Irregular geographic image data slicing processing method and related device

Abstract

The application provides an irregular geographic image data slicing processing method and related equipment. The application can effectively split large-volume irregular geographic image data for encrypted storage, effectively increase the security and privacy of geographic information data storage, effectively reduce the repetitiveness of geographic spatial information collection, better plan the geographic image data collection path, reduce repeated collection due to storage of large geographic image data, and effectively improve the processing speed of geographic image data, improve the read-write efficiency of geographic image data, effectively guarantee the integrity of the original geographic image data, improve the acquisition efficiency of irregular geographic image data, improve the merging display efficiency of irregular geographic image data, and improve the reading experience of users reading the stored geographic image file.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data storage, in particular to a non-regular geographic image data slicing processing method and related equipment. BACKGROUND

[0002] In the field of image data storage technology today, pixels are generally stored in the form of files, of which the representative is the GeoTIFF format. Under this storage mode, when the resolution of image data is continuously improved and the geographical space range covered is increasingly wide, the file volume generated accordingly will show a sharp growth trend. Such large volume of image data files undoubtedly perform well in terms of clarity, and can contain more abundant and detailed data to provide users with clearer and more comprehensive image information. However, behind this advantage is a huge information security risk.

[0003] In actual image storage practice, in order to more effectively manage and process large-scale image data, image data slicing is a common operation strategy. However, the existing numerous image formats do not give clear, unified and authoritative standard specifications in terms of image block size. This lack is particularly prominent when processing image data that has a high requirement for processing speed. For example, due to the lack of clear and explicit standard guidance, in order to pursue the simplicity of the storage process, most image data is only divided into a very limited number of blocks when slicing. This overly rough slicing method directly leads to the fact that the efficiency of these geographic image data in read-write operation is generally at a low level. When it is necessary to read or write these geographic image data, the system often needs to consume a large amount of time and resources, which seriously affects the timeliness and smoothness of data display and subsequent processing, and cannot meet the demand of modern society for efficient and fast processing of non-regular geographic image data. SUMMARY

[0004] The present application aims to at least solve one of the above technical defects, and in view of this, the present application provides a non-regular geographic image data slicing processing method and related equipment to solve the technical defect that the prior art cannot efficiently process image data.

[0005] A method for processing irregular geographic image data by tiling includes: acquiring target geographic image data to be stored; dividing the map corresponding to the target geographic image data into at least one grid based on the coordinates of a first origin point of a preset map space and a preset map space size, wherein the size of each grid does not exceed a preset first threshold; determining the second origin point coordinates of all grids corresponding to the target geographic image data based on the first origin point coordinates and the preset map space size, wherein determining the second origin point coordinates includes: acquiring the first origin point coordinates of the preset map from the metadata or configuration file of the preset map; determining the width and height of the preset map space; determining the resolution of each grid corresponding to the preset map space based on the precision requirements of the preset geographic image data; calculating the number of grids corresponding to the preset map space based on the width and height of the preset map space and the resolution of each grid; traversing all grids using a double loop; the outer loop controls the row from 0 to n. h -1; Inner loop control column, from 0 to n w -1, where, for the th Line number The grid of columns, with its second origin coordinates The calculation formula can be , The coordinates of the first origin are: The size of each grid in the horizontal and vertical directions is denoted as follows: and The number of grids in the horizontal and vertical directions are denoted as follows: and Based on the first origin coordinates, the target geographic image data is segmented into at least one image file; based on each image file, the preset map space size, and the second origin coordinates, a grid corresponding to each image file is determined, wherein each grid corresponds to at least one image file; based on the number of storage machines, the machine code for storing each image file corresponding to each grid of the target geographic image data is determined, wherein the number of storage machines is not less than a preset second threshold; based on the first origin coordinates, the coordinates of each grid relative to the second origin, and the machine code corresponding to each image file, a storage file name for each image file is constructed; based on the storage file name of each image file, each image file is encrypted and stored in the corresponding distributed database.

[0006] Preferably, determining the machine code for storing the image file corresponding to each grid of the target geographic image data based on the number of storage machines includes:

[0007] sorting and numbering all the storage machines according to creation time or capacity size; sorting and numbering all the grids corresponding to the target geographic image data from the first origin coordinate; selecting the same number of grids as the number of storage machines from all the grid queues according to the number of storage machines, and constructing a mapping relationship between the serial numbers of the selected grids and the numbers of the respective storage machines, and marking the grids with the numbers of the storage machines that have a corresponding mapping relationship with the numbers of the storage machines, wherein the serial number of a grid only has a mapping relationship with the number of one storage machine; continuing to construct a mapping relationship between the unmarked grids in all the grid queues and the numbers of the respective storage machines, and re-executing the operation of marking the grids with the numbers of the storage machines that have a corresponding mapping relationship with the numbers of the storage machines, until all the grids are marked, wherein the grids with adjacent serial numbers cannot have a mapping relationship with the number of the same storage machine, and the number of one storage machine has a mapping relationship with the serial numbers of at least one grid; and taking the number of the storage machine corresponding to each grid as the machine code of the image file corresponding to each grid.

[0008] Preferably, the step of dividing the map corresponding to the target geographic image data into at least one grid according to the first origin coordinate of the preset map space and the preset map space size comprises the following steps: determining the number of rows and the number of columns of the target geographic image data with the first origin coordinate as the starting point according to the preset map space size; determining the number of split slices in the horizontal axis direction and the number of split slices in the vertical axis direction of the target geographic image data according to the number of rows and the number of columns of the target geographic image data and a preset grid size; and dividing the map corresponding to the target geographic image data into at least one grid according to the number of split slices in the horizontal axis direction and the number of split slices in the vertical axis direction of the target geographic image data.

[0009] Preferably, the step of splitting the target geographic image data into at least one image file according to the first origin coordinate comprises the following steps: determining the horizontal coordinate and the vertical coordinate of the point where the target geographic image data starts to be stored relative to the first origin coordinate; and splitting the target geographic image data into at least one image file with the point where the target geographic image data starts to be stored as the division starting point according to the number of split slices in the horizontal axis direction and the number of split slices in the vertical axis direction of the target geographic image data and a preset image file size.

[0010] Preferably, the determining the grid corresponding to each of the image files according to each of the image files, the preset map space size and the second origin coordinate comprises: determining the horizontal coordinate and the vertical coordinate of each of the image files relative to the first origin coordinate; determining the longitude and latitude information of each of the image files according to the horizontal coordinate and the vertical coordinate of each of the image files relative to the first origin coordinate; and determining the boundary box range of the grid into which the horizontal coordinate and the vertical coordinate of each of the image files fall according to the longitude and latitude information of each of the image files, the preset map space size and the second origin coordinate.

[0011] Preferably, the determining the boundary box range of the grid into which the horizontal coordinate and the vertical coordinate of each of the image files fall according to the longitude and latitude information of each of the image files, the preset map space size and the second origin coordinate comprises: determining the longitude and latitude information of the second origin coordinate according to the preset map space size and the first origin coordinate; determining the horizontal coordinate of the grid corresponding to each of the image files relative to the second origin coordinate according to the longitude information of each of the image files and the longitude information of the second origin coordinate; determining the vertical coordinate of the grid corresponding to each of the image files relative to the second origin coordinate according to the latitude information of each of the image files and the latitude information of the second origin coordinate; and determining the boundary box range of the grid into which the horizontal coordinate and the vertical coordinate of each of the image files fall according to the horizontal coordinate and the vertical coordinate of the grid corresponding to each of the image files relative to the second origin coordinate.

[0012] Preferably, the preset first threshold value is set to 2 kilometers; and the preset second threshold value is set to 4.

[0013] An irregular geographic image data slicing processing device, comprising: an acquisition unit configured to acquire target geographic image data to be stored; a segmentation unit configured to segment a map corresponding to the target geographic image data into at least one grid according to a first origin coordinate of a preset map space and a preset map space size, wherein a size of each grid is not more than a preset first threshold; a first determination unit configured to determine a second origin coordinate displayed by all grids corresponding to the target geographic image data according to the first origin coordinate and the preset map space size; a slicing unit configured to slice the target geographic image data into at least one image file according to the first origin coordinate; a second determination unit configured to determine a grid corresponding to each image file according to each image file, the preset map space size, and the second origin coordinate, wherein each grid corresponds to at least one image file; a third determination unit configured to determine a machine code of an image file corresponding to each grid of the target geographic image data according to a number of storage machines, wherein the number of storage machines is not less than a preset second threshold; a construction unit configured to construct a storage file name of each image file according to the first origin coordinate, the second origin coordinate of each grid, and the machine code corresponding to each image file; and an encryption unit configured to store each image file in a corresponding distributed database after encryption according to the storage file name of each image file.

[0014] An irregular geographic image data slicing processing device, comprising: one or more processors, and a memory; the memory stores computer readable instructions, when the computer readable instructions are executed by the one or more processors, the steps of the irregular geographic image data slicing processing method according to any one of the preceding descriptions are implemented.

[0015] A readable storage medium, the readable storage medium stores computer readable instructions, when the computer readable instructions are executed by one or more processors, the one or more processors implement the steps of the irregular geographic image data slicing processing method according to any one of the preceding descriptions.

[0016] From the above technical solutions, the method provided by the embodiment of the application can obtain target geographic image data to be stored. In actual application, the geographic image data is generally large in volume, and generally regular image data. If the obtained target geographic image data is directly stored, it may not be stored, and does not meet the relevant image data storage requirements, and information leakage problems are likely to occur. Therefore, the target geographic image data can be considered to be cut and processed before being stored. Therefore, in order to efficiently manage the obtained target geographic image data and improve the reading experience when reading the stored geographic image data, after obtaining the target geographic image data, the map corresponding to the obtained target geographic image data can be divided into at least one grid according to a first origin coordinate of a preset map space and a preset map space size, and the size of each grid is not more than a preset first threshold. Then, the second origin coordinate of the display of all grids corresponding to the target geographic image data is determined according to the first origin coordinate and the preset map space size. Then, the target geographic image data is cut into at least one image file according to the first origin coordinate, wherein the size of each image file obtained by cutting is smaller than the size of the target geographic image data. After dividing the target geographic image data, in order to better store each image file obtained by cutting, the image file to be stored can be efficiently and conveniently read while meeting the relevant legal regulations. Each image file, the preset map space size, and the second origin coordinate can be used to determine the grid corresponding to each image file, wherein each grid corresponds to at least one image file. In this way, the number of storage machines corresponding to each grid of the target geographic image data, i.e., the machine code to be stored, can be better determined according to the number of storage machines, wherein the number of storage machines is not less than a preset second threshold. In order to efficiently store and manage each image file, the image files can be considered to be stored in a distributed manner. Therefore, after obtaining the image file corresponding to each grid, the storage file name of each image file can be constructed according to the first origin coordinate, the second origin coordinate of each grid, and the machine code corresponding to each image file. In this way, each image file can be encrypted and stored in the corresponding distributed database according to the storage file name of each image file.

