Multi-dimensional space storage management method and system for digital archives and storage medium

Abstract

The invention relates to the technical field of digital storage, and particularly discloses a multi-dimensional space storage management method and system for a digital archive and a storage medium, and the method comprises the steps: receiving an archive to be stored, and scanning the archive to obtain a digital archive; the digital archive is a scanning image set; sequentially reading images of any digital file, identifying the images, positioning a textbox, and extracting a characteristic curve of each image based on the positioned textbox; determining the anomaly degree of any image according to the characteristic curve, counting the anomaly degrees of all the images in the same digital file, and determining the universality of the digital file; and archiving and storing the digital archives according to the popularity. According to the method, the archives are scanned to obtain the digital archives, the digital archives are subjected to feature extraction, and then classified, archived and stored, so that the data retention time is greatly prolonged, and the data storage space is reduced.

Description

Technical Field

[0001] The present invention relates to the field of digital storage technology, and in particular to a multi-dimensional space storage management method, system and storage medium for a digital archive. Background Art

[0002] Archives, a Chinese word, means various forms of original records with preservation value directly formed by people in various social activities. It is very common in enterprises. All work involving handover nature in enterprises requires archiving. These archives are mostly paper documents and are very difficult to preserve. How to reduce the difficulty of archive preservation and improve the timeliness of archive retention is the technical problem that the technical solution of the present invention aims to solve. Summary of the invention

[0003] The purpose of the present invention is to provide a multi-dimensional space storage management method, system and storage medium for a digital archive to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: A multi-dimensional space storage management method for a digital archive, the method comprising: Receiving a file to be stored, scanning the file to obtain a digitized file; the digitized file is a set of scanned images; For any digital archive, read the images in sequence, identify the images, locate the text boxes, and extract the characteristic curve of each image based on the located text boxes; Determine the abnormality of any image according to the characteristic curve, count the abnormalities of all images in the same digital archive, and determine the universality of the digital archive; wherein the abnormality is used to characterize the degree of differentiation between the image and other images, and the universality represents the degree of similarity between the digital archive and other digital archives; The digital archive is archived and stored according to the prevalence, and during the archiving, the image is encrypted based on the characteristic curve.

[0005] As a further solution of the present invention: the steps of sequentially reading images, identifying images, locating text boxes, and extracting characteristic curves of each image based on the located text boxes for any digital archive include: For any digitized archive, obtain the image size of each image; Sorting the images in ascending order according to the image sizes, and reading the images in sequence from the images sorted in ascending order; Input the image into the preset contour recognition model to locate the text box; wherein the contour recognition model contains a performance adjustment port, the initial performance is the lowest performance, and when the positioning process of any image fails, a preset performance step is added to the lowest performance; Feature curves are extracted for each image based on the located text boxes.

[0006] As a further solution of the present invention: the step of extracting the characteristic curve of each image based on the located text box includes: Selecting points on the border of the text box based on a preset step length; the step length is the distance between adjacent points; For any point, calculate its distance from the image diagonal and determine the projection point simultaneously; Calculate the distance between adjacent projection points on the diagonal line, and when the distance is less than a preset threshold, classify all points corresponding to the projection points into one category; The distance between similar points is taken as a vector, and the resultant vector is calculated as the vector at the projection point; The characteristic curve is fitted according to the vector end points at all projection points.

[0007] As a further solution of the present invention: the step of determining the abnormality of any image according to the characteristic curve, counting the abnormality of all images in the same digital archive, and determining the prevalence of the digital archive comprises: Record the characteristic curve of each image in real time and build a characteristic curve library; For any image, the feature curve of the image is randomly compared with the feature curves in the feature curve library to calculate the similarity; When the preset comparison jump-out conditions are met, the comparison is stopped; the comparison jump-out conditions include the similarity reaching the preset similarity threshold and the number of random comparisons reaching the preset number threshold; The maximum similarity is selected from all comparison results, and the abnormality is determined based on the maximum similarity and the number of random comparisons; The prevalence of the digitized archive is determined based on the degree of abnormality of each image.

