Distributed structured storage method for geographic image data and related equipment thereof

Abstract

The invention provides a geographic image data distributed structured storage method and related equipment thereof, which can effectively split large-volume geographic image data and then encrypt and store the data, can effectively increase the security and privacy of geographic information data storage, can effectively reduce the repeatability of geographic space information collection, and can improve the efficiency of geographic information collection. Furthermore, large-size image data are divided into small-size data files to be stored, a data acquisition path can be better planned, repeated acquisition caused by storage of overlarge image data is reduced, large-size geographic image data are stored in a fragmented mode, the image data processing speed can be effectively increased, and the processing efficiency of the image data is improved. According to the method, the reading and writing efficiency of the image data is improved, the acquisition efficiency of the geographic image data can be improved while the integrity of the original geographic image data is effectively guaranteed, and the merging display efficiency of the image data is improved.

Description

Technical Field

[0001] The present application relates to the technical field of data storage, and in particular to a distributed structured storage method for geographic image data and related equipment. Background Art

[0002] In today's image data storage technology, geographic image data pixels are generally stored in the form of files, among which the GeoTIFF format is a representative one. Under this storage method, as the resolution of image data continues to increase and the geographic space covered becomes wider and wider, the corresponding generated file size will show a sharp growth trend. This large-volume image data file certainly performs well in terms of clarity, can contain richer and more detailed data, and provide users with clearer and more comprehensive image information. However, behind this advantage lies a huge information security risk. As the file size increases, it faces more security threats during storage, transmission and processing, and the possibility of data leakage, tampering, loss and other problems also increases significantly.

[0003] In actual image storage practice, in order to more effectively manage and process large-scale image data, it is a common operation strategy to slice large-volume image data. Unfortunately, many existing image formats do not provide clear, unified and authoritative standard specifications for the size of image blocks. This leads to particularly prominent problems when processing image data that have high requirements for processing speed. Due to the lack of clear and definite standard guidelines, in order to pursue the simplicity of the storage process, most large-volume image data are only divided into a very limited number of blocks when slicing. This overly rough slicing method directly leads to the generally low efficiency of these image data during read and write operations. When it is necessary to read or write these image data, the system needs to consume a lot of time and resources, which seriously affects the timeliness and smoothness of image data display and subsequent processing, brings great inconvenience to related work and applications, and cannot meet the needs of modern society for efficient and fast processing of image data. Summary of the invention

[0004] The present application aims to solve at least one of the above-mentioned technical defects. In view of this, the present application provides a distributed structured storage method for geographic image data and related equipment, which are used to solve the technical defect of difficulty in storing geographic image data in the prior art.

[0005] A distributed structured storage method for geographic image data includes: obtaining first target geographic image data to be stored; determining a sharding scheme for the first target geographic image data, and sharding the first target geographic image data according to the sharding scheme for the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data; determining a storage directory for each of the first image files, wherein the storage directory for each of the first image files includes a machine identification code to be stored; and encrypting each of the first image files according to the storage directory for each of the first image files and storing them in a corresponding distributed database.

[0006] Preferably, the method also includes: analyzing the user's browsing needs to determine the second target geographic image data requested by the user; determining the various second image files that constitute the second target geographic image data based on the second target geographic image data; determining a distributed database that stores each of the second image files; reading each of the second image files from the distributed database that stores each of the second image files; and merging each of the second image files according to a preset map display size and displaying them to the user.

[0007] Preferably, the determining of the slicing scheme of the first target geographic image data and slicing the first target geographic image data according to the slicing scheme of the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data includes: defining the first origin coordinates of the map space corresponding to the first target geographic image data; determining the pixel size of the length and width of each first image file to be stored in the distributed database; determining, according to the first origin coordinates, the horizontal coordinate and the vertical coordinate corresponding to the point where the first target geographic image data starts to be stored relative to the first origin coordinates; calculating the number of rows and columns of the first target geographic image data; determining the number of slices on the horizontal axis and the number of slices on the vertical axis of the first target geographic image data according to the number of rows and columns of the first target geographic image data; slicing the first target geographic image data according to the number of slices on the horizontal axis and the number of slices on the vertical axis of the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data.

[0008] Preferably, determining the storage directory of each of the first image files includes: determining the horizontal and vertical coordinates of each of the first image files relative to the first origin coordinates; determining the machine identification code where each of the first image files will be stored; and constructing a storage directory for storing each of the first image files based on the horizontal and vertical coordinates of each of the first image files relative to the first origin coordinates and the machine identification code where each of the first image files will be stored.

[0009] Preferably, the determining of each second image file constituting the second target geographic image data based on the second target geographic image data includes: determining the second origin coordinates of the area corresponding to the second target geographic image data based on a preset map display size and a preset first origin coordinate of the map space, wherein the preset map display size is the display area size corresponding to the geographic image data to be displayed, and the preset first origin coordinates of the map space are the origin corresponding to the coordinate system used to store the first target geographic image data; dividing the area displaying the second target geographic image data into at least one grid based on the second origin coordinates, wherein each of the grids is a rectangle of the same size; determining the coordinates of each grid corresponding to the second target geographic image data based on the second origin coordinates. coordinates; according to the second origin coordinates and the coordinates of each of the grids, respectively calculate the row number and column number of each of the grids relative to the second origin coordinates; according to the row number and column number of each of the grids and the first origin coordinates, calculate the longitude and latitude information of each of the grids; according to the longitude and latitude information of each of the grids, construct a bounding box of each of the grids; according to the bounding box of each of the grids, query the first target image files involved in all of the grids to obtain a list of first target image files to be merged and displayed, wherein each of the grids corresponds to a first target image file; use each of the first target image files involved in the list of first target image files to be merged and displayed as a respective second image file corresponding to the second target geographic image data, wherein each of the second image files corresponds to one of the first target image files.

[0010] Preferably, the merging of the second image files and displaying them to the user based on a preset map display size includes: reading, from each distributed database, each first image file involved in the list of first image files to be merged and displayed as each second image file corresponding to the second target geographic image data according to the list of first image files to be merged and displayed; and merging the second image files and displaying them to the user based on the preset map display size, the first origin coordinates and the resolution of each second image file, and the longitude and latitude information corresponding to the bounding box of the grid corresponding to each second image file.

[0011] Preferably, the formula for calculating the row number and column number of each grid relative to the second origin coordinate includes: the calculation formula for the row number of each grid relative to the second origin coordinate is: ; The calculation formula for the column number of each grid relative to the second origin coordinate is: ;in, is the row number of the grid relative to the coordinates of the second origin; is the column number of the grid relative to the coordinates of the second origin; is the horizontal coordinate of the grid relative to the coordinate of the second origin, is the horizontal coordinate of the second origin coordinate; is the ordinate of the grid relative to the coordinates of the second origin, is the ordinate of the second origin coordinate; is the size of the grid; The calculation formula for calculating the longitude and latitude information of each grid according to the row number and column number of each grid and the first origin coordinates includes: the calculation formula for the longitude of the upper left corner point of each grid relative to the first origin coordinates is: ; The calculation formula for the latitude of the upper left corner point of each grid relative to the first origin coordinate is: ; The calculation formula for the longitude of the upper right corner point of each grid relative to the first origin coordinate is: ; The calculation formula for the latitude of the upper right corner point of each grid relative to the first origin coordinate is: ;in, is the longitude of the upper left corner point of each of the grids relative to the first origin coordinates; is the latitude of the upper left corner point of each of the grids relative to the first origin coordinates; is the longitude of the upper right corner point of each of the grids relative to the first origin coordinates; is the latitude of the upper right corner point of each of the grids relative to the first origin coordinates.

[0012] A distributed structured storage device for geographic image data includes: a data acquisition unit, used to acquire first target geographic image data to be stored; a scheme determination unit, used to determine the slicing scheme of the first target geographic image data, and slice the first target geographic image data according to the slicing scheme of the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data; a name determination unit, used for the storage name of each first image file, wherein the storage name of each first image file includes a machine identification code to be stored; an encryption unit, used to encrypt each first image file according to the storage name of each first image file and store it in a corresponding distributed database.

[0013] A distributed structured storage device for geographic image data includes: one or more processors, and a memory; the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the distributed structured storage method for geographic image data as described in any of the above introductions are implemented.

[0014] A 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 distributed structured storage method for geographic image data as described in any of the above introductions.

[0015] It can be seen from the technical solutions introduced above that when it is necessary to store geographic image data, the method provided by the embodiment of the present application can obtain the first target geographic image data to be stored. Since the first target geographic image data is large in size, direct storage is prone to information leakage and does not comply with relevant data storage regulations. Therefore, it can be considered to be sliced ​​before storage. Therefore, after obtaining the first target geographic image data, the slicing scheme of the first target geographic image data can be determined, and according to the slicing scheme of the first target geographic image data, the first target geographic image data is sliced ​​to obtain at least one first image file corresponding to the first target geographic image data; wherein the size of each first image file obtained by segmentation is smaller than the size of the first target geographic image data. After slicing the first target geographic image data, the storage name of each first image file can be further determined, wherein the storage name of each first image file includes the machine identification code to be stored; so that each first image file can be encrypted and stored in the corresponding distributed database according to the storage name of each first image file.

[0016] It can be seen from this that the method provided in the embodiment of the present application can effectively split large-volume geographic image data and then encrypt and store them, which can effectively increase the security and privacy of geographic information data storage, and can effectively reduce the duplication of geographic spatial information collection. Furthermore, by dividing large-volume image data into small-volume data files for storage, data collection paths can be better planned, and repeated collection due to storage of too large image data can be reduced. In addition, by storing large-volume geographic image data in pieces, the processing speed of image data can be effectively improved, and the reading and writing efficiency of image data can be improved. While effectively ensuring the integrity of the original geographic image data, the efficiency of acquiring geographic image data can also be improved, and the efficiency of merging and displaying image data can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0018] Figure 1A flowchart of a method for implementing distributed structured storage of geographic image data provided in an embodiment of the present application; Figure 2-Figure 5 They are schematic diagrams of some image data display effects provided in the embodiments of the present application; Figure 6 A schematic diagram of a distributed structured storage device for geographic image data according to an example of an embodiment of the present application; Figure 7 This is a hardware structure block diagram of a distributed structured storage device for geographic image data disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0020] In view of the fact that most of the current distributed structured storage solutions for geographic image data are difficult to adapt to complex and changeable business needs, the applicant has studied a distributed structured storage solution for geographic image data. The distributed structured storage method for geographic image data effectively splits large volumes of geographic image data and then encrypts and stores them, which can effectively increase the security and privacy of geographic information data storage and effectively reduce the duplication of geographic spatial information collection. Furthermore, by dividing large volumes of image data into small volumes of data files for storage, data collection paths can be better planned and repeated collection due to storage of too large image data can be reduced. In addition, by storing large volumes of geographic image data in pieces, the processing speed of image data can be effectively improved, and the reading and writing efficiency of image data can be improved. While effectively ensuring the integrity of the original geographic image data, the efficiency of acquiring geographic image data can also be improved, and the efficiency of merging and displaying image data can be improved.