[0017] As can be known from the above introduction, the method provided by the embodiment of the present application can effectively split large-volume irregular geographic image data for encrypted storage, can effectively increase the security and privacy of geographic information data storage, can effectively reduce the repetitiveness of geographic spatial information collection, further, the large-volume irregular geographic image data is cut into small-volume image data files for storage, which can better plan the geographic image data collection path, reduce the repeated collection due to the storage of large geographic image data, and the large-volume geographic image data is stored after being split, which can effectively improve the processing speed of geographic image data and the read-write efficiency of geographic image data, while effectively ensuring the integrity of the original geographic image data, the acquisition efficiency of irregular geographic image data can be improved, the combined display efficiency of irregular geographic image data can be improved, and the reading experience of the user in reading the stored geographic image file can be improved. BRIEF DESCRIPTION OF DRAWINGS

[0018] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor. Figure 1 The flowchart of the method for processing irregular geographic image data provided by the embodiment of the present application is shown. Figures 2-6 The experimental effect schematic diagram of the embodiment of the present application is shown. Figure 7 The structural schematic diagram of the irregular geographic image data slicing processing device of the embodiment of the present application is shown. Figure 8 The hardware structural block diagram of the irregular geographic image data slicing processing device disclosed by the embodiment of the present application is shown. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0020] The method provided by the embodiments of the present application can be used in a plurality of general-purpose or special-purpose computing device environments or configurations. For example, a personal computer, a server computer, a handheld device or a portable device, a tablet device, a multiprocessor device, a distributed computing environment including any of the above devices or equipment, and the like. The embodiments of the present application provide a non-regular geographic image data slicing processing method, which can be applied to various geographic image data systems, and can also be applied to various computer terminals or smart terminals, and the execution subject can be a processor or a server of a computer terminal or a smart terminal.

[0021] The non-regular geographic image data slicing processing method provided by the embodiments of the present application will be described below in combination with Figure 1 , and the flow of the non-regular geographic image data slicing processing method provided by the embodiments of the present application will be described below in combination with Figure 1 , and the flow of the non-regular geographic image data slicing processing method provided by the embodiments of the present application will be described below in combination with

[0022] In step S101, target geographic image data to be stored is acquired.

[0023] Specifically, in actual application, geographic image data is basic data for subsequent storage and analysis. For example, the earth surface image obtained by satellite remote sensing, including terrain, vegetation coverage, water body distribution and other information, is the initial data source for various geographic research and application. A complete geographic image data set is necessary for accurately describing geographic phenomena and spatial relationships, and it is very important to acquire and store geographic image data in actual application. Geographic image data can have multiple formats. Commonly used formats include TIFF (Tagged Image File Format) format, JPEG (Joint Photographic Experts Group) format, and GeoTIFF format. The TIFF (Tagged Image File Format) format is a flexible bitmap image format that supports multiple color modes and data types, and can store geographic spatial information such as geographic coordinates, projection information, etc. Many satellite remote sensing images and aerial photography images use this format. The JPEG (Joint Photographic Experts Group) format is also commonly used, which stores images in a compressed manner, which can reduce file size while ensuring a certain image quality, facilitating data transmission and storage, but its storage capacity for geographic spatial information is relatively weak. GeoTIFF is an extension of the TIFF format, which is specially used for the storage of geographic spatial data. On the basis of ordinary TIFF, geographic reference information such as geographic coordinate system, map projection, etc. is added, so that the image data can be accurately corresponded to the actual geographic spatial position.

[0024] In practical application process, the geographic image data usually has large data volume. The geographic image data generally contains rich geographic spatial information, and the higher the resolution is, the larger the data volume is. For example, the data volume of a high-resolution satellite remote sensing image (the resolution can reach 0.3-1 meter) of a full-color band image can reach several hundred megabytes or even several gigabytes. The data volume of multi-spectral or hyperspectral image is even larger because it contains information of multiple bands. The image data volume obtained by aerial photography is also considerable. Especially in a large-area surveying and mapping project, in order to ensure sufficient accuracy and coverage, a large number of images will be taken, and the total data volume of these image data will be very large. In practical application process, the geographic image data has spatial reference information, which mainly includes geographic coordinate systems. These coordinate systems define how the positions on the earth are represented. Different coordinate systems are suitable for different application scenarios and regions. Therefore, in order to better utilize the geographic image data, the collected geographic image data can be saved, and before saving the geographic image data, the target geographic image data to be stored can be acquired, so that the target geographic image data can be stored according to the characteristics of the target geographic image data.

[0025] In step S102, according to the first origin coordinate of the preset map space and the preset map space size, the map corresponding to the target geographic image data is divided into at least one grid.

[0026] Specifically, as known from the above introduction, when the geographic image data to be stored is acquired, the acquired target geographic image data usually has a huge data volume, and the map corresponding to the target geographic image data is also relatively large. If the map can be divided into grids, the geographic image data corresponding to the map can be stored in blocks according to the grids, and the management and quick retrieval of the geographic image data are facilitated. When the image data of a certain area is needed, the corresponding image file data block can be quickly located through grid indexing, without traversing the entire huge image data set, so that the data access speed is greatly improved.

[0027] For example, when the geographic image data needs to be updated, the specific area that needs to be updated can be clearly determined through the grid, and only the corresponding grid data is updated, without affecting the data of other irrelevant areas, thereby reducing the workload and complexity of data updating. After the map is divided into grids, the image data corresponding to different grids can be distributed to different computing nodes or processors for parallel processing, fully utilizing the computing resources and improving the speed of data processing. For example, in image stitching, correction, classification and other processing operations, parallel processing can greatly shorten the processing time. When performing geographic image analysis, sometimes detailed research and analysis of a specific area are needed, and local analysis can be performed on the grid of interest, such as land use change analysis of a certain urban area or vegetation coverage analysis of a certain ecological protection area, which can more accurately obtain and process the required information. Moreover, in map display and visualization applications, dividing the map into grids can realize progressive loading of image data. When a user browses a map, only the grid data of the currently visible area is loaded according to the user's field of view and zoom level, rather than loading the image data of the entire map at once, which can improve the loading speed of the map and enhance user experience.

[0028] In addition, in actual application, different display devices have different resolutions and display area sizes. By dividing the map into grids, the number and resolution of loaded grids can be dynamically adjusted according to the specific circumstances of the device to realize adaptive display of geographic image data and ensure that the map can be clearly and smoothly displayed on various devices. Further, dividing the map into grids also helps to establish spatial indexes, facilitating spatial query and spatial analysis operations. For example, the grid position of a certain geographic entity can be quickly determined based on the grid, and then the geographic image data of the area can be obtained for related analysis, such as distance analysis and buffer zone analysis. When building a geographic information model, such as a land use change model or an ecological environment model, dividing the map into grids can also treat each grid as a basic calculation unit, facilitating the model to process and analyze geographic image data and improving the accuracy and reliability of the model.

[0029] Therefore, after obtaining the target geographic image data to be stored, the map corresponding to the target geographic image data can be divided into at least one grid according to a first origin coordinate of a preset map space and a preset map space size. The first origin coordinate of the preset map space can be the original point of the map corresponding to the obtained target geographic image data, and the preset map space size can be the map space size corresponding to the target geographic image data to be stored. In order to ensure that the stored geographic image data meets the relevant regulations and to ensure the security and stability of the stored image data, the size of each grid can be set to be less than a preset first threshold.

[0030] For example, in actual application, the relevant geographic image data storage provisions that geographic image data exceeding two kilometers cannot be stored in the same storage directory, i.e., the same storage directory cannot store geographic image data exceeding two kilometers. Therefore, the preset first threshold can be set to 2 kilometers.

[0031] In step S103, the second origin coordinates of all grid displays corresponding to the target geographic image data are determined according to the first origin coordinates and the preset map space size.

[0032] Specifically, geographic image data is usually based on a specific geographic coordinate system, and a map also has its corresponding coordinate system. The origin coordinates of the map as the reference point of the map determine the position of the map in the entire geographic space. By determining the origin coordinates of the grid display according to it, it can be ensured that the display of the geographic image data on the map corresponds to the actual geographic space position accurately, avoiding spatial position deviation or misplacement. In actual application, since the obtained target geographic image data can be irregular geographic image data, integration and storage of multi-source geographic image data can be involved in the processing process. Image data of different sources can have different coordinate systems and origins, but in order to accurately display and analyze on the same map, they need to be unified under a common coordinate framework. Determining the origin coordinates of all grid displays based on the first origin coordinates of the map can effectively provide a unified spatial positioning basis for the fusion of multi-source data, so that different data can be correctly superimposed and matched in the same map space. When storing and managing geographic image data, an effective data indexing and querying mechanism also needs to be established. The origin coordinates of the grid display determined based on the origin coordinates and the space size of the map can divide the geographic image data into regular grid units. In this way, when data query and retrieval are performed, the required data area can be quickly located according to the grid coordinates, improving the efficiency of data access.

[0033] In actual application, when spatial analysis such as distance calculation, area measurement, spatial interpolation, etc. is performed, it needs to be performed under a unified coordinate system and spatial framework. Determining the unified origin coordinates of the grid display also helps to clarify the spatial relationship between each grid, facilitating the implementation of various spatial analysis algorithms and reducing the calculation error and complexity caused by inconsistent coordinates.

[0034] When displaying geographic imagery data on a map in grid format, it's crucial to ensure seamless and continuous stitching between grid cells to achieve a complete visual effect. Determining the grid display origin coordinates based on the map's primary origin coordinates and spatial size allows for even spatial distribution and seamless interconnection of the grids, preventing gaps or overlaps and improving visualization quality. Different application scenarios may have varying requirements for map resolution and display area. Flexibly determining the grid display origin coordinates based on the map's primary origin coordinates and spatial size allows for easy adjustment of grid size and density to suit different display devices and user needs, achieving efficient visualization of geographic imagery data. Therefore, to improve the efficiency of reading and managing geographic imagery data, after determining the individual grids corresponding to the target geographic imagery data, the second origin coordinates for all grid displays corresponding to the target geographic imagery data can be determined based on the primary origin coordinates and a preset map spatial size. For example, in practical applications, the process of determining the second origin coordinates for all grid displays corresponding to the target geographic imagery data based on the primary origin coordinates and a preset map spatial size can be as follows:

[0035] (1) Obtain the coordinates of the first origin: Obtain the coordinates of the first origin corresponding to the map from the map's metadata or related configuration files. These coordinates are usually expressed in latitude and longitude, such as... These are the coordinates of the map's reference point in geographic space.

[0036] (2) Determine the size of the map space: Determine the area covered by the map in the horizontal and vertical directions, that is, the width of the map. and height The unit is usually consistent with the coordinate system, such as latitude and longitude in degrees or projected coordinate units.

