographic entity will form a set of multiple VBU grid codes (i.e., the set of virtual BeiDou grid cell codes corresponding to the entity), used to describe its spatial distribution range. Simultaneously, a representative code (the BeiDou grid code corresponding to the smallest 3D coordinates of the entity's smallest bounding rectangle) is selected from this set as the entity's entry identifier, facilitating subsequent code organization and indexing. Ultimately, all geographic entities can be represented as corresponding VBU grid code sets, constituting the geographic entity grid mapping result EG (geographic entity virtual BeiDou grid cell set, geographic entity VBU grid set). The above process can be represented as Algorithm 3, as shown in Table 4.

[0099]

[0100] S204: Obtain the entity codes of all geographic entities based on the set of virtual BeiDou grid units for geographic entities, and construct the set of grid codes for virtual BeiDou grid units for geographic entities based on the entity codes of all geographic entities.

[0101] Specifically, after completing the correspondence between geographic entities and VBU meshes in step S203, each entity This can already be represented as a set of grid codes. To facilitate unified organization and subsequent retrieval, a comprehensive spatiotemporal coding structure containing spatial, temporal, and attribute information needs to be constructed on this basis. The geographic entity coding format designed in this invention is as follows:

[0102]

[0103] In the formula,

[0104]

[0105]

[0106] in, For entity encoding, As a unique identifier for an entity, A spatial code is composed of the spatial extent of an entity and its corresponding VBU mesh set. The overall spatial extent of the entity; Encode the entry grid for the entity. For timecode, The attribute code (encapsulates the non-spatial semantics of an entity (such as drone model, flight status, etc.) in the form of a set of key-value pairs. This encoding changes with the entity's characteristics and is a variable-length structure). This represents the total number of attribute key-value pairs contained in the entity. For a specific key-value pair sequence, This is the set of virtual BeiDou grid cell grid codes corresponding to the entities. Since the number of grid cells varies among different entities, this part has a variable length structure.

[0107] Timecode ( The absolute timestamp is recorded in YYYYMMDDHHMMSS (14-bit) format to record the specific time when the entity's state occurred. This encoding is a fixed-length structure with a fixed byte length. The meanings of the timestamp encoding format are as follows: YYYY for year, MM for month, DD for day, HH for hour, MM for minute, and SS for second.

[0108] After completing the above structure definition, the set of geographic entities obtained in step S203 is processed one by one, and their spatial information, temporal information and attribute information are combined to generate the corresponding entity codes. And summarize them to form a code set The above process can be represented as Algorithm4, as shown in Table 5.

[0109]

[0110] To further illustrate the geographic entity VBU grid coding structure proposed in this invention, a specific coding example is given below in conjunction with unmanned aerial vehicles (UAVs) in near-ground airspace.

[0111] Algorithm input data:

[0112] Assume there is a drone entity performing a patrol mission in the target airspace. After the mapping process in step S203, the entity is in the set The basic data status is as follows:

[0113] Unique Identifier:

[0114] Spatial range:

[0115] VBU mesh set:

[0116]

[0117] Entry mesh coding:

[0118] Time information:

[0119] Attribute information:

[0120] Algorithm execution and encoding process:

[0121] System reads entity The above basic data is used to construct the code in sequence:

[0122] Spatial code: based on logic The assembly yields:

[0123]

[0124] Timecode: based on logic The assigned value is:

[0125] .

[0126] Attribute Code: First, count the number of attributes. Then, according to logic... The assembly yields:

[0127]

[0128] Final encoded output:

[0129] The geographic entity integrated coding encapsulation structure proposed in this invention The final complete spatiotemporal encoded instance of this drone entity is strictly expressed as:

[0130]

[0131] Through the rigorous encoding process described above, it is clear that the composite encoding generated by this invention, while ensuring spatial representation accuracy, orderly encapsulates variable-length entity data and fixed-length index key values. This provides standardized underlying data structure support for the subsequent construction of a multidimensional retrieval system based on B+ trees and inverted indexes (i.e., step 205).

[0132] S205: Using the set of virtual BeiDou grid cell grid codes of geographic entities as the index data source, the spatiotemporal codes to be queried are input into the preset geographic entity retrieval algorithm to obtain the set of geographic entities.

[0133] Specifically, in step S204, the VBU grid encoding of geographic entities has been completed, resulting in a set of virtual BeiDou grid unit grid codes for geographic entities. To support efficient querying and management of geographic entities, this invention constructs a multi-dimensional indexing system based on VBU grids. It is used to enable rapid retrieval of spatial, temporal, and attribute information.

