A method and system for geospatial entity spatiotemporal identification and indexing based on multi-level beidou grid joint coding

Abstract

The application discloses a kind of based on multi-level Beidou grid joint coding geographical entity space-time identification and indexing method and system, the method includes: step one: constructing geographical entity and setting the target level of Beidou grid;Step two: geographical entity is according to the single level gridding processing of Beidou grid target level, obtains Beidou grid code sequence set;Step three: the target level grid is merged, and the Beidou grid code sequence set after merging is obtained;Step four: according to the Beidou grid code sequence set after merging, constructs the space-time coding of geographical entity;Step five: repeatedly execute step one to step four, generate the space-time coding of all geographical entities in target area and as index data source, input the space-time coding to be queried into space-time index, and retrieve to obtain geographical entity ID set.The application can consider precision and efficiency, unify two three-dimensional entity coding and improve retrieval efficiency.

Description

Technical Field

[0001] This invention relates to the field of geographic information systems and spatial data processing technology, and in particular to a method and system for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding. Background Technology

[0002] With the deep integration of Geographic Information Systems (GIS) and the BeiDou Navigation Satellite System, spatiotemporal coding of geographic entities has become a core support for the standardization and efficient processing of spatial data. Its core requirement is to achieve a unique representation of the spatial location, temporal attributes, and inherent characteristics of geographic entities, while simultaneously considering coding accuracy and data processing efficiency. Currently, most existing geographic entity coding methods are based on single-level BeiDou grid codes, strictly adhering to the basic coding rules of GB / T 39409-2020 "BeiDou Grid Location Code." These methods use a fixed-level BeiDou grid to discretize geographic entities, generating spatial codes, and then supplementing them with simple time identifiers to complete preliminary spatiotemporal association.

[0003] In existing technologies, the application of BeiDou grid codes is mostly limited to a single level, that is, setting a grid level according to a certain fixed accuracy requirement and encoding all geographic entities using a uniform grid level. For the encoding of two-dimensional and three-dimensional geographic entities, a separate design is often adopted, with two-dimensional entities only encoding latitude and longitude spatial information and three-dimensional entities only simply supplementing with altitude coordinates, without forming a standardized joint encoding scheme. At the same time, existing encoding methods lack efficient index structures that can adapt to multi-level grids, resulting in low efficiency in large-scale geographic entity data retrieval and matching, making it difficult to meet the practical application requirements of high concurrency and high accuracy. In addition, existing spatiotemporal identification technologies also have encoding redundancy problems, affecting data processing efficiency.

[0004] Based on the actual application scenarios and the requirements of the GB / T 39409-2020 national standard, the existing geographic entity coding methods based on single-level Beidou grids have the following shortcomings. (1) It is difficult to balance accuracy and efficiency. In single-level grid coding, if a high-level grid is selected to pursue high accuracy, it will lead to a surge in the amount of coded data, increase the pressure on data storage and processing, and reduce efficiency; if a low-level grid is selected to pursue high efficiency, it will not meet the requirements of high-precision spatial expression. (2) The coding rules for two-dimensional (point, line, surface) and three-dimensional (volume) geographic entities are not unified. There is a lack of standardized joint coding schemes, which cannot achieve a unified representation of the spatiotemporal attributes of various geographic entities. The compatibility is poor and it is difficult to adapt to the scenario of multi-type geographic data fusion processing. (3) The retrieval efficiency is low. The existing coding methods do not design a dedicated index structure for the nesting characteristics of multi-level Beidou grids. The spatial retrieval and matching of large-scale geographic entities rely on traditional linear retrieval methods. The retrieval time is long and the efficiency is low. Moreover, the core role of the index is not clearly defined, which cannot meet the rapid response requirements in scenarios such as smart cities and emergency command.

[0005] In view of the limitations of the existing technologies and the trend of geographic information systems towards high precision, multiple types, and high efficiency, there is an urgent need for a geographic entity spatiotemporal coding method that can balance accuracy and efficiency, unify 2D and 3D entity coding, and improve retrieval efficiency, so as to promote the standardized and efficient application of geographic entity coding and adapt to various high-precision multi-scale spatial data processing scenarios. Summary of the Invention

[0006] To address the problems of existing BeiDou grid-based geographic entity coding methods, such as the limitation of BeiDou grid codes to a single level, difficulty in balancing accuracy and efficiency, inconsistencies in 2D and 3D entity coding, and low retrieval efficiency, this invention provides a method and system for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding. This invention achieves spatiotemporal identification and indexing of geographic entities through steps including setting grid target level accuracy parameters, single-level grid processing, multi-level grid merging and simplification, integrated spatiotemporal coding and identification according to the "feature code-time code-spatial code" format, and constructing a tree-structured spatial index for efficient querying. This invention can uniformly represent various 2D and 3D geographic entities, possessing the advantages of clear hierarchy, high computational efficiency, and strong scalability. It resolves the contradiction between accuracy and efficiency in existing coding methods and can be widely applied to scenarios requiring high-precision multi-scale spatial representation, such as smart cities and low-altitude airspace control, significantly improving the efficiency of geographic entity spatial retrieval and data processing.

[0007] To achieve the above objectives, the technical solution of the present invention is as follows:

[0008] The first aspect of this invention proposes a method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, comprising:

[0009] Step 1: Construct geographic entities and set the target level of the BeiDou grid; the target 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, so as to meet the accuracy requirements of actual application scenarios.

