Method for slicing and processing regular geographic image data and related devices

Abstract

The present application provides a method for slicing and processing regular geographic image data and related devices. The present application can effectively split large-volume geographic image data and then perform encrypted storage, which can effectively enhance the security and privacy of the storage of geographic information data, can effectively reduce the repetition of geographic space information collection. Further, by slicing the large-volume geographic image data into small-volume image data files for storage, it is possible to better plan the image data acquisition path, reduce repeated acquisition due to storing overly large image data, and storing the large-volume geographic image data after slicing can effectively improve the processing speed of the image data and the read / write efficiency of the image data. While effectively ensuring the integrity of the original geographic image data, it can also improve the acquisition efficiency of regular geographic image data and enhance the merging and display efficiency of regular geographic image data.

Description

Technical Field

[0001] This application relates to the technical field of data storage, and particularly to a method for slicing and processing regular geographic image data and related devices. Background Art

[0002] In the current field of image data storage technology, pixels are generally stored in the form of files, and a representative example is the GeoTIFF format. In this storage method, when the resolution of image data continues to increase and the covered geographical space range becomes wider, the volume of the generated files will show a sharp growth trend. Although these large-volume image data files are excellent in terms of clarity, being able to contain richer and more detailed data and providing users with clearer and more comprehensive image information, 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 possibilities of problems such as data leakage, tampering, and loss also increase significantly.

[0003] In actual image storage practices, in order to more effectively manage and process large-scale image data, slicing the image data is a common operation strategy. However, unfortunately, many existing image formats do not give clear, unified, and authoritative standard specifications for the size of image blocks. This lack is particularly prominent when dealing with image data that has high requirements for processing speed. Especially, there are still great difficulties in processing regular geographic image data. Due to the lack of clear standard guidance, in order to pursue the simplicity of the storage process, the vast majority of image data is only sliced into a very limited number of pieces during slicing. This overly rough slicing method directly leads to generally low efficiency in the read and write operations of these image data. When reading or writing these image data, the system needs to consume a large amount of time and resources, seriously affecting the timeliness and smoothness of image data display and subsequent processing, bringing great inconvenience to related work and applications, and unable to meet the modern society's demand for efficient and fast processing of image data. Summary of the Invention

[0004] This application aims to at least solve one of the above technical defects. In view of this, this application provides a method for slicing and processing regular geographic image data and related devices, which is used to solve the technical defect that image data cannot be efficiently processed in the prior art.

[0005] A method for slicing and processing regular geographic image data includes: obtaining target geographic image data to be stored; performing slicing and processing on the target geographic image data according to a preset slicing rule to obtain at least one first target image file corresponding to the target geographic image data; constructing a storage file name for each first target image file according to the origin coordinate information of the preset map space; and encrypting and storing each first target image file into a corresponding distributed database according to the storage file name of each first target image file.

[0006] Preferably, the step of performing slicing and processing on the target geographic image data according to a preset slicing rule to obtain at least one first target image file corresponding to the target geographic image data includes: calculating the size of the first target image file starting from the origin of the preset map space, and determining whether the origin of the target geographic image data coincides with the origin of the preset map space. If the origin of the target geographic image data coincides with the origin of the preset map space, then slice the target geographic image data starting from the origin of the target geographic image data according to the size of the preset image file. If the origin of the target geographic image data does not coincide with the origin of the preset map space, then slice the target geographic image data starting from the starting point of the first calculated first target image file according to the size of the preset image file. After performing slicing and processing on the target geographic image data, determine whether there is a second target image file with a volume smaller than the size of the preset image file among the first target image files obtained after slicing the target geographic image data. If there is a second target image file, then append a part of pixel points to the second target image file from the first target image file adjacent to the second target image file in the geographic space, so that the size of the second target image file is the same as the size of the preset image file. Use each image file finally obtained after slicing and processing the target geographic image data as each first target image file constituting the target geographic image data.

[0007] Preferably, constructing the storage file name of each of the first target image files according to the origin coordinate information of the preset map space includes: determining the pixel sizes of the length and width of each first target image file to be stored in the distributed database according to the size of the preset image file; determining the abscissa and ordinate corresponding to the origin coordinate of the preset map space of the point where the target geographic image data starts to be stored according to the origin coordinate of the preset map space; calculating the abscissa and ordinate of each first target image file relative to the origin coordinate of the preset map space based on the abscissa and ordinate corresponding to the origin coordinate of the preset map space of the point where the target geographic image data starts to be stored; determining the storage machine code of each first target image file based on the number of each first target image file and the number of storage machines, wherein the image files stored on each storage machine cannot be the image files corresponding to the geographic space with a volume exceeding the preset size; constructing the storage file name of each first target image file according to the storage machine code of each first target image file and the abscissa and ordinate of each first target image file relative to the origin coordinate of the preset map space.

[0008] Preferably, determining the storage machine code of each first target image file based on the number of each first target image file and the number of storage machines includes: judging whether the number of storage machines is greater than a preset first threshold; if the number of storage machines is less than or equal to the preset first threshold, determining the storage machine code of each first target image file according to a preset first storage strategy; if the number of storage machines is greater than the preset first threshold, determining the storage machine code of each first target image file according to a preset second storage strategy.

[0009] Preferably, determining the storage machine code of each of the first target image files according to a preset first storage policy includes: storing the first target image files with odd abscissa values and odd ordinate values in the machine ranked first, and using the serial number corresponding to the stored machine as the storage machine code of the first target image files with odd abscissa values and odd ordinate values; storing the first target image files with odd abscissa values and even ordinate values in the machine ranked second, and using the serial number corresponding to the stored machine as the storage machine code of the first target image files with odd abscissa values and even ordinate values; storing the first target image files with even abscissa values and odd ordinate values in the machine ranked third, and using the serial number corresponding to the stored machine as the storage machine code of the first target image files with odd abscissa values and odd ordinate values; storing the first target image files with even abscissa values and even ordinate values in the machine ranked fourth, and using the serial number corresponding to the stored machine as the storage machine code of the first target image files with even abscissa values and even ordinate values; wherein, the storage machine code of each storage machine is set according to the creation time or the capacity of the storage machine, and the storage machine code of each machine is the serial number corresponding to the storage machine.

[0010] Preferably, determining the storage machine code of each of the first target image files according to a preset second storage policy includes: dividing all storage machines into two groups, namely an odd storage machine group and an even storage machine group, where each storage machine group includes at least two storage machines, and the storage machine codes of the corresponding storage machines in each storage machine group are set according to the creation time or the capacity of the storage machine, and the storage machine code of each storage machine is the serial number corresponding to the storage machine; storing the first target image files with odd abscissa values in the storage machines of the odd storage machine group according to a first storage rule, and using the storage machine code of the storage machine in the odd storage machine group that stores each first target image file with an odd abscissa value as its storage machine code; storing the first target image files with even abscissa values in the storage machines of the even storage machine group according to a second storage rule, and using the storage machine code of the storage machine in the even storage machine group that stores each first target image file with an even abscissa value as its storage machine code.

[0011] Preferably, the first storage rule includes: sorting all the storage machines in the odd-numbered storage machine group according to the creation time or the capacity of the storage machine, randomly selecting one from all the first target image file sets with odd abscissa values that have not been stored and storing it in the first target storage machine at the head of the storage machine queue in the odd-numbered storage machine group, then adjusting the first target storage machine to the end of the storage machine queue, readjusting the storage machine queue, continuing to use the storage machine currently at the head of the storage machine queue as the first target storage machine, and then returning to execute the operation of randomly selecting one from all the first target image file sets with odd abscissa values that have not been stored and storing it in the first target storage machine at the head of the storage machine queue in the odd-numbered storage machine group until all the first target image files with odd abscissa values to be stored are stored in each storage machine in the odd-numbered storage machine group; the second storage rule includes: sorting all the storage machines in the even-numbered storage machine group according to the creation time or the capacity of the storage machine, randomly selecting one from all the first target image file sets with even abscissa values that have not been stored and storing it in the second target storage machine at the head of the storage machine queue in the even-numbered storage machine group, then adjusting the second target storage machine to the end of the storage machine queue, readjusting the storage machine queue, continuing to use the storage machine currently at the head of the storage machine queue as the second target storage machine, and then returning to execute the operation of randomly selecting one from all the first target image file sets with even abscissa values that have not been stored and storing it in the second target storage machine at the head of the storage machine queue in the even-numbered storage machine group until all the first target image files with even abscissa values to be stored are stored in each storage machine in the even-numbered storage machine group.

[0012] A regular geographic image data slicing processing device includes: an acquisition unit for acquiring target geographic image data to be stored; a slicing unit for performing slicing processing on the target geographic image data according to a preset slicing rule to obtain at least one first target image file corresponding to the target geographic image data; a construction unit for constructing a storage file name for each of the first target image files according to the origin coordinate information of a preset map space; and a storage unit for encrypting and storing each of the first target image files in a corresponding distributed database according to the storage file name of each of the first target image files.