[0134] The indexing system includes: .in For spatial B+ tree indexing; A time-based B+ tree index; Inverted index for attributes.

[0135] VBU grid space index construction:

[0136] To support spatial queries of geographic entities, this invention constructs a spatial index structure based on VBU grid coding. Considering that VBU grids have a regular hierarchical structure and that the spatial code contains the entity's entry grid code, an m-order B+Tree is used as the spatial index structure. Specifically, for two-dimensional spatial data, the B+Tree order is typically m=4; for three-dimensional spatial data, due to the increased spatial dimension and the increased number of node branches, m=8 can be used.

[0137] In the specific index construction process, this invention does not change the original spatial code expression form, but selects representative codes as the index basis. Specifically, as described in step S204, the spatial code of a geographic entity is represented as follows: .in, It is the minimum bounding rectangle. Used to record the variable-length VBU mesh sequence corresponding to the entity, and As a representative example, this coding method can be used to quickly locate the approximate spatial position of an entity.

[0138] Therefore, instead of repeatedly parsing complex variable-length grid sets during index building, the system directly traverses the set of virtual BeiDou grid cell grid codes for geographic entities and extracts the fixed-length entry grid code for each entity. As the primary key of the spatial B+ tree Subsequently, a globally unique identifier corresponding to this entity is generated. The data is inserted into the leaf nodes of the corresponding B+ tree (preferably, the association between the index key, the entity's unique identifier, and the set of virtual BeiDou grid cell codes corresponding to the entity is inserted into the spatial index). Further, after the spatial index is constructed, the system will simplify the original geographic entity codes to a fixed length. This is due to the variable-length grid sequence... Now managed by the underlying index tree, the system will separate it from the entity's spatial code, simplifying the spatial code of the geographic entity into a fixed-length representation. Afterward, the entity only needs to retain this fixed-length pointer index to quickly backtrack to the complete grid data when needed through the index tree, thus realizing the replacement of variable-length data with the existing index. The initialization process of the spatial index for geographic entities is shown in Algorithm5, and the details are shown in Table 6:

[0139]

[0140] Time index construction:

[0141] In geographic entity coding, each entity contains corresponding time information describing the specific moment of its occurrence or existence. To support retrieval based on time criteria, an index structure needs to be built for this time information. This invention uses the entity's time code as the index basis. The time code is represented in a unified format, reflecting the entity's temporal characteristics and supporting filtering by time range. Therefore, during the index construction process, the time code is used as the key to organize entity identifiers into the index structure, thus forming a time-oriented index system. Based on this, all geographic entity codes are traversed, their time codes are extracted, and inserted one by one into the time index structure, ultimately obtaining the time index. The above process can be represented as Algorithm6, as shown in Table 7:

[0142]

[0143] Attribute inverted index construction:

[0144] In geographic entity coding, attribute information is used to describe the semantic features of entities. To support retrieval based on attribute conditions, this attribute information needs to be organized and a corresponding index structure needs to be constructed. This invention employs an inverted index approach to establish a relationship between attribute values ​​and corresponding geographic entities. Specifically, attribute content is used as the index basis to classify entity identifiers with the same attributes. Through this structure, relevant entities can be quickly located based on given attribute conditions. Based on this, all geographic entity codes are traversed, their attribute information is extracted, and a mapping relationship between attribute values ​​and entity identifiers is established item by item, ultimately forming an attribute inverted index. The above process can be represented as Algorithm7, as shown in Table 8:

[0145]

[0146] Geographic entity retrieval algorithm:

[0147] After completing the construction of spatial index, time index and attribute index, this invention provides a unified geographic entity retrieval algorithm to support the following query types: 1) grid attribute query; 2) geographic entity spatial range query; 3) time range retrieval; 4) multi-attribute combination retrieval.

[0148] The geographic entity retrieval algorithm calls the corresponding spatial index, time index, or attribute inverted index based on the type of the query conditions to obtain one or more retrieval results. When multiple retrieval results are obtained, the intersection operation of the multiple retrieval results is performed to obtain the geographic entity set. Algorithm 8 describes the retrieval process. The algorithm first obtains a spatial candidate set based on the query conditions; then obtains a time candidate set based on the time conditions; then obtains an attribute candidate set based on the attribute conditions; finally, it obtains the entity set that satisfies all conditions through the set intersection operation and returns the query results to obtain the geographic entity set, as shown in Table 9:

[0149]

[0150] S206: Decode and restore all entities in the geographic entity set to obtain geographic entity information; the geographic entity information includes three-dimensional coordinate range, time code, and attribute code.