[0021] The method provided in the embodiment of the present application can be used in many general or special computing device environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or devices, etc. The embodiment of the present application provides a distributed structured storage method for geographic image data, which can be applied to various geographic image data management systems, and can also be applied to various computer terminals or smart terminals, and its execution subject can be a processor or server of a computer terminal or smart terminal.

[0022] Combine the following Figure 1 , introduces the process of the distributed structured storage method of geographic image data given in the embodiment of the present application, such as Figure 1As shown, the process can include the following steps: Step S101, obtaining first target geographic image data to be stored.

[0023] Specifically, in the actual application process, geographic image data is the basic data for subsequent storage and analysis. Just like building materials are needed to build a house, geographic image data is the "raw material" for building a geographic information system (GIS) and conducting remote sensing analysis. For example, the earth's surface images obtained by satellite remote sensing, including information such as terrain, vegetation coverage, and water distribution, are the initial data source for various geographic research and applications. A complete geographic image data set is necessary to accurately describe geographic phenomena and spatial relationships. If geographic image data is not obtained, there will be no data available in the storage link, and it will be impossible to establish a comprehensive geospatial database. For example, in urban planning, it is necessary to obtain high-resolution image data from different areas of the city, including the distribution of buildings, the direction of roads, etc. Only by obtaining and storing these image data can a complete reference be provided for subsequent planning decisions.

[0024] For example, GIS is a system for collecting, storing, managing, analyzing and displaying geographic spatial data. Geographic image data is a very important data source in GIS. By acquiring geographic image data, it can be stored in a GIS database for various purposes such as making maps and conducting spatial analysis. For example, to make a land use type map, it is necessary to obtain geographic images of the area at different times, store them, and use GIS software to classify and interpret them, dividing different land use types such as farmland, forest, and construction land.

[0025] Furthermore, in actual applications, geographic image data is essential for the investigation and monitoring of natural resources, such as forest resources, mineral resources, and water resources. After acquiring geographic image data and storing them, dynamic monitoring of natural resources can be achieved. Taking forest resources as an example, by acquiring satellite remote sensing image data, changes in forest area and the growth of trees can be monitored. After these geographic image data are stored, they can be compared and analyzed to promptly detect abnormal situations such as deforestation and forest fires.

[0026] Before and after natural disasters (such as earthquakes, floods, landslides, etc.), geographic image data plays a key role. Obtaining image data before a disaster can help assess disaster risks and understand the geographical environment and population distribution of disaster-prone areas. Obtaining image data after a disaster helps assess the extent of disaster losses and provide decision support for emergency rescue. For example, after a flood disaster, by acquiring aerial or satellite images of the affected area, storing and analyzing information such as the flood inundation range and the number of damaged houses, rescue and recovery work can be effectively organized.

[0027] It can be seen that acquiring and storing geographic image data is very important in practical applications.

[0028] In actual application, geographic image data can have multiple formats. The most common one is TIFF (Tagged Image File Format). TIFF (Tagged Image File 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.

[0029] The JPEG (Joint Photographic Experts Group) format is also commonly used. The JPEG (Joint Photographic Experts Group) format stores images in a compressed manner. It can reduce the file size while ensuring a certain image quality, facilitating data transmission and storage, but its storage capacity for geospatial information is relatively weak.

[0030] GeoTIFF is an extension of the TIFF format, specifically used for the storage of geographic spatial data. It adds geographic reference information such as geographic coordinate system, map projection, etc. on the basis of ordinary TIFF, so that the image data can accurately correspond to the actual geographic spatial location.

[0031] In actual applications, geographic image data usually has a large amount of data. This is because geographic image data generally contains rich geographic spatial information, and the higher the resolution, the larger the data volume. For example, a high-resolution satellite remote sensing image, a full-color band image (resolution can reach 0.3-1 meters) may have a data volume of hundreds of megabytes or even several gigabytes. Multispectral or hyperspectral images contain information from multiple bands (such as the multispectral data of Landsat 8 has 11 bands), so the data volume is even larger.

[0032] The amount of image data obtained by aerial photography should not be underestimated. Especially when conducting large-scale surveying and mapping projects, a large number of images will be taken to ensure sufficient accuracy and coverage, and the total amount of data from these images will be very large.

[0033] In actual application, geographic image data carries spatial reference information, which can mainly include geographic coordinate systems (such as WGS84, Beijing 54, Xi'an 80, etc.), which define how locations on the earth are represented. Different coordinate systems are suitable for different application scenarios and regions. There is also map projection information, which is a method of converting three-dimensional coordinates on the earth's surface into two-dimensional plane coordinates. Common projection methods include Gauss-Krüger projection and Mercator projection. Different projection methods will make the geographic features on the earth's surface present different shapes and deformation characteristics. Understanding these projection methods is crucial for the correct use and processing of geographic image data.

[0034] The accuracy of geographic image data varies depending on the acquisition method and equipment. The accuracy of satellite remote sensing images is affected by factors such as the satellite orbit altitude and sensor performance. For example, images acquired by high-orbit satellites may have lower resolution and relatively poor accuracy; while high-resolution commercial satellite images in low orbits have higher accuracy. The accuracy of aerial photography images is related to flight altitude, camera performance, etc. Images acquired by low-altitude flights are generally more accurate, but the coverage is relatively small; images acquired by high-altitude flights have a larger coverage, but the accuracy may be reduced. At the same time, operations such as registration and correction during data processing will also affect the final accuracy of image data.

[0035] Geographic image data reflects geographic information at a specific time. For example, geographic image data can reflect changes in the natural environment (such as seasonal growth and withering of vegetation, changes in water levels of rivers and lakes, etc.) and human activities (such as urban construction, land development, etc.), which makes the timeliness of geographic image data very important. For example, for agricultural monitoring, it is necessary to obtain images of different growing seasons to accurately assess the growth status of crops; for urban expansion research, it is necessary to obtain images regularly to track changes in urban boundaries.

[0036] Therefore, in order to better utilize geographic image data, the collected geographic image data can be saved. Before saving the geographic image data, the first target geographic image data to be stored can be obtained so that it can be stored according to the characteristics of the first target geographic image data.

[0037] For example, in actual application, the image data (in TIFF format) to be stored can be obtained from a specific file transfer protocol (FTP) server.

[0038] Step S102, determining a fragmentation scheme of the first target geographic image data, and performing fragmentation processing on the first target geographic image data according to the fragmentation scheme of the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data.

[0039] 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, it is possible to consider decomposing the first target geographic image data to be stored into multiple smaller and more manageable parts before storing them.

[0040] For example, if a high-resolution satellite image covering a large area is stored as a whole, it will occupy a large amount of continuous space in the storage system, which will put a lot of pressure on the storage device. After sharding, these small image files can be stored in different locations of the storage device, which is convenient for flexible allocation of storage resources. At the same time, when retrieving data, it is also possible to quickly locate the relevant image file fragments according to specific needs, instead of processing the entire huge data body every time.

[0041] Secondly, different storage devices have different storage capacities and performance characteristics. After the first target geographic image data is sliced, the first target geographic image data can be allocated to the most appropriate storage location according to the actual situation of the storage device.

[0042] For example, some frequently accessed image file fragments are stored in high-speed storage devices, while the fragments with less frequent access are stored in large-capacity but slightly slower storage devices, thereby improving the overall utilization efficiency of storage resources.

[0043] Furthermore, in actual applications, when analyzing and processing geographic image data, such as spatial analysis in geographic information systems (GIS), remote sensing image classification, and other operations, the fragmented image files can be easily distributed to multiple computing nodes for distributed processing. This is like a large task being broken down into multiple small tasks, which are completed by different people (computing nodes) at the same time, which can greatly speed up data processing. For example, when classifying land use types of remote sensing images, after fragmenting the image, multiple processors can classify and process different fragments at the same time, and then summarize the results, which improves processing efficiency.

[0044] Furthermore, for complex geographic image data processing algorithms, processing the entire large geographic image data set may face problems such as insufficient memory and long computing time. After slicing large geographic image data, the size of each image file is moderate, and processing tasks can be performed more efficiently within the constraints of existing computing resources. For example, when performing geometric correction of images, operations on the sliced ​​image files only require processing a relatively small part each time, reducing the complexity of the algorithm and the requirements for hardware resources.

[0045] In actual applications, when geographic image data needs to be transmitted over the network, smaller tiled image files are easier to transmit than the entire large geographic image data set. On the one hand, tiled image files can choose appropriate transmission strategies according to network bandwidth and transmission requirements, such as batch and time period transmission, to avoid network congestion caused by transmitting a large amount of data at one time. On the other hand, when the network is interrupted, only part of the tiled image files need to be retransmitted instead of the entire large geographic image data, reducing data transmission losses and duplication of work. For example, when transmitting geographic image data from a data center to a remote user end, after slicing the large geographic image data, the key tiled image data can be transmitted first according to the user's immediate needs, which can effectively improve the timeliness and reliability of data transmission.

[0046] Therefore, in order to better store and utilize the first target geographic image data to be stored, after acquiring the first target geographic image data, the fragmentation scheme of the first target geographic image data can be further determined, and the first target geographic image data can be fragmented according to the fragmentation scheme of the first target geographic image data, so as to obtain at least one first image file corresponding to the first target geographic image data.

[0047] The size of each first image file is smaller than the first target geographic image data. The size of each first image file can be set according to relevant legal provisions or application scenarios. For example, the size of the first image file obtained after slicing the first target geographic image data can be set to be smaller than or equal to the size corresponding to the image data of 2 square kilometers.

[0048] The storage format of each first image file may be set according to the storage format of the first target geographic image data.

[0049] In actual application, the image files after sharding can adopt a more flexible backup strategy. Different backup plans can be formulated for different shards based on factors such as the importance of the data and the update frequency. For example, for image shards containing key geographical elements (such as important transportation hubs and urban central areas), the backup frequency can be increased and safer backup methods such as off-site storage can be adopted; while for shards in relatively less important areas, the backup frequency can be appropriately reduced, thereby effectively saving backup resources while ensuring data security.

[0050] When data is damaged or lost, sharding facilitates fast recovery because only the damaged or lost shards need to be recovered, rather than the entire large geographic image data set. For example, if a disk sector of a storage device is damaged, causing part of the image file to be unreadable, the damaged shard can be quickly located through shard records, and then restored from the backup without having to restore the entire data, greatly shortening the data recovery time.

[0051] Secondly, fragmentation can better implement data access control. Different image fragments may contain information of different sensitivities. For example, some fragments may contain information about military facilities, sensitive ecological protection areas, etc. Through fragmentation, more stringent access rights can be set for these highly sensitive fragments, such as restricting access to specific user groups, requiring multiple authentications, etc., thereby improving data security and privacy protection.

