Multi-level grid-based spatial vector data association method and spatial relation reasoning method

Abstract

This invention relates to a spatial vector data association method and a spatial relationship reasoning method based on a multi-level grid. The data association method includes: establishing a multi-level spatial grid system for the spatial vector data, and encoding each grid cell at each level; when encoding grid cells at levels other than the coarsest level, the sub-grid cell encoding has a coding mapping relationship with its parent grid cell encoding; taking the grid cells in the finest level that spatially intersect with a spatial element in the spatial vector data as the covering grid cells of the current spatial element, and encoding them to form a finest level covering grid encoding set; iteratively merging the encodings in the finest level covering grid encoding set of each spatial element according to the coding mapping relationship to obtain a minimum grid representation encoding set for each spatial element; and establishing a relationship network graph for representing spatial vector data association based on the minimum grid representation encoding set. This method can reduce computational complexity and reduce redundancy in relationship expressions.

Description

Technical Field

[0001] This invention relates to the field of spatial reasoning technology, and in particular to a spatial vector data association method and a spatial relationship reasoning method based on a multi-level grid. Background Technology

[0002] With the rapid development of geographic information systems, remote sensing and mapping, intelligent navigation and other fields, the application of multi-scale spatial vector data is becoming increasingly widespread. The correlation analysis and relationship reasoning of spatial vector data of different scales have become key technologies for important tasks such as spatial decision-making, data fusion and pattern recognition. The analysis and reasoning results can support multiple scenarios such as urban planning, disaster monitoring and resource management. Therefore, it is of great significance to study the efficient reasoning of the correlation relationships between massive multi-scale spatial vector data.

[0003] Traditional techniques use spatial target entities as the core operation unit and determine the relationships between targets, such as inclusion, intersection, and proximity, through direct geometric operations such as polygon Boolean operations and distance calculations. When faced with the overall relationship analysis requirements of multiple spatial vector target sets, pairwise matching operations are performed on the entities within the target set. A relationship graph is constructed with the target entities as nodes and the relationships between entities as edges to carry and express the spatial vector relationships.

[0004] Current technologies suffer from the following main drawbacks: First, they have high computational complexity. When the number of targets is large and the scale differences are significant, especially when it is necessary to determine the overall spatial relationship between two spatial vector data, a massive number of pairwise geometric operations are often required, and the computational complexity increases quadratically, making it difficult to meet the real-time or near-real-time processing requirements of large-scale scenarios. Second, the relationship representation is redundant and the structure is unclear. There are a large number of relationships such as inclusion, intersection, and proximity between multi-scale spatial targets. If the association graph is directly constructed using the edges between target entities, problems such as too many edges between nodes and extremely redundant structure are likely to occur, which is not conducive to storage management and subsequent inference calculations, and it is difficult to support the analysis of complex relationships between multiple spatial vector data. Summary of the Invention

[0005] The first technical problem to be solved by the present invention is to provide a spatial vector data association method that can reduce computational complexity and reduce redundancy in relational expressions, in contrast to the above-mentioned prior art.

[0006] The second technical problem to be solved by the present invention is to provide a spatial relationship reasoning method that applies the above-mentioned spatial vector data association method based on multi-level grids.

[0007] The technical solution adopted by this invention to solve the first technical problem mentioned above is: a spatial vector data association method based on multi-level grids, characterized by including the following steps: Step 1: Establish a multi-level spatial grid system for the spatial vector data, obtain several grid cells corresponding to each level, and encode each grid cell to obtain the code of each grid cell; when encoding grid cells that are not at the coarsest level, ensure that the code of the sub-grid cell has a coding mapping relationship with the code of its corresponding parent grid cell. Step 2: Take the grid cell that intersects with a certain spatial feature in the spatial vector data in the finest level as the covering grid cell of the current spatial feature, and form the finest level covering grid code set of the current spatial feature by encoding all the covering grid cells of the current spatial feature. Step 3: Based on the coding mapping relationship between the sub-grid cell coding and its corresponding parent grid cell coding, iteratively merge the coding in the finest level covering grid coding set of each spatial feature to obtain the minimum grid representation coding set of each spatial feature; Step 4: Based on the minimum grid representation code set for each spatial element in Step 3, establish a relational network diagram to represent the association of spatial vector data.