[0037] (3) Selecting grid resolution: Based on the accuracy requirements of the geographic image data and the actual application needs, determine the resolution of each grid, that is, the size of each grid in the horizontal and vertical directions, respectively denoted as... and .

[0038] (4) Calculate the number of grids: based on the width of the map. and grid width And the height of the map and grid height Calculate the number of grid cells in the horizontal and vertical directions respectively. and The calculation formula is: , All calculation results are rounded up.

[0039] (5) Calculate the second origin coordinate of the grid: use double loop to traverse all grids. The outer loop controls the row from 0 to n h -1; the inner loop controls the column from 0 to n w -1.

[0040] For the grid of the first row and the first column, the calculation formula of the second origin coordinate may be , .

[0041] In particular, if the map adopts a projection coordinate system, there may be a projection distortion problem. In this case, the calculated second origin coordinate needs to be appropriately corrected according to the characteristics and deformation rules of the projection. For the grids at the boundary of the map, special processing needs to be performed according to the actual situation, such as cropping, filling, or splicing with adjacent maps, etc., to ensure the integrity and accuracy of the grid display.

[0042] For example, in actual application, if the point corresponding to the upper left corner of the map is set as the first origin, the point corresponding to the lower left corner can be set as the second origin. In actual application, if the point at the upper left corner is determined as the first origin, starting from this point, the map space is determined, so the point at the lower left corner can also be determined as the second origin for grid display. This can prepare for the conversion from the interface grid data to the stored grid data.

[0043] Step S104, according to the first origin coordinate, the target geographic image data is cut into at least one image file.

[0044] Specifically, in actual application, the amount of geographic image data is often very large. In order to improve the storage efficiency and security of geographic image data, the target geographic image data to be stored can be considered to be decomposed into multiple smaller and more manageable parts before storage. For example, a high-resolution satellite image covering a large area, if stored as a whole, will occupy a large amount of continuous space in the storage system, which will put a lot of pressure on the storage device. The fragmented image files can be stored in different locations of the storage device, facilitating flexible allocation of storage resources. At the same time, when retrieving data, relevant image file segments can be quickly located according to specific needs, rather than processing the entire large data body every time. Secondly, different storage devices have different storage capacities and performance characteristics. After the target geographic image data is fragmented, it can be allocated to the most suitable storage location according to the actual situation of the storage device.

[0045] In practical application process, when analyzing and processing the geographic image data, such as performing spatial analysis in geographic information system (GIS), remote sensing image classification and other operations, the fragmented image files can be conveniently distributed to multiple computing nodes for distributed processing.

[0046] In practical application process, the geographic image data is generally divided into regular geographic image data and irregular geographic image data. Among them, the irregular geographic image data lacks unified standards and specifications in data acquisition, storage, format, distribution and other aspects, and has irregularity, diversity and complexity characteristics, as follows: the irregular geographic image data has a wide range of sources, including images shared by the public using consumer-level devices such as smart phones or ordinary digital cameras, and through the Internet. The collection devices of the irregular geographic image data are not limited to professional satellite and aerial photography devices, but also include various mobile devices, surveillance cameras and the like, and the shooting angle, resolution, shooting time and the like have great randomness. In the irregular geographic image data, the image resolution of different regions may differ greatly, with high resolution and rich details in some places, and low resolution and blurred images in some places, and the data format is various, with common image formats such as JPEG, PNG, TIFF and various self-defined formats, and the data structure and coding method between different formats differ greatly, and format conversion and adaptation are required when processing and analyzing. The irregular geographic image data may use different geographic coordinate systems or projection methods, such as WGS84, GCJ-02, BD-09, which makes the data integration and spatial analysis face the problem of coordinate conversion. Further, due to the difference in professional level of the collection devices and personnel, the data quality of the irregular geographic image data differs significantly. Some images may have noise, distortion, occlusion and other problems, affecting the usability of the data and the accuracy of the analysis results. The collection time of the irregular geographic image data has no uniform rule, and may be concentrated in some time period, while in other time period, the data is missing, and the discontinuity in time sequence brings difficulties to dynamic analysis and change monitoring. The coverage range and density of the irregular geographic image data on the geographic space are not uniform, and some areas may have a large amount of image data, while some remote or difficult-to-reach areas have little data. The irregular geographic image data can be used for city street view update, illegal building monitoring, city facility census and other work. The irregular data such as crowd-sourced images can provide rich ground view information, helping city managers to understand the actual situation of the city in a timely manner. After natural disasters occur, irregular geographic image data such as disaster area images taken by unmanned aerial vehicles and photos taken by on-site personnel can quickly obtain real-time information of the affected area, providing a basis for rescue decision-making.

[0047] Therefore, after obtaining the target geographic image data, in order to efficiently manage the obtained target geographic image data, the target geographic image data can be divided into at least one image file according to the first origin coordinate. The size of each image file is smaller than the target geographic image data. The size of each image file can be set according to relevant legal regulations or application scenarios, for example, the size of the image file obtained after the target geographic image data is divided can be set to be smaller than or equal to the size corresponding to 2 kilometers of image data. The storage format of each image file can be set according to the storage format of the target geographic image data. In actual application process, the image file obtained by dividing can adopt a more flexible backup strategy. Different backup plans can be formulated for different slices according to factors such as data importance and update frequency. For example, for image slices containing key geographic elements (such as important transportation hubs and urban center areas), the backup frequency can be increased, and more secure backup methods such as off-site storage can be used; for slices of relatively secondary areas, the backup frequency can be appropriately reduced, thereby effectively saving backup resources while ensuring data security. When data is damaged or lost, slicing processing is helpful for quick recovery.

[0048] For example, as shown in FIG. 1, Figure 2 Figure 2 The right image in the middle is the geographic image data to be stored, Figure 2 The multiple small images of the coordinates are multiple image files divided according to Figure 2 The 1, 2, 3, and 4 blocks of regions marked on the right are multiple image files divided.

[0049] Step S105, determining the grid corresponding to each of the image files according to each of the image files, the preset map space size, and the second origin coordinate.

[0050] Specifically, in actual application process, in order to meet the needs of data organization and management, it is necessary to establish the spatial index of each image file. By determining the grid corresponding to each image file, a spatial index of geographic image data can be established. This is helpful for quickly locating the image file containing specific geographic location information in subsequent data query and retrieval process, and improves data access efficiency. Secondly, different grids can have different geographic features or data importance. Associating image files with corresponding grids can perform hierarchical storage according to grid division. For example, for grids of urban centers and other key areas, higher storage precision and more frequent update strategies can be used, while for grids of remote areas, appropriate simplified storage can be performed, thereby optimizing the utilization of storage resources.

[0051] ​The analysis and processing of geographic image data is usually based on a specific spatial range. By specifying the grid corresponding to each image file, it can be ensured that the image data used in spatial analysis is accurately matched with the actual geographic spatial range, avoiding deviations in the analysis results due to mismatch between data and spatial range. When performing local processing or updating of geographic image data, such as image enhancement, repair or updating of a certain area, etc., by using the known correspondence between image files and grids, the image files that need to be processed can be quickly determined, without affecting the data of other irrelevant areas, improving the specificity and accuracy of processing.

[0052] When visualizing geographic image data, sometimes multiple image files need to be stitched according to their corresponding grids to present a complete map image. By determining the grid corresponding to each image file, it can be ensured that adjacent image files seamlessly connect in space during the stitching process, avoiding problems such as image misalignment or gaps, and improving the quality of the visualization effect. Different grids can be displayed with different resolutions according to display requirements. By matching image files with grids, image files of corresponding resolutions can be dynamically loaded according to the user's zoom level and display range, realizing multi-resolution display of geographic image data and improving the user's visualization experience.

[0053] Therefore, after obtaining each image file, the grid corresponding to each image file can be determined according to each image file, a preset map spatial size, and a second origin coordinate, wherein each grid corresponds to at least one image file.

[0054] Step S106, according to the number of storage machines, determine the image file corresponding to each grid corresponding to the target geographic image data, i.e. the machine code to be stored.

[0055] Specifically, as known from the above description, geographic image data is usually large in size. If all image files are stored in one machine, it will cause excessive storage pressure on the machine, and may cause problems such as insufficient storage capacity and reduced read-write speed. By determining the machine code of the image file corresponding to each grid according to the number of storage machines, the data can be stored on multiple machines, balancing the storage load, avoiding single point failure, and improving the stability and reliability of the entire storage system. The sizes and access frequencies of image files corresponding to different grids may be different. According to the performance characteristics and remaining storage resources of the storage machines, the image files can be reasonably distributed to different machines, better utilizing the storage capacity and read-write capability of each machine, improving the overall utilization of storage resources, and avoiding the situation where some machine resources are idle while some machine resources are tight.

[0056] When an image file of a specific grid needs to be obtained, the machine code of the storage machine storing the file can be quickly located through the known machine code, reducing the time and complexity of searching for data in the entire storage system, and improving the data access efficiency. This is particularly important for geographic image applications with high real-time requirements, such as map navigation, disaster monitoring, etc. In the data backup and recovery process, the machine code corresponding to the image file storage of each grid is clear, which helps to develop targeted backup strategies and recovery schemes. Data stored on different machines can be classified and backed up according to the machine code, and when data is lost or damaged, the data can be quickly recovered from the corresponding backup machine, reducing the risk of data loss and improving the security and recoverability of data. When processing and analyzing geographic image data, image files of multiple grids often need to be operated simultaneously. Distributing image files corresponding to different grids on multiple storage machines can obtain data from each machine for processing in parallel, fully utilizing the computing resources of multiple machines, greatly improving the speed and efficiency of data processing, and shortening the processing time. When multiple users simultaneously request image files of different grids, the files stored on different machines can simultaneously respond to their respective requests, avoiding the queuing and waiting phenomenon caused by all requests being concentrated on one machine, effectively improving the overall response speed of the system and improving user experience.

[0057] With the continuous accumulation and updating of geographic image data, the data volume will continue to increase. Determining the storage allocation mode of the image files according to the number of storage machines facilitates the flexible addition of storage machines when needed, realizing the smooth expansion of the system. The newly added machines can allocate the storage tasks of the image files corresponding to the new grids according to the established rules, without the need for large-scale reconstruction of the entire storage system, reducing the difficulty and cost of system expansion. Different business scenarios may have different needs and processing methods for geographic image data. By reasonably allocating image files to different machines, the storage tasks and functions of each machine can be flexibly adjusted according to changes in business needs, such as dedicating some machines to store image files of specific types or specific regions, better meeting the needs of business development. Therefore, in order to efficiently manage each image file, the machine code of the image file corresponding to each grid of the target geographic image data to be stored can be determined according to the number of storage machines, wherein the number of storage machines is not less than a preset second threshold. For example, the number of storage machines can be greater than or equal to 4.

[0058] Step S107, constructing a storage file name of each image file according to the first origin coordinate, the second origin coordinate of each grid, and the machine code corresponding to each image file.

