Adaptive vector tile construction optimization method considering spatial distribution characteristics

Abstract

The invention relates to the technical field of network geographic information, in particular to an adaptive vector tile construction optimization method considering spatial distribution characteristics, which comprises the following steps: constructing a vector tile pyramid multi-scale information model, and constructing a multi-scale vector data set based on the vector tile pyramid multi-scale information model, the multi-scale vector data set comprises different levels of geographic vector data; performing slicing and parallel construction on the geographic vector data of different hierarchies to obtain adaptive vector tiles covering all hierarchies; and constructing an index based on the adaptive vector tiles, wherein the index is used for providing an adaptive vector tile map online service. According to the method, the problem of slow loading at a map data intensive part caused by unbalanced vector tile data volume can be solved, and the network map visualization efficiency and the user experience are improved.

Description

Technical Field

[0001] The invention relates to the field of network geographic information technology, and in particular to an adaptive vector tile construction optimization method taking into account spatial distribution characteristics. Background Art

[0002] With the development of geographic information technology, the amount of geographic information data has shown explosive growth, and geographical research has entered the era of big data. Geographic big data has 5V characteristics: volume, variety, velocity, veracity and value. The third national land survey reached 295 million map vector data, and remote sensing data reached hundreds of PB. Due to the characteristics of large capacity and fast update, geographic big data faces many challenges in storage, use and transmission, such as comprehensive compression processing, visualization and timeliness. These challenges need to be solved urgently and have become an important research direction in the field of geographic information.

[0003] The map tile pyramid technology based on WebGIS provides an effective solution to the challenges faced by the above-mentioned geographic big data. This technology divides the map data into different levels according to the pyramid model, and divides the data of each level into a series of tiles of fixed size, such as 256*256 pixels, and stores them on the server. When the user sends a map request from the client, only the tiles within the screen need to be loaded, and the tiles outside the screen do not need to be loaded, so the map data can be displayed quickly. Map tiles include two types of tiles: vector and raster. Compared with raster tiles, vector tiles have the advantages of high definition, high transmission efficiency, flexible style and strong interactivity. Therefore, today's mainstream Internet electronic maps are constructed by cutting map data into vector tiles of uniform size.

[0004] The traditional vector tile partitioning method uses uniformly sized grids for partitioning, but since this method does not fully consider the clustering and heterogeneity of spatial data, it will lead to a series of problems: uneven tile data size, slow loading of dense map data areas, which in turn affects the efficiency of map visualization and reduces user experience. Therefore, the present invention proposes an adaptive vector tile construction optimization method that takes into account spatial distribution characteristics. Summary of the invention

[0005] The purpose of the present invention is to solve the above technical problems and provide an adaptive vector tile construction optimization method taking into account spatial distribution characteristics, so as to solve the problem of slow loading of map data-intensive areas caused by uneven vector tile data size, and improve the visualization efficiency of network maps and user experience.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] An adaptive vector tile construction optimization method taking into account spatial distribution characteristics comprises:

[0008] Constructing a vector tile pyramid multi-scale information model, and constructing a multi-scale vector data set based on the vector tile pyramid multi-scale information model, wherein the multi-scale vector data set includes geographic vector data of different levels;

[0009] Slice and build geographic vector data at different levels in parallel to obtain adaptive vector tiles covering all levels;

[0010] An index is constructed based on the adaptive vector tile to provide an adaptive vector tile map online service.

[0011] Optionally, constructing the vector tile pyramid multi-scale information model includes:

[0012] The national scale is converted into a map resolution, and the map resolution is graded from low to high to construct the vector tile pyramid multi-scale information model.

[0013] Optionally, constructing a multi-scale vector dataset based on the vector tile pyramid multi-scale information model includes:

[0014] Determine the initial level and maximum level based on the grading results;

[0015] Starting from the initial level, the geographic data is mapped and synthesized according to the compilation and mapping requirements corresponding to the level, and the mapping is saved and named according to the level;

[0016] The next level is traversed until the number of levels reaches the maximum level, and the traversal ends, and all the saved cartography is constructed as the multi-scale vector dataset.