[0008] Preferably, the specific method for obtaining the grid cells in step 1 is as follows: Step 1-1: Calculate the minimum and maximum values ​​of the positive outer edge length of all spatial elements in the spatial vector data, and determine the maximum number of levels of the spatial grid system and the edge length of the grid cells at each level based on the minimum and maximum values ​​of the positive outer edge length. Steps 1-2: Determine the side length of the global outer square and the global outer square region of the spatial vector data. Divide the global outer square region according to the maximum number of levels of the spatial grid system and the side length of the grid cells at each level to obtain several grid cells corresponding to each level.

[0009] To facilitate the management of grid cells at each level, step 1 further includes: establishing row and column indices for grid cells at each level, and obtaining the index mapping relationship between grid cells at each level and their parent grid.

[0010] Preferably, in step 1, the coarsest level is taken as the root layer, and the first level in the root layer is... Line number Column grid cells encoding The calculation formula is: ; in, This is a unique single-value mapping function used to map row indices. and column indexes Mapped to a fixed-length string or numeric encoding.

[0011] To enable child and parent meshes to trace each other, step 1 encodes each mesh cell (excluding the coarsest level) in the following manner: The first level Line number Column grid cells encoding The calculation formula is: ; in, , This represents the maximum number of levels in the spatial grid system. The higher the number of levels, the finer the grid. For the first The first level Line number Column grid cells The encoding, for The parent grid, for Relative to its parent grid Quadrant numbering; This is a concatenation function; Encoding with its parent grid cell The encoding mapping relationship is as follows: .

[0012] Preferably, The specific method of obtaining it is as follows: according to with its parent grid Index mapping relationship: ; in, , Corresponding row direction, In the corresponding column direction, the tuples Considering the quadrant position of the sub-mesh relative to the parent mesh, define The quadrant number is , The calculation formula is: ; when hour, ;when hour, ;when hour, ;when hour, ; The above quadrant numbers This is used to represent the fixed position of a sub-mesh relative to its parent mesh, and is encoded according to the object limit numbering formula below, resulting in... The corresponding quadrant number code , , To be Functions that map to single characters or fixed-length bit strings.

[0013] Preferably, the specific process for obtaining the minimum grid representation encoding set of any spatial element in step 3 is as follows: Step 3-1: Use the finest level cover grid encoding set of the current spatial feature as the initial encoding candidate set; Step 3-2: Extract the candidate encoding subset at level g from the candidate encoding set; the initial value of g is... ; Step 3-3: Obtain the parent grid code corresponding to each code in the candidate coding subset of the g-th level, and derive the theoretical subgrid code set corresponding to the parent grid code based on the coding mapping relationship. For each parent grid code, if each subgrid code in its theoretical subgrid code set exists in the candidate coding subset of the g-th level, then it is determined that the parent grid cell can spatially replace its four corresponding subgrid cells, and the parent grid cell satisfies the merging condition. Steps 3-4: Delete the four sub-grid codes corresponding to the parent grid cell that meets the merging condition from the coding candidate set, and then add the parent grid codes that meet the merging condition to the coding candidate set to obtain the updated coding candidate set; Step 3-5: Determine whether g is 1, or whether there are no parent grid cells that can be merged at all levels. If so, update the candidate encoding set to the minimum grid encoding set of spatial vector data; if not, decrement the value of g by 1 and go back to step 3-2.

[0014] Preferably, the specific process of establishing the relationship network diagram in step 4 is as follows: Create a target node for each spatial feature in the spatial vector data; For each grid code in the minimum grid representation code set for each spatial feature, check whether its corresponding grid node already exists in the existing relational network graph. If it does, reuse the grid node; if it does not, create a grid node for the grid cell corresponding to the current grid code. Then, a relationship edge is created between each target node and the grid node of its spatial element to obtain the established relationship network graph.

