A digital archive management method and system

Abstract

The present invention discloses a digital archive management method and system, which specifically relates to the field of digital archive management. By analyzing the continuity of time data in digital archives, the degree of breakage or abnormal jump of time data is evaluated. The integrity analysis of spatial data in digital archives is performed to evaluate the abnormal deviation or missing degree of spatial data. According to the evaluation results of time data and spatial data, archives with abnormal time and space correlation are screened out. Based on the digital archives with abnormal time and space correlation, the time and space correlation of time data and space data is evaluated by analyzing the time and space correlation matrix. A digital archive management model is established according to the degree of breakage or abnormal jump of time data, the abnormal deviation or missing degree of spatial data, and the time and space correlation of time and space data of digital archives. The digital archive management model is established to improve the efficiency of digital archive management and the integrity of digital archives.

Description

Technical Field

[0001] The present invention relates to the field of digital archive management, and more specifically, to a digital archive management method and system. Background Art

[0002] With the in-depth development of informatization, digital archive management has become an important way to manage information. Digital archive management systems face many problems when processing time data and spatial data, especially the correlation between time data and spatial data. Traditional digital archive management systems process time data and spatial data independently, often relying on static classification or simple regular update strategies, lack unified analysis of time data and spatial data, and cannot effectively identify potential time and space correlation problems in archives, including the time data and spatial data in digital archives may be distorted due to changes in data format, time zone definition or geographic reference system. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a digital archive management method to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] The digital archive management method comprises the following steps:

[0006] Step S1: Analyze the continuity of the time data in the digital archives to evaluate the degree of discontinuity or abnormal jump of the time data;

[0007] Step S2: Conduct integrity analysis on the spatial data in the digital archives to assess the degree of abnormal deviation or missingness of the spatial data;

[0008] Step S3: based on the evaluation results of the time data and the space data, screen out the digital archives with abnormal time and space correlation;

[0009] Step S4: Based on the digital archives with abnormal spatiotemporal correlation, a spatiotemporal matrix is ​​constructed to analyze the spatiotemporal correlation of the spatiotemporal data of the digital archives and evaluate the degree of distortion of the spatiotemporal data of the digital archives;

[0010] Step S5: Establish a digital archive management model for managing digital archives based on the degree of discontinuity or abnormal jump of time data, the degree of abnormal deviation or missing of spatial data, and the spatiotemporal correlation of spatiotemporal data of digital archives.

[0011] In a preferred embodiment, in step S1: define the historical time series T in the digital archive h ={th1 ,t h2 ,...,t hn} and the current time series T c ={t c1 ,t c2 ,...,t cm}; where t hn and t cm Respectively represent the timestamp data of historical archives and current archives;

[0012] The dynamic warping algorithm is used to calculate the minimum alignment distance between the historical time series and the current time series. The expression is:

[0013] DTW=min{DTW(n-1,m),DTW(n,m-1),DTW(n-1,m-1)+d(t hn ,t cm )};

[0014] Among them, DTW represents the minimum alignment distance between the historical time series and the current time series; DTW(n-1,m) means aligning the n-1th time point in the historical time series with the m-th time point in the current time series; DTW(n,m-1) means aligning the n-1th time point in the historical time series with the m-1th time point in the current time series; DTW(n-1,m-1) means aligning the n-1th time point in the historical time series with the m-1th time point in the current time series; d(t hn ,t cm ) represents the time point t in the historical time series hn and time point t in the current time series hn The distance between.

[0015] In a preferred embodiment, in step S2: assuming that the digital archive contains spatial coordinates (x, y) representing the geographic coordinates of the spatial data; x represents the longitude of the geographic coordinates; y represents the latitude of the geographic coordinates, the calculation formula of the spherical distance is specifically:

[0016] D=R*arccos(sin(x1)*sin(x2)+cos(x1)*cos(x2)*cos(y1-y2));

[0017] Where D is the spherical distance between two geographic coordinates; (x1, y1) and (x2, y2) are the geographic coordinates of the first spatial data and the second spatial data respectively;

[0018] In order to detect abnormal deviations or missing spatial data, the geographic integrity of each spatial data point is evaluated. By comparing the spatial position of each spatial data point with the adjacent spatial data points, the spatial integrity coefficient is calculated. The expression is:

[0019]

[0020] Among them, C s represents the spatial integrity coefficient; N represents the number of other spatial data points adjacent to the current spatial data point; D i Represents the spherical distance between the current spatial data point and the i-th adjacent spatial data point; Represents the average spherical distance between the current spatial data point and all other adjacent spatial data points.

