Regular geographic image data slicing processing method and related equipment

Abstract

The invention provides a regular geographic image data slicing processing method and related equipment, which can effectively split large-volume geographic image data and then encrypt and store the data, can effectively increase the security and privacy of geographic information data storage, can effectively reduce the repeatability of geographic space information collection, and can effectively improve the efficiency of geographic space information collection. Furthermore, the large-volume geographic image data is cut into small-volume image data files for storage, so that an image data acquisition path can be better planned, repeated acquisition caused by storage of overlarge image data is reduced, the large-volume geographic image data is fragmented and then stored, the processing speed of the image data can be effectively improved, and the processing efficiency of the image data is improved. According to the method, the reading and writing efficiency of the image data is improved, the integrity of the original geographic image data is effectively guaranteed, meanwhile, the obtaining efficiency of the regular geographic image data can be improved, and the merging and displaying efficiency of the regular geographic image data is improved.

Description

Technical Field

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

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

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

[0004] The present application aims to solve at least one of the above-mentioned technical defects. In view of this, the present application provides a regular geographic image data slicing processing method and related equipment to solve the technical defect of the existing technology that image data cannot be processed efficiently.

[0005] A regular geographic image data slicing processing method comprises: obtaining target geographic image data to be stored; 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; 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 encrypting each of the first target image files according to the storage file name of each of the first target image files and storing them in a corresponding distributed database.

[0006] Preferably, the target geographic image data is sliced ​​according to a preset slicing rule to obtain at least one first target image file corresponding to the target geographic image data, including: calculating the size of the first target image file from the origin of a preset map space, judging 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 slicing the target geographic image data 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 calculating the size of the first target image file from the beginning according to the size of the preset image file. The target geographic image data is sliced ​​starting from the starting point of the target image file; after the target geographic image data is sliced, it is determined whether there is a second target image file whose volume is smaller than the size of the preset image file in each first target image file obtained after the target geographic image data is sliced; if the second target image file exists, a part of pixel points is appended to the second target image file from the first target image file corresponding to 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 size of the preset target image file; and each image file finally obtained by slicing the target geographic image data is used as each first target image file constituting the target geographic image data.

[0007] Preferably, constructing the storage file name of each first target image file based on the origin coordinate information of the preset map space includes: determining the pixel size of the length and width of each first target image file to be stored in the distributed database based on the size of the preset image file; determining the horizontal coordinate and the vertical coordinate corresponding to the point where the target geographic image data starts to be stored relative to the origin coordinate of the preset map space based on the origin coordinate of the preset map space; calculating the horizontal coordinate and the vertical coordinate of each first target image file relative to the origin coordinate of the preset map space based on the horizontal coordinate and the vertical coordinate corresponding to the point where the target geographic image data starts to be stored relative to the origin coordinate of the preset map space; 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 file stored on each storage machine cannot be an image file corresponding to a geographic space whose volume exceeds the preset size; 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 horizontal coordinate and the vertical coordinate of each first target image file relative to the origin coordinate of the preset map space.

[0008] Preferably, the storage machine code of each first target image file is determined based on the number of each first target image file and the number of storage machines, including: 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, the storage machine code of each first target image file is determined according to a preset first storage strategy, including: storing the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an odd number to the machine ranked first, and using the serial number corresponding to the stored machine as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an odd number; storing the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an even number to the machine ranked second, and using the serial number corresponding to the stored machine as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an even number; storing the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an even number to the machine ranked second, and using the serial number corresponding to the stored machine as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an even number; , the first target image file whose ordinate value is an odd number is stored in the third-ranked machine, and the serial number corresponding to the stored machine is used as the storage machine code of the first target image file whose ordinate value is an odd number and whose ordinate value is an odd number; the first target image file whose ordinate value is an even number and whose ordinate value is an even number is stored in the fourth-ranked machine, and the serial number corresponding to the stored machine is used as the storage machine code of the first target image file whose ordinate value is an even number and whose ordinate value is an even number; wherein the storage machine code of each storage machine is set according to the creation time of the storage machine 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, the method of determining the storage machine code of each first target image file according to the preset second storage strategy includes: dividing all storage machines into two groups, namely an odd storage machine group and an even storage machine group, wherein 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 of the storage machine 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 file with an odd value of the horizontal coordinate in the storage machine of the odd storage machine group according to the first storage rule, and using the storage machine code of each first target image file with an odd value of the horizontal coordinate stored in the storage machine of the odd storage machine group as its storage machine code; storing the first target image file with an even value of the horizontal coordinate in the storage machine of the even storage machine group according to the second storage rule, and using the storage machine code of each first target image file with an even value of the horizontal coordinate stored in the storage machine of the even storage machine group as its storage machine code.

[0011] Preferably, the first storage rule includes: sorting all storage machines in the odd-numbered storage machine group according to the creation time or the capacity of the storage machine, randomly taking out one from the set of first target image files with odd values ​​of all horizontal coordinates that have not been stored, and storing it in the first target storage machine at the head of the storage machine queue of the odd-numbered storage machine group, then adjusting the first target storage machine to the tail of the storage machine queue, and readjusting the storage machine queue, and then continuing 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 taking out one from the set of first target image files with odd values ​​of all horizontal coordinates that have not been stored, and storing it in the first target storage machine at the head of the storage machine queue of the odd-numbered storage machine group, until all first target image files with odd values ​​of horizontal coordinates to be stored are stored in each storage machine of the odd-numbered storage machine group. The second storage rule includes: sorting all storage machines in the even-numbered storage machine group according to the creation time or the capacity of the storage machine, randomly taking out one from the set of first target image files whose horizontal coordinates have not yet been stored and whose values ​​are even, and storing it in the second target storage machine at the head of the storage machine queue of the even-numbered storage machine group, and then adjusting the second target storage machine to the tail of the storage machine queue, and readjusting the storage machine queue, and then continuing 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 taking out one from the set of first target image files whose horizontal coordinates have not yet been stored and whose values ​​are even, and storing it in the second target storage machine at the head of the storage machine queue of the even-numbered storage machine group, until all first target image files whose horizontal coordinates have even values ​​to be stored are stored in each storage machine of the even-numbered storage machine group.

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

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

[0014] A readable storage medium stores computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors implement the steps of a geographic image data slicing processing method according to any of the rules described in the above introduction.