[0052] When it is necessary to desensitize geographic image data (remove or blur sensitive information), sharding makes the operation more convenient. Shards containing sensitive information can be desensitized separately without affecting the normal use of other shards that do not contain sensitive information. For example, when sharing geographic image data for commercial development, shards containing residents' private information (such as residential details) can be blurred while retaining the original accuracy of other insensitive shards such as geographic features.

[0053] As the geographic environment changes or data quality improves, geographic image data needs to be constantly updated. After tile processing, the image tiles of the local area can be updated without having to reprocess and store the entire dataset. For example, when a large building is newly built in a city, only the image tiles containing the building area need to be updated, without having to update the geographic image of the entire city, which greatly saves update costs and time.

[0054] For different versions of geographic image data (such as images taken at different times, images with different processing precision), tile processing helps to better manage versions. The version changes of each tile can be clearly recorded, making it easy to compare the differences between different versions. For example, when studying the land use changes in a certain area, the change process of land use types in each area can be accurately tracked by comparing different versions of image tiles.

[0055] Step S103, determining the storage directory of each of the first image files.

[0056] Specifically, in the actual application process, after the first target geographic image data is sliced ​​to obtain multiple first image files, in order to better manage and store the obtained first image files, the storage directory of each first image file can be determined so that the numerous sliced ​​image files can be effectively organized. Just like a library classifies and catalogs books, the storage directory provides a systematic way to arrange these files. For example, through the storage directory, the first image files can be classified according to factors such as geographic location, shooting time, image resolution, etc., which is convenient for quickly locating and finding specific image files in the storage system.

[0057] In actual application, the storage directory of each first image file may include the machine identification code to be stored. The storage directory contains information such as the machine identification code to accurately locate the file. In a complex storage environment, there may 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 first image file needs to be accessed, the storage directory is like a map, guiding the user or system to quickly find the specific machine and storage location where the file is located, reducing search time and improving data access efficiency.

[0058] In actual application, as the amount of data continues to increase, the storage system may be expanded, such as adding new storage devices or changing the storage architecture. Determining the storage directory can better adapt to such changes. When a new storage device is added, the new machine identification code can be incorporated into the storage directory system, so that the image file can be smoothly allocated 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 directory and the machine identification code, the new first image file can be stored on the new node, realizing the smooth expansion of the storage system.

[0059] Storage catalogs and machine identification codes can also play a key bridging role when migrating or sharing data between different storage devices or storage systems. Different machines may have different storage format requirements or data access protocols, and storage catalogs can help adjust and adapt to these differences. For example, when migrating the first image file from a local storage device to cloud storage, the storage catalog can guide how to reorganize and identify these files in the cloud system to ensure the compatibility and availability of data in different storage environments.

[0060] Storage directories also help implement data security policies. By controlling access to storage directories, you can restrict unauthorized users from accessing specific primary 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 rights can be set based on storage directories so that only authorized users can access these files stored on specific machines.

[0061] In the data audit process, the storage directory is an important reference. Auditors can view the storage directory to understand the storage location, access history and other information of each first image file. The machine identification code can help trace the changes of data during the storage process, such as whether the file has been moved, copied or deleted. For example, when investigating a data breach, the storage directory and machine identification code can help determine which image files may be affected and on which storage device the abnormal operation occurred.

[0062] Step S104, according to the storage name of each of the first image files, encrypt each of the first image files and store them in the corresponding distributed database.

[0063] Specifically, in today's digital age, data privacy and security are of vital importance. As can be seen from the above introduction, each first image file obtained by slicing the first target geographic image data may contain sensitive information. Therefore, after determining the storage directory of each first image file, each first image file can be encrypted and stored in the corresponding distributed database according to the storage directory of each first image file. Encrypting the first image file and storing it in the distributed database can effectively prevent the data from being unauthorizedly accessed, stolen or tampered with during the storage process.

[0064] Among them, in the actual application process, a first image file can correspond to a distributed database. This situation may be suitable for scenarios with extremely high requirements for data isolation. For example, in some highly confidential scientific research projects, each important experimental image file has a separate distributed database for storage. This ensures that each image file has an independent space in physical storage and management, reducing mutual interference and potential security risks; this storage method has high data security and is easy to manage and maintain separately. If a database has a problem, such as being attacked by hackers or hardware failures, it will only affect the corresponding image file and will not affect other image files. At the same time, it is also relatively simple in terms of permission management. Setting access rights for a single database can accurately control access to specific image files. However, the cost of this storage method is high because it is necessary to establish an independent distributed database for each image file, including hardware resources such as servers and storage devices, as well as related software and maintenance personnel costs. Resource utilization may be low because each database may only store one image file, and a large amount of storage and computing resources will be idle.

[0065] In actual application, a first image file can also be stored in multiple distributed databases. This situation is often used in scenarios with extremely high requirements for data reliability and availability. For example, some key medical image files (such as images of patients' heart bypass surgery) may be stored in multiple distributed databases. This is to prevent data loss due to a single database failure. By storing the same image file in multiple locations, even if one database has a problem, other databases can still provide data access. This storage method has high data reliability and can ensure the availability of image files even if some databases fail. At the same time, users in different regions can obtain image files from databases closer to them, which improves access efficiency. This redundant storage method can also be used for data backup and recovery, making it convenient to obtain complete image files from different databases when needed. However, data update and synchronization of this storage method are more complicated. When an image file needs to be updated, it must be ensured that all distributed databases storing the file can be updated correctly, otherwise data inconsistency will occur. Moreover, this storage method requires more storage space because the same image file will occupy space in multiple databases.

[0066] In the actual application process, in order to improve the efficiency of data storage, multiple first image files can also be stored in the same distributed database while ensuring the security of image data storage. This is a relatively common storage method, which is suitable for a collection of image files with strong correlation. This storage method has high resource utilization. One database can store multiple image files, making full use of storage and computing resources. It is easy to manage, and operations such as maintenance, backup and recovery of the database can be applied to multiple image files at one time. At the same time, when performing data query and analysis, it is convenient to associate multiple image files in the same database, such as finding the changes in X-ray images of all patients in a certain period of time. However, this storage method may have the problem of concentrated security risks. If this distributed database is breached, all image files stored in it may be leaked. In addition, database performance may be affected by the number of stored files. When there are too many stored image files, the query and retrieval speed of the database may decrease.

[0067] In actual application, complex storage relationships can also be built between multiple first image files and multiple distributed databases. This situation may occur in large-scale, cross-departmental or cross-organizational image storage systems. Different image files may be cross-stored in different databases to achieve data sharing and resource integration. This storage method can achieve flexible data sharing and optimal storage configuration. 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 can be facilitated. However, this storage method may make data management and maintenance very complicated. Complex mapping relationships are required to record which databases each image file is stored in and which image files are stored in each database. Maintaining data consistency is also more difficult because it involves interactions between multiple files and multiple databases.

[0068] In actual application, encrypting image data is like adding a solid lock to the data. Only users with the correct key can unlock and view the original data. Although the distributed database itself has certain security mechanisms, encryption provides an additional security layer to ensure that even if some nodes of the database are compromised, the data is still safe.

[0069] Furthermore, by encrypting image files and storing them in a distributed database, organizations can meet the requirements of relevant regulations and avoid facing huge fines and legal liabilities due to data leaks.

[0070] Distributed databases are usually composed of multiple nodes, and data is distributed and stored on these nodes. Although this architecture improves the availability and reliability of data, it also increases the risk points of data exposure. By encrypting image files and storing them, you can fully utilize the advantages of distributed databases (such as high availability and scalability) while reducing the security risks caused by distributed storage. Moreover, in a distributed database environment, data may be transmitted and copied between different nodes. During the transmission and copying process of encrypted image files, even if they are intercepted, it is difficult for attackers to obtain the content, thereby ensuring the security of data throughout the distributed storage life cycle.

[0071] It can be seen from the technical solutions introduced above that when a large volume of geographic image data needs to be stored, the method provided in the embodiment of the present application can effectively split the large volume of geographic image data and then encrypt and store it, which can effectively increase the security and privacy of geographic information data storage, and can effectively reduce the duplication of geographic spatial information collection. Furthermore, dividing the large volume of image data into small volume data files for storage can better plan the data collection path and reduce repeated collection due to storage of too large image data. In addition, storing the large volume of geographic image data in pieces can effectively improve the processing speed of image data and improve the reading and writing efficiency of image data. While effectively ensuring the integrity of the original geographic image data, it can also improve the efficiency of acquiring geographic image data and improve the efficiency of merging and displaying image data.

[0072] In actual application, when a user needs to consult geographic image data, the method provided in the embodiment of the present application can also analyze the user's consultation needs, and extract the geographic image data that the user wants to consult from various storage databases and merge and display them to the user. The process is described below, and the process may include the following steps: Step S201: analyzing the user's browsing needs and determining the second target geographic image data requested by the user.

[0073] Specifically, in actual application, different users have different purposes. For example, in the field of urban planning, planners may need to consult high-resolution geographic image data of specific areas (such as blocks undergoing renovation) to evaluate the distribution of buildings, land use, etc., so as to formulate reasonable renovation plans. Environmental scientists may be interested in geographic image data of coastal areas for studying coastline changes, marine ecological environment, etc. By analyzing user consultation needs, we can accurately provide the second target geographic image data that users really need to meet their professional work requirements.

[0074] In actual applications, geographic image data is usually huge in quantity. For example, satellite remote sensing images generate a huge amount of data every day. If you search blindly without analyzing user needs, you will waste a lot of time and computing resources. By understanding whether the user needs geographic image data for a specific time period (such as a season in the past year) or data for a specific geographic area (such as the area around a mountain), you can narrow the search scope, quickly locate the second target geographic image data, and improve search efficiency.

[0075] Some geographic image data may involve sensitive information. For example, geographic image data in areas where military facilities are located may contain state secrets. Analyzing user access requirements can ensure that only users with legal permissions can obtain the second target geographic image data within the permitted scope to prevent data leakage. If unauthorized users attempt to obtain geographic image data involving sensitive areas such as national security, demand analysis can promptly detect and prevent them.

[0076] The storage and maintenance of geographic image data requires costs. By analyzing user access needs, we can understand which data is frequently requested and which is rarely used. For hot data, its storage method can be optimized, such as storing it in a cache to provide it to users quickly and conveniently; for cold data, we can consider using more economical storage methods, such as low-frequency access storage media. This can make rational use of data resources and reduce storage and maintenance costs.

[0077] Therefore, when a user requests to view certain geographic image data, the user's viewing requirements may be analyzed to determine the second target geographic image data that the user requests to view.

[0078] Step S202: determining each second image file constituting the second target geographic image data according to the second target geographic image data.