[0021] In a preferred embodiment, in step S3: a minimum alignment distance threshold is set, and the minimum alignment distance is compared with the minimum alignment distance threshold:

[0022] When the minimum alignment distance is less than or equal to the minimum alignment distance threshold, it is considered that the current time series matches the historical time series well, and there is no break or abnormal jump in the time data in the digital archives;

[0023] When the minimum alignment distance is greater than the minimum alignment distance threshold, it is considered that the matching degree between the current time series and the historical time series is low, and there are breaks or abnormal jumps in the time data in the digital archives;

[0024] Set the space integrity coefficient threshold and compare the space integrity coefficient with the space integrity coefficient threshold:

[0025] When the spatial integrity coefficient is greater than or equal to the spatial integrity coefficient threshold, it is considered that the spatial data in the digital archive has good integrity and no abnormal deviation or missing occurs;

[0026] When the spatial integrity coefficient is less than the spatial integrity coefficient threshold, it is considered that the spatial data integrity in the digital archive is poor, with abnormal deviations or missing data;

[0027] When the minimum alignment distance is greater than the minimum alignment distance threshold and the spatial integrity coefficient is less than the spatial integrity coefficient threshold, the corresponding digital archives with spatiotemporal correlation anomalies are screened out based on the time data with breaks or abnormal jumps and the spatial data with abnormal deviations or missing data.

[0028] In a preferred embodiment, in step S4: singular value decomposition is performed on the spatiotemporal correlation matrix to extract the main singular values ​​of the matrix, and the spatiotemporal coherence coefficient is calculated based on the main singular values ​​to evaluate the correlation between time data and space data. The spatiotemporal coherence coefficient expression is:

[0029]

[0030] Among them, P st is the spatiotemporal coherence coefficient; σ j represents the jth singular value of the space-time incidence matrix; k is the number of primary singular values; M is the total number of all singular values ​​of the space-time incidence matrix;

[0031] Set the spatiotemporal coherence coefficient threshold and compare the spatiotemporal coherence coefficient with the spatiotemporal coherence coefficient threshold:

[0032] When the spatiotemporal coherence coefficient is greater than or equal to the spatiotemporal coherence coefficient threshold, it indicates that the temporal data and spatial data in the digitized archives with spatiotemporal correlation anomaly are highly correlated, which means that the digitized archives have no spatiotemporal correlation distortion;

[0033] When the spatiotemporal coherence coefficient is less than the spatiotemporal coherence coefficient threshold, it indicates that the correlation between the time data and the spatial data in the digital archives with spatiotemporal correlation anomalies is weak, which means that there is spatiotemporal correlation distortion in the digital archives.

[0034] In a preferred embodiment, in step S5: the minimum alignment distance corresponding to the degree of breakage or abnormal jump of time data, the spatial integrity coefficient corresponding to the degree of abnormal deviation or missing of spatial data, and the spatiotemporal coherence coefficient corresponding to the spatiotemporal correlation of digital archive spatiotemporal data are respectively normalized, and a digital archive management model is established based on the minimum alignment distance, spatial integrity coefficient and spatiotemporal coherence coefficient obtained after normalization:

[0035] OQC=α*DTW+β*(1-C s )+ω*(1-P st );

[0036] Among them, OQC is the comprehensive quality score; DTW represents the minimum alignment distance between the historical time series and the current time series; C s Represents the spatial integrity coefficient; P st is the spatiotemporal coherence coefficient; α, β, and ω represent the minimum alignment distance between the historical time series and the current time series, the spatial integrity coefficient, and the weights of the spatiotemporal coherence coefficient in the digital archive management model;

[0037] Set a comprehensive quality score threshold and compare the comprehensive quality score with the comprehensive quality score threshold:

[0038] When the comprehensive quality score is greater than or equal to the comprehensive quality score threshold, it means that the comprehensive quality score of the digitized archive is poor;

[0039] When the comprehensive quality score is less than the comprehensive quality score threshold, it means that the comprehensive quality of the archive is good, with good usability and reliability.