[0013] A regular geographic image data slicing processing device, comprising: one or more processors, and a memory; computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the one or more processors, the steps of the regular geographic image data slicing processing method described in any one of the foregoing introductions are implemented.

[0014] A readable storage medium, in which computer-readable instructions are stored, and when the computer-readable instructions are executed by one or more processors, the one or more processors are caused to implement the steps of the regular geographic image data slicing processing method described in any one of the foregoing introductions.

[0015] As can be seen from the technical solutions introduced above, the method provided by the embodiments of the present application can obtain target geographic image data to be stored; in the actual application process, since geographic image data generally has a relatively large volume, and generally speaking, geographic image data is regular image data. If the directly obtained target geographic image data is directly stored, it may not be stored and does not meet the relevant requirements for storing image data, and information leakage problems are likely to occur. Therefore, it can be considered to perform slicing processing on the target geographic image data before storing. Therefore, in order to efficiently manage the obtained target geographic image data, after obtaining the target geographic image data, the obtained target geographic image data can be sliced according to a preset slicing rule, and at least one first target image file corresponding to the target geographic image data can be obtained; wherein, the size of each of the segmented first target image files is smaller than the size of the target geographic image data. In order to efficiently store and manage each first target image file, it can be considered to store each first target image file dispersedly. Therefore, after the target geographic image data is sliced, the storage file name of each first target image file can be constructed according to the preset origin coordinate information of the map space; so that each first target image file can be encrypted and then stored in the corresponding distributed database according to the storage file name of each first target image file. As can be seen from the above introduction, through the method provided by the embodiments of the present application, large-volume geographic image data can be effectively split and then encrypted and stored, which can effectively increase the security and privacy of storing geographic information data, can effectively reduce the repetition of geographic space information collection. Further, splitting large-volume geographic image data into small-volume image data files for storage can better plan the image data collection path, reduce repeated collection due to storing overly large image data, and storing large-volume geographic image data in slices can effectively improve the processing speed of image data and the reading and writing efficiency of image data, while effectively ensuring the integrity of the original geographic image data, and can also improve the acquisition efficiency of regular geographic image data and enhance the combined display efficiency of regular geographic image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0017] Figure 1 It is a flowchart of a method for slicing and processing regular geographic image data provided by an embodiment of the present application; Figures 2 - 3 It is a storage schematic diagram exemplified by an embodiment of the present application; Figure 4 It is a schematic structural diagram of a device for slicing and processing regular geographic image data exemplified by an embodiment of the present application; Figure 5 It is a hardware structure block diagram of a device for slicing and processing regular geographic image data disclosed by an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0018] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0019] In view of the fact that most of the current slicing and processing solutions for regular geographic image data are difficult to adapt to complex and changing business requirements, the applicant has studied a slicing and processing solution for regular geographic image data. This method for slicing and processing regular geographic image data can effectively split large-volume geographic image data and then perform encrypted storage, which can effectively increase the security and privacy of geographic information data storage, effectively reduce the repetition of geographic space information collection. Further, slicing large-volume geographic image data into small-volume image data files for storage can better plan the image data acquisition path, reduce repeated acquisition due to storing overly large image data, and storing the large-volume geographic image data in slices can effectively improve the processing speed of the image data and the reading and writing efficiency of the image data. While effectively ensuring the integrity of the original geographic image data, it can also improve the acquisition efficiency of regular geographic image data and enhance the merged display efficiency of regular geographic image data.

[0020] The method provided by the embodiments of the present application can be used in numerous general-purpose or special-purpose 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 equipment, and so on. The embodiments of the present application provide a method for slicing regular geographic image data, which can be applied to various geographic image data systems, and can also be applied to various computer terminals or intelligent terminals. The execution subject can be the processor or server of a computer terminal or intelligent terminal.

[0021] Next, in combination with Figure 1 , the process of the method for slicing regular geographic image data given by the embodiments of the present application will be introduced. As Figure 1 shown, this process may include the following steps:

[0022] Step S101, obtain the 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. For example, the images of the Earth's surface obtained by satellite remote sensing, including information such as terrain, vegetation cover, and water body distribution, are the initial data sources for carrying out various geographic research and applications. A complete geographic image data set is necessary for accurately describing geographic phenomena and spatial relationships, and obtaining and storing geographic image data is very important in the actual application process. Geographic image data can have various formats. Common ones such as the TIFF (Tagged Image File Format) format. The 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 and projection information. Many satellite remote sensing images and aerial photography images use this format. The JPEG (Joint Photographic Experts Group) format is also relatively commonly used. The JPEG (Joint Photographic Experts Group) format stores images in a compressed manner, which can reduce the file size while ensuring a certain image quality, facilitating data transmission and storage, but its ability to store geographic spatial information is relatively weak. GeoTIFF is an extension of the TIFF format, specifically for storing geographic spatial data, adding geographic reference information such as a geographic coordinate system and map projection on the basis of ordinary TIFF, enabling the image data to accurately correspond to the actual geographic spatial position.

[0024] In the actual application process, geographic image data usually has a large data volume. Geographic image data generally contains rich geographic spatial information, and the higher the resolution, the larger the data volume. For example, for high-resolution satellite remote sensing images, the data volume of a panchromatic band image (with a resolution of up to 0.3 - 1 meter) may reach several hundred megabytes or even several gigabytes. For multi-spectral or hyperspectral images, due to containing information of multiple bands, their data volume is even larger.

[0025] The data volume of the image obtained by aerial photography cannot be underestimated either. Especially when conducting large-scale mapping projects, in order to ensure sufficient accuracy and coverage, a large number of images will be taken, and the total data volume of these image data will be very large.

[0026] In the actual application process, geographic image data carries spatial reference information, which mainly includes geographic coordinate systems. These coordinate systems define how positions on the earth are represented. Different coordinate systems are applicable to different application scenarios and regions. There is also map projection information. Map projection is a method of converting the three-dimensional coordinates on the earth's surface into two-dimensional plane coordinates. Common projection methods include Gauss-Kruger projection, Mercator projection, etc. 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 correctly using and processing geographic image data.

[0027] 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 satellite orbit altitude and sensor performance. For example, the images obtained by high-orbit satellites may have lower resolution and relatively poor accuracy; while the images of low-orbit high-resolution commercial satellites have higher accuracy. The accuracy of aerial photography images is related to flight altitude, camera performance, etc. The images obtained by low-altitude flight generally have higher accuracy, but the coverage range is relatively small; the images obtained by high-altitude flight have a large coverage range, but the accuracy may be reduced. At the same time, operations such as registration and calibration in the data processing process will also affect the final accuracy of the data.

[0028] Geographic image data reflects the geographic information at a specific time. For example, geographic image data can reflect the changes in the natural environment (such as the seasonal growth and withering of vegetation, the changes in the water levels of rivers and lakes, etc.) and human activities (such as urban construction, land development, etc.). This makes the timeliness of geographic image data very important. For example, for agricultural monitoring, it is necessary to obtain images in different growth seasons to accurately evaluate the growth status of crops; for urban expansion research, it is necessary to obtain images regularly to track the changes in the urban boundary.

[0029] Therefore, in order to make better use of geographic image data, the collected geographic image data can be saved. Before saving the geographic image data, the target geographic image data to be stored can be obtained so that the target geographic image data can be stored according to the characteristics of the target geographic image data.

[0030] Step S102, slicing the target geographic image data according to a preset slicing rule to obtain at least one first target image file corresponding to the target geographic image data.

[0031] 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 target geographic image data to be stored into multiple smaller and more manageable parts before storing them.

[0032] 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.

[0033] Secondly, different storage devices have different storage capacities and performance characteristics. After the target geographic image data is sliced, the target geographic image data can be allocated to the most appropriate storage location according to the actual situation of the storage device.

[0034] 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.

[0035] 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.

[0036] Furthermore, for complex geographical image data processing algorithms, processing the entire large geographical image dataset may face problems such as insufficient memory and excessive computing time. After slicing the large geographical image data, the size of each image file is appropriate, and the processing tasks can be executed more effectively under the limitations of existing computing resources. For example, when performing geometric correction of an image, operating on the sliced image files only requires processing a relatively small part each time, reducing the algorithm complexity and the requirements for hardware resources.

[0037] In the actual application process, when geographical image data needs to be transmitted over a network, smaller sliced image files are more convenient for transmission than the entire large geographical image dataset. On the one hand, the sliced image files can select appropriate transmission strategies according to network bandwidth and transmission requirements, such as transmitting in batches or at different times, to avoid network congestion caused by transmitting a large amount of data at once. On the other hand, when network interruptions occur, only some of the sliced image files need to be retransmitted instead of the entire large geographical image data, reducing data transmission losses and repetitive work. For example, when transmitting geographical image data from a data center to a remote client, after slicing the large geographical image data, the critical sliced image data can be transmitted first according to the user's immediate needs, effectively improving the timeliness and reliability of data transmission. Therefore, in order to better store and utilize the target geographical image data to be stored, after obtaining the target geographical image data, it can be further sliced and then stored.