[0015] It can be seen from the technical solutions introduced above that the method provided in the embodiment of the present application can obtain the target geographic image data to be stored; in the actual application process, since the geographic image data is generally large in volume, and generally speaking, the geographic image data is regular image data. If the acquired target geographic image data is directly stored, it may not be stored, and it does not meet the relevant image data storage requirements, and information leakage problems are prone to occur. Therefore, it can be considered to store the target geographic image data after slicing. Therefore, in order to efficiently manage the acquired target geographic image data, after acquiring the target geographic image data, the acquired target geographic image data can be sliced ​​according to the preset slicing rules, and at least one first target image file corresponding to the target geographic image data can be obtained; wherein the size of each first target image file obtained by segmentation is smaller than the size of the target geographic image data. In order to efficiently store and manage each first target image file, it is possible to consider dispersing and storing each first target image file. 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. From the above introduction, it can be seen that the method provided by the embodiment 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, and can effectively reduce the repetitiveness of geographic spatial information collection. Further, the large-volume geographic image data is divided into small-volume image data files for storage, which can better plan the image data collection path, reduce repeated collection due to storage of too large image data, and the large-volume geographic image data is sliced ​​and then stored, which can effectively improve the processing speed of image data, improve the reading and writing efficiency of image data, and effectively protect the integrity of the original geographic image data. At the same time, it can also improve the efficiency of obtaining regular geographic image data and improve the efficiency of merging and displaying regular geographic image data. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0017] Figure 1 A flowchart of a method for implementing regular geographic image data slicing processing provided in an embodiment of the present application; Figure 2-Figure 3 A storage schematic diagram of an example of an embodiment of the present application; Figure 4 A schematic diagram of the structure of a regular geographic image data slicing processing device according to an example of an embodiment of the present application; Figure 5 This is a hardware structure block diagram of a regular geographic image data slicing processing device disclosed in an embodiment of the present application. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0019] In view of the fact that most of the current regular geographic image data slicing processing solutions are difficult to adapt to complex and changeable business needs, the applicant has studied a regular geographic image data slicing processing solution. The regular geographic image data slicing processing method can effectively split large volumes of geographic image data and then encrypt and store them, which can effectively increase the security and privacy of geographic information data storage, and can effectively reduce the duplication of geographic spatial information collection. Furthermore, by dividing large volumes of geographic image data into small volumes of image data files for storage, the image data collection path can be better planned, and repeated collection due to storage of too large image data can be reduced. Moreover, by dividing large volumes of geographic image data into slices and then storing them, the processing speed of image data can be effectively improved, and the reading and writing efficiency of image data can be improved. While effectively ensuring the integrity of the original geographic image data, the efficiency of obtaining regular geographic image data can also be improved, and the efficiency of merging and displaying regular geographic image data can be improved.

[0020] The method provided in the embodiment of the present application can be used in many general or special computing device environments or configurations. For example: personal computers, server computers, handheld devices or portable devices, tablet devices, multi-processor devices, distributed computing environments including any of the above devices or devices, etc. The embodiment of the present application provides a regular geographic image data slicing processing method, which can be applied to various geographic image data systems, and can also be applied to various computer terminals or smart terminals, and its execution subject can be a processor or server of a computer terminal or smart terminal.

[0021] Combine the following Figure 1 , introduces the process of the regular geographic image data slicing processing method given in the embodiment of the present application, such as Figure 1 As shown, the process can include the following steps: Step S101, obtaining target geographic image data to be stored.

[0022] Specifically, in the actual application process, geographic image data is the basic data for subsequent storage and analysis. For example, the earth's surface images obtained by satellite remote sensing, including information such as terrain, vegetation cover, and water distribution, are the initial data source for various geographic research and applications. A complete geographic image data set is necessary for accurately describing geographic phenomena and spatial relationships. Acquiring and storing geographic image data is very important in the actual application process. Geographic image data can have multiple formats. Common formats include TIFF (Tagged Image File Format). TIFF (Tagged Image File Format) is a flexible bitmap image format that supports multiple color modes and data types, and can store geographic spatial information, such as geographic coordinates, projection information, etc. Many satellite remote sensing images and aerial photography images use this format. JPEG (Joint Photographic Experts Group) format is also commonly used. JPEG (Joint Photographic Experts Group) format stores images in a compressed manner, which can reduce file size while ensuring a certain image quality, and facilitate data transmission and storage, but its storage capacity for geographic spatial information is relatively weak. GeoTIFF is an extension of the TIFF format, specifically used for the storage of geographic spatial data. It adds geographic reference information such as geographic coordinate system, map projection, etc. on the basis of ordinary TIFF, so that the image data can accurately correspond to the actual geographic spatial location.

[0023] In practical applications, geographic image data usually has a large amount of data. Geographic image data generally contains rich geographic spatial information, and the higher the resolution, the larger the data volume. For example, a high-resolution satellite remote sensing image, a full-color band image (resolution can reach 0.3-1 meters) may have a data volume of hundreds of megabytes or even several gigabytes. Multispectral or hyperspectral images, because they contain information from multiple bands, have even larger data volumes.

[0024] The amount of image data obtained by aerial photography should not be underestimated. Especially when conducting large-scale surveying and mapping projects, a large number of images will be taken to ensure sufficient accuracy and coverage, and the total amount of data from these images will be very large.

[0025] In actual applications, geographic image data carries spatial reference information, which can mainly include geographic coordinate systems, which define how locations on the earth are represented. Different coordinate systems are suitable for different application scenarios and regions. There is also map projection information, which is a method of converting three-dimensional coordinates on the earth's surface into two-dimensional plane coordinates. Common projection methods include Gauss-Krüger projection and Mercator projection. Different projection methods will make the geographic features on the earth's surface present different shapes and deformation characteristics. Understanding these projection methods is crucial for the correct use and processing of geographic image data.

[0026] The accuracy of geographic image data varies depending on the acquisition method and equipment. The accuracy of satellite remote sensing images is affected by factors such as the satellite orbit altitude and sensor performance. For example, images acquired by high-orbit satellites may have lower resolution and relatively poor accuracy; while high-resolution commercial satellite images in low orbits have higher accuracy. The accuracy of aerial photography images is related to flight altitude, camera performance, etc. Images acquired by low-altitude flights are generally more accurate, but the coverage is relatively small; images acquired by high-altitude flights have a larger coverage, but the accuracy may be reduced. At the same time, operations such as registration and correction during data processing will also affect the final accuracy of image data.

[0027] Geographic image data reflects geographic information at a specific time. For example, geographic image data can reflect changes in the natural environment (such as seasonal growth and withering of vegetation, changes in water levels of rivers and lakes, etc.) and human activities (such as urban construction, land development, etc.), which makes the timeliness of geographic image data very important. For example, for agricultural monitoring, it is necessary to obtain images of different growing seasons to accurately assess the growth status of crops; for urban expansion research, it is necessary to obtain images regularly to track changes in urban boundaries.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] Furthermore, for complex geographic image data processing algorithms, processing the entire large geographic image data set may face problems such as insufficient memory and long computing time. After slicing large geographic image data, the size of each image file is moderate, and processing tasks can be performed more efficiently within the constraints of existing computing resources. For example, when performing geometric correction of images, operations on the sliced ​​image files only require processing a relatively small part each time, reducing the complexity of the algorithm and the requirements for hardware resources.

[0036] In actual application, when geographic image data needs to be transmitted over the network, smaller sliced ​​image files are easier to transmit than the entire large geographic image data set. On the one hand, sliced ​​image files can choose appropriate transmission strategies according to network bandwidth and transmission requirements, such as batch and time period transmission, to avoid network congestion caused by transmitting a large amount of data at one time. On the other hand, when the network is interrupted, only part of the sliced ​​image files need to be retransmitted instead of the entire large geographic image data, reducing data transmission losses and duplication of work. For example, when geographic image data is transmitted from a data center to a remote user terminal, after the large geographic image data is sliced, the key sliced ​​image data can be transmitted first according to the user's immediate needs, which can effectively improve the timeliness and reliability of data transmission. Therefore, in order to better store and utilize the target geographic image data to be stored, after acquiring the target geographic image data, the target geographic image data can be further sliced ​​and processed before being stored.

[0037] In practical applications, geographic image data is generally divided into regular geographic image data and irregular geographic image data. Among them, regular geographic image data generally has the following characteristics: (1) Regular geographic image data is usually presented in the form of a regular grid, like a matrix of neatly arranged small squares (pixels). For example, in satellite remote sensing images, these pixels are distributed in geographic space according to a fixed spacing and arrangement, making the entire image spatially regular. This grid structure facilitates spatial positioning and geographic spatial measurement such as distance and area.