[0040] Technical effects and advantages of the method of the present invention:

[0041] Through the analysis of the continuity of time data, the degree of time jump or break in digital archives can be accurately evaluated to ensure the continuity of time information. The integrity analysis of spatial data helps to evaluate the deviation or loss of spatial data and ensure the integrity of geographic location information. By combining the analysis results of time data and spatial data, digital archives with abnormal time and space associations can be screened out, which effectively helps to screen out digital archives with abnormal time and space associations. Through the analysis of the time and space association matrix, the correlation between time data and space data is evaluated to ensure that the time and space data of digital archives are logically consistent. Through the degree of breakage or abnormal jump of time data, the degree of abnormal deviation or loss of spatial data, and the time and space correlation of time and space data of digital archives, a digital archive management model is established to realize the management of digital archive data, improve the efficiency and accuracy of digital archive management, and provide technical support for the long-term management, retrieval and sharing of digital archives. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] Figure 1 A schematic diagram of a digital archive management method of the present invention;

[0043] Figure 2 This is a structural schematic diagram of a digital archive management system of the present invention; DETAILED DESCRIPTION

[0044] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0045] Reference Figure 1 .

[0046] The embodiment provides a digital archive management method of the present invention, which comprises the following steps:

[0047] Step S1: Analyze the continuity of the time data in the digital archives to evaluate the degree of discontinuity or abnormal jump of the time data;

[0048] Step S2: Conduct integrity analysis on the spatial data in the digital archives to assess the degree of abnormal deviation or missingness of the spatial data;

[0049] Step S3: based on the evaluation results of the time data and the space data, screen out the digital archives with abnormal time and space correlation;

[0050] Step S4: Based on the digital archives with abnormal spatiotemporal correlation, a spatiotemporal matrix is ​​constructed to analyze the spatiotemporal correlation of the spatiotemporal data of the digital archives and evaluate the degree of distortion of the spatiotemporal data of the digital archives;

[0051] Step S5: Establish a digital archive management model for managing digital archives based on the degree of discontinuity or abnormal jump of time data, the degree of abnormal deviation or missing of spatial data, and the spatiotemporal correlation of spatiotemporal data of digital archives.

[0052] In step S1: The time data in the digital archive is usually used to record the creation, modification and access time of the archive. Time data is the key to ensure the correct timing of the archive content. The break or abnormal jump of time data may cause the distortion of archive timing, affecting the historical traceability and consistency of time data. When analyzing archives, time breaks may occur, such as a record suddenly jumping to the future or back to the past, which may mean that the time data has been tampered with or lost during the recording or storage process, or the device synchronization has failed. By analyzing the continuity of time data, the continuity of archive content in the time dimension is ensured.

[0053] Conduct continuity analysis on the time data in the digital archives and evaluate the degree of discontinuity or abnormal jump of the time data, specifically:

[0054] Define the historical time series T in the digital archive h ={t h1 ,t h2 ,...,t hn} and the current time series T c ={t c1 ,t c2 ,...,t cm}; where t hn and t cm Respectively represent the timestamp data of historical archives and current archives.

[0055] The dynamic warping algorithm is a time series analysis method that can process time series of different lengths and effectively detect abnormal jumps or breaks in the sequence. The dynamic warping algorithm is used to calculate the minimum alignment distance between the historical time series and the current time series. The expression is:

[0056] DTW=min{DTW(n-1,m),DTW(n,m-1),DTW(n-1,m-1)+d(t hn ,t cm )};

[0057] Among them, DTW represents the minimum alignment distance between the historical time series and the current time series, which is used to evaluate the matching degree between the current time series and the historical time series; DTW(n-1,m) means aligning the n-1th time point in the historical time series with the m-th time point in the current time series; DTW(n,m-1) means aligning the n-1th time point in the historical time series with the m-1th time point in the current time series; DTW(n-1,m-1) means aligning the n-1th time point in the historical time series with the m-1th time point in the current time series; d(t hn ,t cm ) represents the time point t in the historical time series hn and time point t in the current time series hn The distance between.