[0151] Specifically, after completing the VBU grid encoding and index construction of geographic entities, decoding of the geographic entity encoding is required to support the parsing and practical application of the encoding results. This process is used to restore the encoding structure generated in step D to its corresponding spatial range, temporal information, and attribute content. Specifically, the geographic entity encoding is first read... Extract spatial code from it and obtain the ingress grid code. Subsequently, through spatial indexing ,according to Backtrack the VBU mesh set corresponding to this entity. Based on this, targeting Each grid code in the code follows the coding rules of the GB / T 39409-2020 standard, and is combined with the baseline level. The system performs reverse analysis to calculate the corresponding three-dimensional spatial coordinate range (longitude, latitude, and altitude), thereby restoring the spatial coverage of the entity. Simultaneously, the system directly parses the timecode. Obtain the entity's time information and parse the attribute code. The set of attribute key-value pairs is obtained. Through the above process, the geographic entity information is restored from the encoded structure. The above process can be represented as Algorithm9, as shown in Table 10.

[0152]

[0153] Example 3

[0154] Based on the above embodiments, such as Figure 2 As shown, this invention proposes a near-Earth airspace grid location encoding and decoding system based on virtual BeiDou grid cells, comprising:

[0155] The definition module is used to define the target airspace and virtual BeiDou grid cells, and to collect range information of the target airspace.

[0156] The virtual grid division module is used to divide the target airspace according to the range information and preset spatial accuracy to obtain a virtual BeiDou grid unit set;

[0157] The geographic entity gridding module is used to grid geographic entities in the target airspace according to the virtual BeiDou grid unit set, so as to obtain the geographic entity virtual BeiDou grid unit set;

[0158] The entity coding module is used to obtain the entity codes of all geographic entities based on the set of virtual BeiDou grid cells of geographic entities, and to construct a set of grid codes for virtual BeiDou grid cells of geographic entities based on the entity codes of all geographic entities.

[0159] The index module is used to take the set of virtual Beidou grid cell grid codes of geographic entities as the index data source, input the spatiotemporal codes to be queried into the preset geographic entity retrieval algorithm, and obtain the set of geographic entities.

[0160] The decoding and restoration module is used to decode and restore all entities in the geographic entity set to obtain geographic entity information; the geographic entity information includes three-dimensional coordinate range, time code and attribute code.

[0161] It should be noted that the near-Earth airspace grid position encoding and decoding system based on virtual BeiDou grid units provided in this embodiment of the invention is to implement the above-mentioned near-Earth airspace grid position encoding and decoding method based on virtual BeiDou grid units. Its specific functions can be referred to the above-mentioned method embodiments, and will not be repeated here.

[0162] In summary, this invention proposes a VBU grid system construction method based on user-defined spatial precision. By automatically inferring the baseline BeiDou grid hierarchy and calculating virtual grid scale parameters, it achieves adaptive mapping from any spatial scale to the standard BeiDou grid system. This method can convert user-defined precision into a combination of underlying standard BeiDou grids while ensuring spatial coverage integrity, solving the problems of fixed spatial scales and difficulty in adapting to different application requirements in traditional grid coding methods.

[0163] This invention constructs a three-dimensional spatial grid system based on VBU (Virtual Buffer Unit) virtual grid cells and proposes a unified gridding method applicable to various types of geographic entities, including points, lines, surfaces, and volumes, achieving a unified spatial representation of two-dimensional and three-dimensional geographic entities. By filling the spatial domain with three-dimensional VBU grids, complex spatial objects can be represented in a unified grid set form, improving the organization efficiency and consistency of near-Earth spatial data. This invention proposes a geographic entity VBU grid encoding structure composed of spatial codes, temporal codes, and attribute codes. The spatial code consists of the entity's minimum bounding box (MBR) and a VBU grid sequence; the temporal code uses a fixed-time encoding; and the attribute code uses an extensible attribute description structure. This encoding system has a clear structure and standardized expression, simultaneously describing the spatial location, temporal information, and attribute characteristics of geographic entities, and possesses good scalability and readability. This invention constructs a multi-dimensional indexing system based on VBU grid encoding, including spatial indexes, temporal indexes, and attribute inverted indexes. The spatial index employs a B+Tree structure, using the entry VBU grid code as the index key; the temporal index also uses a B+Tree structure to support efficient time range queries; and the attribute index uses an inverted index structure to support attribute-based conditional retrieval. By performing aggregate operations on the results of various indexes using a geographic entity retrieval algorithm, spatial, temporal, and multi-attribute combined queries of geographic entities can be achieved, improving the efficiency of spatiotemporal data retrieval.