[0038] (2) Regular geographic image data often has uniform resolution in the horizontal and vertical directions. For example, a high-resolution remote sensing image may have a resolution of 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. This allows accurate measurements to be made based on a fixed resolution standard when analyzing the image, such as identifying the size of geographic features such as buildings or roads.

[0039] (3) Regular geographic image data generally has a clear geographic coordinate system, the most common of which is the latitude and longitude coordinate system (such as WGS84). This is like installing a precise locator on the earth for the image data, so that each pixel can find the corresponding position on the earth's surface. Through these coordinates, geographic image data can be accurately aligned and integrated with other geographic data (such as vector map data).

[0040] (4) Regular geographic image data generally contains appropriate projection information. Projection is a method of projecting the earth's curved surface onto a flat surface. Different projections are used for different purposes. For example, the Mercator projection is widely used in navigation and large-area mapping, and it can maintain directional accuracy; 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 a plane conform to the actual geographic spatial relationship.

[0041] (5) Regular geographic image data generally has high clarity and can clearly display the details of geographic objects. For example, in images that monitor changes in urban land use, 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 image data is low, avoiding blur or erroneous 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 data on the city and its surrounding areas, and there should be no situation where there is no data in a certain area in the middle. In addition, the accuracy of the data is also high, and the location, shape and other information of the geographic objects reflected are consistent with the actual situation.

[0042] (6) Regular geographic image data general attribute information specifications. For example, for multi-band geographic image data (such as multispectral images in satellite remote sensing), each band has a clear definition and purpose. For example, in the multispectral images of Landsat, different bands can be used to identify different types of land objects such as vegetation, water bodies, and bare land. The data quality and data range (such as wavelength range) of these bands are standardized, which is convenient for professional spectral analysis of land objects. Generally, complete metadata is attached, which includes the data collection date, sensor type, data processing level, etc. For example, metadata records which satellite sensor the image data was collected by and when it was collected, which is very important for understanding the timeliness and quality of the data. Moreover, the format of these metadata usually follows certain standard specifications to facilitate data exchange and sharing.

[0043] In actual application, when the target geographic image data is sliced ​​and processed, based on the above-mentioned characteristics of the regular geographic image data, the acquired geographic image data can be sliced ​​and processed according to the preset slicing rules, so as to obtain at least one first target image file corresponding to the target geographic image data.

[0044] 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: (1) The size of the first target image file to be stored can be calculated 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 cut 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 can be cut from the starting point of calculating the first target image file according to the size of the preset image file.

[0045] Furthermore, in actual application, due to the different volumes of different target geographic image data, it is impossible to guarantee that the entire target geographic image data can be evenly and perfectly cut into several first target image files of equal size. The volume of a first target image file finally cut may be smaller than the size of the preset image file. Therefore, in order to ensure that a standardized first target image file is obtained after the target geographic image data is cut, after the target geographic image data is sliced, it is possible to further determine whether there is a second target image file with a volume smaller than the size of the preset image file in each first target image file obtained after the target geographic image data is cut. If there is a second target image file with a volume smaller than the size of the preset image file, a part of the pixels can be appended to the first target image file corresponding 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 target image file; finally, the image files finally obtained by cutting the target geographic image data are used as the first target image files constituting the target geographic image data.

[0046] 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 legal provisions or application scenarios. For example, the size of the first target image file obtained after 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.

[0047] In actual application, the image files after sharding can adopt a more flexible backup strategy. Different backup plans can be formulated for different shards based on factors such as the importance of the data and the update frequency. For example, for image shards containing key geographical elements (such as important transportation hubs and urban central areas), the backup frequency can be increased and safer backup methods such as off-site storage can be adopted; while for shards in relatively less important areas, the backup frequency can be appropriately reduced, thereby effectively saving backup resources while ensuring data security.

[0048] When data is damaged or lost, sharding facilitates fast recovery because only the damaged or lost shards need to be recovered, rather than the entire large geographic image data set. For example, if a disk sector of a storage device is damaged, causing part of the image file to be unreadable, the damaged shard can be quickly located through shard records, and then restored from the backup without having to restore the entire data, greatly shortening the data recovery time.

[0049] Secondly, fragmentation can better implement data access control. Different image fragments may contain information of different sensitivities. For example, some fragments may contain information about military facilities, sensitive ecological protection areas, etc. Through fragmentation, more stringent access rights can be set for these highly sensitive fragments, such as restricting access to specific user groups, requiring multiple authentications, etc., thereby improving data security and privacy protection.

[0050] When it is necessary to desensitize geographic image data (remove or blur sensitive information), sharding makes the operation more convenient. Shards containing sensitive information can be desensitized separately without affecting the normal use of other shards that do not contain sensitive information. For example, when sharing geographic image data for commercial development, shards containing residents' private information (such as residential details) can be blurred while retaining the original accuracy of other insensitive shards such as geographic features.

[0051] As the geographic environment changes or data quality improves, geographic image data needs to be constantly updated. After tile processing, the image tiles of the local area can be updated without having to reprocess and store the entire dataset. For example, when a large building is newly built in a city, only the image tiles containing the building area need to be updated, without having to update the geographic image of the entire city, which greatly saves update costs and time.

[0052] For different versions of geographic image data (such as images taken at different times, images with different processing precision), tile processing helps to better manage versions. The version changes of each tile can be clearly recorded, making it easy to compare the differences between different versions. For example, when studying the land use changes in a certain area, the change process of land use types in each area can be accurately tracked by comparing different versions of image tiles.

[0053] Step S103: constructing a storage file name for each of the first target image files according to the origin coordinate information of the preset map space.

[0054] Specifically, in the actual application process, after the target geographic image data is sliced ​​to obtain multiple first target image files, in order to better manage and store the obtained first target image files, the storage file name of each first target image file can be constructed based on the origin coordinate information of the preset map space, so that the numerous sliced ​​image files can be effectively organized. 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 geographic location, shooting time, image resolution, etc., which is convenient for quickly locating and finding specific target image files in the storage system.

[0055] In actual application, the storage file name of each first target image file may include the machine identification code to be stored. The 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 a specific first target image file needs to be accessed, the storage file name is like a map, guiding the user or system to quickly find the specific machine and storage location where the file is located, reducing search time and improving data access efficiency.

[0056] In actual application, as the amount of data continues to increase, the storage system may be expanded, such as adding new storage devices or changing the storage architecture. Determining the storage file name can better adapt to such changes. When a new storage device is added, the new machine identification code can be incorporated into the storage file name system, so that the image file can be smoothly allocated and stored in the new storage environment. For example, in a distributed storage system, after a new storage node is added, by updating the association between the storage file name and the machine identification code, the new first target image file can be stored on the new node, thereby achieving smooth expansion of the storage system.

[0057] When migrating or sharing data between different storage devices or storage systems, storage file names and machine identification codes can also play a key bridging role. Different machines may have different storage format requirements or data access protocols, and storage file names 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 data in different storage environments.

[0058] Storage file names also help implement data security policies. By controlling access to storage file names, unauthorized users can be restricted from accessing specific first-target image files. Machine identification codes can also be used to track the storage location of data, making it easier for security personnel to monitor the status of storage devices and prevent data from being stolen or tampered with. For example, for image files containing sensitive geographic information, strict access rights can be set based on the storage file name so that only authorized users can access these files stored on a specific machine.