[0038] In the actual application process, geographical image data is generally divided into regular geographical image data and irregular geographical image data. Among them, regular geographical image data generally has the following characteristics:

[0039] (1) Regular geographical image data usually presents in the form of a regular grid, like a matrix composed of neatly arranged small squares (pixels). For example, in satellite remote sensing images, these pixels are distributed in the geographical space at fixed intervals and arrangements, making the entire image regular in space. This grid structure facilitates spatial positioning and the measurement of geographical spatial quantities such as distance and area.

[0040] (2) Regular geographical image data often has a unified resolution in both the horizontal and vertical directions. Taking high-resolution remote sensing images as an example, its resolution may be 0.5 meters, which means that the actual ground distance represented by each pixel is 0.5 meters in both the x-axis and y-axis directions. In this way, when analyzing the image, such as identifying the dimensions of geographical features such as buildings or roads, accurate measurements can be made based on a fixed resolution standard.

[0041] (3) Regular geographic image data generally has a clear geographic coordinate system. The most common one is the latitude and longitude coordinate system (such as WGS84). This is like installing an accurate locator on the image data on the Earth, enabling each pixel point to find its corresponding position on the Earth's surface. Through these coordinates, the geographic image data can be accurately registered and integrated with other geographic data (such as vector map data).

[0042] (4) Regular geographic image data generally contains information on appropriate projection methods. Projection is a method of projecting the Earth's curved surface onto a plane, and different projection methods are used for different purposes. For example, the Mercator projection is widely used in navigation and large - area map drawing, which can maintain the accuracy of direction; the Gauss - Krüger projection is suitable for high - precision measurements in a smaller area, such as urban planning and land surveying. Complete projection information can ensure that the display and analysis of image data on the plane conform to the actual geographical spatial relationship.

[0043] (5) Regular geographic image data generally has high clarity, capable of clearly showing the details of geographic objects. For example, in the image monitoring urban land - use changes, the boundaries and internal structures of different land - use types such as residential areas, commercial areas, and parks can be clearly distinguished. At the same time, the noise level of the image data is low, avoiding blurred or incorrect information caused by noise interference, and the data is complete within the coverage area, without large areas of missing data. For example, a remote - sensing image covering the entire urban area should completely contain the data of the city and its surrounding related areas, without a situation where there is no data in a certain area in the middle. And the accuracy of the data is also high, and the information such as the position and shape of the geographic objects reflected is consistent with the actual situation.

[0044] (6) Regular geographic image data generally has standardized attribute information. For example, for multi - band geographic image data (such as multi - spectral images in satellite remote sensing), each band has a clear definition and use. For example, in the multi - spectral images of Landsat, different bands can be used to identify different land - cover types such as vegetation, water bodies, and bare land respectively. The data quality and data range (such as wavelength range) of these bands are standardized, facilitating professional spectral analysis of land - cover. Generally, complete metadata is attached, and this metadata includes content such as the data acquisition date, sensor type, and data processing level. For example, the metadata records which satellite sensor collected the image data and when the acquisition time was, which is very important for understanding the timeliness and quality source of the data. Moreover, the format of these metadata usually follows certain standard specifications, facilitating data exchange and sharing.

[0045] In the actual application process, when performing slicing processing on target geographic image data, based on the above characteristics of the rule-based geographic image data, the obtained geographic image data can be sliced according to a preset slicing rule, so as to obtain at least one first target image file corresponding to the target geographic image data.

[0046] Among them, the process of slicing the target geographic image data according to a preset slicing rule to obtain at least one first target image file corresponding to the target geographic image data may include:

[0047] (1) The size of the first target image file to be stored can be calculated starting from the origin of the preset map space, and it can be determined whether the origin of the target geographic image data coincides with the origin of the preset map space. If the origin of the target geographic image data coincides with the origin of the preset map space, the target geographic image data can be sliced starting from the origin of the target geographic image data according to the preset size of the image file. If the origin of the target geographic image data does not coincide with the origin of the preset map space, the target geographic image data can be sliced starting from the starting point of the first first target image file calculated, according to the preset size of the image file.

[0048] Furthermore, in the actual application process, since the volumes of different target geographic image data are different, it is impossible to ensure that the entire target geographic image data can be perfectly sliced into several equal-sized first target image files. It is possible that the volume of the last sliced first target image file is smaller than the preset size of the image file. Therefore, in order to ensure that the sliced first target image files obtained after slicing the target geographic image data are standardized, after performing slicing processing on the target geographic image data, it can be further determined whether there is a second target image file with a volume smaller than the preset size of the image file among the first target image files obtained after slicing the target geographic image data. If there is a second target image file with a volume smaller than the preset size of the image file, a part of the pixel points can be appended to the second target image file from the first target image file corresponding and adjacent to the second target image file in the geographic space, so that the size of the second target image file is the same as the preset size of the target image file; finally, each image file obtained by finally performing slicing processing on the target geographic image data is used as each first target image file constituting the target geographic image data.

[0049] Among them, the size of each first target image file is smaller than the target geographic image data. The size of each first target image file can be set according to relevant laws and regulations or application scenarios. For example, the size of the first target image file obtained by slicing the target geographic image data can be set to be less than or equal to the size corresponding to the image data of 2 kilometers. The storage format of each first target image file can be set according to the storage format of the target geographic image data.

[0050] In the actual application process, more flexible backup strategies can be adopted for the sliced image files. Different backup plans can be formulated for different slices according to factors such as the importance of the data and the update frequency. For example, for the image slices containing key geographic features (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 the slices of relatively less important areas, the backup frequency can be appropriately reduced, so as to effectively save backup resources while ensuring data security.

[0051] When data is damaged or lost, slicing helps with quick recovery. Because only the damaged or lost slices need to be recovered, rather than the entire huge geographic image data set. For example, if a certain disk sector of the storage device is damaged, resulting in some image files being unreadable, the damaged slice can be quickly located through the slice record, and then the slice can be recovered from the backup, without the need to perform a recovery operation on the entire data, greatly shortening the data recovery time.

[0052] Secondly, slicing can better achieve data access control. Different image slices may contain information with different levels of sensitivity. For example, some slices may contain information such as military facilities and sensitive ecological protection areas. Through slicing, more stringent access permissions can be set for these highly sensitive slices, such as restricting access to specific user groups and requiring multi-factor authentication, thereby improving the level of data security and privacy protection.

[0053] When it is necessary to perform desensitization processing (removing or blurring sensitive information) on geographic image data, slicing makes the operation more convenient. The slices containing sensitive information can be desensitized separately without affecting the normal use of other slices that do not contain sensitive information. For example, when sharing geographic image data for commercial development, the slices containing residents' privacy information (such as residential details) can be blurred, while maintaining the original accuracy of other insensitive slices related to geographical features, etc.

[0054] As the geographical environment changes or data quality improves, geographical image data needs to be continuously updated. After piecewise processing, it is possible to update the image pieces of a local area without having to reprocess and store the entire dataset. For example, when a large building is newly built in a certain city, only the image piece containing the area of the building needs to be updated, rather than updating the geographical images of the entire city, which greatly saves the update cost and time.

[0055] For different versions of geographical image data (such as images taken at different times, images with different processing precisions), piecewise processing helps to better manage versions. The version changes of each piece can be clearly recorded, facilitating the comparison of differences between different versions. For example, when studying the land use changes in a certain area, the change process of the land use type in each area can be accurately traced by comparing the image pieces of different versions.

[0056] Step S103: Construct the storage file name of each of the first target image files according to the preset origin coordinate information of the map space.

[0057] Specifically, in the actual application process, after the target geographical image data is piecewise processed to obtain multiple first target image files, in order to better manage and store each of the obtained first target image files, the storage file name of each first target image file can be constructed according to the preset origin coordinate information of the map space, so as to effectively organize the numerous piecewise image files. Just like a library classifies and catalogs books, the storage file name provides a systematic way to arrange these files. For example, through the storage file name, the first target image files can be classified according to factors such as geographical location, shooting time, image resolution, etc., facilitating quick positioning and searching for specific target image files in the storage system.

[0058] In the actual application process, the storage file name of each first target image file can include the machine identification code to be stored. Information such as the machine identification code contained in the storage file name can 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 accessing a specific first target image file, the storage file name is like a map, guiding the user or system to quickly find the specific machine and storage location where the file is located, reducing the search time and improving the data access efficiency.

[0059] In the actual application process, as the amount of data continuously increases, the storage system may be expanded, such as adding new storage devices or changing the storage architecture. Determining the storage file name can better adapt to such changes. When new storage devices are added, the new machine identification code can be incorporated into the storage file name system, enabling the image files to be smoothly distributed and stored in the new storage environment. For example, in a distributed storage system, after a new storage node is added, by updating the association between the storage file name and the machine identification code, the new first target image file can be stored on the new node, realizing the smooth expansion of the storage system.