[0010] Step 2: Perform single-level gridding on geographic entities according to the BeiDou grid target level to obtain the BeiDou grid code sequence set, which facilitates the preliminary spatial discretization representation of geographic entities;

[0011] Step 3: Merge the target-level grids to obtain a merged BeiDou grid code sequence set, which facilitates reducing the number of grids while ensuring accuracy, thus achieving a balance between accuracy and efficiency;

[0012] Step 4: Construct spatiotemporal codes for geographic entities based on the merged BeiDou grid code sequence set, which are used to jointly encode the spatiotemporal attributes and characteristics of geographic entities;

[0013] Step 5: Repeat steps 1 to 4 above to generate spatiotemporal codes for all geographic entities within the target area and use them as index data sources. Input the spatiotemporal codes to be queried into the preset spatiotemporal index to retrieve the set of geographic entity IDs, which facilitates efficient retrieval of "space + time + features" in all dimensions adapted to the gridded coding of geographic entities.

[0014] Furthermore, the geographic entity includes entity categories, spatial attributes, and a set of non-spatial attributes; the entity categories include points, lines, surfaces, and volumes; the spatial attributes include node sequences, the node sequences include longitude, latitude, and altitude; and the set of non-spatial attributes includes multiple non-spatial attributes.

[0015] Furthermore, step two specifically includes:

[0016] Select two endpoints from the geographic entity node sequence in sequence and convert them into 32-bit BeiDou grid location codes. and ;by and Construct a planar rectangle for the diagonal vertices, traverse all grids within the planar rectangle, and combine all grids with the diagonal vertices. and The intersecting line segments form a grid that constructs a BeiDou grid code sequence set, which facilitates the gridding of geographic entity outlines.

[0017] If the geographic entity is of the type of surface or volume, the BeiDou grid location code sequence set is used to fill the internal area of ​​the geographic entity to obtain the final BeiDou grid code sequence set.

[0018] Furthermore, if the geographic entity is of surface or volume type, then the internal region of the geographic entity is filled using the BeiDou grid location code sequence set, specifically including:

[0019] For geographic entities of type polygon, all contour lines are obtained based on spatial attributes. Each contour line is traversed, and the minimum bounding rectangle of the contour line is calculated. It is then determined whether each grid within the minimum bounding rectangle is within the contour line. If the grid is not within the contour line, it is not processed. If the grid is within the contour line and is also within the BeiDou grid code sequence set, it is deleted from the BeiDou grid code sequence set. If the grid is within the contour line but not within the BeiDou grid code sequence set, it is added to the preset filling grid set. After traversing all grids within the minimum bounding rectangle, all grids in the filling grid set are added to the BeiDou grid code sequence set.

[0020] For geographic entities of type volume, fill them according to geographic entities of type surface to obtain a set of BeiDou grid code sequences after surface filling. Construct the minimum bounding cube of multiple geographic entities based on all contour lines. Traverse all minimum bounding cubes and determine whether each grid in the minimum bounding cube is within the contour line. If the grid is not within the contour line, do not process the grid. If the grid is within the contour line and within a preset volume filling grid set, delete the grid from the preset volume filling grid set. If the grid is within the contour line but not within the preset volume filling grid set, add the grid to the preset volume filling grid set. After traversing all grids within the minimum bounding cube, merge the volume filling grid set and the set of BeiDou grid code sequences after surface filling to obtain the final set of BeiDou grid code sequences. The preset volume filling grid set is initially an empty set.

[0021] Furthermore, step three specifically includes:

[0022] The process involves merging all grids in the BeiDou grid code sequence set. If all subgrids of the grid at the next higher level of each grid are located in the BeiDou grid code sequence set, then all corresponding subgrids in the BeiDou grid code sequence set are merged into the grid at the next higher level. This process is repeated until there are no more grids to merge in the BeiDou grid code sequence set. This allows for a reduction in the number of grids while ensuring accuracy, thus achieving a balance between accuracy and efficiency.

[0023] Furthermore, the spatiotemporal coding includes a feature code, a time code, and a spatial code;

[0024] The spatial code is a merged set of BeiDou grid codes; the time code includes the time attributes of geographic entities; and the feature code includes the type and features of geographic entities.

[0025] Furthermore, the spatiotemporal index includes a hierarchical BeiDou grid tree, an m-order B+ tree index, and a feature value inverted index. The final retrieval result of the spatiotemporal index is the intersection of the retrieval results of the hierarchical BeiDou grid tree, the m-order B+ tree index, and the feature value inverted index.

[0026] The hierarchical BeiDou grid tree is used to retrieve geographic entities based on spatial codes; the m-order B+ tree index is used to retrieve geographic entities based on time codes; and the feature value inverted index is used to retrieve geographic entities based on feature codes.

[0027] Furthermore, the formal definition of the hierarchical BeiDou grid tree is expressed by the following formula:

[0028]

[0029]

[0030] in, To create a hierarchical BeiDou grid tree, As the root node, For a set of tree nodes, A collection of geographic entities For the target level parameters of the BeiDou grid, The method for grid subdivision of BeiDou. As the parent node, For child nodes, For grid hierarchy, This is the encoded segment for the corresponding level in the complete BeiDou grid code. For the complete BeiDou grid code corresponding to the grid, It is a set of doubly linked lists, which are used to indicate the linking relationships of grids in a multi-grid joint coding sequence of multiple geographic entities.

[0031] Furthermore, the hierarchical BeiDou grid tree used for retrieving geographic entities based on spatial codes specifically includes:

[0032] Obtain the target tree node corresponding to the BeiDou grid code to be queried from the hierarchical BeiDou grid tree, as well as the set of child nodes of the target tree node;

[0033] Traverse the target tree node and its child node set, extract the geographic entity IDs from the doubly linked list set associated with each tree node, and construct the corresponding geographic entity ID set based on the extracted geographic entity IDs.