[0059] During the data audit process, the storage file name is an important reference. Auditors can view the storage file name to understand the storage location, access history and other information of each first target image file. The machine identification code can help trace the changes of data during the storage process, such as whether the file has been moved, copied or deleted. For example, when investigating a data breach, the storage file name and machine identification code can help determine which image files may be affected and on which storage device the abnormal operation occurred.

[0060] Step S104: encrypt each of the first target image files and store them in a corresponding distributed database according to the storage file name of each of the first target image files.

[0061] Specifically, in today's digital age, data privacy and security are of vital importance. As can be seen from the above introduction, each first target image file obtained by slicing the target geographic image data may contain sensitive information. Therefore, after determining the storage file name of each first target image file, each first target image file can be encrypted and stored in the corresponding distributed database according to the storage file name of each first target image file. Encrypting the first target image file and storing it in the distributed database can effectively prevent the data from being unauthorizedly accessed, stolen or tampered with during the storage process.

[0062] Among them, in the actual application process, a first target image file can correspond to a distributed database. This situation may be suitable for scenarios with extremely high requirements for data isolation. For example, in some highly confidential scientific research projects, each important experimental geographic image file has a separate distributed database for storage. This ensures that each image file has an independent space in physical storage and management, reducing mutual interference and potential security risks; this storage method has high data security and is easy to manage and maintain separately. If a database has a problem, such as being attacked by hackers or hardware failures, it will only affect the corresponding geographic image file and will not affect other geographic image files. At the same time, it is also relatively simple in terms of permission management. Setting access rights for a single database can accurately control access to specific geographic image files. However, the cost of this storage method is high because it is necessary to establish an independent distributed database for each geographic image file, including hardware resources such as servers and storage devices, as well as related software and maintenance personnel costs. Resource utilization may be low because each database may only store one geographic image file, and a large amount of storage and computing resources will be idle.

[0063] In actual application, 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 geographic image files (such as images of key areas) may be stored in multiple distributed databases. This is to prevent data loss due to a single database failure. By storing the same geographic image file in multiple locations, even if one database has a problem, other databases can still provide data access. This storage method has high data reliability and can ensure the availability of geographic image files even if some databases fail. At the same time, users in different regions can obtain geographic image files from databases closer to them, which improves access efficiency. This redundant storage method can also be used for data backup and recovery, making it convenient to obtain complete geographic image files from different databases when needed. However, data update and synchronization of this storage method are more complicated. When a geographic image file needs to be updated, it must be ensured that all distributed databases storing the file can be updated correctly, otherwise data inconsistency will occur. Moreover, this storage method requires more storage space because the same geographic image file will occupy space in multiple databases.

[0064] In the actual application process, in order to improve the efficiency of data storage, multiple first target image files can also be stored in the same distributed database while ensuring the security of geographic image data storage. This is a common storage method, which is suitable for a collection of geographic image files with strong correlation. This storage method has high resource utilization. One database can store multiple geographic image files, making full use of storage and computing resources. It is easy to manage, and operations such as database maintenance, backup and recovery can be applied to multiple geographic image files at one time. At the same time, when performing data query and analysis, it is convenient to associate multiple geographic image files in the same database, but this storage method may have the problem of concentrated security risks. If this distributed database is breached, all geographic image files stored in it may be leaked. In addition, database performance may be affected by the number of stored files. When there are too many stored geographic image files, the query and retrieval speed of the database may decrease.

[0065] In actual application, complex storage relationships can also be built between multiple first target image files and multiple distributed databases. This situation may occur in large, cross-departmental or cross-organizational image storage systems. Different geographic image files may be cross-stored in different databases to achieve data sharing and resource integration. This storage method can achieve flexible data sharing and optimal storage configuration. By reasonably allocating image files to different databases, the resources of each database can be fully utilized, and data exchange and collaboration between different departments or organizations can be facilitated. However, this storage method may make data management and maintenance very complicated. Complex mapping relationships are required to record which databases each geographic image file is stored in, and which geographic image files are stored in each database. Maintaining data consistency is also more difficult because it involves interactions between multiple files and multiple databases.

[0066] In actual applications, encrypting geographic image data is like adding a solid lock to the data. Only users with the correct key can unlock and view the original data. Although the distributed database itself has certain security mechanisms, encryption provides an additional layer of security to ensure that even if some nodes of the database are compromised, the geographic image data is still safe.

[0067] Furthermore, by encrypting geographic image files and storing them in distributed databases, organizations can meet the requirements of relevant regulations and avoid facing huge fines and legal liabilities due to geographic image data leaks. Distributed databases are usually composed of multiple nodes, and data is distributed and stored on these nodes. Although this architecture improves the availability and reliability of data, it also increases the risk points of data exposure. Encrypting geographic image files and storing them can fully utilize the advantages of distributed databases (such as high availability, scalability, etc.) while reducing the security risks caused by distributed storage. Moreover, in a distributed database environment, data may be transmitted and copied between different nodes. During the transmission and copying process of encrypted geographic image files, even if they are intercepted, it is difficult for attackers to obtain the content, thereby ensuring the security of geographic image data throughout the distributed storage life cycle.

[0068] It can be seen from the technical solutions introduced above that when it is necessary to store regular large-volume geographic image data, the method provided in the embodiment of the present application can effectively split the large-volume geographic image data and then encrypt and store it, which can effectively increase the security and privacy of geographic information data storage, and can effectively reduce the duplication of geographic spatial information collection. Furthermore, by dividing the large-volume geographic image data into small-volume image data files for storage, the image data collection path can be better planned, and repeated collection due to storage of too large image data can be reduced. Moreover, by dividing the large-volume geographic image data into pieces and then storing them, the processing speed of the image data can be effectively improved, and the reading and writing efficiency of the image data can be improved. While effectively ensuring the integrity of the original geographic image data, the efficiency of obtaining regular geographic image data can also be improved, and the efficiency of merging and displaying regular geographic image data can be improved.

[0069] As can be seen from the above introduction, the method provided in the embodiment of the present application can construct a storage file name of each first target image file according to the origin coordinate information of the preset map space. The process is described below, and the process may include the following: Step S201 : determining the length and width pixel sizes of each first target image file to be stored in the distributed database according to a preset image file size.

[0070] Specifically, in the actual application process, the storage resources of the distributed database are limited, just like a warehouse has a certain space limitation. Therefore, it is necessary to plan the size of the files stored in each distributed database. Therefore, when it is necessary to store each first target image file corresponding to the target geographic image data in each distributed database, the length and width pixel size of each first target image file to be stored in the distributed database can be determined based on the preset image file size, so that the storage plan can be deployed according to the size of each first target image file.

[0071] Knowing the pixel size 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 require significantly different storage spaces. By determining the pixel size, it is possible to reasonably allocate storage blocks in the database, optimize the storage layout, and avoid space waste or insufficient storage due to unreasonable storage planning. At the same time, this also helps to select a suitable storage strategy. For first target image files with small pixel sizes, a relatively simple storage method may be sufficient to meet the needs; for first target image files with large pixel sizes, it may be necessary to consider special storage strategies such as distributed storage, sharded storage, or compressed storage to ensure efficient storage and fast retrieval.

[0072] When processing the first target image file, pixel size is a key factor. For example, when performing image scaling, cropping, splicing and other operations, the size and effect of the processed image need to be calculated based on the pixel size. If you want to splice multiple first target image files into a large geographic image, you need to know the pixel size of each first target file in order to accurately calculate the size and position relationship after splicing.