[0008] As a further solution of the present invention: the similarity calculation process is: Obtaining a fitting function of the characteristic curve, and calculating the similarity according to the fitting function; ; In the formula, represents the similarity of two characteristic curves, represents the right endpoint of the common segment of the diagonals corresponding to the two characteristic curves, represents the left endpoint of the common segment of the diagonal lines corresponding to the two characteristic curves, Generally set to 0; It represents the length of the longer diagonal line between the two characteristic curves. and Represent two characteristic curves respectively; and Respectively represent the fitting functions of the two characteristic curves; The calculation process of the abnormality degree is: ; In the formula, Indicates abnormality, Indicates the maximum similarity of an image in the random comparison process, represents the number of random comparisons, and is the preset correction factor; The calculation process of the prevalence is: ; In the formula, Indicates universality, Indicates The abnormality of an image, Represents the total number of images in the digitized archive.

[0009] As a further solution of the present invention, the step of archiving and storing the digital archives according to the prevalence, and encrypting the images based on the characteristic curve during archiving includes: Archiving and storing digital files according to the preset prevalence range; When storing, perform text recognition on the image, locate the text, take the center of the text as the point, and update the feature curve; The updated feature curve is input into the trained transcoding model, and the encryption code is output to encrypt the image.

[0010] The technical solution of the present invention also provides a multi-dimensional space storage management system for a digital archive, the system comprising: A digital processing module is used to receive the archive to be stored, scan the archive, and obtain a digital archive; the digital archive is a scanned image set; The feature extraction module is used to read images in sequence for any digital file, identify the images, locate text boxes, and extract the feature curve of each image based on the located text boxes; An image analysis module, used to determine the abnormality of any image according to the characteristic curve, count the abnormalities of all images in the same digital archive, and determine the prevalence of the digital archive; wherein the abnormality is used to characterize the degree of differentiation between the image and other images, and the prevalence indicates the degree of similarity between the digital archive and other digital archives; The image encryption module is used to archive and store the digital archives according to the prevalence, and encrypt the image based on the characteristic curve when archiving.

[0011] As a further solution of the present invention: the feature extraction module includes: An image size acquisition unit, used for acquiring the image size of each image in any digital file; An image sorting unit, used to sort the images in ascending order according to the image sizes, and read images in sequence from the images sorted in ascending order; A text box positioning unit is used to input the image into a preset contour recognition model to locate the text box; wherein the contour recognition model contains a performance adjustment port, the initial performance is the lowest performance, and when the positioning process of any image fails, a preset performance step is added to the lowest performance; The extraction execution unit is used to extract the feature curve of each image based on the located text box.

[0012] As a further solution of the present invention: the extraction execution unit includes: A point selection subunit is used to select points on the border of the text box based on a preset step length; the step length is the distance between adjacent points; The projection subunit is used to calculate the distance between any point and the image diagonal and simultaneously determine the projection point; A merging subunit is used to calculate the distance between adjacent projection points on the diagonal line, and when the distance is less than a preset threshold, all points corresponding to the projection points are classified into one category; A calculation subunit is used to take the distance between similar points as a vector and calculate the resultant vector as the vector at the projection point; The fitting subunit is used to fit the characteristic curve according to the vector end points at all projection points.

[0013] The technical solution of the present invention also provides a storage medium, in which at least one program code is stored. When the program code is loaded and executed by a processor, the multi-dimensional space storage management method of the digital archive is implemented.

[0014] Compared with the prior art, the beneficial effects of the present invention are: the present invention scans the archives to obtain digital archives, extracts features from the digital archives, and then classifies and archives them for storage, which greatly improves the retention time of data and reduces data storage space. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention.

[0016] Figure 1 This is a flowchart of the multi-dimensional space storage management method for digital archives.

[0017] Figure 2 This is a structural block diagram of the multi-dimensional space storage management system for digital archives. DETAILED DESCRIPTION

[0018] In order to make the technical problems, technical solutions and beneficial effects to be solved by the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0019] Figure 1 The flowchart of the multi-dimensional space storage management method of the digital archives is as follows. In the embodiment of the present invention, a multi-dimensional space storage management method of the digital archives is provided. The method includes: Step S100: receiving a file to be stored, scanning the file to obtain a digitized file; the digitized file is a scanned image set; No matter what kind of archives, they are basically in paper form. Archives in paper form have a short preservation period, so they are digitized and then digitally stored. In the technical solution of the present invention, the scenario faced is a scenario with paper archives. The archives to be stored are received and converted into digital archives with the help of a scanner to obtain multiple images, which are called scanned image sets.