[0060] When migrating or sharing data between different storage devices or storage systems, the storage file name and the machine identification code can also play a key bridging role. Different machines may have different storage format requirements or data access protocols, and the storage file name can help adjust and adapt to these differences. For example, when migrating the first target image file from a local storage device to cloud storage, the storage file name can guide how to reorganize and identify these files in the cloud system, ensuring the compatibility and availability of the data in different storage environments.

[0061] The storage file name also helps to implement data security policies. Through access control of the storage file name, unauthorized users can be restricted from accessing specific first target image files. The machine identification code can also be used to track the storage location of the data, facilitating security personnel to monitor the status of the storage device and prevent data theft or tampering. For example, for image files containing sensitive geographical information, strict access permissions can be set according to the storage file name, and only authorized users can access these files stored on specific machines.

[0062] In the data audit process, the storage file name is an important reference basis. Auditors can understand information such as the storage location and access history of each first target image file by viewing the storage file name. The machine identification code can help trace the changes in the data during the storage process, such as whether the file has been moved, copied, or deleted. For example, in investigating a data leakage incident, the storage file name and the machine identification code can help determine which image files may be affected and which abnormal operations occurred on which storage device.

[0063] Step S104, according to the storage file name of each of the first target image files, encrypt each of the first target image files and store them in the corresponding distributed database.

[0064] Specifically, in today's digital age, data privacy and security are of utmost importance. As can be seen from the above introduction, each of the first target image files obtained by fragmenting the target geographical image data may contain sensitive information. Therefore, after determining the storage file names of each of the first target image files, each of the first target image files can be encrypted and stored in the corresponding distributed database according to the storage file name of each first target image file. Encrypting and storing the first target image files in the distributed database can effectively prevent the data from being accessed, stolen, or tampered with without authorization during the storage process.

[0065] Among them, in the actual application process, one first target image file can correspond to one distributed database. This situation may be applicable to scenarios with extremely high requirements for data isolation. For example, in some highly confidential scientific research projects, each important experimental geographical image file is stored in a separate distributed database. This can ensure that each image file has an independent space in physical storage and management, reducing mutual interference and potential security risks; the data security of this storage method is high, and it is convenient for individual management and maintenance. If a problem occurs in a certain database, such as being attacked by hackers or suffering from hardware failures, it will only affect one geographical image file corresponding to it and will not affect other geographical image files. At the same time, the permission management is relatively simple, and setting access permissions for a single database can accurately control the access to specific geographical image files. However, the cost of this storage method is relatively high because an independent distributed database needs to be established for each geographical image file, including hardware resources such as servers and storage devices, as well as related software and maintenance personnel costs. The resource utilization rate may be relatively low because each database may only store one geographical image file, resulting in a large amount of storage and computing resources being idle.

[0066] In the actual application process, a first target 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 geographical image files (such as images of key areas) may be stored in multiple distributed databases. This is to prevent data loss caused by a single database failure. By storing the same geographical image file in multiple locations, even if one database has problems, other databases can still provide data access. This storage method has high data reliability. Even if some databases fail, the availability of geographical image files can still be guaranteed. At the same time, users in different regions can obtain geographical image files from the database closer to them, improving the access efficiency. This redundant storage method can also be used for data backup and recovery, facilitating the acquisition of complete geographical image files from different databases when needed. However, the data update and synchronization of this storage method are relatively complex. When a geographical image file needs to be updated, it is necessary to ensure that all distributed databases storing this file can be correctly updated, otherwise data inconsistency will occur. Moreover, this storage method requires more storage space because the same geographical image file will occupy space in multiple databases.

[0067] In the actual application process, to improve the efficiency of data storage, and under the condition of ensuring the storage security of geographical image data, multiple first target image files can also be stored in the same distributed database. This is a relatively common storage method and is applicable to a set of geographical image files with strong correlations. This storage method has high resource utilization. One database can store multiple geographical image files, making full use of storage and computing resources. It is convenient to manage. Operations such as database maintenance, backup, and recovery can be applied to multiple geographical image files at one time. At the same time, when conducting data query and analysis, it is convenient to perform association operations on multiple geographical image files in the same database. However, this storage method may have the problem of concentrated security risks. If this distributed database is breached, all geographical image files stored in it may be leaked. In addition, the database performance may be affected by the number of stored files. When there are too many geographical image files stored, the query and retrieval speed of the database may decrease.

[0068] In the actual application process, complex storage relationships can also be established between multiple first target image files and multiple distributed databases. This situation may occur in large-scale, cross-departmental or cross-organizational image storage systems. Different geographical 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 lead to very complex data management and maintenance. Complex mapping relationships are required to record which databases store each geographical image file and which geographical image files are stored in each database. Maintaining data consistency is also more difficult because it involves interactions between multiple files and multiple databases.

[0069] In the actual application process, encrypting geographical image data is like putting a strong lock on the data, and 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 geographical image data remains secure.

[0070] Furthermore, by encrypting geographical image files and storing them in a distributed database, it can help organizations meet the requirements of relevant regulations and avoid huge fines and legal liabilities due to the leakage of geographical image data. A distributed database usually consists of multiple nodes, and the data is stored distributedly on these nodes. Although this architecture improves the availability and reliability of the data, it also increases the risk points of data exposure. Encrypting and storing geographical image files can make full use of the advantages of the distributed database (such as high availability, scalability, etc.), while reducing the security risks brought by distributed storage. Moreover, in a distributed database environment, data may be transmitted and replicated between different nodes. During the transmission and replication of encrypted geographical image files, even if intercepted, attackers are difficult to obtain the content, thus ensuring the security of geographical image data throughout the distributed storage life cycle.

[0071] As can be seen from the above-described technical solutions, when it is necessary to store regular large-volume geographic image data, the method provided by the embodiments of the present application can effectively split the large-volume geographic image data and then perform encrypted storage, which can effectively increase the security and privacy of the storage of geographic information data, and can effectively reduce the repetition of geographic space information collection. Further, by slicing the large-volume geographic image data into small-volume image data files for storage, it is possible to better plan the image data acquisition path, reduce repeated acquisition due to storing overly large image data, and by slicing and then storing the large-volume geographic image data, it is possible to effectively improve the processing speed of the image data, improve the read / write efficiency of the image data, while effectively ensuring the integrity of the original geographic image data, and also improve the acquisition efficiency of regular geographic image data and enhance the merging and display efficiency of regular geographic image data.

[0072] As can be known from the above introduction, the method provided by the embodiments of the present application can construct the storage file name of each of the first target image files according to the origin coordinate information of the preset map space. The following is an introduction to this process, which may include the following:

[0073] Step S201: Determine the pixel sizes of the length and width of each first target image file to be stored in the distributed database according to the preset size of the image file.

[0074] Specifically, in the actual application process, the storage resources of the distributed database are limited, just like a warehouse has a certain space limit. Therefore, it is necessary to plan the size of the files to be stored in each distributed database. Thus, when it is necessary to store each of the first target image files corresponding to the target geographic image data in each distributed database, the pixel sizes of the length and width of each first target image file to be stored in the distributed database can be determined according to the preset size of the image file, so as to deploy the storage plan according to the size of each first target image file.

[0075] Knowing the pixel sizes of the length and width of each first target image file can help accurately evaluate the space required to store each first target 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 have significantly different storage space requirements. By determining the pixel size, it is possible to reasonably allocate the storage blocks in the database, optimize the storage layout, and avoid waste or shortage of space caused by unreasonable storage planning. At the same time, this also helps to select an appropriate storage strategy. For the first target image files with small pixel sizes, a relatively simple storage method may be sufficient to meet the requirements; while for the first target image files with large pixel sizes, special storage strategies such as distributed storage, sliced storage, or compressed storage may need to be considered to ensure efficient storage and fast retrieval.

[0076] When processing the first target image file, the pixel size is a key factor. For example, when performing operations such as image scaling, cropping, and stitching, it is necessary to calculate the size and effect of the processed image based on the pixel size. If multiple first target image files are to be stitched into a large geographical image, it is necessary to know the pixel size of each first target file in order to accurately calculate the size and positional relationship after stitching.

[0077] Furthermore, from the perspective of data transmission, the pixel size directly affects the amount of data transmitted. Image files with large pixel sizes will occupy more bandwidth and time when transmitted. In a distributed database environment, data may need to be transmitted between different nodes. Determining the pixel size of the first target image file can help estimate the transmission cost, select an appropriate transmission protocol, and optimize the transmission strategy. For example, for files with large pixel sizes, block transmission or asynchronous transmission methods can be used to reduce the instantaneous impact on network bandwidth.

[0078] The pixel size of the first target image file is closely related to the resolution. Generally, the more pixels, the higher the resolution, and the richer the details contained in the image. For example, when geographical images are used for land use planning, high-resolution (i.e., large pixel size) images can clearly display details such as the boundaries of the land and the types of vegetation, while low-resolution images may only provide a general topographic outline. By determining the pixel size, the data quality of the image file can be better evaluated to meet the requirements of different users for data resolution. For example, for users who need high-precision geographical image data, such as those engaged in fine geological exploration or cultural relic archaeological excavation, providing a first target image file with a large pixel size for both length and width can ensure that sufficient detailed information can be obtained; while for some users who only need a general geographical outline, such as those engaged in regional tourism planning, a file with a smaller pixel size may be sufficient to meet the needs.