[0059] Specifically, in actual application process, after the target geographic image data is processed to obtain a plurality of image files, in order to better manage and store each image file, the storage file name of each image file can be constructed according to the first origin coordinate, the second origin coordinate of each grid and the machine code corresponding to each image file, so that the numerous fragmented image files can be effectively organized. For example, through the storage file name, the image files can be classified according to the geographical position, the shooting time, the image resolution and other factors, so that the specific target image file can be quickly located and searched in the storage system. In actual application process, the storage file name of each image file can include the machine identification code to be stored. The machine identification code and other information contained in the storage file name can accurately locate the file. In a complex storage environment, there can be multiple storage devices (such as servers, disk arrays, etc.), and the machine identification code can uniquely identify the device storing the image file. When a specific image file needs to be accessed, the storage file name is like a map, guiding the user or system to quickly find the specific machine and storage location of the file, reducing the search time and improving the data access efficiency.

[0060] In practical applications, as the amount of data continues to increase, storage systems can be expanded, such as adding new storage devices or changing storage architecture. Determining the storage file name can better adapt to such changes. When new storage devices are added, new machine identification codes can be incorporated into the storage file name system, so that image files can be smoothly distributed and stored in the new storage environment. For example, in a distributed storage system, after a new storage node is added, by updating the association between the storage file name and the machine identification code, new image files can be stored on the new node, realizing the smooth expansion of the storage system. When migrating or sharing data between different storage devices or storage systems, the storage file name and machine identification code can also play a key role as a bridge. Different machines may have different storage format requirements or data access protocols, and the storage file name can help adjust and adapt to these differences. For example, when migrating image files from a local storage device to cloud storage, the storage file name can guide how to reorganize and identify these files in the cloud system, ensuring the compatibility and usability of data in different storage environments. The storage file name also helps implement data security policies. By controlling access to the storage file name, unauthorized users can be restricted from accessing specific image files. Machine identification codes can also be used to track the storage location of data, making it easier for security personnel to monitor the status of storage devices and prevent data from being stolen or tampered with. For example, for image files containing sensitive geographic information, strict access permissions can be set based on the storage file name, and only authorized users can access these files stored on specific machines. In the data audit process, the storage file name is an important reference. Auditors can view the storage file name to understand the storage location, access history, and other information of each image file. Machine identification codes can help trace changes in data during storage, such as whether files have been moved, copied, or deleted. For example, in the investigation of a data breach incident, the storage file name and machine identification code can help determine which image files may be affected and where abnormal operations occurred on the storage device.

[0061] For example, in practical applications, in order to efficiently manage each image file, after determining the storage machine code corresponding to each image file and the horizontal and vertical coordinates of each image file relative to the first origin coordinate, the storage file name of each image file can be constructed based on the storage machine code corresponding to each image file and the horizontal and vertical coordinates of each image file relative to the first origin coordinate, so that each image file can be stored according to the storage file name of each image file.

[0062] For example, the storage file name of each image file can be defined as the horizontal coordinate of the point where the target geographic image data starts to be stored based on the first origin coordinate; the vertical coordinate of the point where the target geographic image data starts to be stored based on the first origin coordinate; The machine code of the image file to be stored; large target geographic image data can be split into multiple blocks according to the horizontal and vertical directions, and the file name is named by the numbers of the blocks in the horizontal and vertical directions and the machine code of the storage machine. The storage file name of each image file is constructed as: .

[0063] Step S108, according to the storage file name of each image file, the image file is encrypted and stored in the corresponding distributed database.

[0064] Specifically, in today's digital age, data privacy and security are crucial. As introduced above, each image file obtained by fragmenting the target geographic image data may contain sensitive information. Therefore, after determining the storage file name of each image file, each image file can be encrypted and stored in the corresponding distributed database according to the storage file name of each image file. Storing the image file in the distributed database after encryption can effectively prevent unauthorized access, theft or tampering of data during storage.

[0065] In actual application, one image file can correspond to one distributed database. This case may be applicable to scenarios with extremely high data isolation requirements. One image file can also be stored in multiple distributed databases. This case is often used in scenarios with extremely high data reliability and availability requirements. In order to improve the efficiency of data storage, while ensuring the security of geographic image data storage, multiple image files can also be stored in the same distributed database. This is a more common storage method, which is suitable for a set of geographic image files with strong correlation. This storage method has high resource utilization, and one database can store multiple geographic image files, making full use of storage and computing resources. It is convenient to manage, and operations such as maintenance, backup and recovery of the database can be applied to multiple geographic image files at a time. A complex storage relationship can also be built between multiple image files and multiple distributed databases. This case may occur in large-scale, cross-department or cross-organization image storage systems. Different geographic image files may be stored in different databases to achieve data sharing and resource integration. This storage method can achieve flexible sharing and optimal storage configuration of data. By reasonably allocating image files to different databases, the resources of each database can be fully utilized, and data exchange and collaboration between different departments or organizations are facilitated. However, this storage method may result in very complex data management and maintenance. Complex mapping relationships are needed to record which databases each geographic image file is stored in and which geographic image files are stored in each database. Data consistency maintenance is also more difficult because it involves the interaction between multiple files and multiple databases.

[0066] It can be seen from the technical solutions described above that the method provided by the embodiments of the present application can effectively split large-volume irregular geographic image data for encrypted storage, can effectively increase the security and privacy of geographic information data storage, can effectively reduce the repetitiveness of geographic spatial information collection, further, the large-volume irregular geographic image data is cut into small-volume image data files for storage, which can better plan the geographic image data collection path, reduce repeated collection due to storage of large geographic image data, and after the large-volume geographic image data is fragmented for storage, the processing speed of the geographic image data can be effectively improved, the read-write efficiency of the geographic image data is improved, while effectively ensuring the integrity of the original geographic image data, the acquisition efficiency of the irregular geographic image data is also improved, and the merging display efficiency of the irregular geographic image data is improved. At the same time, the method of the present application can also improve the reading experience of users when reading the stored geographic image files.

[0067] As introduced above, the method provided by the embodiments of the present application can determine the machine code of the image file to be stored corresponding to each grid corresponding to the target geographic image data according to the number of storage machines. Next, the process will be introduced, which can include the following steps:

[0068] Step S201, sort all storage machines according to creation time or capacity size, and number each storage machine.

[0069] Specifically, in order to efficiently manage and maintain the storage machines, all storage machines can be sorted according to creation time or capacity size, and each storage machine can be numbered. After sorting and numbering according to creation time, the manager can clearly master the development history of the storage system and understand the introduction of storage machines at different stages, which facilitates overall inventory of storage resources. For sorting and numbering according to capacity size, the manager can intuitively know the capacity difference of each storage machine, so as to better plan the allocation and use of storage resources and timely find the capacity bottleneck, providing a basis for subsequent expansion or resource adjustment. When the storage system fails, the machine number can be used to quickly locate the range of storage machines that may have problems. If the fault is related to machine aging or machines of a specific capacity segment, the number sorted according to creation time or capacity size can help the administrator quickly focus on troubleshooting targets, reduce troubleshooting time and workload, and improve troubleshooting efficiency. After sorting and numbering according to creation time, appropriate storage allocation strategies can be developed according to factors such as data timeliness and importance.

[0070] Step S202, sort and number all grids corresponding to the target geographic image data from the first origin coordinate.

[0071] In particular, in actual application, by sorting and numbering, each grid can be given a unique identification, which is equivalent to establishing a unified indexing system for all grids. This enables quick and accurate positioning of any grid when managing massive geographic image data, facilitating data searching, retrieval and calling, and improving data management efficiency. Therefore, in order to establish an indexing system for all grids, all grids corresponding to the target geographic image data can be sorted and numbered starting from the first origin coordinate. Sorting from the first origin coordinate as the starting point can arrange the grids in a certain spatial order, intuitively reflecting the distribution of the grids in geographical space. This helps managers better understand the spatial structure of the data and have a clear grasp of the overall layout of the data, facilitating spatial analysis and data mining operations, etc. When performing data processing and analysis tasks, such as feature extraction and classification of geographic image data, tasks can be assigned to different computing nodes or threads for parallel processing according to the grid number. The numbered grids have a clear order and identification, facilitating task allocation and coordination, improving processing speed, and fully utilizing computing resources. Sorted and numbered grids are conducive to local and overall analysis. Specific numbered grids can be easily selected for detailed analysis of local areas, and overall statistics and analysis of all grids can also be performed, quickly determining the scope and boundaries of the data through numbering, improving the flexibility and accuracy of analysis. When storing grid data in a storage machine, a storage strategy can be developed according to the grid number, such as storing in different storage devices or storage areas in order of numbering, achieving uniform distribution of data and avoiding data storage chaos and imbalance. At the same time, it is also convenient to quickly locate and access stored data according to the number, improving the performance of the storage system. During data transmission, especially in a distributed storage system or network environment, more accurate transmission control can be achieved according to the grid number. Data can be transmitted in blocks according to the numbering order, or specific numbered grid data can be transmitted preferentially according to needs, improving data transmission efficiency and reliability and reducing the risk of transmission errors and data loss.

[0072] In particular, sorting and numbering starting from the first origin coordinate can make the grid coordinate system of geographic image data consistent with other related systems or standard coordinate systems or easily converted. This is conducive to integration and interaction with other professional software such as geographic information systems (GIS) and remote sensing image processing software, enabling data sharing and collaboration. And numbered grid data has a clear identification and order when interacting with other systems, facilitating data transmission and reception according to unified standards. Other systems can quickly identify and process data according to the number, reducing the difficulty and complexity of data interaction and improving the compatibility and collaboration between systems.

[0073] In step S203, according to the number of storage machines, the same number of grids as the number of storage machines is selected from all grid queues, and the serial numbers of the selected grids are respectively mapped to the numbers of the respective storage machines, and the grids with the mapping relationship with the numbers of the storage machines are marked with the numbers of the storage machines.

[0074] Specifically, in order to uniformly allocate data to each storage machine and fully utilize the available storage resources, according to the number of storage machines, the same number of grids as the number of storage machines is selected from all grid queues, and the serial numbers of the selected grids are respectively mapped to the numbers of the respective storage machines, and the grids with the mapping relationship with the numbers of the storage machines are marked with the numbers of the storage machines, wherein the serial number of a grid only has a mapping relationship with the number of one storage machine.