[0015] The technical solution adopted by the present invention to solve the second technical problem mentioned above is: a spatial relationship reasoning method applying the above-mentioned spatial vector data association method based on multi-level grids, characterized in that it includes: In the spatial vector data, select some spatial elements to form a target set and a set to be filtered. The minimum grid representation code set of all spatial elements in the target set is used to form a first grid code set, and the minimum grid representation code set of all spatial elements in the set to be filtered is used to form a second grid code set. If the first grid coding set and the second grid coding set do not meet the matching conditions, then the spatial relationship between the target set and the set to be filtered is deduced to be a separation relationship; If the first grid coding set and the second grid coding set satisfy the matching condition, and the first grid coding set can cover the second grid coding set, then the spatial relationship between the target set and the set to be filtered is inferred to be an inclusion relationship. If the first grid coding set and the second grid coding set meet the matching condition, and the second grid coding set can cover the first grid coding set, then the spatial relationship between the target set and the set to be filtered is inferred to be an inclusion relationship. If the first grid coding set and the second grid coding set meet the matching condition, but the first grid coding set cannot cover the second grid coding set, and the second grid coding set cannot cover the first grid coding set, then the spatial relationship between the target set and the set to be filtered is inferred to be an intersection relationship.

[0016] Preferably, the matching condition determination rule is as follows: Any grid code in the first grid code set With any grid code in the second grid code set The following relationship exists: = ,or Can cover ,or Can cover If the first grid code set and the second grid code set are matched, then the first grid code set and the second grid code set are considered to meet the matching conditions.

[0017] Compared with existing technologies, the advantages of this invention are as follows: when encoding non-coarsest-level grid cells, the encoding of sub-grid cells is made to have an encoding mapping relationship with the encoding of their corresponding parent grid cells; in addition, the grid cells in the finest level that intersect with a certain spatial element in the spatial vector data are taken as the covering grid cells of the current spatial element, and the encoding mapping relationship is used to iteratively merge the encodings of the covering grid cells to obtain the minimum grid representation encoding set. Therefore, this method can uniformly convert each spatial element into the minimum grid representation encoding set for expression, which can reduce computational complexity, reduce the redundancy of relation expression, and realize the efficiency and standardization of spatial vector data association and relation reasoning in multiple scenarios. Attached Figure Description

[0018] Figure 1 This is a flowchart of a spatial vector data association method based on a multi-level grid in an embodiment of the present invention. Detailed Implementation

[0019] The present invention will be further described in detail below with reference to the accompanying drawings and embodiments.

[0020] like Figure 1 As shown, the spatial vector data association method based on multi-level grids in this embodiment includes the following steps: Step 1: Establish a multi-level spatial grid system for the spatial vector data, obtain several grid cells corresponding to each level, and encode each grid cell to obtain the code of each grid cell; when encoding grid cells that are not at the coarsest level, ensure that the code of the sub-grid cell has a coding mapping relationship with the code of its corresponding parent grid cell. Step 2: Take the grid cell that intersects with a certain spatial feature in the spatial vector data in the finest level as the covering grid cell of the current spatial feature, and form the finest level covering grid code set of the current spatial feature by encoding all the covering grid cells of the current spatial feature. Step 3: Based on the coding mapping relationship between the sub-grid cell coding and its corresponding parent grid cell coding, iteratively merge the coding in the finest level covering grid coding set of each spatial feature to obtain the minimum grid representation coding set of each spatial feature; Step 4: Based on the minimum grid representation code set for each spatial element in Step 3, establish a relational network diagram to represent the association of spatial vector data.

[0021] The specific method for obtaining the mesh cells in step 1 of this embodiment is as follows: Step 1-1: Calculate the minimum and maximum values ​​of the positive outer edge length of all spatial elements in the spatial vector data, and determine the maximum number of levels of the spatial grid system and the edge length of the grid cells at each level based on the minimum and maximum values ​​of the positive outer edge length. Steps 1-2: Determine the side length of the global outer square and the global outer square region of the spatial vector data. Divide the global outer square region according to the maximum number of levels of the spatial grid system and the side length of the grid cells at each level to obtain several grid cells corresponding to each level.