[0017] Optionally, the cartographic synthesis of geographic data according to the compilation and mapping requirements corresponding to the levels includes:

[0018] The geographic data is processed by point data, line data and surface data respectively to complete cartographic synthesis, wherein the point data processing is used to retain and select dense areas and sparse areas according to a preset percentage, the line data processing is used to retain and select linear elements according to grade characteristics and preset lengths, and the surface data processing is used to retain and select areas according to a preset area.

[0019] Optionally, slicing and constructing the geographic vector data of different levels in parallel to obtain adaptive vector tiles covering all levels includes:

[0020] Performing adaptive map slicing on the geographic vector data of different levels in parallel, obtaining the vector data and slicing range of each level and saving them as vector tiles;

[0021] The data amount of each vector tile at each level is calculated, a slicing method is selected according to the data amount to slice the vector tiles, and the adaptive vector tiles covering all levels are constructed based on the sliced ​​vector tiles.

[0022] Optionally, obtaining the vector data and slice range of each level and saving them as vector tiles includes:

[0023] Querying and extracting vector data of each level in the multi-scale vector dataset;

[0024] Calculate the maximum circumscribed rectangle based on the vector data as the slice range;

[0025] The vector data and the tile range are saved as the vector tile.

[0026] Optionally, selecting a slicing method according to the data amount to slice the vector tile includes:

[0027] Presetting a first threshold and a second threshold;

[0028] If the data amount is greater than or equal to the first threshold, slicing the vector tile using a quadtree slicing method;

[0029] If the data amount is between the first threshold and the second threshold, slicing the vector tile using a kd tree slicing method;

[0030] If the data amount is less than or equal to the second threshold, the vector tile is not sliced.

[0031] Optionally, slicing the vector tile using a quadtree slicing method includes:

[0032] Reading a slice range in the vector tile, and marking the vector tile as a first parent tile;

[0033] Evenly divide the slice range in a quadtree manner, and slice the first parent tile using the evenly divided slice range to obtain four first child tiles and delete the first parent tile;

[0034] The first sub-tile is judged, and if the data in the first sub-tile is empty, it is not saved; if the data in the first sub-tile is not empty, it is saved, and the evenly divided slice range is written into the first sub-tile.

[0035] Optionally, slicing the vector tile using a kd-tree slicing method includes:

[0036] Reading a slice range in the vector tile, and marking the vector tile as a second parent tile;

[0037] Obtaining equal-division longitudes within the second parent tile as cutting lines, and dividing the slice range based on the cutting lines;

[0038] Slice the second parent tile with the split slice range, obtain two second child tiles, delete the second parent tile, and write the split slice range into the second child tile;

[0039] The second sub-tile is judged. If the data amount of the second sub-tile is less than or equal to the second threshold, no slicing is performed; if the data amount of the second sub-tile is greater than the second threshold, the second sub-tile is subjected to the same slicing processing operation as the second parent tile until there is no tile with a data amount greater than the second threshold.

[0040] The beneficial effects of the present invention are:

[0041] (1) In terms of multi-scale data models, the present invention adopts 11 national basic scales to construct a vector tile pyramid multi-scale information model in view of the important applications of national basic scale topographic maps in urban planning, engineering construction, military, scientific research, education and other fields in order to meet a wide range of needs. According to the compilation and drawing specifications corresponding to each level L, the geographic data is subjected to cartographic synthesis processing to construct a multi-scale vector data set.

[0042] (2) In terms of adaptive data processing, the present invention fully considers the clustering and heterogeneity characteristics of spatial data. For data at different levels, an adaptive slicing method combining quadtree and kd-tree is adopted, and adaptive vector tiles covering all levels and with balanced data volume are constructed through parallel operation.