[0079] Specifically, from the above introduction, it can be seen that geographic image data is divided into multiple small image files and stored in different databases. Geographic image data is stored in the storage system in the form of files. These files are like pieces of a puzzle, and each file contains a part of geographic image information. For example, a large geographic area image may be divided into multiple small tiles for storage, and each tile is a second image file. By determining the various second image files that make up the second target geographic image data, these "fragments" can be accurately found and combined from the storage system, thereby restoring the complete geographic image that the user wants to view.

[0080] Take satellite remote sensing images as an example. When satellites acquire images of the earth's surface, they collect data in a certain orbit and scanning mode. These data are divided into files and stored in a database. Each file may correspond to a specific geographic range, resolution, band and other attributes. When a user requests to view images of a specific geographic area, it is necessary to find these related files to construct a complete image.

[0081] In actual applications, geographic image data will be updated as time and observation conditions change. Different second image files may represent image data at different time points or with different precision. For example, in the process of monitoring urban construction, some second image files may be the old appearance of the city a year ago, while some may be the latest images reflecting newly built buildings. By determining each second image file, it is possible to clarify which files need to be updated and which files can be used as historical data for comparative analysis.

[0082] Furthermore, when the geographic image data is corrected, resampled, or new annotations are added, the version of each second image file can be recorded separately. When providing data to users, the appropriate second image file combination can be selected according to the user's needs (such as whether the latest version is required, whether historical versions need to be included for comparison, etc.).

[0083] Different second image files may require different processing methods to meet the viewing needs of users. For example, some files may require decompression and decoding operations, while other files may require color correction, geometric correction, etc. By determining each second image file, corresponding processing steps can be performed according to the characteristics of each file, thereby optimizing the data to make it more suitable for the user's viewing experience.

[0084] At the same time, in the processing process, appropriate algorithms and tools can be selected according to the resolution, data format and other properties of each second image file. If the resolution of the image file is high, a more efficient rendering algorithm may be required to ensure fast display of the image; if the data format of the file is special, a special decoder may be required for processing.

[0085] Therefore, after determining the second target geographic image data requested by the user, the second image files constituting the second target geographic image data can be further determined based on the second target geographic image data, so that data related to the second target geographic image data can be read from different databases.

[0086] Step S203: determining a distributed database storing each of the second image files.

[0087] Specifically, it can be seen from the above introduction that after being collected, the geographic image data is divided into multiple small files and stored in different distributed databases. Therefore, after determining each second image file constituting the second target geographic image data, the distributed database storing each second image file can be further determined, so that the image file corresponding to the second target geographic image data can be read from each database.

[0088] Step S204: reading each of the second image files from a distributed database storing each of the second image files.

[0089] Specifically, from the above introduction, it can be seen that after determining the various second image files that constitute the second target geographic image data, it is also possible to determine the storage database corresponding to each second image file. Further, each second image file can be read from the distributed database that stores each second image file so that each second image file can be displayed to the user.

[0090] Step S205: merging the second image files and displaying them to the user according to a preset map display size.

[0091] Specifically, it can be seen from the above introduction that the method provided in the embodiment of the present application can read each second image file corresponding to the second target geographic image data from a distributed database.

[0092] In actual application, since the extracted second image files are discretely stored in different databases, if the second image files extracted from different databases are directly displayed to the user, the data may be disorganized, and the display area of ​​the data requested by each user may also be different. Therefore, after the second image files are extracted, the second image files can be combined according to the preset map display size and displayed to the user.

[0093] Among them, the preset map display size can be set according to the specific device and the specific application scenario.

[0094] It can be seen from the technical solutions introduced above that the method provided in the embodiment of the present application can determine the image files corresponding to the geographic image data that the user needs to consult according to the user's consultation needs, and retrieve the image files corresponding to the geographic image data requested by the user from different storage databases. It can effectively determine and merge the image files retrieved from different databases according to the user's consultation needs, thereby improving the user experience.

[0095] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can determine the fragmentation scheme of the first target geographic image data, and perform fragmentation processing on the first target geographic image data according to the fragmentation scheme of the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data. The process is described below, and the process may include the following steps: Step S301, defining the first origin coordinates of the map space corresponding to the first target geographic image data.

[0096] 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 order to better manage the first target geographic image data, the first origin coordinates of the map space corresponding to the first target geographic image data can be defined so that the first target geographic image data can be better segmented according to the defined origin coordinates.

[0097] Defining the first origin coordinates of the geographic space corresponding to the first target geographic image data is like establishing a "reference point" in this space. For example, in a city's geographic image map, we can define a certain landmark location (such as the center of a fountain) in the city's central square as the first origin coordinates (assuming longitude x1, latitude y1). In this way, the positions of all other geographic features (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 images as an example, the image data of the entire earth's surface has a huge range. By defining the first origin coordinates, such as taking the intersection of the equator and the prime meridian as the origin (0,0), the position of the image data of any point on the earth in this space can be easily determined, providing an accurate spatial positioning basis for subsequent geographic spatial analysis, data processing and user viewing.

[0098] Furthermore, geographic image data often comes from multiple different data sources. These data sources may use different coordinate systems or have different spatial references. By defining the first origin coordinates of the map space corresponding to the first target geographic image data, these data from different sources can be unified into a common spatial framework.

[0099] For example, a city planning department may have both overall city image data from satellite remote sensing (using the WGS84 coordinate system) and high-precision building detail image data from a local surveying and mapping department (using a local rectangular coordinate system). By unifying them into a map space based on the first origin coordinate, data fusion can be achieved, thereby presenting the city's geographic spatial information more comprehensively and accurately.

[0100] Furthermore, when performing geospatial analysis, many algorithms and calculations rely on accurate coordinate systems. Defining the first origin coordinates can make these calculations more convenient and accurate. For example, when calculating the distance, area, or relative orientation between two geographic areas (such as two different parks), a coordinate system based on the first origin coordinates can provide a unified calculation framework. Suppose you want to analyze the distance and slope changes between different valleys in a mountainous area. By defining the first 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 geospatial parameters between them, providing strong data support for geological exploration, ecological research and other fields.

[0101] For example, you can define the upper left point of the entire map as the first origin coordinate. .

[0102] Step S302: Determine the length and width pixel sizes of each first image file that needs to be stored in the distributed database.

[0103] Specifically, in the actual application process, the storage resources of the distributed database are limited, just like a warehouse has a certain space limitation. Therefore, it is necessary to plan the size of the files stored in each distributed database. Therefore, when it is necessary to store each first image file corresponding to the first target geographic image data in each distributed database, the pixel size of the length and width of each first image file that needs to be stored in the distributed database can be determined, so that the storage plan can be deployed according to the size of each first image file.

[0104] Knowing the pixel size of the length and width of each first image file can help accurately evaluate the space required to store each first image file. For example, an image file with a pixel size of 1000×800 and an image file with a pixel size of 2000×1600 require significantly different storage spaces. By determining the pixel size, it is possible to reasonably allocate storage blocks in the database, optimize the storage layout, and avoid space waste or insufficient storage due to unreasonable storage planning. At the same time, this also helps to select a suitable storage strategy. For image files with small pixel sizes, a relatively simple storage method may be sufficient to meet the needs; for image files with large pixel sizes, it may be necessary to consider special storage strategies such as distributed storage, sharded storage, or compressed storage to ensure efficient storage and fast retrieval.

[0105] When processing the first image file, pixel size is a key factor. For example, when performing operations such as image scaling, cropping, and splicing, the size and effect of the processed image need to be calculated based on the pixel size. If you want to splice multiple first image files into a large geographic image, you need to know the pixel size of each file in order to accurately calculate the size and position relationship after splicing.

[0106] Furthermore, from the perspective of data transmission, pixel size directly affects the amount of data transmitted. Image files with large pixel sizes will take up more bandwidth and time when transmitted. In a distributed database environment, data may need to be transmitted between different nodes. Determining the pixel size can help us estimate the transmission cost, select the appropriate transmission protocol, and optimize the transmission strategy. For example, for files with large pixel sizes, block transmission or asynchronous transmission can be used to reduce the instantaneous impact on network bandwidth.

[0107] The pixel size of an image file is closely related to its resolution. Generally speaking, the more pixels there are, the higher the resolution is, 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 land boundaries and vegetation types, while low-resolution images may only provide a rough outline of the terrain. By determining the pixel size, the data quality of the image file can be better evaluated to meet the data resolution requirements of different users.

[0108] For example, for users who need high-precision geographic image data, such as those conducting detailed geological exploration or archaeological excavation of cultural relics, providing a first image file with larger length and width pixel sizes can ensure that they obtain sufficiently detailed information; while for some users who only need a rough geographic outline, such as those conducting regional tourism planning, a file with a smaller pixel size may be sufficient to meet their needs.

[0109] Step S303: determining the horizontal coordinate and the vertical coordinate of the point where the first target geographic image data starts to be stored relative to the first origin coordinate according to the first origin coordinate.

[0110] Specifically, in actual application, the position of geographic image data in the map space requires an accurate reference system to determine. As can be seen from the above introduction, the preset first origin coordinates are like the "anchor point" of the map space. Therefore, in order to better manage the first target geographic image data, after determining the first origin coordinates, the horizontal and vertical coordinates of the point where the first target geographic image data starts to be stored relative to the first origin coordinates can be determined based on the preset first origin coordinates.

[0111] By determining the horizontal coordinate and the vertical coordinate of the point where the first target geographic image data starts to be stored relative to the first origin coordinate, the geographic image data can be accurately placed at the correct position in the map space.

[0112] For example, in the storage of geographic image data of a city map, a certain point in the city center square is the first origin coordinate (0,0). If you want to store the image data of a block, you need to determine the horizontal and vertical coordinates of the point where the image data of the block starts to be stored (such as the southwest corner of the block) relative to the origin coordinates of the city center square, so that the location of the block can be accurately represented in the map space, just like accurately locating a figure on a coordinate paper.

[0113] Furthermore, geographic image data is usually composed of multiple parts, especially when dealing with large geographic areas or high-resolution images. Determining the horizontal and vertical coordinates of the point where the first target geographic image data starts to be stored relative to the origin coordinates helps to maintain spatial coherence and accuracy when splicing these image data.

[0114] Assume that there are multiple satellite remote sensing image files, each covering a certain geographic area. In order to construct a complete geographic image, these image files need to be stitched together. By determining the horizontal and vertical coordinates of the point where each image file starts to be stored relative to the origin coordinates, they can be accurately stitched at the correct position to avoid stitching errors such as image overlap or gaps, thus ensuring the integrity and accuracy of the geographic image.

[0115] When conducting geospatial analysis and query, accurate location information is crucial. By determining the horizontal and vertical coordinates of the point where the first target geographic image data begins to be stored, an accurate spatial index is provided for subsequent analysis and query.

[0116] For example, when studying land use changes, these coordinates can be used to quickly locate image data of a specific area, and then compare images from different periods to analyze changes in land use. At the same time, when users query image data of a specific geographic area, these coordinates can also help the database quickly locate and return data that meets the requirements, improving query efficiency and accuracy.