[0022] The minimum value of the positive outer side length of all spatial features in the spatial vector data in step 1-1 The calculation formula is: ; in, This represents the total number of spatial features in the spatial vector data. For the first Spatial elements The positive side length of the outer casing, ; This is a function to find the maximum value. for The width of the outer rectangle, for The height of the outer rectangle, and The calculation formula is: ; , and They are respectively The maximum and minimum x-coordinates among all the vertex coordinates; and They are respectively The maximum and minimum values ​​of the ordinates among all the vertex coordinates.

[0023] The maximum value of the positive outer side length of all spatial features in spatial vector data The calculation formula is: .

[0024] Used to constrain the spatial scale of the finest level of grid cells, preventing individual grid cells from being too large to distinguish smaller spatial features; This is used to constrain the spatial scale of the coarsest level grid cells, avoiding the design of larger grid levels; the finest level grid cell is the grid cell with the smallest side length, and the coarsest level grid cell is the grid cell with the largest side length.

[0025] In this embodiment, the minimum value of the side length of the outer square As the target scale for the finest grid unit, it ensures that a single grid unit does not simultaneously completely cover multiple smaller-scale features, thus avoiding indistinguishability between features. The scale of the coarsest grid unit is derived by merging grid units level by level upwards from the finest level. Assuming that starting from the finest level, each time a layer is merged upwards, 2×2 sub-grids are merged into one parent grid, then the side length of the grid unit doubles in each layer. If ℎ layers are merged upwards, then the side length of the merged grid unit... The calculation formula is: To ensure that the side length of the coarsest level mesh cell is not less than the maximum side length of the enclosing square... It needs to meet the following requirements: The maximum number of levels in the grid system can be calculated from this. , This indicates rounding up to the nearest integer.

[0026] The side length of the global outer square of the spatial vector data in steps 1-2 The calculation formula is: ; and These represent the width and height of the overall outer matrix of the spatial vector data, respectively. ; .

[0027] The global outer square region of the spatial vector data is The coordinates of the four vertices are ( , ), ( , ), ( , ), ( , ), , .Should It can cover all spatial features in spatial vector data.

[0028] Since the side length of a grid cell can become smaller and smaller, but cannot be infinitely large, for ease of description, this embodiment uses the method that the larger the level, the smaller the side length of its corresponding grid cell to record the grid cells of each level.

[0029] Record No. The side length of the grid cell in the hierarchy is , ; in, , The side length of the coarsest level grid cell. This represents the side length of the finest level of the grid cell.

[0030] Step 1 also includes: establishing row and column indices for each level of grid cells, and obtaining the index mapping relationship between each level of grid cells and their parent grid.

[0031] For ease of description, we will use rows as the horizontal axis and columns as the vertical axis, defining the first... The row index of the grid cell in the hierarchy is , No. The column index of the hierarchical grid cell is , , , and The first The number of grid cells in the horizontal and vertical directions of the hierarchy. .

[0032] No. The first in the hierarchy Line number Column grid cells x-coordinate and ordinate The range of values ​​for is as follows: ; ; through the first The first in the hierarchy Line number Column grid cells x-coordinate and ordinate The range of values ​​to be determined Spatial range.

[0033] For any level The grid cell whose parent grid is at a certain level. The smaller the level, the coarser the corresponding mesh and the larger the side length of the mesh cell; the larger the level, the smaller the side length of the corresponding mesh cell. The index mapping relationship between the mesh cells of each level and their parent mesh is described in the following way: for the first level... The first in the hierarchy Line number Column grid cells Its parent grid is: , For the first The first in the hierarchy Line number Column grid cells, , , This is the floor function; with its parent grid It satisfies the spatial containment relationship, that is: ; For the parent grid Theoretically, its four subgrids are as follows: and .

[0034] In this embodiment, step 1 uses the coarsest level (i.e., level 0) as the root level, and the level in the root level is... Line number Column grid cells encoding The calculation formula is: ; in, This is a unique single-value mapping function used to map row indices. and column indexes Map to a fixed-length string or numeric code; ensure that the codes of different root-level grid cells are different and of the same length.

[0035] Each grid cell, except for the coarsest level, is encoded in the following manner: The first level Line number Column grid cells encoding The calculation formula is: ; in, , This represents the maximum number of levels in the spatial grid system. The higher the number of levels, the finer the grid. For the first The first level Line number Column grid cells The encoding, for The parent grid, for Relative to its parent grid Quadrant numbering; This is a concatenation function.