[0073] Furthermore, from the perspective of data transmission, pixel size directly affects the amount of data transmitted. Image files with large pixel sizes will take up more bandwidth and time when transmitted. In a distributed database environment, data may need to be transmitted between different nodes. Determining the pixel size of the first target image file can help estimate the transmission cost, select the appropriate transmission protocol and optimize the transmission strategy. For example, for files with large pixel sizes, block transmission or asynchronous transmission can be used to reduce the instantaneous impact on network bandwidth.

[0074] The pixel size of the first target image file is closely related to the resolution. Generally speaking, the more pixels there are, the higher the resolution is, and the richer the details contained in the image. For example, when geographic images are used for land use planning, high-resolution (i.e., large pixel size) images can clearly show detailed information such as land boundaries and vegetation types, while low-resolution images may only provide a rough terrain outline. By determining the pixel size, the data quality of the image file can be better evaluated to meet the data resolution requirements of different users. For example, for users who need high-precision geographic image data, such as users conducting fine geological exploration or archaeological excavation of cultural relics, providing a first target image file with larger length and width pixel sizes can ensure that sufficiently detailed information can be obtained; while for some users who only need a rough geographic outline, such as users conducting regional tourism planning, files with smaller pixel sizes may be sufficient to meet the needs.

[0075] In particular, in actual application, the pixel size 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 error may be. The smaller the length and width, the smaller the error and the number of first target image files obtained by slicing may be too large. Therefore, in actual application, the pixel size of the length and width of the first target image file can generally be set to 256 pixels. It is found through experiments that when the pixel size of the length and width of the first target image file is set to 256 pixels, the size of the relative grid request and the slice is relatively moderate.

[0076] Step S202, according to the preset origin coordinates of the map space, determining the horizontal coordinate and the vertical coordinate of the point where the target geographic image data starts to be stored relative to the preset origin coordinates of the map space.

[0077] Specifically, in actual application, the map space corresponding to the geographic image data is a complex two-dimensional or even three-dimensional space. Therefore, in actual application, in order to better manage the target geographic image data, the 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.

[0078] For example, in a city's geographic image map, a landmark location in the city's central square (such as the center of a fountain) can be defined as the origin coordinates (assuming longitude x1 and latitude y1). In this way, the locations of all other geographic features (such as streets, buildings, etc.) in the geographic image data can be accurately represented by the offset relative to this origin coordinate.

[0079] Taking satellite remote sensing images as an example, the image data of the entire earth's surface is huge. By defining the origin coordinates, such as the intersection of the equator and the prime meridian as the origin (0,0), the position of the image data of any point on the earth in this space can be easily determined, providing an accurate spatial positioning basis for subsequent geospatial analysis, data processing and user viewing.

[0080] 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.

[0081] For example, a city planning department may have both overall city image data from satellite remote sensing (using the WGS84 coordinate system) and high-precision building detail image data from a local surveying and mapping department (using a local rectangular coordinate system). By unifying them into a map space based on the origin coordinates, data fusion can be achieved, thereby presenting the city's geographic spatial information more comprehensively and accurately.

[0082] Furthermore, when performing geospatial analysis, many algorithms and calculations rely on accurate coordinate systems. Defining origin coordinates can make these calculations more convenient and accurate. For example, when calculating the distance, area, or relative orientation between two geographic areas (such as two different parks), a coordinate system based on origin coordinates can provide a unified calculation framework. Suppose you want to analyze the distance and slope changes between different valleys in a mountainous area. By defining the origin coordinates and the corresponding coordinate system, the geographic image data of each valley can be converted into coordinate values, and then mathematical formulas can be used to calculate the distance, slope and other geospatial parameters between them, providing strong data support for geological exploration, ecological research and other fields.

[0083] For example, in actual application, the upper leftmost point of the entire map corresponding to the target geographic image data can be defined as the origin coordinate of the preset map space. .

[0084] In actual application, the position of geographic image data in the map space needs an accurate reference system to determine. From the above introduction, it can be seen that the origin coordinates of the preset map space are like the "anchor point" of the map space. Therefore, in order to better manage the target geographic image data, the horizontal and vertical coordinates of the point where the target geographic image data starts to be stored relative to the origin coordinates of the preset map space can be determined based on the origin coordinates of the preset map space. In this way, the geographic image data can be accurately placed in the correct position of the map space.

[0085] For example, in the storage of geographic image data of a city map, a certain point in the city center square is taken as the origin coordinate (0,0). If you want to store the image data of a block, you need to determine the horizontal and vertical coordinates of the point where the image data of the block starts to be stored (such as the southwest corner of the block) relative to the origin coordinate of the city center square, so that the location of the block can be accurately represented in the map space, just like accurately locating a figure on a coordinate paper.

[0086] Furthermore, geographic image data is usually composed of multiple parts, especially when dealing with large geographic areas or high-resolution images. Determining the horizontal and vertical coordinates of the point where the target geographic image data begins to be stored relative to the origin coordinates of the preset map space helps to maintain spatial coherence and accuracy when splicing these image data.

[0087] Assume that there are multiple satellite remote sensing image files, each of which covers a certain geographic area. In order to construct a complete geographic image, these image files need to be stitched together. By determining the horizontal and vertical coordinates of the point where each image file starts to be stored relative to the origin coordinates, they can be accurately stitched at the correct position to avoid stitching errors such as image overlap or gaps, thus ensuring the integrity and accuracy of the geographic image.

[0088] Accurate location information is crucial when conducting geospatial analysis and query. By determining the horizontal and vertical coordinates of the point where the target geographic image data begins to be stored, an accurate spatial index is provided for subsequent analysis and query. For example, when studying land use changes, these coordinates can be used to quickly locate image data in a specific area, and then compare images from different periods to analyze changes in land use. At the same time, for users who query image data in a specific geographic area, these coordinates can also help the database quickly locate and return data that meets the requirements, improving query efficiency and accuracy.

[0089] Step S203, based on the horizontal coordinate and vertical coordinate corresponding to the point where the target geographic image data starts to be stored relative to the origin coordinate of the preset map space, calculate the horizontal coordinate and vertical coordinate of each of the first target image files relative to the origin coordinate of the preset map space.

[0090] Specifically, in actual application, 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 position 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 geographic image file is like assigning an accurate "address" to each geographic image file in this coordinate system. This is crucial for the subsequent accurate retrieval, splicing and analysis of these geographic image files in the map space. For example, in a geographic information system (GIS), when images of multiple different regions need to be spliced ​​into a complete map, these precise coordinates can ensure that each geographic image is correctly aligned in space to avoid position confusion.

[0091] Furthermore, understanding the horizontal and vertical coordinates of each first target image file relative to the coordinates of the origin of the preset map space helps to integrate geographic image data with other geospatial data. Geospatial data usually includes multiple types, such as vector data (points, lines, surfaces), terrain data, etc. By clarifying the coordinates of each first target image file relative to the origin of the map space, it is convenient to associate these first target image files with other types of data according to spatial positions. For example, when conducting urban planning, geographic image data is combined with vector data of urban roads. Through coordinate matching, the land use or building distribution around the road can be accurately analyzed, thereby providing more comprehensive information for planning decisions.