[0043] (3) In terms of map online service optimization, the present invention addresses the problem of balanced but uneven data size of adaptive vector tiles by constructing an index in the database for information such as the encoding, level, and range of the vector tiles, thereby providing users with efficient adaptive vector tile map online services. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1A flow chart of an adaptive vector tile construction optimization method taking into account spatial distribution characteristics according to an embodiment of the present invention;

[0046] Figure 2 A schematic diagram of a vector tile pyramid multi-scale information model according to an embodiment of the present invention;

[0047] Figure 3 This is a multi-scale vector data set result diagram of an embodiment of the present invention;

[0048] Figure 4 A schematic diagram of adaptive slicing according to an embodiment of the present invention;

[0049] Figure 5 This is a result diagram of using a uniform grid slicing method according to an embodiment of the present invention;

[0050] Figure 6 This is a result diagram of using a quadtree slicing method according to an embodiment of the present invention;

[0051] Figure 7 This is a result diagram of using the kd tree slicing method according to an embodiment of the present invention;

[0052] Figure 8 This is a result diagram of an adaptive slicing method using a quadtree combined with a kd tree according to an embodiment of the present invention;

[0053] Fig. 9 This is a partial display result diagram of the effects of different slicing methods at the same level in an embodiment of the present invention;

[0054] Fig.10 This is a diagram showing a comparison of slicing time results of different methods according to an embodiment of the present invention;

[0055] Fig.11 This is a diagram showing a comparison of tile quantities of different methods according to an embodiment of the present invention;

[0056] Fig.12 This is a comparison result diagram of the coefficient of variation of tile data volume of different methods according to an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0059] This embodiment provides an adaptive vector tile construction optimization method taking into account spatial distribution characteristics, including:

[0060] Construct a vector tile pyramid multi-scale information model, and construct a multi-scale vector dataset based on the vector tile pyramid multi-scale information model, where the multi-scale vector dataset includes geographic vector data of different levels; slice and construct geographic vector data of different levels in parallel to obtain adaptive vector tiles covering all levels; construct an index based on the adaptive vector tiles to provide adaptive vector tile map online services.

[0061] Specifically, this embodiment performs comprehensive cartographic processing on geographic data in accordance with the compilation and drawing specification requirements corresponding to each level L, and then constructs a multi-scale vector data set; fully considers the agglomeration and heterogeneity characteristics of spatial data, adopts different slicing methods for data at different levels, and constructs adaptive vector tiles covering all levels and with balanced data volume through parallel operation; indexes the encoding, level, range and other information of vector tiles in the database, thereby providing users with efficient adaptive vector tile map online services; can solve the problem of slow loading of map data intensive areas caused by uneven vector tile data size, and improve the visualization efficiency of network maps and user experience.

[0062] Furthermore, constructing a vector tile pyramid multi-scale information model includes:

[0063] The national scale is converted into map resolution, and the map resolution is graded from low to high to construct a vector tile pyramid multi-scale information model.

[0064] Furthermore, constructing a multi-scale vector dataset based on the vector tile pyramid multi-scale information model includes:

[0065] Determine the initial level and maximum level based on the grading results;

[0066] Starting from the initial level, the geographic data is mapped and synthesized according to the compilation and mapping requirements corresponding to the level, and the mapping is saved and named according to the level;

[0067] Traverse the next level until the number of levels reaches the maximum level, and then the traversal ends, constructing all the saved cartography into a multi-scale vector dataset.

[0068] Furthermore, the cartographic synthesis of geographic data according to the compilation and mapping requirements corresponding to the levels includes:

[0069] The geographic data is processed by point data, line data and surface data respectively to complete cartographic synthesis. Among them, point data processing is used to retain and select dense areas and sparse areas according to preset percentages, line data processing is used to retain and select linear elements according to grade characteristics and preset lengths, and surface data processing is used to retain and select areas according to preset areas.