[0075] Due to the storage requirements of relevant geographic image data, geographic image data exceeding two kilometers cannot be stored in the same storage directory. Therefore, by selecting the same number of grids from all grid queues according to the number of storage machines and constructing a one-to-one mapping relationship, it can be ensured that each storage machine is allocated approximately the same amount of grid data, avoiding the situation where some storage machines are overloaded while others are idle, thereby achieving load balancing of the storage system, improving overall storage efficiency and performance, and also avoiding storing geographic image data exceeding two kilometers in the same storage machine. In this way, grid data can be reasonably allocated according to the actual processing capacity and storage capacity of the storage machines, making full use of the resources of each storage machine, avoiding waste or overuse of resources, prolonging the service life of the storage machines, and reducing hardware costs. The grid is labeled with the number of storage machines that constitute the mapping relationship, so that each grid is clearly associated with a storage machine. During data management, the storage location of a grid can be quickly determined through its label, facilitating data searching, updating, and deletion operations, and improving the convenience and accuracy of data management. This mapping relationship and labeling method establishes a direct connection between the grid and the storage machine, eliminating the need for complex search algorithms or traversing the entire storage system when storing and retrieving data. Directly according to the label of the grid, the corresponding storage machine can be found, simplifying the data management process and reducing management costs. By allocating different grids to different storage machines, distributed storage of data is achieved. When a storage machine fails, only the grid data corresponding to it will be affected, without causing the loss or inaccessibility of the entire geographic image data, improving the reliability and availability of the data. During data recovery, since the mapping relationship between each grid and the storage machine is clear, the affected grid can be quickly determined, and the corresponding data can be obtained from other normal storage machines for recovery, reducing the time and workload of data recovery and improving the efficiency of data recovery. In addition, when the storage system needs to expand the storage machines, it only needs to re-establish the mapping relationship between the grid and the storage machine according to the same rules, without the need for large-scale adjustment of the entire data storage structure. This approach has good scalability and can easily adapt to changes in the size of the storage system. In actual application, the mapping relationship between the grid and the storage machine can be flexibly adjusted according to the actual operation of the storage system and the needs. For example, when it is found that some storage machines are overloaded or underloaded, the mapping relationship can be adjusted to redistribute the grid data, achieving dynamic balancing of the load and improving the flexibility and adaptability of the system.

[0076] Step S204: Continue to construct mapping relationships between all unlabeled grids in the grid queue and the numbers of each storage machine, and re-execute the operation of labeling grids with corresponding storage machine numbers using the storage machine numbers that form mapping relationships with the grids, until all grids are labeled. Among them, grids with adjacent sequence numbers cannot form mapping relationships with the numbers of the same storage machine, and a storage machine number forms a mapping relationship with the sequence number corresponding to at least one grid.

[0077] Specifically, in practical applications, the number of grids may far exceed the number of storage machines. After the above steps, it may not be possible to establish a mapping relationship between all grids and storage machines. Therefore, it is possible to continue to establish a mapping relationship between each unlabeled grid in the grid queue and the number of each storage machine, and to re-execute the operation of labeling the grids with corresponding storage machine numbers using the storage machine numbers that have a mapping relationship with the grids, until all grids are labeled. Since the geographic image data corresponding to a grid is geographic image data of no more than 2 kilometers, in order to avoid storing geographic image data of more than 2 kilometers on one storage machine, it can be set that grids with adjacent sequence numbers cannot establish a mapping relationship with the same storage machine number. Since the number of grids is greater than the number of storage machines, the number of a storage machine can establish a mapping relationship with the sequence number corresponding to at least one grid.

[0078] For example, such as Figure 3 and Figure 4 As shown, Figure 3 This means that after the initial annotation process, storage machines numbered 1-9 can be labeled with corresponding grid cells numbered 1-9. To ensure that no two consecutive 2-kilometer grid cells are stored on the same storage machine, each time a machine group projects onto an unlabeled map grid, the unlabeled grid is labeled with the machine group's machine number after each projection. After multiple annotation processes, the following can be obtained: Figure 4 The annotation effect shown.

[0079] Step S205: The number of the storage machine corresponding to each grid is used as the machine code for storing the image file corresponding to each grid.

[0080] Specifically, as introduced above, each grid has a mapping relationship with the storage machine, and therefore, the number of the storage machine corresponding to each grid can be used as the machine code of the image file corresponding to each grid, i.e., the machine to be stored. Determining the machine code of each image file to be stored helps implement the data redundancy strategy. Copies of important image files can be stored on different storage machines, and the storage locations of these copies can be conveniently planned and managed through the code. For example, for geographic image data of some critical areas, the main file thereof can be stored on one machine, and a copy can be stored on another machine, so that when a machine fails, a data copy can be obtained from another machine to ensure data security and availability.

[0081] Therefore, in actual application, in order to efficiently manage each image file, the storage machine code of each image file can be determined based on the number of image files and the number of storage machines. By determining the storage machine code of each file based on the number of image files and the number of storage machines, each image file can be evenly distributed to each storage machine.

[0082] Secondly, in actual application, the storage machines can also have differences in data reading and writing speed and other processing capabilities. Reasonably allocating the storage machines of each image file can balance the workloads of each storage machine and ensure that each machine can work efficiently within its capability range during data retrieval, processing, and other operations.

[0083] Each image file stored on each storage machine cannot be an image file corresponding to a geographic space whose volume exceeds a preset size.

[0084] For example, according to relevant legal regulations, the volume of geographic image data stored on each storage machine cannot be the volume of geographic image data corresponding to an area of more than 2 kilometers.

[0085] As can be seen from the above technical solutions, the method provided by the embodiments of the present application can determine the machine code of the image file corresponding to each grid corresponding to the target geographic image data to be stored according to the number of storage machines, and can make full use of limited storage resources to better manage each image file.

[0086] As introduced above, the method provided by the embodiments of the present application can divide the map corresponding to the target geographic image data into at least one grid according to the first origin coordinate of the preset map space and the preset map space size. Next, the process will be introduced, which can include the following:

[0087] In step S301, the number of rows and columns of the target geographic image data is determined based on the first origin coordinate as the starting point according to the preset map space size.

[0088] Specifically, as introduced above, the map space size is a known fixed parameter, in order to establish accurate spatial location index in the entire map space. The number of rows and columns of the target geographic image data can be determined based on the pre-set map space size, with the first origin coordinate as the starting point. The number of rows and columns of the target geographic image data is equivalent to the height and width of the target geographic image data. Determining the number of rows and columns of the target geographic image data is to determine how many 256*256 small image files the target geographic image data can be cut into, and at the same time, subsequent search, access and processing of the image data can be based on this explicit spatial location, improving the accuracy and efficiency of data positioning. Secondly, different map systems or geographic information platforms have their specific spatial reference and coordinate system. The number of rows and columns determined in this way can better match the pre-set map space size, ensuring the correct display and use of geographic image data in the map system. After the number of rows and columns is determined, the target geographic image data can be divided into multiple data blocks or grids according to the rows and columns. This division method helps to organize and manage data, facilitates operation according to data blocks in the storage and processing process, and improves the parallelism and efficiency of data processing. According to the number of rows and columns, the total amount and distribution of data can be calculated, so that storage resources can be allocated more reasonably. For example, according to the number and size of data blocks, they can be evenly distributed to different storage devices or storage areas to achieve load balancing and resource optimization. When processing and analyzing geographic image data, such as data cropping, splicing, filtering and other operations, the scope and boundary of the processing need to be clear. By determining the number of rows and columns, the scope of the target geographic image data can be accurately defined, unnecessary data processing can be avoided, and the processing efficiency and accuracy of the results can be improved.

[0089] In practical application, many geographic image processing algorithms are designed based on the row and column structure of data, for example, convolutional neural network needs to clearly define the row and column dimensions of the image when performing image recognition. After determining the number of rows and columns of the target geographic image data, these algorithms can be better adapted to facilitate various data processing and analysis operations. When visualizing and displaying geographic image data, it needs to be displayed correctly according to its position and size in the map space. Clear number of rows and columns and first origin coordinate can ensure the accurate display position of image data on the map, avoid image stretching, distortion or misplacement, and improve the visualization effect. According to different application requirements, it may be necessary to display geographic image data at multiple resolutions. By determining the number of rows and columns, data can be easily sampled and scaled according to certain rules to generate images of different resolutions, meeting the visualization needs of users in different scenarios.

[0090] Step S302, according to the number of rows and the number of columns of the target geographic image data and the preset grid size, determine the number of slices in the horizontal direction and the number of slices in the vertical direction of the target geographic image data.

[0091] Specifically, as can be known from the above description, determining the number of rows and the number of columns of the target geographic image data can help determine the number of small image files that the target geographic image data can be divided into. Therefore, in order to determine how many image files the target geographic image data is divided into, the number of slices in the horizontal direction and the number of slices in the vertical direction of the target geographic image data can be determined according to the number of rows and the number of columns of the target geographic image data and the preset grid size, so that the target geographic image data can be sliced in the horizontal and vertical directions, thereby being able to divide a large data block into multiple small slices. When processing and analyzing data, these small slices can be distributed in parallel to multiple computing nodes or processors for processing, thereby greatly improving processing speed, fully utilizing computing resources, and shortening overall processing time. Geographic image data in different regions may have different characteristics and processing requirements. By determining the number of slices, the data can be divided into local regions of appropriate size, facilitating targeted processing for each slice, such as feature extraction, data enhancement, and other operations on specific regions, improving processing flexibility and accuracy. The number of slices can also be used to more accurately allocate slices to different storage devices or storage areas, achieving balanced allocation of storage resources. Avoiding the problem of uneven storage caused by too large or too small data blocks, improving the overall utilization and performance of the storage system.

[0092] The preset grid size can be set according to the actual application scenario, for example, the relevant geographic image data storage regulations cannot store geographic image data exceeding 2 kilometers in the same storage directory. In order to avoid the image files obtained by slicing being too large and not meeting the requirements, the preset grid size can be set to a rectangle with a length and a width of 2000 meters.

[0093] Step S303, according to the number of slices in the horizontal direction and the number of slices in the vertical direction of the target geographic image data, divide the map corresponding to the target geographic image data into at least one grid.

[0094] Specifically, in actual application, after the map is divided into multiple grids, the geographic image data processing can be carried out in parallel on each grid. Different grids can be allocated to different computing resources or processors for simultaneous processing, greatly improving the overall efficiency of data processing, especially for complex operations on large-scale geographic image data such as geographic feature extraction, image analysis, etc., which can significantly shorten the processing time. The geographic areas covered by different grids may have different geographic features and data characteristics. By dividing the map into grids, more targeted local processing can be performed on the geographic image data within each grid, such as focusing on building identification in urban area grids and focusing on terrain analysis in mountain area grids, etc., to improve the accuracy and reliability of the processing results. Grid division facilitates more detailed storage management of geographic image data. Each grid can serve as an independent storage unit, facilitating data classification, indexing, and retrieval. The required geographic image data can be quickly located based on the grid number or location, improving the management efficiency and data search speed of the storage system. The grid size and data volume can be used to flexibly allocate them to different storage devices or storage areas, optimizing the allocation of storage resources. For grids with large amounts of geographic image data, high-performance storage devices can be allocated, and for grids with small amounts of geographic image data, more economical storage methods can be used, improving the utilization rate of storage resources. Sometimes, during network transmission, users often do not need to obtain the entire geographic image data of the map at once, but are only interested in the geographic image data of a specific area. After the map is divided into multiple grids, when users need to consult geographic image data, only the geographic image data corresponding to the grid of interest can be transmitted based on user needs, rather than the entire map image data, greatly saving network bandwidth and improving the efficiency of geographic data transmission. The segmented grid corresponding geographic image data is more convenient for sharing and collaboration between different users or departments. Different users can obtain the corresponding grid data for analysis and processing according to their own needs, while better controlling the sharing range and permissions of geographic image data to avoid unnecessary data leakage and improve the security and flexibility of data sharing.