[0036] The specific method of obtaining it is as follows: according to with its parent grid Index mapping relationship: ; in, , Corresponding row direction, In the corresponding column direction, the tuples Considering the quadrant position of the sub-mesh relative to the parent mesh, define The quadrant number is , The calculation formula is: ; when hour, ;when hour, ;when hour, ;when hour, ; The above quadrant numbers This is used to represent the fixed position of a sub-mesh relative to its parent mesh, and is encoded according to the object limit numbering formula below, resulting in... The corresponding quadrant number code , , To be Functions that map to single characters or fixed-length bit strings.

[0037] Encoding with its parent grid cell The encoding mapping relationship is as follows: .

[0038] Since the length of the code for each quadrant is fixed, encoding It is composed of the code of a grid cell in the root layer and the quadrant code sequence on the path from the root layer to that grid cell.

[0039] For any non-root layer mesh element Its parent grid cell encoding Just for The strict prefix, i.e. for The prefix ensures that the encoding naturally has a parent-child prefix relationship, and the prefix truncation can be used to trace back from the child grid to any parent grid.

[0040] A bidirectional mapping relationship between any code and its corresponding grid cell A hierarchical index structure with prefix properties supports the following operations: 1. By inputting any grid code and removing the last quadrant number code, you can trace back to its parent grid code level by level. 2. Input any parent grid code, and enumerate the set of its child grid codes by appending possible quadrant number codes to it.

[0041] In this embodiment, step 2 uses a spatial intersection determination function. Determine whether the space of a spatial feature in the finest level grid cell intersects with that of a spatial feature in the spatial vector data.

[0042] In this embodiment, the specific process for obtaining the minimum grid representation encoding set of any spatial element in step 3 is as follows: Step 3-1: Use the finest level cover grid encoding set of the current spatial feature as the initial encoding candidate set; Step 3-2: Extract the candidate encoding subset at level g from the candidate encoding set; the initial value of g is... ; Step 3-3: Obtain the parent grid code corresponding to each code in the candidate coding subset of the g-th level, and derive the theoretical subgrid code set corresponding to the parent grid code based on the coding mapping relationship. For each parent grid code, if each subgrid code in its theoretical subgrid code set exists in the candidate coding subset of the g-th level, then it is determined that the parent grid cell can spatially replace its four corresponding subgrid cells, and the parent grid cell satisfies the merging condition. In this embodiment, for each code in the candidate coding subset of the g-th level, the parent grid can be obtained according to the index mapping relationship between the sub-grid and the parent grid, and the parent grid code can be obtained. According to the coding mapping relationship (i.e. the parent-child prefix relationship mentioned above), its theoretical sub-grid code set can be obtained. This is something that those skilled in the art can know from the content described in this embodiment, and will not be elaborated here. Steps 3-4: Delete the four sub-grid codes corresponding to the parent grid cell that meets the merging condition from the coding candidate set, and then add the parent grid codes that meet the merging condition to the coding candidate set to obtain the updated coding candidate set; Step 3-5: Determine whether g is 1, or whether there are no parent grid cells that can be merged at all levels. If so, update the candidate encoding set to the minimum grid encoding set of spatial vector data; if not, decrement the value of g by 1 and go back to step 3-2.

[0043] The specific process of establishing the relationship network diagram in step 4 of this embodiment is as follows: Create a target node for each spatial feature in the spatial vector data; For each grid code in the minimum grid representation code set for each spatial feature, check whether its corresponding grid node already exists in the existing relational network graph. If it does, reuse the grid node; if it does not, create a grid node for the grid cell corresponding to the current grid code. Then, a relationship edge is created between each target node and the grid node of its spatial element to obtain the established relationship network graph.