[0070] Furthermore, different levels of geographic vector data are sliced ​​and constructed in parallel to obtain adaptive vector tiles covering all levels, including:

[0071] Perform adaptive map slicing on geographic vector data of different levels in parallel, obtain vector data and slicing range of each level and save them as vector tiles;

[0072] Calculate the data volume of each vector tile at each level, select a slicing method to slice the vector tiles according to the data volume, and build adaptive vector tiles covering all levels based on the sliced ​​vector tiles.

[0073] Furthermore, obtaining the vector data of each level, slicing range and saving as vector tiles includes:

[0074] Query and extract vector data at each level in a multi-scale vector dataset; calculate the maximum bounding rectangle based on the vector data as the slice range; save the vector data and slice range as vector tiles.

[0075] Further, selecting a slicing method to slice the vector tile according to the amount of data includes:

[0076] Presetting a first threshold and a second threshold;

[0077] If the data volume is greater than or equal to the first threshold, the vector tile is sliced ​​using a quadtree slicing method;

[0078] If the data volume is between the first threshold and the second threshold, the kd-tree slicing method is used to slice the vector tiles;

[0079] If the data amount is less than or equal to the second threshold, the vector tile is not sliced.

[0080] Specifically, the first threshold is 4 times the second threshold. In this embodiment, the first threshold is threshold 1, and the second threshold is threshold 2.

[0081] Threshold 1 is:

[0082] Threshold 1 = 4 * Threshold 2

[0083] Threshold 2 is:

[0084]

[0085] Among them, threshold 2 represents the maximum data volume that a single tile should reach in theory to ensure transmission efficiency, in kb; Bandwidth represents the Internet bandwidth, in Mbps, 1Mbps represents 1000000 bits transmitted per second, that is, the amount of data transmitted per second is 1000000 / 8=125000Byte / s=0.125MB / s; T represents the transmission time required for a single tile to ensure transmission efficiency, in seconds.

[0086] Furthermore, slicing the vector tile using the quadtree slicing method includes:

[0087] Read the tile range in the vector tile and mark the vector tile as the first parent tile;

[0088] The slice range is evenly divided in a quadtree manner, and the evenly divided slice range is sliced ​​for the first parent tile, four first child tiles are obtained, and the first parent tile is deleted;

[0089] The first sub-tile is judged. If the data in the first sub-tile is empty, it is not saved; if the data in the first sub-tile is not empty, it is saved, and the evenly divided slice range is written into the first sub-tile.

[0090] Furthermore, slicing the vector tile using the kd-tree slicing method includes:

[0091] Read the tile range in the vector tile and mark the vector tile as the second parent tile;

[0092] Obtaining equal-division longitudes within the second parent tile as cutting lines, and dividing the slice range based on the cutting lines;

[0093] Slice the second parent tile with the split slice range, obtain two second child tiles, delete the second parent tile, and write the split slice range into the second child tile;

[0094] The second child tile is judged. If the data amount of the second child tile is less than or equal to the second threshold, no slicing is performed; if the data amount of the second child tile is greater than the second threshold, the second child tile is subjected to the same slicing processing operation as the second parent tile until there is no tile with a data amount greater than the second threshold.

[0095] Combine the following Figure 1-Figure 4 An adaptive vector tile construction optimization method taking into account spatial distribution characteristics proposed in this embodiment is described in detail, and specifically includes the following steps:

[0096] Step 1, construct a multi-scale vector data set: According to the national basic scale map series, construct a multi-scale information model of vector tile pyramids, and synthesize geographic vector data into a multi-scale vector data set covering different levels and details;

[0097] Further, Step 1 of this embodiment includes the following steps:

[0098] Step 1.1, convert 11 national basic scales (1:1,000,000, 1:500,000, 1:250,000, 1:100,000, 1:50,000, 1:25,000, 1:10,000, 1:5,000, 1:2,000, 1:1,000, 1:500) into map resolutions;

[0099] Step 1.2, rank and classify in ascending order of map resolution, and construct a multi-scale information model of vector tile pyramids as Figure 2 shown. The scales and map resolutions corresponding to levels 1 to 11 under the multi-scale information model of vector tile pyramids are shown in Table 1;