[0058] The larger the minimum alignment distance, the worse the match between the time data in the digital archive and the historical time series, and the higher the degree of discontinuity or abnormal jump in the time data. This means that the time information in the digital archive may have obvious time jumps, breaks or discontinuities, and cannot accurately reflect the actual order of events.

[0059] In step S2: The spatial data in the digital archives are usually used to identify the physical location or geographical area related to the archives, such as the location where the archives were created and the location where the archives were stored, to ensure the relevance of the archives to the actual geographic information. Abnormal deviations or missing spatial data in the archives may cause the archive content to be unable to be accurately mapped to the actual geographic location, affecting the application of the archive content in various geographic information systems, archive management, emergency response and other scenarios. For example, biased spatial data may lead to the use of incorrect location information in emergency decision-making, affecting the accuracy of subsequent actions.

[0060] Conduct integrity analysis on spatial data in digital archives and assess the degree of abnormal deviation or missingness of spatial data, specifically:

[0061] Spatial data is stored in the form of geographic coordinates. Since geographic coordinates are given in the form of spherical coordinates, the spherical distance formula is used to calculate the spherical distance between two geographic coordinates to ensure the accuracy of geographic deviation. Assuming that the digital archive contains spatial coordinates (x, y) representing the geographic coordinates of spatial data; x represents the longitude of the geographic coordinate; y represents the latitude of the geographic coordinate, the specific formula for calculating the spherical distance is:

[0062] D=R*arccos(sin(x1)*sin(x2)+cos(x1)*cos(x2)*cos(y1-y2));

[0063] Where D is the spherical distance between two geographic coordinates; (x1, y1) and (x2, y2) are the geographic coordinates of the first spatial data and the second spatial data respectively.

[0064] In order to detect abnormal deviations or missing spatial data, the geographic integrity of each spatial data point is evaluated. By comparing the spatial position of each spatial data point with the adjacent spatial data points, the spatial integrity coefficient is calculated. The expression is:

[0065]

[0066] Among them, C s represents the spatial integrity coefficient; N represents the number of other spatial data points adjacent to the current spatial data point; D i Represents the spherical distance between the current spatial data point and the i-th adjacent spatial data point; Represents the average spherical distance between the current spatial data point and all other adjacent spatial data points.

[0067] The lower the spatial integrity coefficient, the worse the integrity of the spatial data, indicating that the abnormal deviation or missing degree of the spatial data in the digital archive is also greater, which means that the geographical location information recorded in the digital archive cannot accurately reflect the actual situation, and there may be problems such as missing data, erroneous records or geographic coordinate offsets. A low spatial integrity coefficient indicates that the spatial information involved in the digital archive is unreliable, which weakens the overall reference value of the digital archive.

[0068] In step S3: In the digital archive system, time data and space data are important dimensions of archive content, ensuring that the archive content is consistent with events and places in the real world. Space-time correlation anomalies are abnormalities in the correspondence between time data and space data in archives, such as inconsistency between the time information and geographic location of digital archives, abnormal time sequence, or large spatial position deviation.

[0069] According to the judgment results of time data and space data, digital archives with abnormal time and space correlation are screened out, specifically:

[0070] In order to evaluate the matching degree between the current time series and the historical time series, the minimum alignment distance threshold is set. The minimum alignment distance threshold is set according to the accuracy requirements of the specific application scenario. The minimum alignment distance is compared with the minimum alignment distance threshold:

[0071] When the minimum alignment distance is less than or equal to the minimum alignment distance threshold, it is considered that the current time series matches the historical time series well, and there is no break or abnormal jump in the time data in the digital archives;

[0072] When the minimum alignment distance is greater than the minimum alignment distance threshold, it is considered that the current time series has a low match with the historical time series, and there are breaks or abnormal jumps in the time data in the digital archives.