[0095] In map visualization, dividing the map into multiple grids can achieve progressive loading and fast display. First, display the geographic image data corresponding to the low-resolution grid to allow users to quickly understand the general situation of the map, and then gradually load and display high-resolution grid data based on user operations such as zooming in, zooming out, panning, etc., to improve user experience and the smoothness of map browsing. When users interact with the map, such as selecting a specific area for measurement or query, the segmented grid can provide more accurate operation range and objects. The corresponding operation and processing can be directly performed on the user-selected grid, improving the accuracy and response speed of interactive operations.

[0096] Therefore, after determining the number of sub-slices of the target geographic image data in the horizontal axis direction and the number of sub-slices of the target geographic image data in the vertical axis direction, the map corresponding to the target geographic image data can be divided into at least one grid according to the number of sub-slices of the target geographic image data in the horizontal axis direction and the number of sub-slices of the target geographic image data in the vertical axis direction. Each grid can be represented by (R****C****), and (R0001C0001) can represent a grid with a horizontal coordinate and a vertical coordinate of 1 relative to the second origin coordinate.

[0097] As can be seen from the above technical solutions, the method provided in the embodiments of the present application can divide the map corresponding to the target geographic image data into at least one grid according to the first origin coordinate of the preset map space and the preset map space size, so that the target geographic image data can be more efficiently managed.

[0098] As can be seen from the above technical solutions, the method provided in the embodiments of the present application can divide the target geographic image data into at least one image file according to the first origin coordinate. Next, the process will be introduced, which can include the following:

[0099] Step S401: Determine the horizontal coordinate and the vertical coordinate of the point where the target geographic image data starts to store relative to the first origin coordinate.

[0100] Specifically, in actual application, the map space corresponding to the geographic image data is a complex two-dimensional or even three-dimensional space. Therefore, in actual application, in order to better manage the target geographic image data, the first origin coordinate of the map space corresponding to the target geographic image data can be defined, so that the target geographic image data can be better divided according to the defined first origin coordinate. Defining the origin coordinate of the geographic space corresponding to the target geographic image data is like establishing a "reference point" in this space. For example, in the geographic image map of a city, a landmark position (such as the center of a fountain) in the city center square can be defined as the origin coordinate (assuming the longitude x1 and the latitude y1). In this way, the positions of all other geographic elements (such as streets, buildings, etc.) in the geographic image data can be accurately represented by the offset relative to this origin coordinate. Taking satellite remote sensing image as an example, the range of image data of the entire earth surface is huge. By defining the origin coordinate, such as taking the intersection of the equator and the prime meridian as the origin (0, 0), the position of image data of any point on the earth in this space can be conveniently determined, providing an accurate spatial positioning basis for subsequent geographic space analysis, data processing and user viewing.

[0101] Further, geographic image data often comes from multiple different sources. These sources can use different coordinate systems or have different spatial references. By defining the origin coordinates of the target geographic image data corresponding to the map space, these different sources of data can be unified under a common spatial framework. For example, a city planning department can have both overall city image data from satellite remote sensing (using the WGS84 coordinate system) and high-precision building detail image data from the local surveying and mapping department (using the local plane rectangular coordinate system). By unifying them into a map space based on the origin coordinates, data fusion can be achieved, thus more comprehensively and accurately presenting the geographic spatial information of the city.

[0102] Further, in geographic spatial analysis, many algorithms and calculations rely on accurate coordinate systems. Defining the origin coordinates can make these calculations more convenient and accurate. For example, in calculating the distance, area or relative orientation between two geographic regions (such as two different parks), a coordinate system based on origin coordinates can provide a unified calculation framework. Suppose we want to analyze the distance and slope changes between different valleys in a mountainous area. By defining the origin coordinates and the corresponding coordinate system, the geographic image data of each valley can be converted into coordinate values, and then mathematical formulas can be used to calculate the distance, slope and other geographic spatial parameters between them, providing strong data support for geological exploration, ecological research and other fields.

[0103] For example, in practical applications, the top-left point of the entire map corresponding to the target geographic image data can be defined as the first origin coordinate of the preset map space, i.e. .

[0104] In practical applications, the position of geographic image data in the map space needs a precise reference system to determine. As introduced above, the origin coordinates of the preset map space are like the "anchor point" of the map space. In practical applications, when the origin of the map space is determined, the horizontal and vertical coordinates of each image file relative to the origin coordinates of the map space can be calculated to accurately locate the position of each image file in the entire map space. Just like in a huge coordinate system, the origin is the reference point, and knowing the relative coordinates of each geographic image file is like assigning each geographic image file a precise "address" in this coordinate system. This is crucial for subsequent accurate retrieval, splicing and analysis of these geographic image files in the map space. For example, in geographic information systems (GIS), when multiple images of different regions need to be spliced into a complete map, these precise coordinates can ensure that each geographic image is correctly aligned in space, avoiding position errors.

[0105] Further, understanding the horizontal and vertical coordinates of each image file relative to the origin coordinates of the preset map space helps integrate the geographic image data with other geospatial data. Geospatial data often includes various types, such as vector data (points, lines, surfaces), terrain data, etc. By specifying the coordinates of each image file relative to the origin of the map space, it is convenient to associate these image files with other types of data according to the spatial position. For example, when planning a city, combining geographic image data with vector data of urban roads, through coordinate matching, the land use situation or building distribution around the road can be accurately analyzed, thereby providing more comprehensive information for planning decisions.

[0106] For large-volume target geographic image data sets, understanding the horizontal and vertical coordinates of each image file relative to the origin coordinates of the preset map space can effectively manage the blocks. This is like dividing a large jigsaw puzzle into multiple small puzzle pieces. By knowing the position of each small puzzle piece (image file) in the entire puzzle (preset map space), they can be more efficiently stored, transmitted, and processed. For example, when processing geographic image data in a cloud computing environment, these coordinate information can help the system reasonably allocate processing tasks for each image file block according to computing resources and task requirements, improving data processing efficiency.

[0107] Understanding the horizontal and vertical coordinates of each image file relative to the origin coordinates of the preset map space is the basis for various spatial analysis and queries. For example, when querying geographic images within a specific area (defined by the horizontal and vertical coordinate range) in the map space, by comparing the relative coordinates of each image file with the coordinate range of the query area, the image files that meet the requirements can be quickly filtered out. This spatial query function has wide applications in many fields such as land resource management and ecological environment monitoring. For example, when monitoring forest fires, the geographic image of the specific fire area can be quickly obtained by querying the coordinate range of the area to analyze the spread of the fire.

[0108] Therefore, in order to better manage the target geographic image data, the horizontal and vertical coordinates corresponding to the origin coordinates of the preset map space of the point where the target geographic image data starts to be stored can be determined according to the first origin coordinates. So that the geographic image data can be accurately placed in the correct position of the map space.

[0109] For example, in the geographic image data storage of a city map, a certain point in the central square of the city is taken as the origin coordinate (0, 0). If the image data of a block needs to be stored, the horizontal and vertical coordinates of the starting point of the block image data (such as the southwest corner of the block) relative to the origin coordinate of the central square of the city need to be determined, so that the position of the block in the map space can be accurately represented, just like accurately positioning a figure on a coordinate paper.

[0110] Further, geographic image data is usually spliced from multiple parts, especially when dealing with large geographic areas or high-resolution images. Determining the horizontal and vertical coordinates of the starting point of the target geographic image data relative to the origin coordinate of the preset map space helps to maintain the spatial continuity and accuracy when splicing these image data. Assuming there are multiple satellite remote sensing image files, each geographic image file covers a certain geographic area. In order to construct a complete geographic image, these image files need to be spliced together. By determining the horizontal and vertical coordinates of the starting point of each image file relative to the origin coordinate, they can be accurately spliced in the correct position, avoiding splicing errors such as image overlap or gaps, and ensuring the integrity and accuracy of the geographic image.

[0111] When performing geographic spatial analysis and queries, accurate location information is crucial. By determining the horizontal and vertical coordinates of the starting point of the target geographic image data, accurate spatial indexing is provided for subsequent analysis and queries. For example, when studying land use changes, the image data of a specific area can be quickly located according to these coordinates, and then compared with images of different periods to analyze the changes in land use. At the same time, for user queries of image data of a specific geographic area, these coordinates can also help the database quickly locate and return the required data, improving query efficiency and accuracy.

[0112] Step S402, according to the number of horizontal and vertical slices of the target geographic image data and the preset image file size, taking the starting point of the target geographic image data as the starting point of division, dividing the target geographic image data into at least one image file.

[0113] Specifically, in actual application, the storage resources of distributed databases are limited, just like a warehouse has certain space limitations. Therefore, the size of files stored to each distributed database needs to be planned. Therefore, when each image file corresponding to the target geographic image data needs to be stored to each distributed database, the pixel size of the length and width of each image file that needs to be stored to the distributed database can be determined according to the preset size of the image file, so that the storage plan can be deployed according to the size of each image file. Knowing the pixel size of the length and width of each image file can help accurately assess the space required to store each image file. For example, an image file with a pixel size of 1000x800 and an image file with a pixel size of 2000x1600, their required storage space is obviously different. By determining the pixel size, the storage blocks in the database can be reasonably allocated, the storage layout can be optimized, and the waste of space or insufficient storage caused by unreasonable storage planning can be avoided. At the same time, this also helps to choose the appropriate storage strategy. For small pixel size image files, a relatively simple storage method can meet the demand; while for large pixel size image files, special storage strategies such as distributed storage, sharding storage or compressed storage may need to be considered to ensure efficient storage and fast retrieval. When processing image files, pixel size is a key factor. For example, when performing image scaling, cropping, splicing and other operations, the image size and effect after processing need to be calculated according to the pixel size. If multiple image files are spliced into a large geographic image, the pixel size of each first target file needs to be known to accurately calculate the size and positional relationship after splicing.

[0114] Further, from the perspective of data transmission, pixel size directly affects the amount of data transmitted. Large pixel size image files will occupy more bandwidth and time when transmitted. In a distributed database environment, data may need to be transmitted between different nodes. Determining the pixel size of the image file can help estimate the transmission cost, select the appropriate transmission protocol and optimize the transmission strategy. For example, for large pixel size files, block transmission or asynchronous transmission can be used to reduce the instantaneous impact on network bandwidth.