[0164] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells, characterized in that, include: Step 1: Define the target airspace and virtual BeiDou grid cells, and collect the range information of the target airspace; Step 2: Divide the target airspace according to the range information and preset spatial accuracy to obtain a virtual BeiDou grid unit set; Step 3: Grid the geographic entities in the target airspace according to the virtual BeiDou grid unit set to obtain the virtual BeiDou grid unit set of geographic entities; Step 4: Obtain the entity code of all geographic entities based on the set of virtual BeiDou grid cells of geographic entities, and construct the set of grid codes of virtual BeiDou grid cells of geographic entities based on the entity codes of all geographic entities. Step 5: Using the set of virtual BeiDou grid cell grid codes for geographic entities as the index data source, input the spatiotemporal codes to be queried into the preset geographic entity retrieval algorithm to obtain the set of geographic entities; Step 6: Decode and restore all entities in the geographic entity set to obtain geographic entity information; the geographic entity information includes three-dimensional coordinate range, time code, and attribute code.

2. The near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells according to claim 1, characterized in that, The target airspace is represented by the following formula: in, For the target airspace, P represents the spatial range of the target airspace, G represents the set spatial accuracy requirement, E represents the grid structure built based on virtual BeiDou grid units, I represents the set of gridded geographic entities within the target airspace, and I represents the index structure that supports queries.

3. The near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells according to claim 1, characterized in that, The virtual BeiDou grid unit is represented by the following formula: in, For virtual BeiDou grid units, For the entry grid encoding of the virtual BeiDou grid unit, It is the three-dimensional minimum bounding rectangle of the virtual BeiDou grid unit.

4. The near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells according to claim 1, characterized in that, The process of dividing the target airspace according to its range and preset spatial precision to obtain a virtual BeiDou grid unit set specifically includes: A virtual grid parameter set is obtained based on the target airspace range and a preset spatial accuracy. The virtual grid parameter set includes a reference level, virtual grid scale parameters, and the BeiDou grid boundary range of the target airspace. The reference level is the level with the smallest level number among all BeiDou grid levels with accuracy values ​​less than or equal to the preset accuracy value. The virtual grid scale parameters include the number of reference level grids contained in a single virtual grid unit in the longitude, latitude, and altitude directions. The BeiDou grid boundary range of the target airspace includes the BeiDou grid codes of the reference level corresponding to the maximum and minimum three-dimensional coordinates of the target airspace. Along the longitude, latitude, and altitude directions, all positions are traversed with virtual grid scale parameters as the step size to obtain multiple starting point 3D coordinates. For each starting point 3D coordinate, it is converted into a reference-level BeiDou grid code. The ending point 3D coordinate is determined according to the grid span corresponding to the virtual grid scale parameters. The minimum bounding rectangle of the virtual BeiDou grid unit is constructed based on the starting point 3D coordinate and the ending point 3D coordinate. The virtual BeiDou grid unit is obtained based on the reference parameter set and the minimum bounding rectangle of the virtual BeiDou grid unit. A virtual BeiDou grid cell set is constructed based on all virtual BeiDou grid cells.