[0073] When there is a break or abnormal jump in the time data in the digital archive, the minimum alignment distance is calculated for each time point to locate the time point where the break or abnormal jump occurred. For the time point where the break or abnormal jump is identified, the time data is restored by recalibrating or correcting the timestamp.

[0074] In order to determine whether there is abnormal deviation or missing in the spatial data, a spatial integrity coefficient threshold is set. The method for setting the spatial integrity coefficient threshold includes:

[0075] By analyzing the spatial integrity coefficients of normal spatial data points, an average spatial integrity coefficient value is obtained and set as a spatial integrity coefficient threshold;

[0076] The threshold is adjusted according to the needs of the actual application. In high-precision scenarios, a higher spatial integrity coefficient threshold can be set; in loose scenarios (spatial data in a larger range), a lower spatial integrity coefficient threshold can be set.

[0077] Compare the spatial completeness factor to the spatial completeness factor threshold:

[0078] When the spatial integrity coefficient is greater than or equal to the spatial integrity coefficient threshold, it is considered that the spatial data in the digital archive has good integrity and no abnormal deviation or missing occurs;

[0079] When the spatial integrity coefficient is less than the spatial integrity coefficient threshold, it is considered that the spatial data integrity in the digital archive is poor, and there are abnormal deviations or omissions.

[0080] When there may be abnormal deviations or missing spatial data in the digital archives, the spatial integrity coefficient is calculated to find the spatial data points with poor integrity. For the spatial data points with poor integrity, they can be repaired by re-acquiring the spatial data, or spatial interpolation can be performed on the spatial data with poor integrity to infer the missing spatial data.

[0081] When the minimum alignment distance is greater than the minimum alignment distance threshold and the spatial integrity coefficient is less than the spatial integrity coefficient threshold, it means that there are breaks or abnormal jumps in the time data of the digital archives, and there are abnormal deviations or missing spatial data in the digital archives. According to the time data with breaks or abnormal jumps and the spatial data with abnormal deviations or missing, the corresponding digital archives with abnormal time and space correlation are screened out.

[0082] In step S4: Digital archives involve not only the time dimension but also the space dimension. When the correlation between time data and space data is poor, the archives may have time-space correlation distortion, which affects the accuracy and reliability of the archives, resulting in the data recorded in the archives not being able to truly reflect the time and place of occurrence, making the archives lose their reference value.

[0083] Based on the digitized archives with abnormal spatiotemporal correlation, a spatiotemporal matrix is ​​constructed to analyze the spatiotemporal correlation of the spatiotemporal data of the digitized archives and evaluate the degree of distortion of the spatiotemporal data of the digitized archives. Specifically:

[0084] The spatiotemporal correlation matrix is ​​a two-dimensional matrix used to describe the relationship between time and space. Each row of the matrix represents a time point of the digital archive record of spatiotemporal correlation anomalies, and the column represents a spatial point of the digital archive record of spatiotemporal correlation anomalies. The matrix elements represent the degree of correlation between the time point and the spatial point.

[0085] Perform singular value decomposition on the spatiotemporal correlation matrix and decompose the matrix into three parts. According to the decomposition results, extract the main singular values ​​of the matrix. The main singular values ​​are usually defined as the singular values ​​that can explain most of the information in the matrix. Based on the main singular values, calculate the spatiotemporal coherence coefficient to evaluate the correlation between time data and spatial mutual data. The expression of the spatiotemporal coherence coefficient is:

[0086]

[0087] Among them, P st is the spatiotemporal coherence coefficient, which is used to evaluate the correlation between temporal data and spatial data; σ j represents the jth singular value of the space-time incidence matrix; k is the number of primary singular values; M is the total number of all singular values ​​of the space-time incidence matrix.