[0079] Particularly, in the actual application process, the pixel sizes of the length and width of each first target image file can be determined according to the actual application scenario. The larger the length and width, the greater the possible error; the smaller the length and width, the smaller the error and the number of first target image files obtained by splitting may be excessive. Therefore, in the actual application process, the pixel sizes of the length and width of the first target image file can generally be set to 256 pixels. Through experiments, it is found that when the pixel settings of the length and width of the first target image file are 256 pixels, its relative grid requests and slice sizes are relatively moderate.

[0080] Step S202: Determine the abscissa and ordinate corresponding to the origin coordinates of the preset map space of the point where the target geographical image data starts to be stored.

[0081] Specifically, in the actual application process, the map space corresponding to the geographic image data is a complex two-dimensional or even three-dimensional space. Therefore, in the actual application process, in order to better manage the target geographic image data, the origin coordinates of the map space corresponding to the target geographic image data can be defined, so that the target geographic image data can be better segmented according to the defined origin coordinates. Defining the origin coordinates of the geographic space corresponding to the target geographic image data is like establishing a "reference point" in this space.

[0082] For example, in the geographic image map of a city, a certain landmark position in the city center square (such as the center of a fountain) can be defined as the origin coordinates (assumed to be longitude x1 and latitude y1). In this way, the positions of geographical elements (such as streets, buildings, etc.) in all other geographic image data can be accurately represented by the offset relative to this origin coordinate.

[0083] Taking satellite remote sensing images as an example, the image data range of the entire earth's surface is huge. By defining the 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 conveniently determined, providing an accurate spatial positioning basis for subsequent geographic space analysis, data processing, and user viewing.

[0084] 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 origin coordinates of the map space corresponding to the target geographic image data, these data from different sources can be unified into a common spatial framework.

[0085] For example, a city planning department may simultaneously have the overall city image data from satellite remote sensing (using the WGS84 coordinate system) and the high-precision building detail image data from the local surveying and mapping department (using the local plane rectangular coordinate system). By unifying them into a map space based on the origin coordinates, data fusion can be achieved, thus presenting the geographic space information of the city more comprehensively and accurately.

[0086] Furthermore, when performing geospatial analysis, many algorithms and calculations rely on an accurate coordinate system. Defining the origin coordinates can make these calculations more convenient and accurate. For example, when calculating the distance, area, or relative orientation between two geographical regions (such as two different parks), a coordinate system based on the origin coordinates can provide a unified calculation framework. Suppose we want to analyze the distance and slope changes between different valleys in a mountainous area. By defining the origin coordinates and the corresponding coordinate system, the geographical image data of each valley can be converted into coordinate values, and then mathematical formulas can be used to calculate geographical spatial parameters such as the distance and slope between them, providing strong data support for fields such as geological exploration and ecological research.

[0087] For example, in the actual application process, the top-leftmost point of the entire map corresponding to the target geographical image data can be defined as the origin coordinates of the preset map space. .

[0088] In the actual application process, the position of the geographical image data in the map space requires an accurate reference system to determine. As introduced above, the origin coordinates of the preset map space are like the "anchor points" of the map space. Therefore, in order to better manage the target geographical image data, the abscissa and ordinate of the point where the target geographical image data starts to be stored relative to the origin coordinates of the preset map space can be determined. So as to be able to accurately place the geographical image data in the correct position in the map space.

[0089] For example, in the storage of geographical image data of a city map, a certain point in the city center square is used as the origin coordinates (0, 0). If we want to store the image data of a block, we need to determine the abscissa and ordinate of the point where the image data of this block starts to be stored (such as the southwest corner of the block) relative to the origin coordinates of the city center square, so as to accurately represent the position of this block in the map space, just like accurately positioning a figure on graph paper.

[0090] Furthermore, geographical image data is usually composed of multiple parts spliced together, especially when dealing with large geographical areas or high-resolution images. Determining the abscissa and ordinate of the point where the target geographical image data starts to be stored relative to the origin coordinates of the preset map space helps to maintain the spatial coherence and accuracy when splicing these image data.

[0091] Suppose there are multiple satellite remote sensing image files, and each geographical image file covers a certain geographical area. To construct a complete geographical image, these image files need to be stitched together. By determining the horizontal and vertical coordinates of the starting storage point of each image file relative to the origin coordinates of the origin of the map space, they can be accurately stitched in the correct position, avoiding stitching errors such as image overlap or gaps, and ensuring the integrity and accuracy of the geographical image.

[0092] When performing geospatial analysis and queries, accurate location information is crucial. By determining the horizontal and vertical coordinates of the starting storage point of the target geographical image data, it provides an accurate spatial index for subsequent analysis and queries. For example, when studying land use changes, the image data of a specific area can be quickly located based on these coordinates, and then the images of different periods can be compared to analyze the land use changes. At the same time, for users querying the image data of a specific geographical area, these coordinates can also help the database quickly locate and return the data that meets the requirements, improving the query efficiency and accuracy.

[0093] Step S203: Calculate the horizontal and vertical coordinates of each of the first target image files relative to the origin coordinates of the preset map space based on the horizontal and vertical coordinates corresponding to the starting storage point of the target geographical image data relative to the origin coordinates of the preset map space.

[0094] Specifically, in the actual application process, after determining the origin of the map space, by calculating the horizontal and vertical coordinates of each first target image file relative to the origin coordinates of the map space, the positions of each first target image file in the entire map space can be accurately located. Just like in a huge coordinate system, the origin is the reference point. Knowing the relative coordinates of each geographical image file is like assigning an accurate "address" to each geographical image file in this coordinate system. This is crucial for accurately retrieving, stitching, and analyzing these geographical image files in the map space subsequently. For example, in a Geographic Information System (GIS), when multiple images of different regions need to be stitched into a complete map, these accurate coordinates can ensure that the geographical images are correctly aligned in space, avoiding misalignment.

[0095] Furthermore, understanding the abscissa and ordinate of each first target image file relative to the origin coordinates of the preset map space helps integrate geographical image data with other geospatial data. Geospatial data usually includes various types, such as vector data (points, lines, surfaces), terrain data, etc. By clarifying the coordinates of each first target image file relative to the map space origin, these first target image files can be easily associated with other types of data according to their spatial positions. For example, when conducting urban planning, combining geographical image data with vector data of urban roads, through coordinate matching, the land use situation or building distribution around the roads can be accurately analyzed, thus providing more comprehensive information for planning decisions.

[0096] For a large-volume target geographical image dataset, understanding the abscissa and ordinate of each first target image file relative to the origin coordinates of the preset map space can effectively manage it in blocks. This is like dividing a large jigsaw puzzle into multiple small puzzle pieces. By knowing the position of each small puzzle piece (first target image file) in the whole jigsaw puzzle (preset map space), they can be stored, transmitted, and processed more efficiently. For example, when processing geographical image data in a cloud computing environment, these coordinate information can help the system reasonably allocate the processing tasks of each first target image file block according to the computing resources and task requirements, improving the efficiency of data processing.

[0097] Understanding the abscissa and ordinate of each first target image file relative to the origin coordinates of the preset map space is the basis for various spatial analyses and queries. For example, when querying geographical images within a specific area (defined by the abscissa and ordinate ranges) in the map space, by comparing the relative coordinates of each first target image file with the coordinate range of the query area, the first target image files that meet the requirements can be quickly filtered out. This spatial query function has extensive applications in many fields such as land resource management and ecological environment monitoring. For example, when monitoring forest fires, by querying the coordinate range of a specific fire area, the geographical images of that area can be quickly obtained to analyze the spread of the fire.

[0098] Step S204: Determine the storage machine code of each first target image file based on the quantity of each first target image file and the quantity of storage machines.

[0099] Specifically, when storing geographical image data, different storage machines may have different storage capacities and performance characteristics. If each first target image file has a corresponding storage machine code, it can enable quick location of the storage machine where the file is located when managing data. Just like books in a library all have corresponding shelf numbers, when it is necessary to search for or manage a specific first target image file, the storage machine where the first target file is located can be directly found according to the storage machine code. This location method greatly improves the efficiency of data management, especially in the case of a large amount of data and numerous storage machines. For example, when it is necessary to update or back up a specific first target image file, its location can be quickly determined through the storage machine code and the corresponding operation can be performed.

[0100] During the maintenance or upgrade process of the storage system, the storage machine code can help the administrator operate on some storage machines in a targeted manner. For example, when it is necessary to perform hardware upgrades or software updates on several storage machines, the first target image files stored on these machines can be easily identified according to the storage machine code, and preparations such as data migration or backup can be made in advance, thereby reducing the impact of system maintenance and upgrade on data storage and use.