5. The near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells according to claim 4, characterized in that, Step three specifically includes: Collect geographic entities in the target airspace and construct a set of geographic entities; Iterate through each entity in the set of geographic entities. For each entity, construct the minimum bounding rectangle of the entity and convert the minimum and maximum 3D coordinates of the minimum bounding rectangle of the entity into minimum and maximum grid codes. Along the longitude, latitude and altitude directions, traverse all positions between the minimum and maximum 3D coordinates with the virtual grid scale parameter as the step size to obtain multiple 3D grid coordinates. Traverse the three-dimensional grid coordinates. For each three-dimensional grid coordinate, obtain its corresponding BeiDou grid code. Use the BeiDou grid code as the entry grid code of the current virtual BeiDou grid unit. With the entry grid code as the starting point and the virtual grid scale parameter as the step size, obtain the endpoint BeiDou grid code. Based on the entry grid code and the endpoint BeiDou grid code, obtain the three-dimensional minimum bounding rectangle of the current virtual BeiDou grid unit. Determine whether there is an intersection between the current virtual BeiDou grid cell's minimum 3D bounding rectangle and the actual geometry of the entity. If there is an intersection, add the current virtual BeiDou grid cell's entry grid code to the preset set of virtual BeiDou grid cell codes corresponding to the entity. After traversing all 3D grids, select the BeiDou grid code corresponding to the minimum 3D coordinates of the entity's minimum bounding rectangle from the set of virtual BeiDou grid cell codes corresponding to the entity as the entity's entry grid code. Then, add the set of virtual BeiDou grid cell codes corresponding to the entity and the entity with the entry grid code to the preset set of virtual BeiDou grid cells for geographic entities. The preset set of virtual BeiDou grid cell codes corresponding to the entity and the preset set of virtual BeiDou grid cells for geographic entities are initially empty sets.

6. The near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells according to claim 5, characterized in that, The entity code is represented by the following formula: In the formula, in, For entity encoding, As a unique identifier for an entity, For spatial codes, The overall spatial extent of the entity; Encode the entry grid for the entity; This is the set of virtual BeiDou grid unit grid codes corresponding to the entity. For timecode, For attribute codes, This represents the total number of attribute key-value pairs contained in the entity. For a specific sequence of key-value pairs.

7. The near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells according to claim 1, characterized in that, The preset geographic entity retrieval algorithm includes a spatial index, a time index, and an attribute inverted index; the geographic entity retrieval algorithm calls the corresponding spatial index, time index, or attribute inverted index according to the type of the query condition to obtain one or more retrieval results; when multiple retrieval results are obtained, the intersection operation of the multiple retrieval results is performed to obtain a set of geographic entities; both the spatial index and the time index include an m-order B+ tree.

8. A near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells according to any one of claims 6 or 7, characterized in that, The initialization process of the spatial index includes: Initialize an m-order B+ tree as a spatial index; Initialize the fixed-length entity set, traverse all entities in the set of virtual Beidou grid unit grid codes of geographic entities, and for each entity, extract the entry grid code from the entity spatial code, use the entry grid code as the index key, and insert the association between the index key, the entity's unique identifier, and the set of virtual Beidou grid unit grid codes corresponding to the entity into the spatial index; The minimum bounding rectangle and entrance mesh of the entity are encoded as fixed-length spatial codes. The fixed-length spatial codes replace the original spatial codes of the entity, and the processed entity is added to the fixed-length entity set. After traversing all entities, we obtain the constructed spatial index and the fixed-length entity set.

9. A near-Earth airspace grid location encoding and decoding method based on virtual BeiDou grid cells according to any one of claims 6 or 7, characterized in that, Step six specifically includes: Traverse all entities in the geographic entity set. For each entity, obtain the entry grid code from the spatial code corresponding to the entity, input the entry grid code into the spatial index, and obtain the set of virtual Beidou grid unit grid codes corresponding to the entity. Traverse each BeiDou grid code in the set of virtual BeiDou grid unit grid codes corresponding to the entity, and convert the BeiDou grid code into a three-dimensional coordinate range according to the reference level; Based on the entity, obtain the corresponding entity code, and based on the entity code, obtain the time code and attribute code.

10. A near-Earth airspace grid location encoding and decoding system based on virtual BeiDou grid cells, characterized in that, include: The definition module is used to define the target airspace and virtual BeiDou grid cells, and to collect range information of the target airspace. The virtual grid division module is used to divide the target airspace according to the range information and preset spatial accuracy to obtain a virtual BeiDou grid unit set; The geographic entity gridding module is used to grid geographic entities in the target airspace according to the virtual BeiDou grid unit set, so as to obtain the geographic entity virtual BeiDou grid unit set; The entity coding module is used to obtain the entity codes of all geographic entities based on the set of virtual BeiDou grid cells of geographic entities, and to construct a set of grid codes for virtual BeiDou grid cells of geographic entities based on the entity codes of all geographic entities. The index module is used to take the set of virtual Beidou grid cell grid codes of geographic entities as the index data source, input the spatiotemporal codes to be queried into the preset geographic entity retrieval algorithm, and obtain the set of geographic entities. The decoding and restoration module is used to decode and restore all entities in the geographic entity set to obtain geographic entity information; the geographic entity information includes three-dimensional coordinate range, time code and attribute code.

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