[0034] The second aspect of this invention proposes a system for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, comprising:

[0035] The geographic entity module is used to construct geographic entities and set the target level of the BeiDou grid. The target level is the level with the smallest level number among all BeiDou grid levels with a precision value less than or equal to a preset precision value, so as to meet the precision requirements of actual application scenarios.

[0036] The single-level gridding module is used to perform single-level gridding processing on geographic entities according to the BeiDou grid target level to obtain the BeiDou grid code sequence set, which facilitates the initial spatial discretization representation of geographic entities.

[0037] The merging module is used to merge the target-level grids to obtain a merged BeiDou grid code sequence set, which facilitates the reduction of the number of grids while ensuring accuracy, thus achieving a balance between accuracy and efficiency.

[0038] The spatiotemporal coding module is used to construct spatiotemporal codes for geographic entities based on the merged BeiDou grid code sequence set, and is used to jointly encode the spatiotemporal attributes and characteristics of geographic entities.

[0039] The index module is used to repeatedly execute the above-mentioned geographic entity module, single-level gridding module, merging module and spatiotemporal coding module to generate spatiotemporal codes of all geographic entities in the target area and use them as index data sources. The spatiotemporal codes to be queried are input into the preset spatiotemporal index to retrieve the set of geographic entity IDs, which facilitates efficient retrieval of "space + time + features" in all dimensions adapted to geographic entity gridding codes.

[0040] The beneficial effects of this invention are:

[0041] This invention relies on the BeiDou grid hierarchy division of the national standard GB / T 39409-2020 to realize multi-level spatial coding and indexing structure of geographic entities, solve the problem of excessively long spatial codes, and achieve both accuracy and efficiency through hierarchy merging, adapting to the accuracy requirements of different scenarios.

[0042] This invention proposes a multi-level spatial coding and indexing method for geographic entities that is compatible with the standard BeiDou grid coding scheme. It can uniformly encode various two-dimensional and three-dimensional geographic entities such as points, lines, surfaces, and volumes, and has good practicality.

[0043] The multi-level spatial coding indexing scheme proposed in this invention uses fixed-length spatial codes and time codes to finely describe geographic entities, which facilitates rapid identification of entity type, time range and spatial location, ensures uniqueness and readability, and facilitates standardized sharing and interaction of spatial data.

[0044] This invention proposes a hybrid spatiotemporal indexing mechanism consisting of "Hierarchical Tree Spatial Index (HBDTree) + Time Code m-order B + Tree Index + Feature Value Inverted Index", and describes in detail the implementation scheme of key processes, providing a feasible approach for efficient retrieval of geographic entities in all dimensions of "space + time + feature". Attached Figure Description

[0045] Figure 1 The flowchart illustrates a method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, as provided in an embodiment of the present invention.

[0046] Figure 2 This is an architecture diagram of a system for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, provided for an embodiment of the present invention. Detailed Implementation

[0047] 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.

[0048] Example 1

[0049] like Figure 1 As shown, this invention proposes a method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, including:

[0050] S101: Construct geographic entities and set the target level of the BeiDou grid; the target level is the level with the smallest level number among all BeiDou grid levels with precision values ​​less than or equal to the preset precision value;

[0051] S102: Perform single-level gridding on geographic entities according to the BeiDou grid target level to obtain the BeiDou grid code sequence set;

[0052] S103: Merge the target-level grids to obtain the merged BeiDou grid code sequence set;

[0053] S104: Construct spatiotemporal coding of geographic entities based on the merged BeiDou grid code sequence set;

[0054] S105: Repeat steps S101-S104 above to generate spatiotemporal codes of all geographic entities within the target area and use them as index data sources. Input the spatiotemporal codes to be queried into the preset spatiotemporal index to retrieve the set of geographic entity IDs.

[0055] This invention first constructs geographic entities and sets the target level of the BeiDou grid. Then, it performs single-level gridding processing on the geographic entities to obtain a BeiDou grid code sequence set, which is then merged. Finally, it constructs spatiotemporal codes for geographic entities based on the merged BeiDou grid code sequence set. The spatiotemporal codes to be queried are input into a preset spatiotemporal index to retrieve a set of geographic entity IDs. This invention achieves a unified representation of the spatiotemporal attributes of various two-dimensional and three-dimensional geographic entities, balancing coding accuracy and data processing efficiency. It optimizes the coding structure and order, clarifies the role and process of the tree-like spatial index, improves the efficiency of geographic entity spatial retrieval, and adapts to the high-precision spatial representation needs of multiple scenarios. This invention can be directly applied to various scenarios requiring high-precision, multi-scale spatial representation and spatial analysis. For example, in low-altitude airspace management, it can achieve rapid retrieval of airspace entities and spatiotemporal trajectory analysis; in emergency command, it can support multi-scale retrieval of geographic entities in disaster areas and the scheduling of rescue resources, etc. This invention solves several pain points of existing technologies, has high practical value, and broad prospects for promotion.

[0056] Example 2

[0057] Based on the above embodiments, this invention proposes a method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, comprising:

[0058] S201: Construct geographic entities and set the target level of the BeiDou grid; the target level is the level with the smallest level number among all BeiDou grid levels with precision values ​​less than or equal to the preset precision value.

[0059] Specifically, the standardized definition of a geographic entity is first given, allowing... Represents a geographical entity. For entity categories, including points ,Wire ,noodle ,body Four types, The spatial attribute of an entity, consisting of a sequence of nodes. Composition, each node Includes spatial location attribute: longitude ,latitude and height , It is a set of non-spatial attributes, containing multiple non-spatial attributes, such as the building owner and the year.