[0101] Determining the storage machine code helps to implement the data redundancy strategy. Copies of important first target image files can be stored on different storage machines, and the storage locations of these copies can be conveniently planned and managed through the code. For example, for the geographical image data in some key areas, the main file can be stored on one machine, and the copy can be stored on other machines at the same time. In this way, when a machine fails, the data copy can be obtained from other machines to ensure the security and availability of the data.

[0102] When a storage machine fails, the storage machine code can help quickly determine the affected first target image files, so as to take timely measures for data recovery. For example, after a storage machine fails, it can be quickly judged which files are lost or damaged according to the code, and then backup data can be obtained from other normal storage machines or the storage location can be reallocated, improving the fault tolerance and data recovery ability of the entire storage system.

[0103] Therefore, in the actual application process, in order to efficiently manage each first target image file, the storage machine code of each first target image file can be determined based on the number of each first target image file and the number of storage machines. By determining the storage machine code of each file based on the number of the first target image files and the number of storage machines, each first target image file can be evenly distributed to each storage machine. This is like having many goods (first target image files) to be stored in multiple warehouses (storage machines). To avoid the situation where a certain warehouse is overcrowded while other warehouses are idle, it is necessary to reasonably distribute the goods to each warehouse. For example, assume there are 100 first target image files and 5 storage machines. The files are evenly distributed to each machine, and each machine stores 20 files. In this way, the storage resources of each machine can be fully utilized, and individual machines can be prevented from being overloaded due to storing too many files.

[0104] Secondly, in the actual application process, there may also be differences in the processing capabilities such as data reading and writing speeds among storage machines. Reasonably distributing the storage machines for each first target image file can balance the workloads of each storage machine and ensure that each machine can work efficiently within its capabilities during operations such as data retrieval and processing. For example, during a large-scale geographical image data analysis task, the first target image files need to be frequently read from the storage machines. If the distribution of the first target image files is unreasonable, it may cause some machines to frequently respond to read requests and become performance bottlenecks, while other machines are rarely used.

[0105] Among them, the image files stored on each storage machine cannot be the image files corresponding to the geographical space with a volume exceeding a preset size.

[0106] For example, according to relevant legal regulations, the volume of the geographical image files stored on each storage machine cannot be the volume corresponding to the geographical image data of an area exceeding 2 kilometers.

[0107] Step S205: Construct the storage file name of each first target image file according to the storage machine code of each first target image file and the abscissa and ordinate of the origin coordinate of each first target image file relative to the preset map space.

[0108] Specifically, in the actual application process, in order to efficiently manage each first target image file, after determining the storage machine code of each first target image file and the abscissa and ordinate of the origin coordinates of each first target image file relative to the origin coordinates of the preset map space, the storage file name of each first target image file can be constructed based on the storage machine code of each first target image file and the abscissa and ordinate of the origin coordinates of each first target image file relative to the origin coordinates of the preset map space, so that each first target image file can be stored according to the storage file name of each first target image file.

[0109] For example, it can be defined that is the abscissa of the origin coordinates of the preset map space based on the point where the target geographic image data starts to be stored; is the ordinate of the origin coordinates of the preset map space based on the point where the target geographic image data starts to be stored; is the machine identification code where the first target image file is about to be stored; the large target geographic image data can be split into multiple blocks horizontally and vertically, and the files can be named with the numbers of the horizontal and vertical blocks plus the storage machine identification code, and the storage file name of each first target image file is constructed as: .

[0110] As can be seen from the technical solutions introduced above, the method provided by the embodiments of the present application can construct the storage file name of each first target image file based on the origin coordinate information of the preset map space, so that each first target image file can be efficiently managed and stored.

[0111] As can be known from the above introduction, the method provided by the embodiments of the present application can determine the storage machine code of each first target image file based on the number of each first target image file and the number of storage machines. Next, this process will be introduced, and this process can include the following steps:

[0112] Step S301, determine whether the number of storage machines is greater than a preset first threshold.

[0113] Specifically, as can be known from the above introduction, since the storage machines generally have limited resources, therefore, when storing each first target image file, it is necessary to determine the storage strategy according to the number of storage machines. Therefore, when obtaining each first target image file, it can be first determined whether the number of storage machines is greater than a preset first threshold. Among them, the preset first threshold can be set to 4. If the number of storage machines is less than or equal to the preset first threshold, step S302 can be executed. If the number of storage machines is greater than the preset first threshold, step S303 can be executed.

[0114] Step S302, determine the storage machine code of each said first target image file according to the preset first storage strategy.

[0115] Specifically, if it is determined that the number of storage machines is less than or equal to a preset first threshold, it indicates that the number of storage machines is limited and the storage machines need to be fully utilized. Then, the storage machine codes of each first target image file can be determined according to a preset first storage policy.

[0116] Among them, the process of determining the storage machine codes of each first target image file according to the preset first storage policy can include the following:

[0117] (1) Store the first target image files with odd abscissa values and odd ordinate values in the machine ranked first, and use the serial number corresponding to the stored machine as the storage machine code of the first target image files with odd abscissa values and odd ordinate values;

[0118] (2) Store the first target image files with odd abscissa values and even ordinate values in the machine ranked second, and use the serial number corresponding to the stored machine as the storage machine code of the first target image files with odd abscissa values and even ordinate values;

[0119] (3) Store the first target image files with even abscissa values and odd ordinate values in the machine ranked third, and use the serial number corresponding to the stored machine as the storage machine code of the first target image files with odd abscissa values and odd ordinate values;

[0120] (4) Store the first target image files with even abscissa values and even ordinate values in the machine ranked fourth, and use the serial number corresponding to the stored machine as the storage machine code of the first target image files with even abscissa values and even ordinate values;

[0121] Among them, the storage machine code of each storage machine can be set according to the creation time of the storage machine or the capacity size of the storage machine, and the storage machine code of each machine can be set as the serial number corresponding to the storage machine.

[0122] Step S303, determine the storage machine code of each of the first target image files according to a preset second storage policy.

[0123] Specifically, if it is determined that the number of storage machines is greater than a preset first threshold, it indicates that the number of storage machines is sufficient. Then, the storage machine codes of each first target image file can be determined according to a preset second storage policy. Among them, the process of determining the storage machine codes of each first target image file according to the preset second storage policy can include the following:

[0124] All storage machines are divided into two groups, namely the odd storage machine group and the even storage machine group. Each group of storage machines includes at least two storage machines. The storage machine codes of the corresponding storage machines in each group of storage machines are set according to the creation time of the storage machine or the capacity size of the storage machine. The storage machine code of each storage machine is the serial number corresponding to the storage machine.

[0125] The first target image files with odd values on the abscissa can be stored in the storage machines of the odd storage machine group according to the first storage rule, and the storage machine code of the storage machine storing each first target image file with an odd value on the abscissa in the storage machines of the odd storage machine group is used as its storage machine code. Among them, the first storage rule may include: all storage machines in the odd storage machine group can be sorted according to the creation time or the capacity size of the storage machine, and a storage is randomly selected from the set of all first target image files with odd values on the abscissa that have not been stored and stored in the first target storage machine at the head of the storage machine queue in the odd storage machine group, then the first target storage machine is adjusted to the end of the storage machine queue, and the storage machine queue is readjusted. Then, the storage machine currently at the head of the storage machine queue is continued to be used as the first target storage machine, and the operation of randomly selecting a storage from the set of all first target image files with odd values on the abscissa that have not been stored and storing it in the first target storage machine at the head of the storage machine queue in the odd storage machine group is returned to be executed until all the first target image files with odd values on the abscissa to be stored are completely stored in the respective storage machines of the odd storage machine group.

[0126] The first target image files with even numerical values on the abscissa can be stored in the storage machines of the even-numbered storage machine group according to the second storage rule, and the storage machine codes of the storage machines storing each first target image file with an even numerical value on the abscissa in the storage machines of the even-numbered storage machine group are used as their storage machine codes. Among them, the second storage rule may include the following: Sort all the storage machines in the even-numbered storage machine group according to the creation time or the capacity of the storage machine, randomly select one from all the first target image file sets with even numerical values on the abscissa that have not been stored and store it in the second target storage machine at the head of the storage machine queue in the even-numbered storage machine group, then move the second target storage machine to the end of the storage machine queue, readjust the storage machine queue, and continue to use the storage machine currently at the head of the storage machine queue as the second target storage machine, and then return to execute the operation of randomly selecting one from all the first target image file sets with even numerical values on the abscissa that have not been stored and storing it in the second target storage machine at the head of the storage machine queue in the even-numbered storage machine group until all the first target image files with even numerical values on the abscissa to be stored are stored in each storage machine of the even-numbered storage machine group.