[0092] For large-volume target geographic image data sets, knowing the horizontal and vertical coordinates of each first target image file relative to the origin coordinates of the preset map space can effectively manage the blocks. This is like dividing a large puzzle into multiple small puzzle pieces. By knowing the position of each small puzzle piece (first target image file) in the entire puzzle (preset map space), they can be stored, transmitted and processed more efficiently. For example, when processing geographic 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 computing resources and task requirements, thereby improving the efficiency of data processing.

[0093] Understanding the horizontal and vertical coordinates 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 it is necessary to query the geographic image within a specific area (defined by the horizontal and vertical coordinate 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 screened out. This spatial query function is widely used in many fields such as land resource management and ecological environment monitoring. For example, when monitoring forest fires, the geographic image of a specific fire area can be quickly obtained by querying the coordinate range of the area to analyze the spread of the fire.

[0094] Step S204: 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.

[0095] Specifically, when storing geographic 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 the storage machine where the file is located to be quickly located when managing data. Just like the books in a library have corresponding shelf numbers, when you need to find or manage a specific first target image file, you can directly find the storage machine where the first target file is located based on the storage machine code. This positioning method greatly improves the efficiency of data management, especially when the amount of data is huge and there are many storage machines. For example, when you need to update or back up a specific first target image file, you can quickly determine its location and perform the corresponding operation through the storage machine code.

[0096] During the storage system maintenance or upgrade process, the storage machine code can help administrators perform targeted operations on some storage machines. For example, when it is necessary to upgrade the hardware or update the software of several storage machines, the first target image files stored on these machines can be easily identified based on 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 upgrades on data storage and use.

[0097] Determining the storage machine code helps implement data redundancy strategies. Copies of important first-target image files can be stored on different storage machines. Coding can be used to easily plan and manage the storage locations of these copies. For example, for geographic image data in some key areas, the master file can be stored on one machine, and the copies can be stored on other machines. In this way, when a machine fails, data copies can be obtained from other machines to ensure data security and availability.

[0098] When a storage machine fails, the storage machine code can help quickly identify the first target image file affected, so that timely measures can be taken to recover data. For example, after a storage machine fails, you can quickly determine which files are lost or damaged based on the code, and then obtain backup data from other normal storage machines or reallocate storage locations, thereby improving the fault tolerance and data recovery capabilities of the entire storage system.

[0099] 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 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 there are many goods (first target image files) to be stored in multiple warehouses (storage machines). In order to avoid a warehouse being overcrowded while other warehouses are idle, it is necessary to reasonably distribute the goods to each warehouse. For example, assuming 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 to prevent individual machines from being overloaded due to storing too many files.

[0100] Secondly, in actual applications, storage machines may also have differences in processing capabilities such as data reading and writing speeds. Reasonable allocation of storage machines for each first target image file can balance the workload of each storage machine and ensure that each machine can work efficiently within its capabilities during data retrieval, processing and other operations. For example, when performing large-scale geographic image data analysis tasks, it is necessary to frequently read the first target image file from the storage machine. If the first target image file is not allocated reasonably, some machines may frequently respond to read requests and become performance bottlenecks, while other machines are rarely used.

[0101] The image files stored on each storage machine cannot be image files corresponding to a geographic space whose volume exceeds a preset size.

[0102] For example, according to relevant legal provisions, the volume of geographic image files stored on each storage machine cannot exceed the volume corresponding to geographic image data of an area of ​​2 kilometers.

[0103] Step S205, constructing a storage file name of each first target image file according to the storage machine code of each first target image file and the horizontal coordinate and the vertical coordinate of each first target image file relative to the origin coordinate of the preset map space.

[0104] Specifically, in actual application, in order to efficiently manage each first target image file, after determining the storage machine code of each first target image file and the horizontal and vertical 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 according to the storage machine code of each first target image file and the horizontal and vertical 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.

[0105] For example, you can define The horizontal coordinate of the point where the target geographic image data starts to be stored based on the origin coordinate of the preset map space; The ordinate of the point where the target geographic image data starts to be stored is based on the origin coordinates of the preset map space; is the machine identification code of the first target image file to be stored; the large target geographic image data can be split into multiple blocks horizontally and vertically, and the files are named by the block numbers of the horizontal and vertical axes plus the storage machine identification code, and the storage file name of each first target image file is constructed as: .

[0106] It can be seen from the technical solution introduced above that the method provided in the embodiment 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.

[0107] As can be seen from the above introduction, the method provided in the embodiment 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. The process is described below, and the process may include the following steps: Step S301, determining whether the number of storage machines is greater than a preset first threshold.

[0108] Specifically, as can be seen from the above introduction, since the storage machines generally have limited resources, 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. 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.

[0109] Step S302, determining a storage machine code of each of the first target image files according to a preset first storage strategy.

[0110] Specifically, if it is determined that the number of storage machines is less than or equal to the preset first threshold, it means that the number of storage machines is limited and the storage machines need to be fully utilized. Then, the storage machine code of each first target image file can be determined according to the preset first storage strategy.

[0111] The process of determining the storage machine code of each first target image file according to the preset first storage strategy may include the following: (1) The first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an odd number is stored in the machine ranked first, and the serial number corresponding to the stored machine is used as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an odd number; (2) storing the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an even number in the second ranked machine, and using the serial number corresponding to the stored machine as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an even number; (3) storing the first target image file whose horizontal coordinate value is an even number and whose vertical coordinate value is an odd number in the third ranked machine, and using the serial number corresponding to the stored machine as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an odd number; (4) storing the first target image file whose horizontal coordinate value is an even number and whose vertical coordinate value is an even number in the fourth ranked machine, and using the serial number corresponding to the stored machine as the storage machine code of the first target image file whose horizontal coordinate value is an even number and whose vertical coordinate value is an even number; The storage machine code of each storage machine may be set according to the creation time of the storage machine or the capacity of the storage machine, and the storage machine code of each machine may be set to the serial number corresponding to the storage machine.

[0112] Step S303: determining the storage machine code of each of the first target image files according to a preset second storage strategy.

[0113] Specifically, if it is determined that the number of storage machines is greater than the preset first threshold, it means that the number of storage machines is sufficient, and the storage machine code of each first target image file can be determined according to the preset second storage strategy. The process of determining the storage machine code of each first target image file according to the preset second storage strategy may include the following: All storage machines are divided into two groups, namely, an odd-numbered storage machine group and an even-numbered storage machine group, wherein each storage machine group includes at least two storage machines, and the storage machine code of each storage machine corresponding to each storage machine in each storage machine group is set according to the creation time of the storage machine 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.

[0114] The first target image file with an odd value of the horizontal coordinate can be stored in the storage machine of the odd storage machine group according to the first storage rule, and the storage machine code of each first target image file with an odd value of the horizontal coordinate stored in the storage machine of the odd storage machine group is used as its storage machine code. 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 of the storage machine, and one is randomly taken out from the set of all the first target image files with odd values ​​of the horizontal coordinates that have not been stored and stored in the first target storage machine at the head of the storage machine queue of the odd storage machine group, and then the first target storage machine is adjusted to the end of the storage machine queue, and the storage machine queue is readjusted, and then the storage machine currently at the head of the storage machine queue continues to be the first target storage machine, and then returns to perform the operation of randomly taking out from the set of all the first target image files with odd values ​​of the horizontal coordinates that have not been stored and storing in the first target storage machine at the head of the storage machine queue of the odd storage machine group, until all the first target image files with odd values ​​of the horizontal coordinates to be stored are stored in each storage machine of the odd storage machine group.