[0060] Because the spatial morphological representation methods for various types of geographic entities are diverse and the data formats are numerous, this invention, in order to simplify the problem expression, refers to the definition of the "shapefile (.shp)" vector data file format and uniformly defines the spatial attributes of geographic entities as multiple node sequences. The resulting set eliminates the need to distinguish between special representations of different spatial forms, reducing the complexity of subsequent meshing processes while ensuring the standardization and compatibility of spatial attribute representation. Combined with the shapefile white paper (ESRI official technical documentation) specification, which explicitly requires surface solids ( The node sequence of the shapefile must be forced to close loops, meaning the coordinates of the last node must completely coincide with the coordinates of the first node. If it contains holes (inner loops), then the outer node sequence and each inner node sequence must close loops separately, with opposite closing directions. Additionally, the shapefile is a two-dimensional vector format; the white paper does not define three-dimensional entities (…). The closed-loop rule of the three-dimensional entity is based on the closed-loop rule of the surface entity. Therefore, the closed-loop logic of the three-dimensional entity is based on the closed-loop rule of the surface entity. That is, each surface contour node sequence of the three-dimensional entity needs to be closed separately. In other words, the vertices of each closed contour are on the same plane. This invention is aimed at the mainstream regular three-dimensional geographic entities in engineering practice. It does not consider non-coplanar irregular three-dimensional contour lines. It can be adapted to such scenarios through subsequent extensions. In this way, a complete three-dimensional boundary is formed, ensuring the simplicity and accuracy of subsequent meshing processing.

[0061] Next, based on the accuracy requirements of actual application scenarios and referring to the BeiDou grid hierarchy division standard specified in GB / T 39409-2020, the target hierarchy parameters of the BeiDou grid code were set. This establishes the highest precision benchmark for the spatial representation of geographic entities. According to GB / T 39409-2020, the spatial precision of each level of the BeiDou grid is shown in Table 1. The higher the level, the higher the grid accuracy.

[0062]

[0063] Let the required accuracy value be , Calculate using the following formula:

[0064]

[0065] in, For each level, the minimum resolution from the corresponding column in Table 1 is used to determine the planar accuracy / height accuracy values. Equals to satisfy The minimum BeiDou grid level for accuracy requirements.

[0066] S202: Perform single-level gridding processing on geographic entities according to the BeiDou grid target level to obtain the BeiDou grid code sequence set.

[0067] Specifically, based on the above geographical entity definition, the target hierarchy set in S101 is adopted. The corresponding BeiDou grid performs single-level gridding on geographic entities, completing the initial spatial discretization representation of the geographic entities. Specific steps include: vector contour gridding and entity interior filling.

[0068] Vector contour meshing: The contour lines of geographic entities are either vector points (point entities) or vector polylines (line-polygons). Therefore, the vector contour meshing operation for all four types of geographic entities—points, lines, polygons, and solids—can be unified into multiple executions of the "vector polyline meshing" method. Algorithm 1 provides the basic flow of the "vector polyline meshing" method, whose input is the vertex set of the vector polyline. (i.e., node sequence) and target mesh level parameters The output is a set of BeiDou grid code sequences in a polygonal format. Algorithm 1 for Perform segment-by-segment processing (line 2), first converting the two endpoints of the line segment into 32-bit BeiDou grid location codes. and The one used here The method involves converting spatial point coordinates into a three-dimensional BeiDou grid code string. This method is implemented according to the latitude, longitude, and altitude encoding method specified in GB / T 39409-2020. The specific conversion process is not the focus of this invention and will not be elaborated here. Then, the calculation is performed... and All within the plane rectangle with opposite diagonal vertices Level of grid set And iterate through all the grids in the rectangle, if the grid With line segment If they intersect, the grid is included in the result set. (Fill the grid set), and finally return the BeiDou grid code sequence set of the outline. The details are shown in Table 2.

[0069]

[0070] for Entity and Entities, their outlines are the entities themselves; and for Entity and The solids, their internal spaces still need to be filled with mesh.

[0071] Internal padding of an entity: Based on the aforementioned definition, The solid is composed of several closed loops enclosed by closed contour lines on the same plane, and its internal filling process is shown in Algorithm 2. The input to Algorithm 2 is... Mesh outline set of entities and target mesh level parameters , It is an outline The set of data points outputs a set of BeiDou grid code sequences for geographic entities. This method iterates through each closed-loop contour line. First use Method for calculating the minimum bounding rectangle of the contour line Then determine Are each grid within the outline? Inside, at this point, the "ring" shape needs to be considered additionally. In the case of an entity, if a certain mesh On the outline It already exists within the BeiDou grid code sequence set. The middle line (line 7) indicates the existence of a "circular" structure, therefore execution is performed. The operation removes the grid from the BeiDou grid code sequence set; otherwise, it is a normal surface entity case (line 9), and the grid is removed. join in until the outline is complete. The internal fill of [the object], and its internal fill result. Add to BeiDou grid code sequence set (Line 11), output returned. See Table 3 for details.

[0072]

[0073] and The physical objects are slightly different. The entity has a height attribute, and its closed contour lines are multiple and not located in the same plane. Therefore, after filling the closed contour lines of each plane, it is also necessary to fill in three-dimensional space. Specifically, this invention uses... calculate The solution involves finding the minimum bounding volume (mbv) of the solid and traversing all its meshes. The problem of filling a solid's 3D mesh. The specific process of "entity interior filling" is shown in Algorithm 3, and its input is... entity Mesh contour set and target mesh level parameters The output is Physical BeiDou grid code sequence Algorithm 3 first uses Algorithm 2 to fill the interior of all contour lines, obtaining the internal mesh filling results for all contour lines. (Lines 2-4), then calculate The smallest outer cube of a solid (Line 5), then iterate through all the grids, similar to Algorithm 2, if a certain grid... On the outline It already exists within the BeiDou grid code sequence set. The middle line (line 8) indicates the existence of a "hole" structure, so execute... The operation removes the mesh from the result set; otherwise, it's a normal solid entity case (line 10), and the mesh is removed. join in In the middle (line 11), the last one will be The internal grid of the entity is stored in the BeiDou grid code sequence set. And output it. See Table 4 for details.