[0100] Table 1

[0101]

[0102] Step 1.3, define the initial level L = 1, max = 11;

[0103] Step 1.4, perform cartographic generalization on geographic data according to the compilation specifications required for level L, save it to the database and name it after level L, including:

[0104] Point data processing, select 60%-70% in dense areas and retain all in sparse areas;

[0105] Line data processing, for elements such as roads, rivers, and pipe networks, select according to grade characteristics; for other elements, if the length on the map exceeds 1 cm, discard it;

[0106] Polygon data processing, adopt the strategy of mainly merging and supplemented by deleting, and discard data with an area less than 2 mm 2 on the map.

[0107] Step 1.5, when level L < max, increment L and repeat Step 1.4; when level L >= max, the loop ends, and a multi-scale vector data set is constructed, as Figure 3 shown.

[0108] Step 2, adaptive slicing and parallel construction: According to the single-tile data volume threshold, adopt an adaptive slicing method combining quadtree and k-d tree for data at different levels, and run in parallel to construct adaptive vector tiles with balanced data volume covering all levels;

[0109] Furthermore, step 2 of this embodiment includes the following steps:

[0110] Step 2.1, select the multi-scale vector dataset constructed in step 1, and take level L as the processing unit;

[0111] Step 2.2, start max threads and perform adaptive map slicing in parallel to fully utilize the computing power of multi-core CPUs;

[0112] Step 2.3, export vector data, calculate the slice range and save it as vector tiles in GeoJSON format;

[0113] Step 2.4, calculate the data volume of each vector tile at level L;

[0114] Step 2.5, according to the relationship between the data volume Q and threshold 1 and threshold 2, select quadtree slicing or kd tree slicing, or do not slice if the requirements are met;

[0115] Step 2.6: If there are vector tiles with data volume not less than threshold 2 under level L, repeat steps 2.4 and 2.5; all levels run in parallel at the same time to construct adaptive vector tiles with balanced data volume covering different levels, such as Figure 4 shown.

[0116] Specifically, the implementation of step 2.3 of this embodiment includes:

[0117] Step 2.3.1, access the database, query and extract the vector data of the specified level L;

[0118] Step 2.3.2, for the exported L-level vector data, calculate its maximum enclosing rectangle and use it as the slice range;

[0119] Step 2.3.3, save the exported vector data and the calculated slice range together as a file in GeoJSON format, which is the initial vector tile.

[0120] Specifically, the implementation of step 2.5 of this embodiment includes:

[0121] Step 2.5.1, if Q>=threshold 1, use the quadtree slicing method;

[0122] Step 2.5.2, if threshold 1>Q>threshold 2, then use the kd tree slicing method;

[0123] In step 2.5.3, if Q <= threshold 2, no slicing is performed and the data volume requirement is deemed to have been met.

[0124] Furthermore, threshold 1 and threshold 2 in step 2.5 are implemented by the following formula:

[0125] Threshold 1 = 4 * Threshold 2

[0126]

[0127] Among them, threshold 2 represents the maximum data volume that a single tile should reach in theory to ensure transmission efficiency, in kb; Bandwidth represents the Internet bandwidth, in Mbps, 1Mbps represents 1000000 bits transmitted per second, that is, the amount of data transmitted per second is 1000000 / 8=125000Byte / s=0.125MB / s; T represents the transmission time required for a single tile to ensure transmission efficiency, in seconds.

[0128] Specifically, the quadtree slicing in step 2.5.1 of this embodiment is specifically implemented as follows:

[0129] Step 2.5.1.1, read the slice range information in the current vector tile, and mark the tile as the parent tile;

[0130] Step 2.5.1.2, divide the parent tile slice range evenly into four child tile slice ranges in a quadtree manner;

[0131] Step 2.5.1.3, slice the parent tile with four child tile slice ranges to obtain four child tiles, and delete the parent tile;

[0132] Step 2.5.1.4, determine whether the data in the generated sub-tile is empty. If it is empty, it will not be saved. If it is not empty, it will be saved and the corresponding sub-tile slice range information will be written into the sub-tile.