[0127] For example, as Figure 2 and Figure 3 shown, Figure 2 and Figure 3 illustrate the storage effect of 4 image files. The first target image files obtained by slicing the geographical image data to be stored can be regarded as Block 1, Block 2, Block 3, and Block 4 respectively. Since in the actual application process, map data over 2 kilometers cannot be stored on one machine, if Block 1 is the same as any of Block 2, Block 3, and Block 4, it does not meet the requirement of being stored on one machine. Therefore, Block 1 needs to be different from Block 2, Block 3, and Block 4, and at least four storage machines are required to store the data. The four required storage machines are numbered 1, 2, 3, and 4 respectively. When there are only four storage machines, the blocks with even numerical values on the abscissa and odd numerical values on the ordinate among Block 1, Block 2, Block 3, and Block 4 can be placed in Machine 3; the blocks with even numerical values on the abscissa and even numerical values on the ordinate can be placed in Machine 4. The blocks with odd numerical values on the abscissa and odd numerical values on the ordinate are placed in Machine 1, and the blocks with odd numerical values on the abscissa and even numerical values on the ordinate are placed in Machine 2. The data on each storage machine does not exceed 2 kilometers of geographical image data.

[0128] When there are more than four machines, all storage machines can be sorted according to certain rules first. For example, all storage machines can be sorted by the machine creation time or the machine storage size. The machines are split into two groups A and B according to the parity of the serial number or a custom rule, and each group forms a queue. When the value of the abscissa of the first target image file is odd, a machine from group A is selected for storage. The machines in group A that have stored image files are continuously placed at the end of the queue in group A and queued up in a loop until all the first target image files with odd abscissa values are stored in the storage of group A. When the value of the abscissa of the first target image file is even, a machine from group B is selected for storage. The machines in group B that have stored image files are continuously placed at the end of the queue in group B and queued up in a loop until all the first target image files with even abscissa values are stored in the storage of group B.

[0129] Specifically, the reason for repeatedly selecting machines from group A or group B to store image files is that the number of storage machines is definitely insufficient, so it is necessary to retrieve them in a loop. Another reason for using a queue to retrieve is that if retrieved randomly, there is a probability that the machine numbers retrieved multiple times are the same, which cannot guarantee the requirement that data over 2 kilometers cannot be stored on one machine. For example, for five machines numbered 1, 2, 3, 4, and 5, sorted by serial number, they can be divided into group A (1, 3, 5) and group B (2, 4). When the abscissa of the first target image file is 1 and the ordinate of the first target image file is equal to 1, the storage machine numbered 1 in group A is selected for storage; when the abscissa of the first target image file is 1 and the ordinate is 2, the machine numbered 3 in group A is selected for storage; when the abscissa of the first target image file is 1 and the ordinate is 3, the machine numbered 5 in group A is selected for storage; when the abscissa of the first target image file is 1 and the ordinate is 4, the machine numbered 1 in group A is selected for storage.

[0130] As can be seen from the technical solutions described above, the method provided by the embodiments of the present application can determine the storage machine code of each first target image file based on the quantity of each first target image file and the quantity of storage machines, so as to efficiently utilize limited storage resources and efficiently manage each first target image file at the same time.

[0131] Next, a device for slicing and processing regular geographic image data provided by the embodiments of the present application will be described. The device for slicing and processing regular geographic image data described below can be correspondingly referred to the method for slicing and processing regular geographic image data described above. Refer to Figure 4 , Figure 4 which is a schematic structural diagram of a device for slicing and processing regular geographic image data disclosed by the embodiments of the present application. As shown in Figure 4 the device for slicing and processing regular geographic image data may include:

[0132] An acquisition unit 101, configured to acquire target geographic image data to be stored;

[0133] A slicing unit 102, configured to perform slicing processing on the target geographic image data according to a preset slicing rule, so as to obtain at least one first target image file corresponding to the target geographic image data;

[0134] A construction unit 103, configured to construct a storage file name for each of the first target image files according to the origin coordinate information of a preset map space;

[0135] A storage unit 104, configured to encrypt each of the first target image files according to the storage file name of each of the first target image files, and store them in a corresponding distributed database.

[0136] As can be seen from the technical solutions introduced above, the device provided in the embodiments of the present application can acquire target geographic image data to be stored; in actual application processes, since geographic image data generally has a relatively large volume, and generally speaking, geographic image data is regular image data. If the acquired target geographic image data is directly stored, it may not be possible to store it, and it does not meet the relevant image data storage requirements, and information leakage problems are likely to occur. Therefore, it can be considered to perform slicing processing on the target geographic image data before storing it. Therefore, in order to efficiently manage the acquired target geographic image data, after acquiring the target geographic image data, according to a preset slicing rule, the acquired target geographic image data can be sliced to obtain at least one first target image file corresponding to the target geographic image data; among them, the size of each of the segmented first target image files is smaller than the size of the target geographic image data. In order to efficiently store and manage each of the first target image files, it can be considered to store each of the first target image files dispersedly. Therefore, after slicing the target geographic image data, according to the origin coordinate information of the preset map space, a storage file name for each of the first target image files can be constructed; so that according to the storage file name of each of the first target image files, each of the first target image files can be encrypted and then stored in a corresponding distributed database.

[0137] As can be seen from the above introduction, the device provided by the embodiments of the present application can effectively split large-volume geographic image data and then encrypt and store it, which can effectively increase the security and privacy of geographic information data storage, can effectively reduce the repetition of geographic space information collection. Further, by slicing the large-volume geographic image data into small-volume image data files for storage, the image data acquisition path can be better planned, and repeated acquisition due to storing overly large image data can be reduced. Moreover, by slicing and storing the large-volume geographic image data, the processing speed of the image data can be effectively improved, and the read / write efficiency of the image data can be enhanced. While effectively ensuring the integrity of the original geographic image data, the acquisition efficiency of regular geographic image data can also be improved, and the merging and display efficiency of regular geographic image data can be enhanced.

[0138] Among them, for the specific processing flow of each unit included in the above-mentioned regular geographic image data slicing and processing device, reference can be made to the relevant introduction in the previous part of the regular geographic image data slicing and processing method, which will not be elaborated here. The regular geographic image data slicing and processing device provided by the embodiments of the present application can be applied to regular geographic image data slicing and processing equipment, such as terminals: mobile phones, computers, etc. Optionally, Figure 5 shows the hardware structure block diagram of the regular geographic image data slicing and processing equipment. Refer to Figure 5, the hardware structure of the device for slicing and processing regular 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 embodiments of the present application, the number of the processor 1, the communication interface 2, the memory 3, and the communication bus 4 is at least one, and the processor 1, the communication interface 2, and the memory 3 complete communication with each other through the communication bus 4. The 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 embodiments of the present application, etc.; the memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory, etc., 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 process in the above-mentioned terminal's scheme for slicing and processing regular geographic image data. The embodiments of the present application also provide a readable storage medium, which can store a program suitable for the processor to execute, and the program is used to: implement each processing process in the above-mentioned terminal's scheme for slicing and processing regular geographic image data. Finally, it should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including the element. The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same and similar parts among the various embodiments can be referred to each other. The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments can be combined with each other. Therefore, the present application will not be limited to these embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for slicing and processing regular geographic image data, characterized in that, Including: Obtain target geographic image data to be stored, where the target geographic image data includes regular geographic image data; Calculate the size of the first target image file starting from the origin of the preset map space, and determine whether the origin of the target geographic image data coincides with the origin of the preset map space. If the origin of the target geographic image data coincides with the origin of the preset map space, then slice the target geographic image data starting from the origin of the target geographic image data according to the size of the preset image file. If the origin of the target geographic image data does not coincide with the origin of the preset map space, then slice the target geographic image data starting from the starting point of the first target image file calculated from the beginning according to the size of the preset image file; After slicing the target geographic image data, determine whether there is a second target image file with a volume smaller than the size of the preset image file among the first target image files obtained after slicing the target geographic image data. If there is the second target image file, then append a part of pixel points to the second target image file from the first target image file adjacent to the second target image file in the geographic space, so that the size of the second target image file is the same as the size of the preset image file; Use each image file finally obtained by slicing the target geographic image data as each first target image file constituting the target geographic image data; Determine the pixel sizes of the length and width of each first target image file to be stored in the distributed database according to the size of the preset image file; Determine the abscissa and ordinate corresponding to the origin coordinates of the preset map space of the point where the target geographic image data starts to be stored according to the origin coordinates of the preset map space; Based on the abscissa and ordinate corresponding to the origin coordinates of the preset map space of the point where the target geographic image data starts to be stored, calculate the abscissa and ordinate of each first target image file relative to the origin coordinates of the preset map space; Determine whether the number of storage machines is greater than a preset first threshold; When the number of storage machines is less than or equal to a preset first threshold, the first target image files with odd values of the abscissa and odd values of the ordinate are stored in the machine ranked first, and the serial number of the stored machine is used as the storage machine code of the first target image files with odd values of the abscissa and odd values of the ordinate; the first target image files with odd values of the abscissa and even values of the ordinate are stored in the machine ranked second, and the serial number of the stored machine is used as the storage machine code of the first target image files with odd values of the abscissa and even values of the ordinate; the first target image files with even values of the abscissa and odd values of the ordinate are stored in the machine ranked third, and the serial number of the stored machine is used as the storage machine code of the first target image files with odd values of the abscissa and odd values of the ordinate; the first target image files with even values of the abscissa and even values of the ordinate are stored in the machine ranked fourth, and the serial number of the stored machine is used as the storage machine code of the first target image files with even values of the abscissa and even values of the ordinate; wherein, the storage machine code of each storage machine is set according to the creation time of the storage machine or the capacity size of the storage machine, and the storage machine code of each machine is the serial number corresponding to the storage machine; When the number of storage machines is greater than a preset first threshold, all storage machines are divided into two groups, namely an odd storage machine group and an even storage machine group. Each group of storage machine groups includes at least two storage machines. The storage machine codes of the respective storage machines in each group of storage machine groups are set according to the creation time of the storage machine or the capacity size of the storage machine. The storage machine code of each storage machine is the serial number corresponding to the storage machine. The first target image files with odd abscissa values are stored in the storage machines of the odd storage machine group according to the first storage rule, and the storage machine code of the storage machine in the odd storage machine group that stores each first target image file with an odd abscissa value is used as its storage machine code. The first target image files with even abscissa values are stored in the storage machines of the even storage machine group according to the second storage rule, and the storage machine code of the storage machine in the even storage machine group that stores each first target image file with an even abscissa value is used as its storage machine code. Among them, the first storage rule includes: sorting all the storage machines in the odd storage machine group according to the creation time or the capacity size of the storage machine, randomly selecting one from all the first target image file sets with odd abscissa values that have not been stored and storing it in the first target storage machine at the head of the storage machine queue in the odd storage machine group, then adjusting the first target storage machine to the end of the storage machine queue, readjusting the storage machine queue, and then continuing to use the storage machine currently at the head of the storage machine queue as the first target storage machine, and then returning to execute the operation of randomly selecting one from all the first target image file sets with odd abscissa values that have not been stored and storing it in the first target storage machine at the head of the storage machine queue in the odd storage machine group until all the first target image files with odd abscissa values to be stored are completely stored in the respective storage machines of the odd storage machine group;The second storage rule includes: sorting all the storage machines in the even-numbered storage machine group according to the creation time or the capacity of the storage machines, randomly selecting one from all the first target image file sets with even-numbered abscissa values that have not been stored and storing it in the second target storage machine at the head of the storage machine queue in the even-numbered storage machine group, then adjusting the second target storage machine to the end of the storage machine queue, readjusting the storage machine queue, continuing to use the storage machine currently at the head of the storage machine queue as the second target storage machine, and then returning to execute the operation of randomly selecting one from all the first target image file sets with even-numbered abscissa values that have not been stored and storing it in the second target storage machine at the head of the storage machine queue in the even-numbered storage machine group until all the first target image files with even-numbered abscissa values to be stored are stored in each storage machine in the even-numbered storage machine group. Among them, the image files stored on each storage machine cannot be image files corresponding to a geographical space with a volume exceeding a preset size, where the preset size is less than or equal to 2 kilometers; According to the storage machine code of each of the first target image files and the abscissa and ordinate of each of the first target image files relative to the origin coordinates of the preset map space, construct the storage file name of each of the first target image files; According to the storage file name of each of the first target image files, encrypt and store each of the first target image files in the corresponding distributed database.