[0115] The pixel size of an image file is closely related to the resolution. Generally, the more pixels, the higher the resolution, and the richer the details contained in the image. For example, when geographic images are used for land use planning, high-resolution (i.e., large pixel size) images can clearly show details such as the boundaries of the land and the types of vegetation, while low-resolution images can only provide a general topographic outline. By determining the pixel size, the data quality of the image file can be better evaluated to meet the resolution requirements of different users. For example, for users who need high-precision geographic image data, such as users who conduct fine geological exploration or archaeological excavation, providing image files with larger long and wide pixel sizes can ensure that sufficient detailed information can be obtained; while for some users who only need a general geographic outline, such as users who conduct regional tourism planning, smaller pixel size files can be sufficient to meet the needs.

[0116] In particular, in actual application, the long and wide pixel sizes of each image file can be determined according to the actual application scenario. The larger the long and wide pixel sizes, the greater the error is likely to be, and the smaller the long and wide pixel sizes, the smaller the error is and the number of image files obtained by splitting is likely to be too large. Therefore, in actual application, the long and wide pixel sizes of the image file can generally be set to 256 pixels. It has been found through experiments that when the long and wide pixel sizes of the image file are set to 256 pixels, the relative grid request and the size of the slice are relatively moderate. Therefore, in order to efficiently manage the target geographic image data, after determining the horizontal coordinate and the vertical coordinate of the starting point of the target geographic image data relative to the first origin coordinate, the target geographic image data can be further split into at least one image file according to the number of horizontal split slices and the number of vertical split slices of the target geographic image data and the preset image file size, with the starting point of the split being the starting point of the target geographic image data. The plurality of image files obtained by the split can meet the storage requirements of the related geographic image data.

[0117] As can be seen from the above description, the present application can split the target geographic image data into at least one image file according to the first origin coordinate, thereby efficiently managing the target geographic image data.

[0118] As can be seen from the above description, the method provided by the embodiments of the present application can determine the grid corresponding to each image file according to each image file, the preset map space size, and the second origin coordinate. Next, the process will be introduced, which can include the following:

[0119] Step S501: determining the horizontal coordinate and the vertical coordinate of each image file relative to the first origin coordinate.

[0120] Specifically, as known from the above description, the method provided in the embodiments of the present application can split the target geographic image data into a plurality of image files, and split the map corresponding to the target geographic image data into a plurality of grids, in order to build a correlation between the grids and the image files, the horizontal coordinate and the vertical coordinate of each image file relative to the first original point coordinate can be determined, so that the longitude and latitude information of each image file can be determined according to the horizontal coordinate and the vertical coordinate of each image file relative to the first original point coordinate.

[0121] In step S502, the longitude and latitude information of each image file is determined according to the horizontal coordinate and the vertical coordinate of each image file relative to the first original point coordinate.

[0122] In particular, the geographic image data can only be determined in the large geographic coordinate system of the earth after its corresponding latitude and longitude information is determined. Latitude and longitude are the basic coordinate system for representing geographic positions on the earth. By determining the longitude and latitude of the image file, the specific position of the image covered area on the earth can be known, and accurate positioning in geographic space can be realized. Image files obtained from different sources and at different times may differ in space. By converting them into latitude and longitude coordinates, they can be unified into the same spatial reference system. This enables the integration, analysis and comparison of multi-source data within the same geographic framework, providing a unified basis for subsequent geographic information processing and application. In practical applications, it is often necessary to integrate multiple different image files, such as combining satellite images with aerial images, ground photography images, etc. After determining the latitude and longitude information according to the horizontal and vertical coordinates, these images from different sources can be easily matched in space to ensure their consistency in geographic space, thus realizing seamless data fusion. In addition to image data, geographic information systems also have various vector data, terrain data and other types of geographic data. These data are usually stored and managed with latitude and longitude as coordinates. By determining the latitude and longitude of the image file, it can be accurately overlaid and analyzed with other geographic data in space, fully utilizing the comprehensive advantages of multi-source data. When performing spatial analysis, sometimes the geographic coordinates need to be converted and projected according to specific requirements. After the latitude and longitude information of the image file is determined, various coordinate conversion operations can be easily performed to convert it into a projection coordinate system suitable for specific analysis or application, thus improving the accuracy and reliability of spatial analysis. Latitude and longitude information provides convenience for spatial query and retrieval. Users can set query conditions according to specific geographic locations or regional ranges through latitude and longitude to quickly filter out the required data from a large number of image files. In map visualization applications, latitude and longitude is the key to accurately displaying images on the map. By converting the coordinates of the image file to latitude and longitude, it can be correctly placed on the corresponding position of the map, realizing seamless integration with the map. When browsing the map, users can intuitively see the area covered by the image and its relationship with the surrounding geographic environment, making it easy to view and interact with geographic information. Therefore, after determining the horizontal and vertical coordinates of each image file relative to the first origin coordinate, the longitude and latitude information of each image file can be determined based on the horizontal and vertical coordinates of each image file relative to the first origin coordinate.

[0123] Step S503, according to the longitude and latitude information of each image file, the pre-set map space size and the second origin coordinate, determine the boundary box range of the grid where the horizontal and vertical coordinates of each image file fall into.

[0124] Specifically, in order to efficiently manage various image files, after the longitude and latitude information of each image file is determined, the boundary box range of the grid in which the horizontal coordinate and the vertical coordinate of each image file fall can be further determined according to the longitude and latitude information of each image file, the preset map space size, and the second origin coordinate.

[0125] By determining the boundary box range of the image file in the grid, the image data can be classified and stored according to the grid, facilitating the establishment of an efficient index structure. In a storage system, data is organized in units of grids, which can quickly locate the grid containing a specific image file, improve data retrieval speed, and reduce data access time. Especially when processing large-scale geographic image data, this organization method can significantly improve storage and query efficiency. Different display devices and map zoom levels have different resolution requirements for image data. Through the grid boundary box range, the display accuracy of the image files in each grid can be dynamically adjusted according to the device resolution and display requirements, realizing adaptive display of geographic image data and ensuring that the map can be clearly and smoothly displayed on various devices. When performing spatial queries, such as finding image files in a specific area or judging the spatial relationship between image files and other geographic features, the grid boundary box range provides a fast preliminary screening method. By judging whether the grid boundary box of the image file intersects with the grid boundary box of the query area or other geographic features, a large amount of irrelevant data can be quickly excluded, the query range can be narrowed, and the efficiency of spatial queries can be improved.

[0126] The process of determining the boundary box range of the grid in which the horizontal coordinate and the vertical coordinate of each image file fall according to the longitude and latitude information of each image file, the preset map space size, and the second origin coordinate can be as follows:

[0127] (1) Determine the longitude and latitude information of the second origin coordinate according to the preset map space size and the first origin coordinate.

[0128] (2) Determine the horizontal coordinate of the grid corresponding to each image file relative to the second origin coordinate according to the longitude information of each image file and the longitude information of the second origin coordinate.

[0129] (3) Determine the vertical coordinate of the grid corresponding to each image file relative to the second origin coordinate according to the latitude information of each image file and the latitude information of the second origin coordinate.

[0130] (4) Determine the boundary box range of the grid in which the horizontal coordinate and the vertical coordinate of each image file fall according to the horizontal coordinate and the vertical coordinate of the grid corresponding to each image file relative to the second origin coordinate.

[0131] For example, in actual application, the grid to which the small tif belongs can be determined according to the horizontal coordinate and the vertical coordinate of each image file, the size of the image file, the image pixel size p (the image pixel size can be set according to actual application requirements, for example, 0.09999999999999942268 is generally used, which represents the spatial distance represented by one pixel, that is, one pixel represents 0.09999999999999942268 meters), the coordinates of the upper left corner of the small tif cut out by the horizontal coordinate and the vertical coordinate, and the first origin and the preset map space size corresponding to the map space.

[0132] For example, as shown in Figure 5 , it can be seen that Figure 5 The length and width of an example grid are 256 and 256 respectively, and the size of the pixel is about 0.1 meters.

[0133] The latitude and longitude information of the lower left corner of the map space can be determined according to the first origin and the preset map space size corresponding to the map space.

[0134] A tif file has its own latitude and longitude coordinates of the upper left corner. According to the coordinates of the tif and the latitude and longitude information of the lower left corner of the map space, the second grid to which the third grid belongs can be determined. The approximate calculation method is as follows:

[0135] The x of the second grid = (the longitude of the tif - the longitude of the lower left corner) / 2000 meters, and the decimal places are discarded.

[0136] The y of the second grid = (the latitude of the tif - the longitude of the lower left corner) / 2000 meters, and the decimal places are discarded.

[0137] As shown in Figure 6 , the position of the slice 18905_10936_1 is indicated by the left arrow in the middle, Figure 6 and the machine stored in the grid to which the slice 18905_10936_1 belongs is indicated by the right arrow. Figure 6

[0138] As can be seen from the above technical solutions, the method provided by the embodiments of the present application can determine the grid corresponding to each image file according to each image file, the preset map space size and the second origin coordinates, so as to establish the index relationship between the grid and the image file, and realize efficient management of each image file.

[0139] ​The irregular geographic image data slicing processing device provided by the embodiment of the present application is described below. The irregular geographic image data slicing processing device described below can be referred to in correspondence with the irregular geographic image data slicing processing method described above. Referring to Figure 7 , Figure 7 FIG. 1 is a structural schematic diagram of an irregular geographic image data slicing processing device disclosed by the embodiment of the present application. As shown in Figure 7 , the irregular geographic image data slicing processing device can include:

[0140] an acquisition unit configured to acquire target geographic image data to be stored;

[0141] a division unit configured to divide a map corresponding to the target geographic image data into at least one grid according to a first origin coordinate of a preset map space and a preset map space size, wherein a size of each grid is not more than a preset first threshold;

[0142] a first determination unit 101 configured to determine a second origin coordinate displayed by all grids corresponding to the target geographic image data according to the first origin coordinate and the preset map space size, wherein the process of determining the second origin coordinate includes: acquiring the first origin coordinate of the preset map from metadata or a configuration file of the preset map; determining a width and a height of the preset map space; determining a resolution of each grid corresponding to the preset map space according to an accuracy requirement of the preset geographic image data; calculating a number of grids corresponding to the preset map space according to the width and the height of the preset map space and the resolution of each grid; and using double loops to traverse all grids, wherein an outer loop controls rows from 0 to n h -1, and an inner loop controls columns from 0 to n w -1. For a grid in the i th row and the j th column, a calculation formula of the second origin coordinate of the grid can be , ; wherein the first origin coordinate is , and sizes of each grid in horizontal and vertical directions are respectively denoted as and , and numbers of grids in the horizontal and vertical directions are respectively denoted as and ;

[0143] a slicing unit 102 configured to slice the target geographic image data into at least one image file according to the first origin coordinate;

[0144] The second determining unit 103 is configured to determine a grid corresponding to each of the image files according to each of the image files, the preset map space size and the second origin coordinate, wherein each of the grids corresponds to at least one of the image files.