[0133] Specifically, the kd tree slicing in step 2.5.2 of this embodiment is specifically implemented as follows:

[0134] Step 2.5.2.1, read the slice range information in the current vector tile, and mark the tile as the parent tile;

[0135] Step 2.5.2.2, sort all the coordinates in the tile by longitude, obtain equal-division longitudes as cutting lines, and divide the parent tile slice range into two sub-slice ranges;

[0136] Step 2.5.2.3, slice the parent tile with two sub-slice ranges to obtain two sub-tiles, delete the parent tile, and write the corresponding sub-slice range information in the sub-tiles;

[0137] Step 2.5.2.4, if the data volume Q of the child tile is less than or equal to threshold 2, no slicing is performed; if Q> threshold 2, the tile is marked as a parent tile, sorted by latitude, and equal-value split latitudes are obtained as cutting lines. The tiles are split again to obtain two child-slice ranges, and the slicing process step 2.5.2.3 is repeated. At this time, the kd tree slicing method ends.

[0138] Step 3, build index and provide service: According to the encoding, level and range of vector tiles, build index in the database to provide users with adaptive vector tile map online service.

[0139] Furthermore, the specific implementation of step 3 of this embodiment includes:

[0140] Step 3.1, select the constructed adaptive vector tiles of different levels, take level L as the processing unit, extract the coding and range information and store it in the geographic database;

[0141] Step 3.2, using the geographic database index function, find the intersecting tile code between the user screen display range and the required map level;

[0142] Step 3.3 sends the vector tile corresponding to the tile encoding to the user to provide online services.

[0143] Comparison and verification:

[0144] In order to demonstrate the superiority of this method, this method was compared with the existing technology, including uniform grid slicing, quadtree slicing, kd tree slicing, and the adaptive slicing method of quadtree combined with kd tree. The comparative experiment selected vector tiles of levels 1, 3, 5, and 7, and conducted a comprehensive analysis from the aspects of slicing results, slicing time, number of tiles, and coefficient of variation of tile data volume. For specific comparison results, see Figures 5 to 12 .

[0145] right Figure 5-Figure 12 After in-depth analysis, the following conclusions can be drawn: from the perspective of slicing time of different methods, this method shows the shortest running time and the highest efficiency; this method is a further optimization of the kd-tree slicing method, making the size and shape of tiles more regular, effectively avoiding the appearance of narrow and long tiles; narrow and long tiles are often easy to exceed the screen range, and the data outside the screen range is invalid; compared with the quadtree slicing method, this method significantly reduces the number of tiles and makes the tile data volume more balanced; compared with the uniform grid slicing method, this method can construct adaptive vector tiles covering all levels and with balanced data volume in parallel, effectively solving the problem of slow loading of dense map data caused by uneven vector tile data volume, thereby improving the visualization efficiency of network maps and user experience. In summary, this method has shown superiority in many aspects and has practical application value.

[0146] The embodiments described above are only descriptions of the preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Without departing from the design spirit of the present invention, various modifications and improvements made to the technical solutions of the present invention by ordinary technicians in this field should all fall within the protection scope determined by the claims of the present invention.

Claims

1. An adaptive vector tile construction optimization method taking into account spatial distribution characteristics, characterized in that: include: Constructing a vector tile pyramid multi-scale information model, and constructing a multi-scale vector data set based on the vector tile pyramid multi-scale information model, wherein the multi-scale vector data set includes geographic vector data of different levels; Slice and build geographic vector data at different levels in parallel to obtain adaptive vector tiles covering all levels; An index is constructed based on the adaptive vector tile to provide an adaptive vector tile map online service.