2. A regular geographical image data slicing processing device, characterized in that, Including: An acquisition unit, configured to acquire target geographic image data to be stored, wherein the target geographic image data includes regular geographic image data; The slitting unit is used to calculate the size of the first target image file starting from the origin of the preset map space, and determine whether the origin of the target geographic image data coincides with the origin of the preset map space. If the origin of the target geographic image data coincides with the origin of the preset map space, the target geographic image data is slit starting from the origin of the target geographic image data according to the size of the preset image file. If the origin of the target geographic image data does not coincide with the origin of the preset map space, the target geographic image data is slit starting from the starting point of the first calculated first target image file according to the size of the preset image file; after the target geographic image data is sliced, it is determined whether there is a second target image file with a volume smaller than the size of the preset image file among the first target image files obtained after the target geographic image data is slit. If there is the second target image file, a part of pixel points is appended from the first target image file corresponding and adjacent to the second target image file in the geographic space to the second target image file so that the size of the second target image file is the same as the size of the preset image file; each image file finally obtained by slitting the target geographic image data is used as each first target image file slice for composing the target geographic image data to obtain at least one first target image file corresponding to the target geographic image data; The construction unit is used to determine the pixel sizes of the length and width of each first target image file to be stored in the distributed database according to the size of the preset image file; determine the abscissa and ordinate corresponding to the origin coordinates of the preset map space of the point where the target geographic image data starts to be stored; calculate the abscissa and ordinate of each first target image file relative to the origin coordinates of the preset map space based on the abscissa and ordinate corresponding to the origin coordinates of the preset map space of the point where the target geographic image data starts to be stored; determine whether the number of storage machines is greater than a preset first threshold; When the number of storage machines is less than or equal to a preset first threshold, the first target image files with odd values of the abscissa and odd values of the ordinate are stored in the machine ranked first, and the serial number of the stored machine is used as the storage machine code of the first target image files with odd values of the abscissa and odd values of the ordinate; the first target image files with odd values of the abscissa and even values of the ordinate are stored in the machine ranked second, and the serial number of the stored machine is used as the storage machine code of the first target image files with odd values of the abscissa and even values of the ordinate; the first target image files with even values of the abscissa and odd values of the ordinate are stored in the machine ranked third, and the serial number of the stored machine is used as the storage machine code of the first target image files with odd values of the abscissa and odd values of the ordinate; the first target image files with even values of the abscissa and even values of the ordinate are stored in the machine ranked fourth, and the serial number of the stored machine is used as the storage machine code of the first target image files with even values of the abscissa and even values of the ordinate; wherein, the storage machine code of each storage machine is set according to the creation time of the storage machine or the capacity size of the storage machine, and the storage machine code of each machine is the serial number corresponding to the storage machine. When the number of storage machines is greater than a preset first threshold, all storage machines are divided into two groups, namely an odd storage machine group and an even storage machine group. Each group of storage machine groups includes at least two storage machines. The storage machine codes of the corresponding storage machines in each group of storage machine groups are set according to the creation time of the storage machines or the capacity size of the storage machines. The storage machine code of each storage machine is the serial number corresponding to the storage machine. The first target image files with odd abscissa values are stored in the storage machines of the odd storage machine group according to the first storage rule, and the storage machine code of the storage machine in the odd storage machine group that stores each first target image file with an odd abscissa value is used as its storage machine code. The first target image files with even abscissa values are stored in the storage machines of the even storage machine group according to the second storage rule, and the storage machine code of the storage machine in the even storage machine group that stores each first target image file with an even abscissa value is used as its storage machine code. Among them, the first storage rule includes: sorting all the storage machines in the odd storage machine group according to the creation time or the capacity size of the storage machines, randomly selecting one from the set of all un-stored first target image files with odd abscissa values and storing it in the first target storage machine at the head of the storage machine queue in the odd storage machine group, then adjusting the first target storage machine to the end of the storage machine queue, and readjusting the storage machine queue. Then, the storage machine currently at the head of the storage machine queue is continued to be used as the first target storage machine, and then the operation of randomly selecting one from the set of all un-stored first target image files with odd abscissa values and storing it in the first target storage machine at the head of the storage machine queue in the odd storage machine group is returned to be executed until all the first target image files with odd abscissa values to be stored are stored in each storage machine of the odd storage machine group;The second storage rule includes: sorting all the storage machines in the even-numbered storage machine group according to the creation time or the capacity of the storage machines, randomly selecting one from all the first target image file sets with even abscissa values that have not been stored and storing it in the second target storage machine at the head of the storage machine queue in the even-numbered storage machine group, then adjusting the second target storage machine to the end of the storage machine queue, readjusting the storage machine queue, continuing to use the storage machine currently at the head of the storage machine queue as the second target storage machine, and then returning to execute the operation of randomly selecting one from all the first target image file sets with even abscissa values that have not been stored and storing it in the second target storage machine at the head of the storage machine queue in the even-numbered storage machine group until all the first target image files with even abscissa values to be stored are stored in each storage machine in the even-numbered storage machine group. Among them, the image files stored on each storage machine cannot be image files corresponding to a geographical space with a volume exceeding a preset size, where the preset size is less than or equal to 2 kilometers; constructing the storage file name of each first target image file based on the storage machine code of each first target image file and the abscissa and ordinate of each first target image file relative to the origin coordinates of the preset map space. A storage unit, configured to encrypt each of the first target image files according to the storage file name of each first target image file and store them in a corresponding distributed database.

3. A regular geographical image data slicing and processing device, characterized in that, Comprising: One or more processors, and a memory; Computer-readable instructions are stored in the memory, and when the computer-readable instructions are executed by the one or more processors, the steps of the method for slicing and processing geographic image data according to any one of the rules in claim 1 are implemented.

4. A readable storage medium, characterized in that: Computer-readable instructions are stored in the readable storage medium, and when the computer-readable instructions are executed by one or more processors, one or more processors are caused to implement the steps of the method for slicing and processing geographic image data according to any one of the rules in claim 1.

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