[0117] Step S304, calculating the number of rows and columns of the first target geographic image data.

[0118] Specifically, in actual applications, when storing geographic image data, it is necessary to plan the layout of data storage. For example, in a distributed database, data storage needs to efficiently use storage space. By storing geographic image data in a matrix-like manner, by determining the number of rows and columns, the storage location of image data can be accurately arranged, just like arranging a graph on a piece of graph paper.

[0119] For example, assuming that the storage nodes of a distributed database are divided according to a certain block size, if the number of rows and columns of geographic image data can be determined, it is possible to calculate how many storage blocks are needed to store the image data and which part of the image data should be stored in each storage block. This helps to avoid the waste of storage space and improve the utilization of storage resources.

[0120] Secondly, when processing geographic image data, the number of rows and columns is an important parameter. For example, when performing operations such as scaling, rotating, and cropping an image, the image size after the operation needs to be calculated based on the number of rows and columns of the original image data.

[0121] If you want to convert the first target geographic image data from one resolution to another, or from one coordinate system to another, the calculation of the number of rows and columns can help determine the size and shape of the converted image data. This is like when redrawing a painting, the size of the new painting is calculated based on the size of the original painting to ensure the accuracy and completeness of the content of the picture.

[0122] Knowing the number of rows and columns of geographic image data helps optimize the data transmission and retrieval process. In terms of data transmission, by knowing the size of the image data in advance (the number of rows and columns determines the amount of data), you can choose the appropriate transmission protocol and bandwidth allocation method. For example, for large geographic image data, if there are many rows and columns, it means that the amount of data is large, and you may need to use block transmission or compressed transmission to improve transmission efficiency. In terms of retrieval, the database can quickly locate the part of the image data required by the user based on the number of rows and columns. For example, the user may only need to retrieve certain rows or columns of the image data. Knowing the total number of rows and columns can make this partial retrieval more efficient.

[0123] Therefore, in order to better store the first target geographic image data, the number of rows and columns of the first target geographic image data may be calculated.

[0124] Step S305 : determining the number of slices on the horizontal axis and the number of slices on the vertical axis of the first target geographic image data according to the number of rows and columns of the first target geographic image data.

[0125] Specifically, in actual application, geographic image data is usually huge. In order to better slice the first target geographic image data reasonably, the number of slices on the horizontal axis and the number of slices on the vertical axis of the first target geographic image data can be determined according to the number of rows and columns of the first target geographic image data.

[0126] By sharding the first target geographic image data on the horizontal and vertical axes, it can better adapt to the storage structure of the distributed database. For example, the storage nodes of the distributed database may have storage capacity limitations. After sharding the image data, these shards can be stored on different nodes respectively to avoid excessive storage pressure on a single node, make full use of the storage space of each node, and improve the storage efficiency of the entire storage system. And the sharded geographic image data is easier to manage. Just like dividing the files in a large filing cabinet into multiple small drawers for storage, each shard can be used as an independent management unit. For example, during the data backup and recovery process, only part of the shards need to be operated instead of processing the entire huge image data, which can reduce the difficulty and time cost of backup and recovery.

[0127] Furthermore, when users request access to geographic image data, slicing can speed up data transmission. If the user only needs to view a portion of the image data (such as a local area), the horizontal and vertical slicing can quickly locate and transmit the required slice without transmitting the entire large image. For example, when network bandwidth is limited, transmitting a small slice of data is much faster than transmitting the entire large data set, which can improve the user's access experience.

[0128] Furthermore, multiple users may access different parts of the first target geographic image data at the same time. By slicing large image data, different users can access different slicings at the same time, achieving concurrent access. For example, in a geographic information system (GIS) application, some users may be viewing images of the city center, while other users are viewing images of the suburbs. The slicing of image data can meet the needs of such concurrent access and improve the overall response speed of the system.

[0129] Secondly, when processing and analyzing data, the sharded geographic image data can take advantage of distributed computing. For example, when performing some complex geographic spatial analysis on image data (such as terrain analysis, vegetation cover analysis, etc.), these analysis tasks can be assigned to different shards and performed simultaneously, and then the results can be aggregated, which can greatly shorten the data processing time and improve processing efficiency.

[0130] In actual application, sometimes, only a local area of ​​the first target geographic image data needs to be analyzed or updated. By determining the number of slices on the horizontal and vertical axes, it is easy to locate the slices that need to be processed and perform local operations. For example, when the geographical environment of a certain area changes (such as a new building), only the corresponding slices need to be updated instead of reprocessing the entire image data.

[0131] Step S306 , segmenting the first target geographic image data according to the number of segments on the horizontal axis and the number of segments on the vertical axis of the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data.

[0132] Specifically, from the above introduction, it can be known that the first target geographic image data can be considered to be sliced ​​from the horizontal axis and the vertical axis. Therefore, after determining the number of slices on the horizontal axis and the number of slices on the vertical axis of the first target geographic image data, the first target geographic image data can be sliced ​​according to the number of slices on the horizontal axis and the number of slices on the vertical axis of the first target geographic image data, so as to obtain at least one first image file corresponding to the first target geographic image data.

[0133] For example, Figure 2 and Figure 3 As shown, Figure 2 The image data indicated by the middle arrow is the original geographic image data to be stored; Figure 3 The image data indicated by the arrow in the middle is based on Figure 2 The middle arrow corresponds to an image file (ie, a first image file) obtained after slicing the original geographic image data.

[0134] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can determine the row number and column number of the first target geographic image data according to the size of the first target geographic image data, and further determine the number of slices of the first target geographic image data on the horizontal axis and the vertical axis, and on this basis, perform slice processing on the first target geographic image data, and further determine the various first image files obtained after the slice, which can effectively perform slice processing on large-volume geographic image data, effectively improve the processing efficiency of large-volume image data, and effectively ensure the security and integrity of geographic image data.

[0135] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can determine the storage directory of each first image file. The process is described below, and the process may include the following steps: Step S401, determining the horizontal coordinate and the vertical coordinate of each of the first image files relative to a preset first origin coordinate.

[0136] Specifically, it can be seen from the above introduction that using the coordinate information of each image file relative to the origin coordinate of the map space to store image files can more efficiently manage each image file. Therefore, in order to better store each first image file, after determining the first origin coordinate, the horizontal coordinate and the vertical coordinate of each first image file relative to the preset first origin coordinate can be determined. So that the storage name of each first image file can be determined according to the horizontal coordinate and the vertical coordinate of each first image file relative to the preset first origin coordinate.

[0137] Step S402, determining the machine identification code where each of the first image files is to be stored.

[0138] Specifically, in actual application, a distributed database is usually composed of multiple storage machines, each of which has its own unique identification code. Determining the machine identification code where each first image file is to be stored is like assigning a specific "room number" to each file in the storage system. For example, in a large-scale enterprise geographic image data storage system, the storage machines may be distributed in different data centers. The machine identification code can accurately know which specific server the image file will be stored on. This helps to achieve orderly storage of data and avoid data storage chaos.

[0139] Different storage machines may have differences in performance, storage capacity, etc. By determining the machine identification code where the first image file is to be stored, the file can be reasonably allocated according to the performance and load of the machine. For example, if the storage capacity of a machine is still sufficient and its processing power is strong, more first image files can be allocated to this machine for storage. In this way, the load balancing of the storage system can be achieved, avoiding the situation where some machines are overloaded while other machines are idle, thereby optimizing the resource utilization efficiency of the entire distributed database.

[0140] Furthermore, determining the machine identification code of each image file storage is crucial for data security and disaster recovery backup. In a multi-machine backup strategy, a specific machine identification code can help determine the storage location of the backup file. For example, in order to prevent data loss, some important first image files may be backed up and stored on multiple machines. The machine identification code can be used to clearly identify which machines the backup files are stored on. Moreover, in the event of an emergency such as a machine failure, the affected files can be quickly located based on the machine identification code, and appropriate recovery measures can be taken, such as obtaining data from other backup machines.

[0141] In actual application, when a user requests to access the first image file, the machine identification code can help quickly locate the location of the file, thereby improving the efficiency of data access. For example, in a distributed storage system, data access requests can be directly routed to the machine storing the file based on the machine identification code, reducing the time for data search and transmission. At the same time, for data transmission management, such as data migration or synchronization operations between different machines, the machine identification code can serve as an important identifier to ensure the accuracy and orderliness of the transmission process.

[0142] Therefore, in order to better store each first image file, the machine identification code of each first image file to be stored can be further determined, so that each first image file can be managed by using the machine identification code of each first image file to be stored.

[0143] Step S403, storing a storage directory of each first image file according to the horizontal coordinate and the vertical coordinate of each first image file relative to the first origin coordinate and the machine identification code of each first image file to be stored.

[0144] Specifically, in actual application, in order to efficiently manage each first image file, after determining the horizontal and vertical coordinates of each first image file relative to the first origin coordinates and the machine identification code where each first image file is to be stored, a storage name for each first image file can be constructed based on the horizontal and vertical coordinates of each first image file relative to the first origin coordinates and the machine identification code where each first image file is to be stored.

[0145] For example, you can define The horizontal coordinate of the point where the first target image data starts to be stored is based on the coordinates of the first origin; A ordinate of a point where the first target image data starts to be stored based on the coordinates of the first origin; is the machine identification code of the first image file to be stored; the large first target geographic image data can be split into multiple blocks horizontally and vertically, and the files are named by the block numbers of the horizontal and vertical axes plus the storage machine identification code, and the storage file name of each first image file is constructed as follows: .

[0146] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can construct a corresponding storage directory according to the storage machine identification code corresponding to the first image file, and can efficiently manage each first image file to be stored.

[0147] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can determine the second image files constituting the second target geographic image data according to the second target geographic image data. The process is described below, and the process may include: Step S501 : determining the second origin coordinates of the area corresponding to the second target geographic image data to be displayed according to a preset map display size and a preset first origin coordinate of the map space.

[0148] Specifically, in actual application, the map display size determines the spatial range of the geographic image data presented on the user interface. Just like displaying a photo in a fixed-size frame, the map display size limits the portion of the geographic image that the user can see. The first origin coordinate is the reference point of the entire geographic image data in the map space. By determining the second origin coordinate through these two factors, a specific area in the geographic image data can be accurately corresponded to the starting position of the display interface. For example, the preset map display size mentioned in the embodiment of the present application is the display area size corresponding to the geographic image data to be displayed, and the preset first origin coordinate of the map space is the origin corresponding to the coordinate system used to store the first target geographic image data; For example, in a car navigation system, the map display size is set according to the size of the car screen. The preset first origin coordinates may be a fixed point on the earth (such as the intersection of the prime meridian and the equator). When the user wants to view the geographic image data of a city, the starting position of the city image data on the display interface (i.e., the second origin coordinates) needs to be determined based on the map display size and the first origin coordinates, so that the city image can be correctly presented on the car screen.