[0115] The first target image file whose horizontal coordinate value is an even number can be stored in the storage machine of the even-numbered storage machine group according to the second storage rule, and the storage machine code of each first target image file whose horizontal coordinate value is an even number in the storage machine of the even-numbered storage machine group can be used as its storage machine code. Among them, the second storage rule may include the following: sorting all storage machines in the even-numbered storage machine group according to the creation time or the capacity of the storage machine, randomly taking out one from the set of first target image files with even values ​​of all horizontal coordinates that have not been stored and storing it in the second target storage machine at the head of the storage machine queue of the even-numbered storage machine group, then adjusting the second target storage machine to the end of the storage machine queue, and readjusting the storage machine queue, and then continuing 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 taking out one from the set of first target image files with even values ​​of all horizontal coordinates that have not been stored and storing it in the second target storage machine at the head of the storage machine queue of the even-numbered storage machine group, until all first target image files with even values ​​of horizontal coordinates to be stored are stored in each storage machine of the even-numbered storage machine group.

[0116] For example, Figure 2 and Figure 3 As shown, Figure 2 and Figure 3 The storage effect of 4 image files is illustrated. The first target image file obtained by segmenting the geographic image data to be stored can be regarded as block 1, block 2, block 3 and block 4 respectively. In actual application, map data over 2 kilometers cannot be stored in one machine. If block 1 is the same as any one of block 2, block 3 and block 4, it does not meet the requirement of being stored in one machine. Therefore, block 1 needs to be different from block 2, block 3 and block 4. At least four storage machines are needed to store data. The four required storage machines are numbered as 1, 2, 3 and 4 respectively. When there are only four storage machines, the blocks with even values ​​of horizontal coordinates and odd values ​​of vertical coordinates in blocks 1, 2, 3 and 4 can be placed in machine 3; the blocks with even values ​​of horizontal coordinates and even values ​​of vertical coordinates can be placed in machine 4. The blocks with odd values ​​of horizontal coordinates and odd values ​​of vertical coordinates can be placed in machine 1, and the blocks with odd values ​​of horizontal coordinates and even values ​​of vertical coordinates can be placed in machine 2. The data on each storage machine will not exceed 2 kilometers of geographic image data.

[0117] When there are more than four machines, all storage machines can be sorted according to certain rules. For example, all storage machines can be sorted by machine creation time or machine storage size, and the machines can be split into two groups A and B according to the parity of the serial number or custom rules, and each group is a queue. When the value of the horizontal coordinate of the first target image file is an odd number, one machine in group A is selected for storage, and the machines in group A that have stored image files continue to be placed at the end of the queue of group A, and the cycle repeats until all first target image files with odd horizontal coordinate values ​​are stored in group A storage. When the value of the horizontal coordinate of the first target image file is an even number, one machine in group B is selected for storage, and the machines in group B that have stored image files continue to be placed at the end of the queue of group B, and the cycle repeats until all first target image files with even horizontal coordinate values ​​are stored in group B storage.

[0118] In particular, the reason why the machines in group A or group B are repeatedly used to store image files is that the number of storage machines is definitely not enough, so it is necessary to cyclically obtain them. In addition, the reason why a queue is used to obtain is that if it is obtained randomly, there is a probability that the machine number obtained multiple times is the same, which cannot guarantee that the data of more than 2 kilometers cannot be stored in one machine. For example, there are five machines 1, 2, 3, 4, and 5, which can be divided into group A (1, 3, 5) and B (2, 4) according to the sequence number. When the horizontal coordinate of the first target image file is 1 and the vertical coordinate of the first target image file is equal to 1, the storage machine No. 1 in group A is taken for storage; when the horizontal coordinate of the first target image file is 1 and the vertical coordinate is 2, the machine No. 3 in group A is taken for storage; when the horizontal coordinate of the first target image file is 1 and the vertical coordinate is 3, the machine No. 5 in group A is taken for storage; when the horizontal coordinate of the first target image file is 1 and the vertical coordinate is 4, the machine No. 1 in group A is taken for storage.

[0119] It can be seen from the technical solution introduced above that the method provided in the embodiment of the present application can determine the storage machine code of each first target image file based on the number of each first target image file and the number of storage machines, so as to efficiently utilize limited storage resources while efficiently managing each first target image file.

[0120] The following is a description of a regular geographic image data slicing processing device provided by an embodiment of the present application. The regular geographic image data slicing processing device described below and the regular geographic image data slicing processing method described above can be referred to in correspondence with each other. Figure 4 , Figure 4 FIG. 1 is a schematic diagram of a structure of a regular geographic image data slicing processing device disclosed in an embodiment of the present application. Figure 4 As shown, the regular geographic image data slicing processing device may include: An acquisition unit 101 is used to acquire target geographic image data to be stored; A slicing unit 102 is used to slice 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 103, configured to construct a storage file name of each of the first target image files according to the origin coordinate information of a preset map space; The storage unit 104 is used to encrypt each of the first target image files and store them in a corresponding distributed database according to the storage file name of each of the first target image files.

[0121] It can be seen from the technical solutions introduced above that the device provided by the embodiment of the present application can obtain the target geographic image data to be stored; in the actual application process, since the geographic image data is generally large in volume, and generally speaking, the geographic image data is regular image data. If the acquired target geographic image data is directly stored, it may not be stored, and it does not meet the relevant image data storage requirements, and information leakage problems are prone to occur. Therefore, it can be considered to store the target geographic image data after slicing. Therefore, in order to efficiently manage the acquired target geographic image data, after acquiring the target geographic image data, the acquired target geographic image data can be sliced ​​according to the preset slicing rules, and at least one first target image file corresponding to the target geographic image data can be obtained; wherein the size of each first target image file obtained by segmentation is smaller than the size of the target geographic image data. In order to efficiently store and manage each first target image file, it is possible to consider dispersing the storage of each first target image file. Therefore, after slicing the target geographic image data, the storage file name of each first target image file can be constructed based on the origin coordinate information of the preset map space; so that each first target image file can be encrypted and stored in the corresponding distributed database based on the storage file name of each first target image file.

[0122] From the above introduction, it can be seen that the device provided by the embodiment of the present application can effectively split large-volume geographic image data and then encrypt and store them, which can effectively increase the security and privacy of geographic information data storage, and can effectively reduce the duplication of geographic spatial information collection. Furthermore, by dividing large-volume geographic image data into small-volume image data files for storage, the image data collection path can be better planned, and repeated collection due to storage of too large image data can be reduced. Moreover, by dividing large-volume geographic image data into pieces and then storing them, the processing speed of image data can be effectively improved, and the reading and writing efficiency of image data can be improved. While effectively ensuring the integrity of the original geographic image data, the efficiency of obtaining regular geographic image data can also be improved, and the efficiency of merging and displaying regular geographic image data can be improved.