[0020] Step S200: for any digital file, read images in sequence, identify the images, locate text boxes, and extract characteristic curves of each image based on the located text boxes; For any digital file, images are read in sequence, the images are recognized, and the text boxes are located. Since the pixels in the file are limited, such as black text on a white background, and occasionally some red borders, the process of locating the text boxes is very simple; after locating the text boxes, the positions and sizes of all text boxes are recorded, and the characteristics of the image are determined according to the positions and sizes of each text box. In the technical solution of the present invention, it is represented by a curve, which is called a characteristic curve; in layman's terms, a curve is extracted from the image as the feature of the image, and the characteristic curve can be obtained.

[0021] Step S300: determining the abnormality of any image according to the characteristic curve, counting the abnormality of all images in the same digital archive, and determining the prevalence of the digital archive; wherein the abnormality is used to characterize the degree of differentiation between the image and other images, and the prevalence indicates the degree of similarity between the digital archive and other digital archives; By analyzing the characteristic curve of an image, the abnormality of the image can be calculated. The calculation process of the abnormality is to compare the difference between the characteristic curve of the image and the characteristic curves of other images to determine a parameter called the abnormality. On this basis, a digital archive is a collection of images. According to the abnormality of each image in the digital archive, the universality of the digital archive can be determined. The universality indicates the similarity between a digital archive and other digital archives, and its actual meaning is to indicate the degree of commonality.

[0022] Step S400: Archiving and storing the digital archive according to the prevalence, and encrypting the image based on the characteristic curve when archiving; Similar digital archives are grouped together according to their prevalence. When a query is needed, the prevalence is first used as an index to locate the corresponding category of digital archives, and then subsequent traversal retrieval is performed to optimize the retrieval architecture. In addition, during specific storage, since each image has undergone a feature extraction process, the image can be encrypted based on the feature extraction results to ensure the security of the digital archive.

[0023] Regarding step S200, the steps of sequentially reading images, identifying images, locating text boxes, and extracting characteristic curves of each image based on the located text boxes for any digitized file include: For any digitized archive, obtain the image size of each image; Sorting the images in ascending order according to the image sizes, and reading the images in sequence from the images sorted in ascending order; Input the image into the preset contour recognition model to locate the text box; wherein the contour recognition model contains a performance adjustment port, the initial performance is the lowest performance, and when the positioning process of any image fails, a preset performance step is added to the lowest performance; Feature curves are extracted for each image based on the located text boxes.

[0024] The above content explains the image recognition process and feature extraction process. Any digital archive is an image set. The image size of each image is obtained, and the images are sorted in ascending order according to the image size. The images are read one by one in the ascending order. The practical significance is that the small image is read first, and then the large image is read. The advantage of doing this is that at the beginning, the contour recognition model can be operated in a low-performance mode. If the recognition fails, the performance is improved and the process is repeated until all images are recognized. In this process, since the image size is increasing, the performance of the contour recognition model is also increasing. Compared with the existing contour recognition model with fixed performance, the resource utilization rate is higher.

[0025] By applying the contour recognition model to recognize the image, the text box can be located, and the position and size of each text box can be counted to fit a curve, which is called the feature curve.

[0026] Furthermore, the step of extracting a characteristic curve of each image based on the located text box includes: Selecting points on the border of the text box based on a preset step length; the step length is the distance between adjacent points; For any point, calculate its distance from the image diagonal and determine the projection point simultaneously; Calculate the distance between adjacent projection points on the diagonal line, and when the distance is less than a preset threshold, classify all points corresponding to the projection points into one category; The distance between similar points is taken as a vector, and the resultant vector is calculated as the vector at the projection point; The characteristic curve is fitted according to the vector end points at all projection points.

[0027] In an example of the technical solution of the present invention, a specific description is given of the process of generating a characteristic curve. First, the text box is actually some continuous boundary lines. During analysis, continuous line segments are not easy to handle, so they need to be discretized. The discretization method is to select points on the boundary of the text box based on a preset step size; for the selected points, calculate their distances from the image diagonal lines. There are actually two image diagonals. In the technical solution of the present invention, a pre-regulation is required first. It is best to use diagonals in the same direction for all images, for example, from the upper left corner to the lower right corner; then, calculate the projection point of each selected point on the diagonal line, connect the selected point and the projection point, take the projection point as the starting point and the selected point as the end point, and you can get a vector. If the projection points of the two vectors are close enough, the corresponding vectors are regarded as similar vectors, and they are added to finally get a resultant vector; finally, fit the end points of all resultant vectors, and the resulting curve is the characteristic curve.