2. The adaptive vector tile construction optimization method considering spatial distribution characteristics according to claim 1, characterized in that: Constructing the vector tile pyramid multi-scale information model includes: The national scale is converted into a map resolution, and the map resolution is graded from low to high to construct the vector tile pyramid multi-scale information model.

3. The adaptive vector tile construction optimization method taking into account spatial distribution characteristics according to claim 2, characterized in that: Constructing a multi-scale vector data set based on the vector tile pyramid multi-scale information model includes: Determine the initial level and maximum level based on the grading results; Starting from the initial level, the geographic data is mapped and synthesized according to the compilation and mapping requirements corresponding to the level, and the mapping is saved and named according to the level; The next level is traversed until the number of levels reaches the maximum level, and the traversal ends, and all the saved cartography is constructed as the multi-scale vector dataset.

4. The adaptive vector tile construction optimization method considering spatial distribution characteristics according to claim 3, characterized in that: The mapping and generalization of geographic data according to the hierarchical compilation requirements include: The geographic data is processed by point data, line data and surface data respectively to complete cartographic synthesis, wherein the point data processing is used to retain and select dense areas and sparse areas according to a preset percentage, the line data processing is used to retain and select linear elements according to grade characteristics and preset lengths, and the surface data processing is used to retain and select areas according to a preset area.

5. The adaptive vector tile construction optimization method considering spatial distribution characteristics according to claim 1, characterized in that: Slicing and parallel construction of the geographic vector data at different levels to obtain adaptive vector tiles covering all levels includes: Performing adaptive map slicing on the geographic vector data of different levels in parallel, obtaining the vector data and slicing range of each level and saving them as vector tiles; The data amount of each vector tile at each level is calculated, a slicing method is selected according to the data amount to slice the vector tiles, and the adaptive vector tiles covering all levels are constructed based on the sliced ​​vector tiles.

6. The adaptive vector tile construction optimization method considering spatial distribution characteristics according to claim 5, characterized in that: Obtaining the vector data and slice range of each level and saving them as vector tiles includes: Querying and extracting vector data of each level in the multi-scale vector dataset; Calculate the maximum circumscribed rectangle based on the vector data as the slice range; The vector data and the tile range are saved as the vector tile.

7. The adaptive vector tile construction optimization method considering spatial distribution characteristics according to claim 5, characterized in that: Selecting a slicing method according to the data amount to slice the vector tile includes: Presetting a first threshold and a second threshold; If the data amount is greater than or equal to the first threshold, slicing the vector tile using a quadtree slicing method; If the data amount is between the first threshold and the second threshold, slicing the vector tile using a kd tree slicing method; If the data amount is less than or equal to the second threshold, the vector tile is not sliced.

8. The adaptive vector tile construction optimization method considering spatial distribution characteristics according to claim 7, characterized in that: Slicing the vector tile using a quadtree slicing method includes: Reading a slice range in the vector tile, and marking the vector tile as a first parent tile; Evenly divide the slice range in a quadtree manner, and slice the first parent tile using the evenly divided slice range to obtain four first child tiles and delete the first parent tile; The first sub-tile is judged, and if the data in the first sub-tile is empty, it is not saved; if the data in the first sub-tile is not empty, it is saved, and the evenly divided slice range is written into the first sub-tile.

9. The adaptive vector tile construction optimization method considering spatial distribution characteristics according to claim 7, characterized in that: Slicing the vector tile using the kd-tree slicing method includes: Reading a slice range in the vector tile, and marking the vector tile as a second parent tile; Obtaining equal-division longitudes within the second parent tile as cutting lines, and dividing the slice range based on the cutting lines; Slice the second parent tile with the split slice range, obtain two second child tiles, delete the second parent tile, and write the split slice range into the second child tile; The second sub-tile is judged. If the data amount of the second sub-tile is less than or equal to the second threshold, no slicing is performed; if the data amount of the second sub-tile is greater than the second threshold, the second sub-tile is subjected to the same slicing processing operation as the second parent tile until there is no tile with a data amount greater than the second threshold.

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