[0074]

[0075] S203: Merge the target-level grids to obtain the merged BeiDou grid code sequence set.

[0076] Specifically, S202 is based on While a single-level grid can guarantee the spatial representation accuracy of geographic entities, it generates a large number of redundant grids for large-area, low-complexity geographic entities, increasing storage and retrieval overhead. Therefore, based on the hierarchical nesting characteristics of the BeiDou grid, this invention designs a lossless, multi-level grid merging and simplification algorithm. This algorithm reduces the number of grids while maintaining accuracy, achieving a balance between accuracy and efficiency. The levels below the target BeiDou grid level are BeiDou grid levels with level numbers lower than the target level number. The specific process is shown in Algorithm 4, which involves processing the BeiDou grid code sequence set... any grid ,like All subgrids of the upper-level grid are in the BeiDou grid code sequence set. In the middle, merge and update. until Until there are no more grids to merge. See Table 5 for details.

[0077]

[0078] S204: Construct spatiotemporal codes for geographic entities based on the merged BeiDou grid code sequence set.

[0079] Specifically, in addition to spatial features, geographic entities also possess distinct temporal features and semantic attributes. Therefore, this invention uses a "feature code-time code-spatial code" format to jointly encode the spatiotemporal attributes and inherent features of geographic entities, resulting in a gridded spatiotemporal encoding of geographic entities. It is a spatial code timecode Feature code The combination of these three elements is separated by a semicolon (;). See the formula below for a specific example:

[0080]

[0081] Spatial Code (SC) is a simplified version of S203, using a multi-level BeiDou grid joint coding system, including... Multiple fields are separated by commas (,), and multiple values ​​within a field are separated by colons (:), as shown in the following formula.

[0082]

[0083] in, , indicating the range of the grid level, As the lowest level, The highest level; , represents the minimum bounding shape of a geographic entity. , and These are the minimum longitude, minimum latitude, and minimum altitude, respectively. , and These are the maximum longitude, maximum latitude, and maximum altitude, respectively. Indicates the number of grid cells; Indicates the first Each grid code, .

[0084] Time Code (TC) expresses the time attributes of geographic entities, using the UTC timestamp encoding format YYYYMMDDHHMMSS (14 bits), which can accurately represent the relevant time attributes of geographic entities. 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.

[0085] Feature codes (FCs) primarily express the type and related characteristics of geographic entities. Their basic structure is shown in the following formula, including... Multiple fields, separated by commas.

[0086]

[0087] in, Entity type code (point=P, line=L, surface=A, volume=V); The number of features; This represents "feature name: feature value", separated by a colon. .

[0088] S205: Repeat steps S201-S204 above to generate spatiotemporal codes of all geographic entities within the target area and use them as index data sources. Input the spatiotemporal codes to be queried into the preset spatiotemporal index to retrieve the set of geographic entity IDs.

[0089] Specifically, since this invention focuses on refined spatiotemporal identification of geographic entities, and the spatiotemporal encoding designed in step 4 covers a wide range of attributes, a dedicated hybrid spatiotemporal indexing mechanism is needed to support efficient spatiotemporal feature queries. This invention designs a hybrid spatiotemporal indexing mechanism consisting of a hierarchical tree spatial index (HBDTree), a time-code m-order B+ tree index, and a feature-value inverted index, achieving efficient full-dimensional retrieval of "space + time + feature" to adapt to the gridded encoding of geographic entities. The final retrieval result of the spatiotemporal index is the intersection of the retrieval results from the hierarchical BeiDou grid tree, the m-order B+ tree index, and the feature-value inverted index.

[0090] Hierarchical Tree Spatial Index (HBDTree): Spatial code for geographic entities Containing multiple BeiDou grids of different levels, precise spatial retrieval involves a huge amount of computation and is inefficient. This invention designs a hierarchical BeiDou grid tree index structure, Hierarchy BeiDou Grid Tree (HBDTree).

[0091] HBDTree is a non-balanced tree-shaped spatial index based on the 10-level partitioning structure of the BeiDou grid. Its definition satisfies the following conditions:

[0092] The HBDTree structure strictly follows the BeiDou grid hierarchy division standard specified in GB / T 39409-2020, providing a unified gridded spatial index for all geographic entities within a specified spatial range.

[0093] HBDTree is an unbalanced tree structure, and the branch depth is related to the multi-level grid encoding of geographic entities.

[0094] The root node of HBDTree represents the entire Earth space, the leaf nodes store specific BeiDou grids, and the intermediate nodes are of two types to support multi-level grid representations of geographic entities. One type of intermediate node does not store specific grids, but only grid hierarchy-related information; the other type of intermediate node stores the BeiDou grid and has a structure similar to the leaf nodes.

[0095] HBDTree mesh accuracy and BeiDou level parameters defined in step 1 Consistent.

[0096] The HBDTree tree node also includes a doubly linked list structure, organized by geographic entity. The grid sequence of the code links the multi-grid joint coding sequence of geographic entities.

[0097] Based on this, the formal definition of HBDTree is shown in the following formula:

[0098]

[0099] in, To create a hierarchical BeiDou grid tree, It is the root node (the entry point of the tree index).