[0028] Regarding step S300, the step of determining the abnormality of any image according to the characteristic curve, counting the abnormality of all images in the same digital file, and determining the prevalence of the digital file includes: Record the characteristic curve of each image in real time and build a characteristic curve library; For any image, the feature curve of the image is randomly compared with the feature curves in the feature curve library to calculate the similarity; When the preset comparison jump-out conditions are met, the comparison is stopped; the comparison jump-out conditions include the similarity reaching the preset similarity threshold and the number of random comparisons reaching the preset number threshold; The maximum similarity is selected from all comparison results, and the abnormality is determined based on the maximum similarity and the number of random comparisons; The prevalence of the digitized archive is determined based on the degree of abnormality of each image.

[0029] The above content limits the generation process of abnormality and universality, records the feature curve of each image in real time, and builds a feature curve library. The feature curve library does not need to record which image each feature curve corresponds to, which saves storage space and does not have the storage pressure of storing corresponding relationships. For any image, a feature curve is randomly read from the feature curve library, and the randomly read feature curve is compared with the feature curve of the current image to calculate the similarity. When the similarity obtained by a random selection reaches a threshold, it means that it is similar enough to some curves in the feature curve library. At this time, the random selection process (which is a cyclic process itself) is exited. When the number of random comparisons reaches the preset number threshold, it means that after multiple random selection processes, no sufficiently similar feature curves are found. At this time, the random selection process is also exited.

[0030] Finally, the degree of abnormality is determined based on the maximum similarity and the number of random comparisons. The greater the maximum similarity, the smaller the degree of abnormality, and the greater the number of random comparisons, the greater the degree of abnormality. Each digital archive is an image set. Based on the degree of abnormality of each image in the image set, the prevalence of the digital archive can be calculated together.

[0031] Furthermore, the similarity calculation process is: Obtaining a fitting function of the characteristic curve, and calculating the similarity according to the fitting function; ; In the formula, represents the similarity of two characteristic curves, represents the right endpoint of the common segment of the diagonals corresponding to the two characteristic curves, represents the left endpoint of the common segment of the diagonals corresponding to the two characteristic curves, Generally set to 0; It represents the length of the longer diagonal line between the two characteristic curves. and Represent two characteristic curves respectively; and Respectively represent the fitting functions of the two characteristic curves; The calculation process of the abnormality degree is: ; In the formula, Indicates abnormality, Indicates the maximum similarity of an image in the random comparison process, represents the number of random comparisons, and is the preset correction factor; The calculation process of the prevalence is: ; In the formula, Indicates prevalence, Indicates The abnormality of an image, Represents the total number of images in the digitized archive.

[0032] Regarding step S400, the step of archiving and storing the digital archive according to the prevalence, and encrypting the image based on the characteristic curve during archiving includes: Archiving and storing digital files according to the preset prevalence range; When storing, perform text recognition on the image, locate the text, take the center of the text as the point, and update the feature curve; The updated feature curve is input into the trained transcoding model, and the encryption code is output to encrypt the image.

[0033] The above content limits the specific storage stage. The management party pre-sets some ranges, which are called universality ranges. For any digital archive, storing it within the corresponding universality range is the process of archiving and storing. Furthermore, during storage, the technical solution of the present invention can also perform text recognition on the image. After locating the text, the center of the text is used as a point and added to the process of "selecting a point on the boundary of the text box based on a preset step length". That is, on the basis of determining the point based on the text box, a new point is determined again based on the text recognition result. After the point is updated, the fitted feature curve is updated. Since there are more points, the feature curve is more refined, and the differentiation of the feature curve is more obvious. The feature curves of different images almost always have slight differences. In other words, the probability of the same feature curve is smaller. Previously, as long as the text box was the same, the feature curve was the same. Now, the text needs to be exactly the same for the feature curve to be the same. The purpose of refining the feature curve is to input the updated feature curve into the trained transcoding model, output the encryption code, and encrypt the image. Thus, each image is independently encrypted.