[0149] Geographic image data is data stored based on geographic space coordinates. Data from different regions must follow certain spatial rules when splicing and displaying. Determining the second origin coordinates can ensure that when the area corresponding to the second target geographic image data is displayed, it is spatially coherent with the surrounding area (if displayed).

[0150] For example, in a large-scale geographic information system (GIS) software, users may zoom in and out on a map or pan to view different areas. By properly determining the coordinates of the second origin, when users switch from one area to an adjacent area, the image data can be seamlessly connected without any confusion or blanks. Figure 1 In this way, the starting position (second origin coordinates) of each small area (the area corresponding to the second target geographic image data) is accurate, and the display of the entire map can be accurate and coherent.

[0151] Different users may have different viewing priorities and scope requirements for geographic image data. By determining the second origin coordinates based on the map display size and the first origin coordinates, the area desired by the user can be flexibly displayed according to the user's request. For example, in a land planning department, staff may need to focus on viewing the detailed geographic image data of a certain development zone. By adjusting the map display size and using the first origin coordinates to determine the second origin coordinates, the most critical part of the development zone image data can be displayed in the center of the screen or other user-specified locations, making it easier for users to analyze and make decisions.

[0152] Therefore, when it is necessary to present the image data that the user wants to view, the second origin coordinates of the area corresponding to the second target geographic image data can be determined based on the preset map display size and the first origin coordinates of the preset map space, so that the reference point for presenting the second target geographic image data that the user wants to view can be determined.

[0153] Step S502: dividing the area displaying the second target geographic image data into at least one grid according to the second origin coordinates.

[0154] Specifically, in actual application, in order to efficiently process and manage data, after determining the second origin, the area displaying the second target geographic image data can be divided into at least one grid according to the coordinates of the second origin, wherein each grid is set to be a rectangle of the same size.

[0155] For example, in actual application, according to the origin of the map space and the pre-designed map display space size, the point in the lower left corner can be calculated as the origin coordinate of the grid display (that is, the second origin coordinate), and the map that the user wants to consult can be divided into small grids. Each grid can be a square with fixed length and width, and each grid can be represented by R****C****. For example, R0001C0001 represents a grid with both horizontal and vertical coordinates of 1.

[0156] For example, the lower left corner of the entire map to be displayed can be defined as the second origin coordinate. .

[0157] In actual application, after the display area is divided into grids, each grid is like a small data unit, and an index can be created for it. This is similar to numbering each small grid on the bookshelf in a library. For example, in a geographic information system (GIS), the grid index can be used to quickly locate and access geographic image data in a specific area. When data query, update or analysis is required for a small area, the grid index can be used to directly find the corresponding grid without searching in the entire large image data area, which greatly improves the efficiency of data processing. Secondly, grid division makes data storage and operation more modular. The data in each grid can be stored, backed up or restored independently. For example, in the data backup strategy, backup can be performed in units of grids. In this way, when data is lost or damaged, only specific grid data needs to be restored, rather than the entire huge geographic image data, which reduces the difficulty and cost of data management. In map display or geographic image display applications, grid division can achieve progressive loading and rendering. When a user opens a map or views a geographic image, the grid data near the center of the user's field of view is loaded and displayed first, and the data of other grids are gradually loaded as the user operates (such as panning and zooming). This method can effectively reduce the initial loading time and improve the user experience. For example, in an online map application, when a user views a map of a city, the grid data in the center area of ​​the screen is quickly displayed first, and the grid data in the surrounding areas is dynamically loaded according to the user's operation, making the map display smoother. Different grids can be processed and rendered in different degrees of detail according to their importance or the user's attention. For example, for the key area that the user is viewing (such as the grid around the city landmark), a higher resolution or more sophisticated rendering method can be used to display the geographic image, while for the less important surrounding areas, a lower resolution can be used, thereby optimizing the display performance while ensuring the display quality and reducing the occupation of system resources (such as memory and graphics card resources).

[0158] Grid division is conducive to regional statistics and analysis of geographic space. For example, in environmental monitoring, grids can be used to count vegetation coverage, land use type and other information in each small area. After dividing the display area into grids, it is convenient to perform separate calculations and analyses on each grid, and then summarize the analysis results of the entire area. This grid-based analysis method is more accurate and efficient than directly analyzing the entire complex geographic image data.

[0159] Grids provide a convenient framework for studying the spatial relationship between geographic elements. For example, when studying the relationship between urban traffic flow and surrounding land use, we can better understand the urban spatial structure and function by analyzing the relationship between traffic flow data and land use type data in different grids. Each grid can be regarded as an independent research unit. By comparing and analyzing the correlation between grids, various laws and patterns in geographic space can be revealed.

[0160] Step S503: determining the coordinates of each grid corresponding to the second target geographic image data according to the second origin coordinates.

[0161] Specifically, in actual application, the display of geographic image data in the user interface requires precise spatial positioning. From the above introduction, it can be seen that the method provided in the embodiment of the present application can divide the display area corresponding to the geographic image data that the user wants to consult into various grids. The coordinates of each grid are like the address of each small square on the map, which can accurately indicate its position in the entire geographic space. For example, in an urban geographic information system (GIS), a user may need to view the geographic image details of a specific block. By calculating the coordinates of each grid, each part of the block (such as the grids corresponding to buildings, streets, etc.) can be accurately positioned on the user interface, which helps users accurately understand the spatial relationship between geographic elements.

[0162] Take satellite remote sensing image data as an example. These data cover a wide geographical range and are rich in details. Computing grid coordinates allows image data acquired at different resolutions and at different times to be accurately stitched and displayed on the user interface. For example, when displaying an image of a mountainous area, the coordinates of different grids can ensure that the image parts corresponding to geographical features such as valleys and peaks are correctly combined together, providing users with accurate geospatial cognition.

[0163] When users interact with geographic image data, calculating each grid coordinate can improve the efficiency of data retrieval. For example, when a user clicks a location or delimits an area on the interface, the system can quickly locate the part that the user is interested in based on the grid coordinates. Just like in a file cabinet with a coordinate index, the corresponding file can be quickly found by the coordinates.

[0164] For some geographic information analysis functions, such as querying the land use type within a certain coordinate range or counting the number of geographic elements in a specific area, grid coordinates are the basis for realizing these functions. By calculating the grid coordinates, the system can accurately screen out grids that meet the conditions, and then analyze the geographic image data in these grids to provide users with accurate information feedback.

[0165] In the visualization process of geographic image data, grid coordinates help optimize the display effect. For example, in map zooming and panning operations, the display content can be dynamically adjusted according to the grid coordinates. When the user zooms in on the map, the system can determine the area that needs to be displayed in detail based on the grid coordinates, and present the data of the corresponding grid at a higher resolution; when the user pans the map, the grid coordinates can quickly load and update the new grid data that enters the field of view, making the display of geographic images smoother and more natural. At the same time, for some situations where other information (such as traffic flow signs, meteorological data icons, etc.) needs to be superimposed on geographic image data, grid coordinates can provide accurate location references to ensure that this information can be correctly displayed in the corresponding geographic area, enhancing the practicality and readability of geographic image data on the user interface.

[0166] Therefore, after the second origin coordinates are determined, the coordinates of each grid corresponding to the second target geographic image data can be further determined based on the second origin coordinates.

[0167] Step S504, calculating the row number and column number of each grid relative to the second origin coordinates according to the second origin coordinates and the coordinates of each grid.

[0168] Specifically, in the display and processing of geographic image data, accurate spatial positioning is crucial. Each grid has a row number and a column number corresponding to the coordinates of the second origin, just like a more detailed coordinate index for each small area on the map. For example, in a large urban geographic information system (GIS), the map data displayed on the user interface is divided into many grids. Row and column numbers can help quickly locate a specific grid, just like locating cells by rows and columns in a table. This indexing method enables the required small area to be quickly found when processing complex geographic image data, facilitating data retrieval and operation.

[0169] The row and column numbers can clearly describe the spatial relationship between each grid. For example, when analyzing the distribution pattern of geographical elements, by comparing the row and column numbers of adjacent grids, you can intuitively understand how they are arranged in space. This is very helpful for studying the continuity or change trend of geographical phenomena. For example, when studying the change of urban land use types, the change of row and column numbers from one grid to the adjacent grid can help users determine whether the land use type is gradually transitioning or suddenly changing.

[0170] In terms of map display, knowing the row and column numbers of each grid relative to the second origin coordinates helps with local rendering. For example, when the user zooms in or out of the map, the row and column numbers can be used to determine which grids need to be rendered at a higher or lower resolution. For areas that the user focuses on (which can be located by row and column numbers), high-quality rendering can be prioritized, while for less important surrounding areas, the rendering quality can be appropriately reduced. This can improve display efficiency and reduce system resource usage while ensuring display effects.

[0171] When users operate on the map interface, such as selecting an area or querying specific geographic features, the row and column numbers can help the system quickly respond to the user's request. For example, when a user clicks a point on the map, the system can quickly determine the area of ​​interest to the user based on the row and column number of the grid where the point is located, and then provide relevant geographic information, such as the place name and altitude of the area. This quick response can improve the user experience and make map applications more convenient and practical.

[0172] In some advanced geospatial analysis functions, row and column numbers are indispensable. For example, when calculating geospatial parameters such as the distance and area between two regions, the row and column numbers can accurately define the region range and then perform corresponding mathematical calculations. This provides a basis for more complex geographic analysis and decision support, such as land assessment in urban planning, traffic flow analysis, etc.

[0173] Therefore, after determining the second origin coordinates and the coordinates of each grid, the row number and column number of each grid relative to the second origin coordinates can be calculated based on the second origin coordinates and the coordinates of each grid. The formula for calculating the row number and column number of each grid relative to the second origin coordinates can include the following: The calculation formula for the row number of each grid relative to the second origin coordinates is: ; The calculation formula for the column number of each grid relative to the second origin coordinate is: ;in, is the row number of the grid relative to the coordinates of the second origin; is the column number of the grid relative to the coordinates of the second origin; is the horizontal coordinate of the grid relative to the coordinate of the second origin, is the horizontal coordinate of the second origin coordinate; is the ordinate of the grid relative to the second origin coordinate, is the ordinate of the second origin coordinate; For each grid size; Step S505, calculating the longitude and latitude information of each grid according to the row number and column number of each grid and the first origin coordinates.

[0174] Specifically, in practical applications of geographic image data, longitude and latitude are the most basic geographic spatial positioning information. On the user interface, although the relative position of each grid in the display area can be determined by the row and column numbers of the grid, this is only the position relationship at the map display level. However, the longitude and latitude information of each grid is the only thing that can link these grids with the real earth geographic coordinate system, thereby achieving accurate geographic positioning. For example, in a marine navigation system, only when the longitude and latitude information of each sea area grid displayed on the map is available can the crew accurately know their actual geographical location and ensure navigation safety.