[0100] For a set of tree nodes, when the tree nodes do not store mesh entities, Including pointers to higher-level nodes Lower-level node pointer set BeiDou grid hierarchy And the corresponding level of the encoded fragment in the complete BeiDou grid code When storing BeiDou grid data in tree nodes, it is also necessary to store geographic entity encoding information, i.e.: ,in, As the parent node, For child nodes, For grid hierarchy, This is the encoded segment for the corresponding level in the complete BeiDou grid code. For the complete BeiDou grid code corresponding to the grid, This is a doubly linked list set, indicating the possible link relationships of this grid in multiple geographic entity multi-grid joint coding sequences.

[0101] A collection of geographic entities Geographic entity information includes unique identifiers. The gridded spatiotemporal code generated by S204 .

[0102] For the target level parameters of the BeiDou grid, This refers to the BeiDou grid partitioning method (the BeiDou grid partitioning method specified in the GB / T 39409-2020 standard; a detailed description of the BeiDou grid hierarchical partitioning principle can be found in the relevant content of the GB / T 39409-2020 standard, which is not a major innovation of this invention and will not be repeated here).

[0103] Based on the above definitions, the initialization process of the HBDTree index is shown in Algorithm 5. Algorithm 5 takes a set of geographic entities as input. Mesh parameters and the BeiDou grid segmentation method The output is the completed HBDTree index tree. Algorithm 5 traverses the set of geographic entities and obtains each... Grid-coded sequence The key process involves adding the mesh code one by one into the HBDTree structure, and then how to integrate the entities... The grid sequence information is added to the HBDTree index. For geographic entities... Each grid code First, determine whether the HBDTree structure contains (Line 5) If it already exists, then use it. The method will Add the entity's doubly linked list information In the corresponding tree node (line 6); if the HBDTree structure does not contain (Line 7), then use Method based on and Create tree nodes (Line 8), then use Method given Add parent and child pointers and linked list pointers to the node, and based on... The BeiDou grid partitioning method will The node is inserted into the HBDTree index (row 9). After completing the geographic entity traversal, the HBDTree initialization is completed, and the output is returned. See Table 6 for details.

[0104]

[0105] The HBDTree index not only constructs the three-dimensional geospatial BeiDou grid subdivision system specified in the GB / T 39409-2020 standard, but also enables BeiDou grid positioning for specific spatial ranges, demonstrating high flexibility and scalability. Furthermore, the HBDTree index supports doubly linked lists of multi-level grid sequences for geographic entities. By finding any grid within a geographic entity's grid sequence, the entire sequence can be quickly retrieved, providing a convenient entry point for retrieving spatial features of geographic entities. This means that the spatial code of geographic entities... Only the grid sequence at the entry point of the HBDTree index needs to be stored; the entire grid sequence does not need to be stored, thus simplifying the spatial code. It features a concise structure, providing reliable support for the widespread application of geographic entity grid-based spatiotemporal coding. (Previous spatial code) All grid codes need to be stored, and the data volume increases linearly with the number of grids; optimized spatial codes Store only the entry code The complete grid sequence definition can be obtained using the doubly linked list of HBDTree, as shown in the following formula:

[0106]

[0107] in, The range of the grid level. The minimum bounding shape of a geographic entity. This is the BeiDou grid code of the first grid in the geographic entity grid sequence. This represents the number of grid cells.

[0108] To support efficient retrieval of geographic entities using multi-level spatial grid coding, this invention presents a geographic entity retrieval scheme based on BeiDou grid codes. As shown in Algorithm 6, the input is a BeiDou grid code. The spatial index tree HBDTree; the output is the set of geographic entities covered by the grid. The calculation process mainly relies on the HBDTree index, first querying... The tree node The set of its child nodes (Lines 2-3); then iterate through... Retrieve each tree node linked list structure Based on this, query the geographic entity's Add to result set (Lines 4-7); final output The details are shown in Table 7.

[0109]

[0110] Algorithm 6 enables efficient retrieval of geographic entity sets based on given spatial grid locations. This not only supports geographic entity overlap detection at a small scale but also solves spatial range queries at a large scale, providing a reliable approach for geographic entity spatial analysis based on the BeiDou grid.

[0111] An m-order B+ tree index is constructed for the 14-bit timecode (YYYYMMDDHHMMSS) of geographic entities to support precise time-point queries, time-range queries, and time-series sorted retrieval. The B+ tree is a balanced multi-way ordered search tree, whose structure is highly compatible with the ordered, continuous, and frequent range queries characteristics of timecodes. The index uses the timecode as the key and is divided into non-leaf node layers and leaf node layers. The basic characteristics of the m-order B+ tree index are described below:

[0112] (1) m-order refers to the storage of m-1 keywords in a tree node, with each node having m children (child nodes). The value of m is adaptively determined by the system storage block size, usually ranging from 64 to 4096, which is suitable for time indexing of massive geographic entities.

[0113] (2) Non-leaf nodes are used only for index navigation and do not store actual geographic entity data. The nodes store ordered time code keys and pointers to child nodes. The number of keys and child nodes is constrained by the order m of the B+ tree. Its function is to quickly locate the query to the corresponding leaf node through layer-by-layer binary comparison, ensuring that the path length from the root node to any leaf node is the same, thus stabilizing the query efficiency.

[0114] (3) All leaf nodes are located at the same level, storing complete time code keywords and their associated geographic entity IDs. Leaf nodes are linked together in ascending order of time code through a doubly linked list, so that time range queries can be completed by traversing the linked list after locating the starting position, without having to repeatedly access the upper-level index.

[0115] The initialization algorithm for constructing an m-order B+ tree index for n geographic entities is shown in Algorithm 7, where the input is a set of geographic entities. And order m; the output is an m-order B+ index tree of timecode. Algorithm 7 first constructs a tree containing the root. index tree (Line 1); then iterate through the set of geographic entities. Query the time code of the adapted geographic entity leaf nodes and entity identifier And timecodes are inserted (lines 3-4); then, based on the construction characteristics of an m-order B+ tree, from Update the index tree upwards, including operations such as updating linked lists, splitting nodes, and updating parent nodes; finally, output the initialized m-order B+ index tree. The details are shown in Table 8.