[0034] Figure 2 The structure diagram of the multi-dimensional space storage management system of a digital archive is shown in FIG. 1 . In an embodiment of the present invention, a multi-dimensional space storage management system of a digital archive is provided. The system 10 includes: The digital processing module 11 is used to receive the archive to be stored, scan the archive, and obtain a digital archive; the digital archive is a scanned image set; The feature extraction module 12 is used to read images in sequence for any digital file, identify the images, locate text boxes, and extract feature curves of each image based on the located text boxes; The image analysis module 13 is used to determine the abnormality of any image according to the characteristic curve, count the abnormality of all images in the same digital archive, and determine the prevalence of the digital archive; wherein the abnormality is used to characterize the degree of differentiation between the image and other images, and the prevalence indicates the degree of similarity between the digital archive and other digital archives; The image encryption module 14 is used to archive and store the digital archives according to the prevalence, and encrypt the images based on the characteristic curve when archiving.

[0035] Furthermore, the feature extraction module 12 includes: An image size acquisition unit, used for acquiring the image size of each image in any digital file; An image sorting unit, used to sort the images in ascending order according to the image sizes, and read images in sequence from the images sorted in ascending order; A text box positioning unit is used to input the image into a preset contour recognition model to locate the text box; wherein the contour recognition model contains a performance adjustment port, the initial performance is the lowest performance, and when the positioning process of any image fails, a preset performance step is added to the lowest performance; The extraction execution unit is used to extract the feature curve of each image based on the located text box.

[0036] Wherein, the extraction execution unit includes: A point selection subunit is used to select points on the border of the text box based on a preset step length; the step length is the distance between adjacent points; The projection subunit is used to calculate the distance between any point and the image diagonal and simultaneously determine the projection point; A merging subunit is used to calculate the distance between adjacent projection points on the diagonal line, and when the distance is less than a preset threshold, all points corresponding to the projection points are classified into one category; A calculation subunit is used to take the distance between similar points as a vector and calculate the resultant vector as the vector at the projection point; The fitting subunit is used to fit the characteristic curve according to the vector end points at all projection points.

[0037] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-dimensional space storage management method for a digital archive, characterized in that: The method comprises: Receiving a file to be stored, scanning the file to obtain a digitized file; the digitized file is a set of scanned images; For any digital archive, read the images in sequence, identify the images, locate the text boxes, and extract the characteristic curve of each image based on the located text boxes; Determine the abnormality of any image according to the characteristic curve, count the abnormalities of all images in the same digital archive, and determine the universality of the digital archive; wherein the abnormality is used to characterize the degree of differentiation between the image and other images, and the universality represents the degree of similarity between the digital archive and other digital archives; The digital archive is archived and stored according to the prevalence, and during the archiving, the image is encrypted based on the characteristic curve.

2. The multi-dimensional space storage management method of a digital archive according to claim 1 is characterized in that: The steps of sequentially reading images, identifying images, locating text boxes, and extracting characteristic curves of each image based on the located text boxes for any digital file include: For any digitized archive, obtain the image size of each image; Sorting the images in ascending order according to the image sizes, and reading the images in sequence from the images sorted in ascending order; Input the image into the preset contour recognition model to locate the text box; wherein the contour recognition model contains a performance adjustment port, the initial performance is the lowest performance, and when the positioning process of any image fails, a preset performance step is added to the lowest performance; Feature curves are extracted for each image based on the located text boxes.

3. The multi-dimensional space storage management method of a digital archive according to claim 2 is characterized in that: The step of extracting a characteristic curve of each image based on the located text box comprises: Selecting points on the border of the text box based on a preset step length; the step length is the distance between adjacent points; For any point, calculate its distance from the image diagonal and determine the projection point simultaneously; Calculate the distance between adjacent projection points on the diagonal line, and when the distance is less than a preset threshold, classify all points corresponding to the projection points into one category; The distance between similar points is taken as a vector, and the resultant vector is calculated as the vector at the projection point; The characteristic curve is fitted according to the vector end points at all projection points.