[0044] This embodiment also relates to a spatial relationship reasoning method that applies the above-mentioned spatial vector data association method based on multi-level grids, including: In the spatial vector data, select some spatial elements to form a target set and a set to be filtered. The minimum grid representation code set of all spatial elements in the target set is used to form a first grid code set, and the minimum grid representation code set of all spatial elements in the set to be filtered is used to form a second grid code set. Based on the first grid coding set and the second grid coding set, the spatial relationship between the target set and the set to be filtered is inferred. The spatial relationship between the target set and the set to be filtered is specifically as follows: If the first grid coding set and the second grid coding set do not meet the matching conditions, then the spatial relationship between the target set and the set to be filtered is deduced to be a separation relationship; If the first grid coding set and the second grid coding set satisfy the matching condition, and the first grid coding set can cover the second grid coding set, then the spatial relationship between the target set and the set to be filtered is inferred to be an inclusion relationship. If the first grid coding set and the second grid coding set meet the matching condition, and the second grid coding set can cover the first grid coding set, then the spatial relationship between the target set and the set to be filtered is inferred to be an inclusion relationship. If the first grid coding set and the second grid coding set satisfy the matching condition, but the first grid coding set cannot cover the second grid coding set, and the second grid coding set cannot cover the first grid coding set, then it is inferred that the spatial relationship between the target set and the set to be filtered is an intersection relationship; if the spatial relationship between the target set and the set to be filtered is an intersection relationship, then a subset that can satisfy the matching condition with the first grid coding set is selected from the second grid coding set, and this subset is the target subset of the set to be filtered relative to the target set.

[0045] The matching condition determination rule is as follows: any grid code in the first grid code set With any grid code in the second grid code set The following relationship exists: = ,or Can cover ,or Can cover If the first grid code set and the second grid code set are matched, then the first grid code set and the second grid code set are considered to meet the matching conditions.

[0046] The determination rule covered in this embodiment is: for any grid encoding and ,when ,or for When the prefix is ​​specified (as mentioned in the index mapping relationship between sub-grids and parent grids), it is considered that... cover Its meaning is: encoding The corresponding grid cells cover the encoding in spatial range. The corresponding grid cell.

[0047] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A spatial vector data association method based on multi-level grids, characterized in that... Includes the following steps: Step 1: Establish a multi-level spatial grid system for the spatial vector data, obtain several grid cells corresponding to each level, and encode each grid cell to obtain the code of each grid cell; when encoding grid cells that are not at the coarsest level, ensure that the code of the sub-grid cell has a coding mapping relationship with the code of its corresponding parent grid cell. Step 2: Take the grid cell that intersects with a certain spatial feature in the spatial vector data in the finest level as the covering grid cell of the current spatial feature, and form the finest level covering grid code set of the current spatial feature by encoding all the covering grid cells of the current spatial feature. Step 3: Based on the coding mapping relationship between the sub-grid cell coding and its corresponding parent grid cell coding, iteratively merge the coding in the finest level covering grid coding set of each spatial feature to obtain the minimum grid representation coding set of each spatial feature; Step 4: Based on the minimum grid representation code set for each spatial element in Step 3, establish a relational network diagram to represent the association of spatial vector data.

2. The spatial vector data association method according to claim 1, characterized in that: The specific method for obtaining the grid cells in step 1 is as follows: Step 1-1: Calculate the minimum and maximum values ​​of the positive outer edge length of all spatial elements in the spatial vector data, and determine the maximum number of levels of the spatial grid system and the edge length of the grid cells at each level based on the minimum and maximum values ​​of the positive outer edge length. Steps 1-2: Determine the side length of the global outer square and the global outer square region of the spatial vector data. Divide the global outer square region according to the maximum number of levels of the spatial grid system and the side length of the grid cells at each level to obtain several grid cells corresponding to each level.

3. The spatial vector data association method according to claim 2, characterized in that: Step 1 further includes: establishing row and column indices for each level of grid cells, and obtaining the index mapping relationship between each level of grid cells and its parent grid.

4. The spatial vector data association method according to claim 3, characterized in that: In step 1, the coarsest level is taken as the root level, and the first level in the root level is... Line number Column grid cells encoding The calculation formula is: ； in, This is a unique single-value mapping function used to map row indices. and column indexes Mapped to a fixed-length string or numeric encoding.