[0123] Among them, the specific processing flow of each unit included in the above-mentioned regular geographic image data slicing processing device can refer to the relevant introduction of the regular geographic image data slicing processing method in the previous text, and will not be repeated here. The regular geographic image data slicing processing device provided in the embodiment of the present application can be applied to regular geographic image data slicing processing equipment, such as terminals: mobile phones, computers, etc. Optionally, Figure 5 The hardware structure diagram of the regular geographic image data slicing processing device is shown in FIG. Figure 5, the hardware structure of the regular geographic image data slicing processing device may include: at least one processor 1, at least one communication interface 2, at least one memory 3 and at least one communication bus 4. In the embodiment of the present application, the number of processor 1, communication interface 2, memory 3 and communication bus 4 is at least one, and the processor 1, communication interface 2 and memory 3 communicate with each other through the communication bus 4. 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 embodiment of the present application; the memory 3 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory; wherein the memory stores a program, and the processor can call the program stored in the memory, and the program is used to: implement each processing flow in the aforementioned terminal regular geographic image data slicing processing scheme. The embodiment of the present application also provides a readable storage medium, which can store a program suitable for execution by a processor, and the program is used to: implement each processing flow of the aforementioned terminal in the regular geographic image data slicing processing scheme. 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 terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method, article or device including the elements. In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same and similar parts between the embodiments can be referred to each other. The above description of the disclosed embodiments enables professionals and technicians in this field to implement or use the present application. The various modifications to these embodiments will be obvious to professionals and technicians in this field, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. The various embodiments can be combined with each other. Thus, the present application will not be limited to the embodiments shown herein but will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A regular geographic image data slicing processing method, characterized in that: include: Acquire target geographic image data to be stored; According to a preset slicing rule, the target geographic image data is sliced ​​to obtain at least one first target image file corresponding to the target geographic image data; Constructing a storage file name for each of the first target image files according to the origin coordinate information of the preset map space; According to the storage file name of each of the first target image files, each of the first target image files is encrypted and stored in a corresponding distributed database.

2. The method according to claim 1, characterized in that The step of 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 includes: Calculating the size of the first target image file from the origin of the preset map space, 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 cutting the target geographic image data 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 cutting the target geographic image data from the starting point of calculating the first target image file 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 whose volume is smaller than the size of the preset image file in each first target image file obtained after slicing the target geographic image data; if the second target image file exists, add a portion of pixel points to the second target image file from the first target image file corresponding to and adjacent to the second target image file in geographic space, so that the size of the second target image file is the same as the size of the preset target image file; The image files finally obtained by segmenting the target geographic image data are used as the first target image files constituting the target geographic image data.

3. The method according to claim 2, characterized in that The step of constructing a storage file name of each first target image file according to the origin coordinate information of the preset map space includes: Determine the length and width pixel size of each first target image file to be stored in the distributed database according to the preset image file size; According to the coordinates of the origin of the preset map space, determine the horizontal coordinate and the vertical coordinate corresponding to the point where the target geographic image data starts to be stored relative to the coordinates of the origin of the preset map space; Calculate the abscissa and ordinate of each of the first target image files relative to the origin coordinates of the preset map space based on the abscissa and ordinate corresponding to the point where the target geographic image data starts to be stored relative to the origin coordinates of the preset map space; 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, wherein the image file stored on each storage machine cannot be an image file corresponding to a geographic space with a volume exceeding a preset size; The storage file name of each first target image file is constructed according to the storage machine code of each first target image file and the horizontal coordinate and the vertical coordinate of each first target image file relative to the origin coordinate of the preset map space.

4. The method according to claim 3, characterized in that The step of 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: Determine 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 a preset first threshold, determining the storage machine code of each of the first target image files according to a preset first storage strategy; If the number of storage machines is greater than a preset first threshold, the storage machine code of each of the first target image files is determined according to a preset second storage strategy.

5. The method according to claim 4, characterized in that The step of determining the storage machine code of each of the first target image files according to the preset first storage strategy includes: The first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an odd number is stored in the machine ranked first, and the serial number corresponding to the stored machine is used as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an odd number; The first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an even number is stored in the second ranked machine, and the serial number corresponding to the stored machine is used as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an even number; The first target image file whose horizontal coordinate value is an even number and whose vertical coordinate value is an odd number is stored in the third ranked machine, and the serial number corresponding to the stored machine is used as the storage machine code of the first target image file whose horizontal coordinate value is an odd number and whose vertical coordinate value is an odd number; The first target image file whose horizontal coordinate value is an even number and whose vertical coordinate value is an even number is stored in the fourth ranked machine, and the serial number corresponding to the stored machine is used as the storage machine code of the first target image file whose horizontal coordinate value is an even number and whose vertical coordinate value is an even number; The storage machine code of each storage machine is set according to the creation time of the storage machine or the capacity of the storage machine, and the storage machine code of each machine is the serial number corresponding to the storage machine.

6. The method according to claim 4, characterized in that The step of determining the storage machine code of each of the first target image files according to the preset second storage strategy includes: Divide all storage machines into two groups, namely an odd-numbered storage machine group and an even-numbered storage machine group, wherein each storage machine group includes at least two storage machines, and the storage machine code of each storage machine corresponding to each storage machine in each storage machine group is set according to the creation time of the storage machine 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; The first target image file whose horizontal coordinate value is an odd number is stored in the storage machine of the odd-numbered storage machine group according to the first storage rule, and the storage machine code of each first target image file whose horizontal coordinate value is an odd number in the storage machine of the odd-numbered storage machine group is used as its storage machine code; The first target image file whose horizontal coordinate value is an even number is stored in the storage machine of the even-numbered storage machine group according to the second storage rule, and the storage machine code of each first target image file whose horizontal coordinate value is an even number stored in the storage machine of the even-numbered storage machine group is used as its storage machine code.

7. The method according to claim 6, characterized in that The first storage rule includes: All storage machines in the odd-numbered storage machine group are sorted according to the creation time or the capacity of the storage machines, and one is randomly taken out from the set of first target image files whose horizontal coordinates are odd numbers and not yet stored, and stored in the first target storage machine at the head of the storage machine queue of the odd-numbered storage machine group, and then the first target storage machine is adjusted to the tail of the storage machine queue, and the storage machine queue is readjusted, and then the storage machine currently at the head of the storage machine queue continues to be the first target storage machine, and then returns to execute the operation of randomly taking out from the set of first target image files whose horizontal coordinates are odd numbers and not yet stored, and storing in the first target storage machine at the head of the storage machine queue of the odd-numbered storage machine group, until all the first target image files whose horizontal coordinates are odd numbers to be stored are stored in each storage machine of the odd-numbered storage machine group; The second storage rule includes: All storage machines in the even-numbered storage machine group are sorted according to the creation time or the capacity of the storage machines, and one is randomly taken out from the set of first target image files whose horizontal coordinates have not yet been stored and whose values ​​are even, and stored in the second target storage machine at the head of the storage machine queue of the even-numbered storage machine group, and then the second target storage machine is adjusted to the tail of the storage machine queue, and the storage machine queue is readjusted, and then the storage machine currently at the head of the storage machine queue continues to be the second target storage machine, and then the operation of randomly taking out from the set of first target image files whose horizontal coordinates have not yet been stored and whose values ​​are even, and storing in the second target storage machine at the head of the storage machine queue of the even-numbered storage machine group is returned to be executed, until all first target image files whose horizontal coordinates have even values ​​to be stored are stored in each storage machine of the even-numbered storage machine group.

8. A device for processing regular geographic image data slices, characterized in that: include: An acquisition unit, used for acquiring target geographic image data to be stored; A slicing unit, configured to perform 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, used to construct a storage file name of each of the first target image files according to the origin coordinate information of the preset map space; The storage unit is used to encrypt each of the first target image files and store them in a corresponding distributed database according to the storage file name of each of the first target image files.

9. A regular geographic image data slicing processing device, characterized in that: include: one or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, implement the steps of the regular geographic image data slicing processing method as described in any one of claims 1 to 7.

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

Images