4. The multi-dimensional space storage management method for digital archives according to claim 1 is characterized in that: The step of determining the abnormality of any image according to the characteristic curve, counting the abnormality of all images in the same digital file, and determining the prevalence of the digital file comprises: Record the characteristic curve of each image in real time and build a characteristic curve library; For any image, the feature curve of the image is randomly compared with the feature curves in the feature curve library to calculate the similarity; When the preset comparison jump-out conditions are met, the comparison is stopped; the comparison jump-out conditions include the similarity reaching the preset similarity threshold and the number of random comparisons reaching the preset number threshold; The maximum similarity is selected from all comparison results, and the abnormality is determined based on the maximum similarity and the number of random comparisons; The prevalence of the digitized archive is determined based on the degree of abnormality of each image.

5. The multi-dimensional space storage management method of a digital archive according to claim 4 is characterized in that: The similarity calculation process is: Obtaining a fitting function of the characteristic curve, and calculating the similarity according to the fitting function; ; In the formula, represents the similarity of two characteristic curves, represents the right endpoint of the common segment of the diagonal lines corresponding to the two characteristic curves, represents the left endpoint of the common segment of the diagonal lines corresponding to the two characteristic curves, Generally set to 0; It represents the length of the longer diagonal line between the two characteristic curves. and Represent two characteristic curves respectively; and Respectively represent the fitting functions of the two characteristic curves; The calculation process of the abnormality degree is: ; In the formula, Indicates abnormality, Indicates the maximum similarity of an image in the random comparison process, represents the number of random comparisons, and is the preset correction factor; The calculation process of the prevalence is: ; In the formula, Indicates prevalence, Indicates The abnormality of an image, Represents the total number of images in the digitized archive.

6. The multi-dimensional space storage management method of a digital archive according to claim 3 is characterized in that: The step of archiving and storing the digital archive according to the prevalence and encrypting the image based on the characteristic curve during archiving includes: Archiving and storing digital files according to the preset prevalence range; When storing, perform text recognition on the image, locate the text, take the center of the text as the point, and update the feature curve; The updated feature curve is input into the trained transcoding model, and the encryption code is output to encrypt the image.

7. A multi-dimensional space storage management system for digital archives, characterized in that: The system comprises: A digital processing module is used to receive the archive to be stored, scan the archive, and obtain a digital archive; the digital archive is a scanned image set; The feature extraction module is used to read images in sequence for any digital file, identify the images, locate text boxes, and extract the feature curve of each image based on the located text boxes; An image analysis module, used to determine the abnormality of any image according to the characteristic curve, count the abnormalities of all images in the same digital archive, and determine the prevalence of the digital archive; wherein the abnormality is used to characterize the degree of differentiation between the image and other images, and the prevalence indicates the degree of similarity between the digital archive and other digital archives; The image encryption module is used to archive and store the digital archives according to the prevalence, and encrypt the image based on the characteristic curve when archiving.

8. The multi-dimensional space storage management system for digital archives according to claim 7 is characterized in that: The feature extraction module comprises: An image size acquisition unit, used for acquiring the image size of each image in any digital file; An image sorting unit, used to sort the images in ascending order according to the image sizes, and read images in sequence from the images sorted in ascending order; A text box positioning unit is used to input the image into a preset contour recognition model to locate the text box; wherein the contour recognition model contains a performance adjustment port, the initial performance is the lowest performance, and when the positioning process of any image fails, a preset performance step is added to the lowest performance; The extraction execution unit is used to extract the feature curve of each image based on the located text box.

9. The multi-dimensional space storage management system for digital archives according to claim 8 is characterized in that: The extraction execution unit comprises: A point selection subunit is used to select points on the border of the text box based on a preset step length; the step length is the distance between adjacent points; The projection subunit is used to calculate the distance between any point and the image diagonal and simultaneously determine the projection point; A merging subunit is used to calculate the distance between adjacent projection points on the diagonal line, and when the distance is less than a preset threshold, all points corresponding to the projection points are classified into one category; A calculation subunit is used to take the distance between similar points as a vector and calculate the resultant vector as the vector at the projection point; The fitting subunit is used to fit the characteristic curve according to the vector end points at all projection points.

10. A storage medium, characterized in that: At least one program code is stored in the medium, and when the program code is loaded and executed by the processor, the multi-dimensional space storage management method for a digital archive as described in any one of claims 1 to 6 is implemented.

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