[0175] Different geographic information system (GIS) platforms or data sources may use different coordinate systems to represent geographic image data. As a universal geographic spatial identification method, longitude and latitude information of each grid can ensure that the spatial position of geographic image data can be consistent when integrated between different platforms and with other external geographic data. For example, a project to study global climate change may need to integrate weather station data and satellite remote sensing image data from multiple countries. Only when these data are unified under the longitude and latitude coordinate system can they be accurately spatially associated and analyzed.

[0176] When conducting geospatial analysis, longitude and latitude information is the basis of many analysis methods. For example, when studying the distribution of wildlife habitats, by calculating the longitude and latitude of the grid, the geographical area where each grid is located can be accurately determined, and then the impact of natural factors such as climate and topography on wildlife habitats can be analyzed. At the same time, for users' spatial query needs, such as querying geographic image features within a certain longitude and latitude range, longitude and latitude information allows the system to quickly locate and return relevant grid data, improving query efficiency.

[0177] Many geographic models (such as flood simulation models, atmospheric circulation models, etc.) need to be run based on accurate geographic spatial locations. The longitude and latitude of the grid can provide accurate input data for these models, ensuring that the models can truly reflect the spatial distribution and changing patterns of geographical phenomena. For example, in a flood simulation model, only by knowing the longitude and latitude of each grid can we accurately simulate the flooding range and flow path of the flood in combination with geographical elements such as terrain and rivers.

[0178] For users, longitude and latitude are an intuitive way to represent geographic space. During the map display process, displaying the longitude and latitude information of each grid can help users better understand the actual geographic location represented by the geographic image data. For example, in a tourist map application, when a user views a map of a scenic spot, displaying the longitude and latitude of each grid allows the user to know the specific location of each part of the scenic spot on the earth, increasing the user's cognition and understanding of geographic space. Secondly, longitude and latitude information enables the grid of the map display area to be used in conjunction with other geographic tools, such as global positioning system (GPS) devices. For example, when a user is outdoors using a device with GPS function, the longitude and latitude information of the grid in the map application can be matched with the GPS positioning information to help users find their location on the map more accurately, and realize functions such as navigation and location sharing.

[0179] Therefore, after determining the row number and column number of each grid, the longitude and latitude information of each grid can be calculated according to the row number and column number of each grid and the coordinates of the first origin, wherein the calculation formula for calculating the longitude and latitude information of each grid can include the following: The calculation formula for the longitude of the upper left corner of each grid relative to the first origin coordinate is: ; The calculation formula for the latitude of the upper left corner of each grid relative to the first origin coordinate is: ; The calculation formula for the longitude of the upper right corner of each grid relative to the first origin coordinate is: ; The calculation formula for the latitude of the upper right corner of each grid relative to the first origin coordinate is: ;in, is the longitude of the upper left corner of each grid relative to the first origin coordinates; is the latitude of the upper left corner of each grid relative to the coordinates of the first origin; is the longitude of the upper right corner of each grid relative to the first origin coordinates; is the latitude of the top right corner of each grid cell relative to the first origin coordinates.

[0180] Step S506: construct a bounding box of each grid according to the longitude and latitude information of each grid.

[0181] Specifically, in actual application, the latitude and longitude information of each grid determines its position on the earth's surface, but is not accurate enough to define its range. In actual application, the specific boundaries of each grid in geographic space can be clarified by constructing a bounding box.

[0182] For example, in urban planning, when analyzing land use in different blocks (which can be seen as grids), the bounding box can clearly divide the scope of each block and avoid data confusion. This is just like dividing a piece of land, the bounding box is a fence that clearly defines the boundaries of each piece of land, ensuring that the geographic image data of each grid is spatially independent and clearly defined.

[0183] When processing large amounts of geographic image data, bounding boxes help isolate data from different grids. For example, in a distributed storage system, the data of each grid can be stored in different storage units or areas based on the bounding box, which facilitates data management and maintenance. At the same time, when updating or modifying data, only the data within a specific bounding box needs to be operated without affecting the data of other grids, which improves the accuracy and efficiency of data processing.

[0184] The bounding box provides a clear area range for various geospatial analyses. For example, in environmental monitoring, to calculate the vegetation coverage in an area, statistics can be performed using the bounding box of each grid. The bounding box can accurately determine which geographic elements (such as vegetation) are located within the grid, allowing for accurate calculations and analysis. This bounding box-based analysis method is more intuitive and convenient than directly using longitude and latitude information, and can better support quantitative analysis of geospatial data.

[0185] Bounding boxes are an important tool when studying the spatial relationship between geographic features. For example, when analyzing the relationship between traffic flow and surrounding land use, by comparing the intersection and inclusion of different grid bounding boxes, the spatial connection between traffic nodes and different functional areas (such as commercial areas and residential areas) can be determined. This helps to deeply understand the interaction between geographical phenomena and provide decision support for urban planning, traffic optimization and other fields.

[0186] During the map display process, the bounding box can enhance the visualization of geographic image data. For example, when users view the geographic features of different areas in a map application, displaying the bounding box of each grid can make the regional division more obvious. This is very helpful for users to understand the hierarchy and distribution patterns of geographic space, just like outlining the scope of each area on the map with clear lines, so that users can see it at a glance. When users interact with the map, the bounding box can provide better support. For example, when a user selects an area on the map for detailed viewing or inquiries about related information, the bounding box can help the system quickly determine the grid corresponding to the area selected by the user, and then provide accurate geographic image data and related information. This improves the efficiency and accuracy of user interaction and enhances the user experience.

[0187] Therefore, after determining the longitude and latitude information of each grid, the bounding box of each grid can be constructed according to the longitude and latitude information of each grid.

[0188] Step S507 , querying the first target image files involved in all grids according to the boundary box of each grid, and obtaining a list of first target image files to be merged and displayed.

[0189] Specifically, after constructing the bounding box of each grid, the boundary of the image data corresponding to each grid can be well determined. Therefore, after determining the bounding box of each grid, the first target image files involved in all grids can be queried based on the bounding box of each grid to obtain a list of first target image files to be merged and displayed.

[0190] Each grid may correspond to a first target image file, or may correspond to a portion of data in the first target image file.

[0191] Step S508: taking each first target image file involved in the list of first target image files to be merged and displayed as a respective second image file corresponding to the second target geographic image data, wherein each second image file corresponds to one first target image file.

[0192] Specifically, it can be seen from the above introduction that the area corresponding to the display of the second target geographic image data is divided into multiple grids. Then the image files corresponding to each grid can be pieced together to form the second target geographic image data. Therefore, after determining the first target image file list corresponding to the first target image files of all grid designs, each first target image file involved in the first target image file list to be merged and displayed can be used as a respective second image file corresponding to the second target geographic image data, wherein each second image file can correspond to a first target image file, or each second image file can correspond to a portion of data in a first target image file.

[0193] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can divide the corresponding display area according to the second target geographic image data that the user wants to view, and refer to the first origin corresponding to the first target geographic image data to determine the various second image files that constitute the second target geographic image data, so as to efficiently extract the geographic image data files that the user wants to view.

[0194] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can merge the second image files and display them to the user according to the preset map display size. The process is described below, and the process may include the following steps: Step S601 , according to the first image file list to be combined and displayed, each first image file involved in the first image file list to be combined and displayed is read from each distributed database as each second image file corresponding to the second target geographic image data.

[0195] Step S602, based on the preset map display size, the first origin coordinates, the resolution of each of the second image files, and the longitude and latitude information corresponding to the bounding box of the grid corresponding to each of the second image files, merge the second image files and display them to the user.

[0196] Specifically, from the above introduction, it can be seen that the map display size determines the physical space range of the geographic image data presented in the user interface. Just like putting a large painting into a fixed-size frame, the painting (geographic image) needs to be arranged reasonably according to the size of the frame (map display size). By considering the map display size and the resolution of each second image file and the latitude and longitude information of the grid bounding box, it can be ensured that the geographic image can be completely and accurately filled in the space specified by the user interface. For example, in a map application on a mobile device, the screen size (map display size) is small, and the way to merge images needs to be adjusted according to this size and the resolution and bounding box information of the image file to make the geographic image fit the screen display and avoid the image exceeding the screen range or being incompletely displayed on the screen.

[0197] The first origin coordinates corresponding to the stored geographic image data are the reference of the entire geographic image space. The longitude and latitude information of the grid bounding box corresponding to each second image file is used to determine the position of the image in the geographic space. When merging the second image files, these coordinate information ensures the correct splicing of the images in space. For example, when splicing multiple satellite remote sensing images to construct a complete map of the earth's surface, the first origin coordinates and the longitude and latitude information of the bounding box are like reference marks for the puzzle, allowing the images to be merged according to the correct geographical location, thereby providing users with an accurate geospatial view.

[0198] Each secondary image file has its own resolution, which determines the level of detail in the image. During the merging process, these different resolutions need to be taken into account to ensure that the geographic image displayed to the user is coherent and reasonable in detail. For example, when merging a high-resolution image of the city center area with a relatively low-resolution image of the city suburbs, it is necessary to reasonably transition and integrate them according to their resolutions and corresponding grid bounding boxes, so that the geographic image of the entire city can show both the fine details of the city center and the relatively simple image part of the suburbs naturally, avoiding abrupt changes in image details.

[0199] Therefore, by combining the preset map display size, resolution and bounding box information, the quality of the merged image can be optimized. For example, for the area of ​​focus in the center of the user's field of view (determined by the bounding box and display size), a high-resolution second image file can be used for a more detailed display; for the surrounding area, the display requirements can be appropriately lowered to balance the image quality and system performance. This can improve display efficiency and reduce resource usage while ensuring the overall quality of the geographic image. Merging the various second image files can integrate the scattered geographic image data into complete map information for display to the user. For example, a user wants to view a map of a larger geographic area (such as a state in a country), which may be composed of multiple second image files. By merging these files, the user can see a complete map containing various geographic elements such as the state's terrain, cities, and transportation, meeting the user's needs for obtaining comprehensive geographic information.

[0200] Therefore, based on the preset map display size, the coordinates of the first origin, the resolution of each second image file, and the latitude and longitude information corresponding to the bounding box of the grid corresponding to each second image file, each second image file is merged and displayed to the user, which can provide the user with a seamless geographic image display experience. When the user performs operations such as zooming and panning, the image can dynamically adjust the merging method according to factors such as the map display size, resolution, and bounding box, so that the changes in the geographic image are natural and smooth. For example, when the user zooms in on the map to view the details of a town, the new second image file can be merged in the correct way, allowing the user to clearly see the details of the town's streets, buildings, etc., without image tearing or discontinuity, thereby improving the user experience.