5. The spatial vector data association method according to claim 4, characterized in that: In step 1, each grid cell (excluding the coarsest level) is encoded in the following manner: The first level Line number Column grid cells encoding The calculation formula is: in, , This represents the maximum number of levels in the spatial grid system. The higher the number of levels, the finer the grid. For the first The first level Line number Column grid cells The encoding, for The parent grid, for Relative to its parent grid Quadrant numbering; This is a concatenation function; Encoding with its parent grid cell The encoding mapping relationship is as follows: 。 6. The spatial vector data association method according to claim 5, characterized in that: The specific method of obtaining it is as follows: according to with its parent grid Index mapping relationship: in, , Corresponding row direction, In the corresponding column direction, the tuples Considering the quadrant position of the sub-mesh relative to the parent mesh, define The quadrant number is , The calculation formula is: ; when hour, ;when hour, ;when hour, ;when hour, ; The above quadrant numbers This is used to represent the fixed position of a sub-mesh relative to its parent mesh, and is encoded according to the object limit numbering formula below, resulting in... The corresponding quadrant number code , , To be Functions that map to single characters or fixed-length bit strings.

7. The spatial vector data association method according to any one of claims 1 to 6, characterized in that: The specific process for obtaining the minimum grid representation encoding set of any spatial element in step 3 is as follows: Step 3-1: Use the finest level cover grid encoding set of the current spatial feature as the initial encoding candidate set; Step 3-2: Extract the candidate encoding subset at level g from the candidate encoding set; the initial value of g is... ; Step 3-3: Obtain the parent grid code corresponding to each code in the candidate coding subset of the g-th level, and derive the theoretical subgrid code set corresponding to the parent grid code based on the coding mapping relationship. For each parent grid code, if each subgrid code in its theoretical subgrid code set exists in the candidate coding subset of the g-th level, then it is determined that the parent grid cell can spatially replace its four corresponding subgrid cells, and the parent grid cell satisfies the merging condition. Steps 3-4: Delete the four sub-grid codes corresponding to the parent grid cell that meets the merging condition from the coding candidate set, and then add the parent grid codes that meet the merging condition to the coding candidate set to obtain the updated coding candidate set; Step 3-5: Determine whether g is 1, or whether there are no parent grid cells that can be merged at all levels. If so, update the candidate encoding set to the minimum grid encoding set of spatial vector data; if not, decrement the value of g by 1 and go back to step 3-2.

8. The spatial vector data association method according to claim 7, characterized in that: The specific process of establishing the relationship network diagram in step 4 is as follows: Create a target node for each spatial feature in the spatial vector data; For each grid code in the minimum grid representation code set for each spatial feature, check whether its corresponding grid node already exists in the existing relational network graph. If it does, reuse the grid node; if it does not, create a grid node for the grid cell corresponding to the current grid code. Then, a relationship edge is created between each target node and the grid node of its spatial element to obtain the established relationship network graph.

9. A spatial relationship reasoning method applying the spatial vector data association method based on a multi-level grid as described in any one of claims 1 to 8, characterized in that... include: In the spatial vector data, select some spatial elements to form a target set and a set to be filtered. The minimum grid representation code set of all spatial elements in the target set is used to form a first grid code set, and the minimum grid representation code set of all spatial elements in the set to be filtered is used to form a second grid code set. If the first grid coding set and the second grid coding set do not meet the matching conditions, then the spatial relationship between the target set and the set to be filtered is deduced to be a separation relationship; If the first grid coding set and the second grid coding set satisfy the matching condition, and the first grid coding set can cover the second grid coding set, then the spatial relationship between the target set and the set to be filtered is inferred to be an inclusion relationship. If the first grid code set and the second grid code set meet the matching condition, and the second grid code set can cover the first grid code set, then the spatial relationship between the target set and the set to be filtered is inferred to be an inclusion relationship. If the first grid coding set and the second grid coding set meet the matching condition, but the first grid coding set cannot cover the second grid coding set, and the second grid coding set cannot cover the first grid coding set, then the spatial relationship between the target set and the set to be filtered is inferred to be an intersection relationship.

10. The spatial relationship reasoning method according to claim 9, characterized in that: The matching condition determination rule is as follows: Any grid code in the first grid code set With any grid code in the second grid code set The following relationship exists: = ,or Can cover ,or Can cover If the first grid code set and the second grid code set are matched, then the first grid code set and the second grid code set are considered to meet the matching conditions.

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