[0145] The third determining unit 104 is configured to determine a machine code of an image file corresponding to each of the grids to be stored according to a number of storage machines corresponding to the target geographic image data, wherein the number of the storage machines is not less than a preset second threshold.

[0146] The constructing unit 105 is configured to construct a storage file name of each of the image files according to the first origin coordinate, the second origin coordinate of each of the grids and the machine code corresponding to each of the image files.

[0147] The encrypting unit 106 is configured to store each of the image files in a corresponding distributed database after encryption according to the storage file name of each of the image files.

[0148] As can be seen from the above technical solutions, the device provided by the embodiments of the present application can effectively split large-volume irregular geographic image data and then store the data after encryption, which can effectively increase the security and privacy of geographic information data storage, effectively reduce the repetitiveness of geographic spatial information collection, further split large-volume irregular geographic image data into small-volume image data files for storage, which can better plan the geographic image data collection path, reduce repeated collection due to storage of large geographic image data, and store large-volume geographic image data after fragmentation, which can effectively improve the processing speed of geographic image data and the read-write efficiency of geographic image data, while effectively ensuring the integrity of the original geographic image data, it can also improve the acquisition efficiency of irregular geographic image data and enhance the merging display efficiency of irregular geographic image data. At the same time, the method of the present application can also improve the reading experience of users when reading stored geographic image files.

[0149] The specific processing procedures of each unit included in the irregular geographic image data slicing processing device can be referred to the related description in the foregoing irregular geographic image data slicing processing method, which will not be described here.

[0150] The irregular geographic image data slicing processing device provided by the embodiments of the present application can be applied to irregular geographic image data slicing processing equipment, such as terminals: mobile phones, computers, etc. Optionally, Figure 8 The hardware structure block diagram of the irregular geographic image data slicing processing equipment is shown, which can be referred to Figure 8The hardware structure of the irregular geographic image data slicing processing device can include at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4. In the embodiments of the present application, the number of the processor 1, the communication interface 2, the memory 3 and the communication bus 4 is at least one, and the processor 1, the communication interface 2 and the memory 3 complete the communication with each other through the communication bus 4. The processor 1 can be a central processing unit CPU, or an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement the embodiments of the present application, etc.; the memory 3 can include a high-speed RAM memory, and can also include a non-volatile memory, etc., for example, at least one disk memory; wherein the memory stores a program, and the processor can call the program stored in the memory, and the program is used to: implement each processing flow in the terminal irregular geographic image data slicing processing scheme. The embodiments of the present application also provide a readable storage medium, which can store a program suitable for the processor to execute, and the program is used to: implement each processing flow in the terminal irregular geographic image data slicing processing scheme. Finally, it should be noted that, in this paper, relationship terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between the entities or operations. Moreover, the terms "include", "contain" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "including a" does not exclude the presence of another identical element in the process, method, article or device including the element. The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same or similar parts between the embodiments can be referred to each other. The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications of the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments can be combined with each other. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for slicing non-regular geographic image data, characterized in that, The method comprises the following steps: acquiring target geographic image data to be stored; segmenting a map corresponding to the target geographic image data into at least one grid according to a first original point coordinate of a preset map space and a preset map space size, wherein a size of each grid is not greater than a preset first threshold; According to the first origin coordinate and the preset map space size, a second origin coordinate of all grid displays corresponding to the target geographic image data is determined, wherein the process of determining the second origin coordinate comprises: obtaining the first origin coordinate corresponding to the preset map from the metadata or configuration file of the preset map; determining the width and height of the preset map space; determining the resolution of each grid corresponding to the preset map space according to the accuracy requirement of the preset geographic image data; calculating the number of grids corresponding to the preset map space according to the width and height of the preset map space and the resolution of each grid; using double loops to traverse all grids; the outer loop controls the row from 0 to n h -1; the inner loop controls the column from 0 to n w -1, wherein for the grid of the i th row and the j th column, the calculation formula of the second origin coordinate is , ; wherein the first origin coordinate is , the size of each grid in the horizontal and vertical directions is respectively denoted as and , and the number of grids in the horizontal and vertical directions is respectively denoted as and . cutting the target geographic image data into at least one image file according to the first original point coordinate; determining a grid corresponding to each image file according to each image file, the preset map space size and the second original point coordinate, wherein each grid corresponds to at least one image file; determining a machine code of an image file corresponding to each grid corresponding to the target geographic image data to be stored according to a number of storage machines, wherein the number of storage machines is not less than a preset second threshold; constructing a storage file name of each image file according to the first original point coordinate, each grid relative to the second original point coordinate and the machine code of each image file; storing each image file in a corresponding distributed database after encryption according to the storage file name of each image file.

2. The method of claim 1, wherein, The method of determining the machine code of the image file corresponding to each grid corresponding to the target geographic image data to be stored according to the number of storage machines comprises: sorting all storage machines according to creation time or capacity size and numbering each storage machine; sorting and numbering all grids corresponding to the target geographic image data from the first original point coordinate; selecting the same number of grids from all grid queues according to the number of storage machines, constructing a mapping relationship between the serial numbers of the selected grids and the numbers of the storage machines respectively, and marking the grids having the mapping relationship with the numbers of the storage machines with the numbers of the storage machines, wherein the serial number of a grid only has a mapping relationship with the number of one storage machine; continuing to construct a mapping relationship between the unmarked grids in all grid queues and the numbers of the storage machines respectively, and re-executing the operation of marking the grids having the mapping relationship with the numbers of the storage machines with the numbers of the storage machines until all grids are marked, wherein the grids having adjacent serial numbers cannot have a mapping relationship with the number of the same storage machine, and the number of one storage machine has a mapping relationship with the serial numbers of at least one grid; taking the number of the storage machine corresponding to each grid as the machine code of the image file corresponding to each grid to be stored.

3. The method of claim 1, wherein, The method of segmenting the map corresponding to the target geographic image data into at least one grid according to the first original point coordinate of the preset map space and the preset map space size comprises: determining the number of rows and columns of the target geographic image data with the first original point coordinate as a starting point according to the preset map space size. determine the number of sub-slices in the horizontal axis direction and the number of sub-slices in the vertical axis direction of the target geographic image data according to the number of rows and the number of columns of the target geographic image data and the preset grid size; divide the map corresponding to the target geographic image data into at least one grid according to the number of sub-slices in the horizontal axis direction and the number of sub-slices in the vertical axis direction of the target geographic image data.

4. The method of claim 3, wherein, The dividing the target geographic image data into at least one image file according to the first origin coordinate comprises: determining the horizontal coordinate and the vertical coordinate of the point where the target geographic image data starts to store relative to the first origin coordinate; dividing the target geographic image data into at least one image file according to the number of sub-slices in the horizontal axis direction and the number of sub-slices in the vertical axis direction of the target geographic image data and the preset image file size, and taking the point where the target geographic image data starts to store as the division starting point.

5. The method of claim 4, wherein, The determining the grid corresponding to each image file according to each image file, the preset map space size and the second origin coordinate comprises: determining the horizontal coordinate and the vertical coordinate of each image file relative to the first origin coordinate; determining the longitude and latitude information of each image file according to the horizontal coordinate and the vertical coordinate of each image file relative to the first origin coordinate; determining the boundary box range of the grid into which the horizontal coordinate and the vertical coordinate of each image file fall according to the longitude and latitude information of each image file, the preset map space size and the second origin coordinate.

6. The method of claim 5, wherein, The determining the boundary box range of the grid into which the horizontal coordinate and the vertical coordinate of each image file fall according to the longitude and latitude information of each image file, the preset map space size and the second origin coordinate comprises: determining the longitude and latitude information of the second origin coordinate according to the preset map space size and the first origin coordinate; determining the horizontal coordinate of the grid corresponding to each image file relative to the second origin coordinate according to the longitude information of each image file and the longitude information of the second origin coordinate; determining the vertical coordinate of the grid corresponding to each image file relative to the second origin coordinate according to the latitude information of each image file and the latitude information of the second origin coordinate; determining the boundary box range of the grid into which the horizontal coordinate and the vertical coordinate of each image file fall according to the horizontal coordinate and the vertical coordinate of the grid corresponding to each image file relative to the second origin coordinate.

7. The method according to any one of claims 1 to 6, characterized in that, The preset first threshold is set to 2 kilometers; and the preset second threshold is set to 4.

8. A device for processing irregular geographic image data slices, characterized in that, comprises: an acquisition unit configured to acquire target geographic image data to be stored; a division unit configured to divide a map corresponding to the target geographic image data into at least one grid according to a first origin coordinate of a preset map space and a preset map space size, and the size of each grid is not greater than a preset first threshold; The first determining unit is configured to determine a second origin coordinate of all grid displays corresponding to the target geographic image data according to the first origin coordinate and the preset map space size, wherein the process of determining the second origin coordinate comprises: obtaining the first origin coordinate corresponding to the preset map from metadata or a configuration file of the preset map; determining the width and height of the preset map space; determining the resolution of each grid corresponding to the preset map space according to the accuracy requirement of the preset geographic image data; calculating the number of grids corresponding to the preset map space according to the width and height of the preset map space and the resolution of each grid; using double loops to traverse all grids; the outer loop controls the row from 0 to n h -1; the inner loop controls the column from 0 to n w -1, wherein for the grid in the i-th row and the j-th column, the calculation formula of the second origin coordinate is , ; wherein the first origin coordinate is , the size of each grid in the horizontal and vertical directions is respectively denoted as and , and the number of grids in the horizontal and vertical directions is respectively denoted as and .​​ a division unit configured to divide the target geographic image data into at least one image file according to the first origin coordinate; A second determining unit is configured to determine a grid corresponding to each of the image files according to each of the image files, the preset map space size, and the second origin coordinate, wherein each of the grids corresponds to at least one of the image files; A third determining unit is configured to determine a machine code corresponding to an image file to be stored in each of the grids corresponding to the target geographic image data according to a number of storage machines, wherein the number of the storage machines is not less than a preset second threshold value; A constructing unit is configured to construct a storage file name of each of the image files according to the first origin coordinate, the second origin coordinate relative to each of the grids, and the machine code corresponding to each of the image files; An encrypting unit is configured to store each of the image files in a corresponding distributed database after encryption according to the storage file name of each of the image files.

9. A device for processing irregular geographic image data slices, characterized in that, One or more processors, and a memory; The memory stores computer readable instructions, and the computer readable instructions are executed by the one or more processors to implement the steps of the irregular geographic image data slicing processing method in any one of claims 1 to 7. The readable storage medium stores computer readable instructions, and the computer readable instructions are executed by one or more processors to make the one or more processors implement the steps of the irregular geographic image data slicing processing method in any one of claims 1 to 7.

10. A readable storage medium, characterized by: ​

Images