[0116]

[0117] The time-code m-order B+ tree index used in this invention possesses strict balance, ordering, and efficient range query capabilities. It can stably support time-series condition filtering under large-scale spatiotemporal data and can be used in conjunction with spatial and feature indexes to achieve integrated spatiotemporal rapid retrieval. The time-condition-based geographic entity retrieval method is shown in Algorithm 8, with the index tree as input. and time retrieval interval The output is a set of geographic entity IDs. Because... The leaf nodes exist in a strictly ordered doubly linked list structure, therefore it is only necessary to query the... The corresponding leaf node (line 2) is then used to traverse the linked list and filter by time value to obtain all geographic entity IDs that meet the conditions (lines 3-9). Finally, the set of geographic entity IDs that meet the conditions is returned. The details are shown in Table 9.

[0118]

[0119] Geographic entity feature codes Based on its structural characteristics, this invention constructs an inverted index based on feature values ​​to support feature-value-based geographic entity retrieval. An inverted index is a reverse mapping index of "feature → entity," primarily addressing the efficient query problem of "which geographic entities contain certain features." The inverted index uses an inverted list as its core structure, with... The approach involves constructing an inverted list for each feature value, storing information on all geographic entities containing that feature value. Inverted indexes can be built for any feature value based on its importance. The algorithms for constructing inverted indexes and for feature-based retrieval are relatively simple, primarily involving single-feature traversal and matching calculations.

[0120] As shown in Algorithm 9, the input to the feature-value inverted index initialization algorithm is a set of geographic entities. The output is the inverted index of the feature values. Algorithm 9 sets up an inverted index for feature values. The first row is a two-dimensional empty array, and then the set of geographic entities is traversed. Extract each entity Feature name and eigenvalues (Lines 2-4), then the entity information join in Finally return The details are shown in Table 10.

[0121] Algorithm 10 describes the specific process of the multi-feature value combination retrieval algorithm, where the input is the inverted index of the feature values. and a set of multiple search criteria The output is a set of retrieved geographic entity IDs. Algorithm 10 first uses the inverted index of the feature values. The system retrieves the results for a single search condition and then returns the intersection of these results. See Table 11 for details.

[0122]

[0123] Example 3

[0124] Based on the above embodiments, such as Figure 2 As shown, this invention proposes a system for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, comprising:

[0125] The geographic entity module is used to construct geographic entities and set the target level of the BeiDou grid; the target level is the level with the smallest level number among all BeiDou grid levels with a precision value less than or equal to a preset precision value.

[0126] The single-level gridding module is used to perform single-level gridding processing on geographic entities according to the BeiDou grid target level to obtain the BeiDou grid code sequence set.

[0127] The merging module is used to merge the target-level grids to obtain the merged BeiDou grid code sequence set.

[0128] The spatiotemporal coding module is used to construct spatiotemporal codes for geographic entities based on the merged BeiDou grid code sequence set.

[0129] The index module is used to repeatedly execute the above-mentioned geographic entity module, single-level gridding module, merging module and spatiotemporal coding module to generate spatiotemporal codes of all geographic entities in the target area and use them as index data sources. The spatiotemporal codes to be queried are input into the preset spatiotemporal index to retrieve the set of geographic entity IDs.

[0130] It should be noted that the system for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding provided in this embodiment of the invention is to implement the above-mentioned method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding. Its specific functions can be referred to the above-mentioned method embodiments, and will not be repeated here.

[0131] In summary, this invention relies on the BeiDou grid hierarchical division of the national standard GB / T 39409-2020 to realize a multi-level spatial coding and indexing structure for geographic entities, solving the problem of excessively long spatial codes. Simultaneously, it achieves a balance between accuracy and efficiency through hierarchical merging, adapting to the accuracy requirements of different scenarios. This invention proposes a multi-level spatial coding and indexing method for geographic entities that is compatible with the standard BeiDou grid coding scheme, enabling unified coding of various two- and three-dimensional geographic entities such as points, lines, surfaces, and volumes, and possesses good practicality. The multi-level spatial coding indexing scheme proposed in this invention uses fixed-length spatial codes and time codes to finely describe geographic entities, facilitating rapid identification of entity type, time range, and spatial location, ensuring uniqueness and readability, and facilitating standardized sharing and interaction of spatial data. This invention proposes a hybrid spatiotemporal indexing mechanism of "Hierarchical Tree Spatial Index HBDTree + Time Code m-order B + Tree Index + Feature Value Inverted Index," and describes in detail the implementation scheme of key processes, providing a feasible approach for efficient "spatial + temporal + feature" multi-dimensional retrieval of geographic entity grid coding.

[0132] 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 method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, characterized in that, include: Step 1: Construct geographic entities and set the target hierarchy of the BeiDou grid; The target level is the level with the smallest level number among all BeiDou grid levels with precision values ​​less than or equal to the preset precision value; Step 2: Perform single-level gridding on geographic entities according to the BeiDou grid target level to obtain the BeiDou grid code sequence set; Step 3: Merge the target-level grids to obtain the merged BeiDou grid code sequence set; Step 4: Construct spatiotemporal codes for geographic entities based on the merged BeiDou grid code sequence set; Step 5: Repeat steps 1 to 4 above to generate spatiotemporal codes for all geographic entities within the target area and use them as index data sources. Input the spatiotemporal codes to be queried into the preset spatiotemporal index to retrieve the set of geographic entity IDs.