[0201] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can merge the second image files and display them to the user based on the preset map display size, the first origin coordinates, the resolution of each second image file, and the longitude and latitude information corresponding to the bounding box of the grid corresponding to each second image file, thereby effectively improving the processing efficiency and display effect of the geographic image data that the user wants to view, and can efficiently process and stitch image data files stored in different data, so that the stitched image files are closer to the original geographic image data effect, effectively improving the user experience.

[0202] For example, Figure 4 and Figure 5 As shown, Figure 4 It is the original geographic image data before being fragmented, corresponding to the slice data stored in each distributed database. Figure 5 The image data in the part indicated by the arrow is the combined image effect after the image files extracted from the distributed database are spliced ​​together, where: Figure 5The image of the part indicated by the arrow is Figure 4 The images indicated by the arrows in the figure are images with the same longitude and latitude information. Figure 4 and Figure 5 It can be seen from the display effect that the image effect spliced ​​together from the various image files extracted from the distributed database is almost the same as the original geographic image data effect.

[0203] The following describes a distributed structured storage device for geographic image data provided by an embodiment of the present application. The distributed structured storage device for geographic image data described below and the distributed structured storage method for geographic image data described above can be referred to in correspondence with each other. Figure 6 , Figure 6 FIG. 1 is a schematic diagram of a distributed structured storage device for geographic image data disclosed in an embodiment of the present application. Figure 6 As shown, the geographical image data distributed structured storage device may include: The data acquisition unit 101 is used to acquire the first target geographic image data to be stored; A scheme determining unit 102 is used to determine a slicing scheme of the first target geographic image data, and perform slicing processing on the first target geographic image data according to the slicing scheme of the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data; A directory determining unit 103, used for determining a storage directory of each of the first image files, wherein the storage directory of each of the first image files includes a machine identification code to be stored; The encryption unit 104 is used to encrypt each of the first image files and store them in the corresponding distributed database according to the storage name of each of the first image files.

[0204] It can be seen from the technical solutions introduced above that when it is necessary to store geographic image data, the device provided by the embodiment of the present application can obtain the first target geographic image data to be stored. Since the first target geographic image data is large in size, direct storage is prone to information leakage and does not comply with relevant data storage regulations. Therefore, it can be considered to be sliced ​​before storage. Therefore, after obtaining the first target geographic image data, the slicing scheme of the first target geographic image data can be determined, and according to the slicing scheme of the first target geographic image data, the first target geographic image data is sliced ​​to obtain at least one first image file corresponding to the first target geographic image data; wherein the size of each first image file obtained by slicing is smaller than the size of the first target geographic image data. After slicing the first target geographic image data, the storage name of each first image file can be further determined, wherein the storage name of each first image file includes the machine identification code to be stored; so that each first image file can be encrypted and stored in the corresponding distributed database according to the storage name of each first image file.

[0205] It can be seen from this that the device provided by the embodiment of the present application can effectively split large volumes of geographic image data and then encrypt and store them, which can effectively increase the security and privacy of geographic information data storage, and can effectively reduce the duplication of geographic spatial information collection. Furthermore, by dividing large volumes of image data into small volumes of data files for storage, the data collection path can be better planned, and repeated collection due to storage of too large image data can be reduced. In addition, by storing large volumes of geographic image data in pieces, the processing speed of image data can be effectively improved, and the reading and writing efficiency of image data can be improved. While effectively ensuring the integrity of the original geographic image data, the efficiency of acquiring geographic image data can also be improved, and the efficiency of merging and displaying image data can be improved.

[0206] Among them, the specific processing flow of each unit included in the above-mentioned distributed structured storage device for geographic image data can refer to the relevant introduction of the distributed structured storage method for geographic image data in the previous text, and will not be repeated here.

[0207] The distributed structured storage device for geographic image data provided in the embodiment of the present application can be applied to distributed structured storage devices for geographic image data, such as terminals: mobile phones, computers, etc. Optionally, Figure 7 The hardware structure diagram of the distributed structured storage device for geographic image data is shown in FIG. Figure 7, the hardware structure of the distributed structured storage device for geographic image data may 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 embodiment of the present application, the number of processor 1, communication interface 2, memory 3 and communication bus 4 is at least one, and the processor 1, communication interface 2 and memory 3 communicate with each other through the communication bus 4. Processor 1 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement the embodiment of the present application; memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as 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 distributed structured storage solution of the aforementioned terminal geographic image data. The embodiment of the present application also provides a readable storage medium, which can store a program suitable for execution by the processor, and the program is used to: implement each processing flow of the aforementioned terminal in the distributed structured storage solution of geographic image data.

[0208] Finally, it should be noted that, in this article, relational terms such as first and second, etc. 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 these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0209] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0210] The above description of the disclosed embodiments enables professionals and technicians in the field to implement or use the present application. Various modifications to these embodiments will be apparent to professionals and technicians in the field, 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 distributed structured storage method for geographic image data, characterized in that: include: Acquire first target geographic image data to be stored; Determine a slicing scheme for the first target geographic image data, and perform slicing processing on the first target geographic image data according to the slicing scheme for the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data; Determining a storage directory for each of the first image files, wherein the storage directory for each of the first image files includes a machine identification code to be stored; According to the storage name of each of the first image files, each of the first image files is encrypted and stored in a corresponding distributed database.

2. The method according to claim 1, characterized in that: The method further includes: Analyze the user's browsing needs and determine the second target geographic image data requested by the user; Determining, according to the second target geographic image data, each second image file constituting the second target geographic image data; Determine a distributed database storing each of the second image files; Reading each of the second image files from a distributed database storing each of the second image files; According to a preset map display size, each of the second image files is merged and displayed to the user.

3. The method according to claim 1, characterized in that The step of determining a slicing scheme for the first target geographic image data and slicing the first target geographic image data according to the slicing scheme for the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data includes: Define the first origin coordinates of the map space corresponding to the first target geographic image data; Determine the length and width pixel size of each first image file to be stored in the distributed database; Determine, based on the first origin coordinates, the horizontal coordinate and the vertical coordinate corresponding to the point where the first target geographic image data starts to be stored relative to the first origin coordinates; Calculate the number of rows and columns of the first target geographic image data; Determine the number of slices on the horizontal axis and the number of slices on the vertical axis of the first target geographic image data according to the number of rows and columns of the first target geographic image data; The first target geographic image data is segmented according to the number of segments on the horizontal axis and the number of segments on the vertical axis of the first target geographic image data to obtain at least one first image file corresponding to the first target geographic image data.

4. The method according to claim 3, characterized in that The step of determining the storage directory of each of the first image files comprises: Determine the horizontal coordinate and the vertical coordinate of each of the first image files relative to the first origin coordinate; Determining a machine identification code where each of the first image files is to be stored; A storage directory for each of the first image files is stored according to the horizontal and vertical coordinates of each of the first image files relative to the first origin coordinates and the machine identification code structure where each of the first image files is to be stored.

5. The method according to claim 2, characterized in that: The step of determining, based on the second target geographic image data, each second image file constituting the second target geographic image data comprises: Determining the second origin coordinates of the area corresponding to the second target geographic image data according to a preset map display size and a preset first origin coordinate of the map space, wherein the preset map display size is the display area size corresponding to the geographic image data to be displayed, and the preset first origin coordinate of the map space is the origin corresponding to the coordinate system used to store the first target geographic image data; Dividing the area displaying the second target geographic image data into at least one grid according to the second origin coordinates, wherein each of the grids is a rectangle of the same size; Determine the coordinates of each grid corresponding to the second target geographic image data according to the second origin coordinates; According to the coordinates of the second origin and the coordinates of each of the grids, respectively calculating the row number and column number of each of the grids relative to the coordinates of the second origin; Calculating the longitude and latitude information of each grid according to the row number and column number of each grid and the first origin coordinates; Constructing a bounding box of each of the grids according to the latitude and longitude information of each of the grids; According to the bounding box of each of the grids, query the first target image files involved in all the grids to obtain a list of first target image files to be merged and displayed, wherein each of the grids corresponds to a first target image file; Each first target image file involved in the list of first target image files to be merged and displayed is used as a respective second image file corresponding to the second target geographic image data, wherein each second image file corresponds to one first target image file.

6. The method according to claim 5, characterized in that The step of merging the second image files and displaying the combined images to the user according to the preset map display size includes: According to the list of first image files to be combined and displayed, each first image file involved in the list of first image files to be combined and displayed is read from each distributed database as each second image file corresponding to the second target geographic image data; Based on the preset map display size, the first origin coordinates, the resolution of each of the second image files, and the longitude and latitude information corresponding to the bounding box of the grid corresponding to each of the second image files, the second image files are merged and displayed to the user.

7. The method according to claim 5, characterized in that The formula for calculating the row number and column number of each grid relative to the second origin coordinate includes: The calculation formula of the row number of each grid relative to the second origin coordinate is: ; The calculation formula of the column number of each grid relative to the second origin coordinate is: ; in, is the row number of the grid relative to the coordinates of the second origin; is the column number of the grid relative to the coordinates of the second origin; is the horizontal coordinate of the grid relative to the coordinate of the second origin, is the horizontal coordinate of the second origin coordinate; is the ordinate of the grid relative to the coordinates of the second origin, is the ordinate of the second origin coordinate; is the size of the grid; The calculation formula for calculating the longitude and latitude information of each grid according to the row number and column number of each grid and the first origin coordinates includes: The calculation formula for the longitude of the upper left corner point of each grid relative to the first origin coordinate is: ; The calculation formula for the latitude of the upper left corner point of each grid relative to the first origin coordinate is: ; The calculation formula for the longitude of the upper right corner point of each grid relative to the first origin coordinate is: ; The calculation formula for the latitude of the upper right corner point of each grid relative to the first origin coordinate is: ; in, is the longitude of the upper left corner point of each of the grids relative to the first origin coordinates; is the latitude of the upper left corner point of each of the grids relative to the first origin coordinates; is the longitude of the upper right corner point of each of the grids relative to the first origin coordinates; is the latitude of the upper right corner point of each of the grids relative to the first origin coordinates.

8. A distributed structured storage device for geographic image data, characterized in that: include: A data acquisition unit, used for acquiring first target geographic image data to be stored; a scheme determining unit, configured to determine a slicing scheme for the first target geographic image data, and perform slicing processing on the first target geographic image data according to the slicing scheme for the first target geographic image data, so as to obtain at least one first image file corresponding to the first target geographic image data; A name determination unit, used for a storage name of each of the first image files, wherein the storage name of each of the first image files includes a machine identification code to be stored; The encryption unit is used to encrypt each of the first image files according to the storage name of each of the first image files and store them in the corresponding distributed database.

9. A distributed structured storage device for geographic image data, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, and when the computer-readable instructions are executed by the one or more processors, the steps of the distributed structured storage method for geographic image data as described in any one of claims 1 to 7 are implemented.

10. A readable storage medium, characterized in that: The readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by one or more processors, the one or more processors implement the steps of the distributed structured storage method for geographic image data as described in any one of claims 1 to 7.

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