2. The method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding as described in claim 1, characterized in that, The geographic entity includes entity category, spatial attributes, and non-spatial attribute set; the entity category includes point, line, area, and volume; the spatial attribute includes node sequence, the node sequence includes longitude, latitude, and altitude; and the non-spatial attribute set includes multiple non-spatial attributes.

3. The method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding as described in claim 1, characterized in that, Step two specifically includes: Select two endpoints from the geographic entity node sequence in sequence and convert them into 32-bit BeiDou grid location codes. and ;by and Construct a planar rectangle for the diagonal vertices, traverse all grids within the planar rectangle, and combine all grids with the diagonal vertices. and The intersecting line segments form a grid that constructs a BeiDou grid code sequence set; If the geographic entity is of the type of surface or volume, the BeiDou grid location code sequence set is used to fill the internal area of ​​the geographic entity to obtain the final BeiDou grid code sequence set.

4. The method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding according to claim 3, characterized in that, If the geographic entity is of type surface or volume, then the internal region of the geographic entity is filled using the BeiDou grid location code sequence set, specifically including: For geographic entities of type polygon, all contour lines are obtained based on spatial attributes. Each contour line is traversed, and the minimum bounding rectangle of the contour line is calculated. It is then determined whether each grid within the minimum bounding rectangle is within the contour line. If the grid is not within the contour line, it is not processed. If the grid is within the contour line and is also within the BeiDou grid code sequence set, it is deleted from the BeiDou grid code sequence set. If the grid is within the contour line but not within the BeiDou grid code sequence set, it is added to the preset filling grid set. After traversing all grids within the minimum bounding rectangle, all grids in the filling grid set are added to the BeiDou grid code sequence set. For geographic entities of type volume, fill them according to geographic entities of type surface to obtain a set of BeiDou grid code sequences after surface filling. Construct the minimum bounding cube of multiple geographic entities based on all contour lines. Traverse all minimum bounding cubes and determine whether each grid in the minimum bounding cube is within the contour line. If the grid is not within the contour line, do not process the grid. If the grid is within the contour line and within a preset volume filling grid set, delete the grid from the preset volume filling grid set. If the grid is within the contour line but not within the preset volume filling grid set, add the grid to the preset volume filling grid set. After traversing all grids within the minimum bounding cube, merge the volume filling grid set and the set of BeiDou grid code sequences after surface filling to obtain the final set of BeiDou grid code sequences. The preset volume filling grid set is initially an empty set.

5. The method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding according to claim 1, characterized in that, Step three specifically includes: The process involves merging all grids in the BeiDou grid code sequence set. If all subgrids of the grid at the next higher level of each grid are located in the BeiDou grid code sequence set, then all corresponding subgrids in the BeiDou grid code sequence set are merged into the grid at the next higher level. This process is repeated until there are no more grids to merge in the BeiDou grid code sequence set.

6. The method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding according to claim 1, characterized in that, The spatiotemporal coding includes feature code, time code, and spatial code; The spatial code is a merged set of BeiDou grid codes; the time code includes the time attributes of geographic entities; and the feature code includes the type and features of geographic entities.

7. The method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding according to claim 1, characterized in that, The spatiotemporal index includes a hierarchical BeiDou grid tree, an m-order B+ tree index, and a feature value inverted index. The final retrieval result of the spatiotemporal index is the intersection of the retrieval results of the hierarchical BeiDou grid tree, the m-order B+ tree index, and the feature value inverted index. The hierarchical BeiDou grid tree is used to retrieve geographic entities based on spatial codes; the m-order B+ tree index is used to retrieve geographic entities based on time codes; and the feature value inverted index is used to retrieve geographic entities based on feature codes.

8. The method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding according to claim 7, characterized in that, The formal definition of the hierarchical BeiDou grid tree is expressed by the following formula: in, To create a hierarchical BeiDou grid tree, As the root node, For a set of tree nodes, A collection of geographic entities For the target level parameters of the BeiDou grid, The method for grid partitioning in BeiDou. As the parent node, For child nodes, For grid hierarchy, This is the encoded segment for the corresponding level in the complete BeiDou grid code. For the complete BeiDou grid code corresponding to the grid, It is a set of doubly linked lists, which are used to indicate the linking relationships of grids in a multi-grid joint coding sequence of multiple geographic entities.

9. A method for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding as described in claim 8, characterized in that, The hierarchical BeiDou grid tree is used to retrieve geographic entities based on spatial codes, specifically including: Obtain the target tree node corresponding to the BeiDou grid code to be queried from the hierarchical BeiDou grid tree, as well as the set of child nodes of the target tree node; Traverse the target tree node and its child node set, extract the geographic entity IDs from the doubly linked list set associated with each tree node, and construct the corresponding geographic entity ID set based on the extracted geographic entity IDs.

10. A system for spatiotemporal identification and indexing of geographic entities based on multi-level BeiDou grid joint coding, characterized in that, include: The geographic entity module is used to construct geographic entities and set the target level of the BeiDou grid; The target level is the level with the smallest level number among all BeiDou grid levels with precision values ​​less than or equal to the preset precision value; The single-level gridding module is used to perform single-level gridding processing on geographic entities according to the BeiDou grid target level to obtain the BeiDou grid code sequence set; The merging module is used to merge the target-level grids to obtain the merged BeiDou grid code sequence set; The spatiotemporal coding module is used to construct spatiotemporal codes for geographic entities based on the merged BeiDou grid code sequence set; The index module is used to repeatedly execute the above-mentioned geographic entity module, single-level gridding module, merging module and spatiotemporal coding module to generate spatiotemporal codes of all geographic entities in the target area and use them as index data sources. The spatiotemporal codes to be queried are input into the preset spatiotemporal index to retrieve the set of geographic entity IDs.

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