[0088] The lower the spatiotemporal coherence coefficient is, the worse the correlation between time data and spatial data in the digital archives is, and the greater the distortion of the spatiotemporal data in the digital archives is, which means that the correspondence between the time series and spatial position of the digital archives cannot accurately reflect the spatiotemporal relationship in reality.

[0089] Set the spatiotemporal coherence coefficient threshold. The spatiotemporal coherence coefficient threshold is set according to the requirements of the digital archive application scenario. Compare the spatiotemporal coherence coefficient with the spatiotemporal coherence coefficient threshold:

[0090] When the spatiotemporal coherence coefficient is greater than or equal to the spatiotemporal coherence coefficient threshold, it indicates that the temporal data and spatial data in the digitized archives with spatiotemporal correlation anomaly are highly correlated, which means that the digitized archives have no spatiotemporal correlation distortion;

[0091] When the spatiotemporal coherence coefficient is less than the spatiotemporal coherence coefficient threshold, it indicates that the correlation between the time data and the spatial data in the digital archives with spatiotemporal correlation anomalies is weak, which means that there is spatiotemporal correlation distortion in the digital archives.

[0092] In step S5: a digital archive management model is established according to the degree of discontinuity or abnormal jump of time data, the degree of abnormal deviation or missing of spatial data, and the spatiotemporal correlation of spatiotemporal data of digital archives, for managing digital archives, specifically:

[0093] The minimum alignment distance corresponding to the degree of breakage or abnormal jump of time data, the spatial integrity coefficient corresponding to the degree of abnormal deviation or missing of spatial data, and the spatiotemporal coherence coefficient corresponding to the spatiotemporal correlation of digital archive spatiotemporal data are normalized respectively. According to the minimum alignment distance, spatial integrity coefficient and spatiotemporal coherence coefficient obtained after normalization, a digital archive management model is established:

[0094] OQC=α*DTW+β*(1-C s )+ω*(1-P st );

[0095] Among them, OQC is the comprehensive quality score; DTW represents the minimum alignment distance between the historical time series and the current time series; C s Represents the spatial integrity coefficient; P st is the spatiotemporal coherence coefficient; α, β, and ω represent the minimum alignment distance between the historical time series and the current time series, the spatial integrity coefficient, and the weights of the spatiotemporal coherence coefficient in the digital archive management model.

[0096] Set the comprehensive quality score threshold. The comprehensive quality score threshold is set according to the requirements of the historical digital archive management system. Compare the comprehensive quality score with the comprehensive quality score threshold:

[0097] When the comprehensive quality score is greater than or equal to the comprehensive quality score threshold, it means that the comprehensive quality score of the digitized archive is poor, and the following measures should be taken:

[0098] Check the time data in the archive, especially those with time jumps or breaks, and recheck the timestamps. Correct the time sequence manually or automatically by referring to adjacent time points or historical data to ensure the consistency of the time data.

[0099] Re-collect the geolocation data of the archive, especially using accurate geolocation equipment. Calibrate with historical spatial data or external geographic databases to ensure that the current spatial data is consistent with the historical digitized archive.

[0100] For time data and space data with poor temporal and spatial correlation, optimize the storage and representation of temporal and spatial data, enhance the archive management system's ability to process time and space data, and ensure a closer correlation between temporal and spatial data.

[0101] When the comprehensive quality score is less than the comprehensive quality score threshold, it means that the comprehensive quality of the archive is good, with good usability and reliability.

[0102] The digital archive management system disclosed in the present invention comprises: a time data evaluation module, a space data evaluation module, a time-space correlation anomaly screening module, a time-space correlation evaluation module, and a digital archive management module;

[0103] Time data evaluation module: conducts continuity analysis on time data in digital archives and evaluates the degree of discontinuity or abnormal jump of time data;

[0104] Spatial data evaluation module: conduct integrity analysis on spatial data in digital archives and evaluate the abnormal deviation or missing degree of spatial data;

[0105] Temporal and spatial correlation anomaly screening module: based on the evaluation results of time data and spatial data, screen out digital archives with temporal and spatial correlation anomalies;

[0106] Spatiotemporal correlation evaluation module: Based on the digital archives with spatiotemporal correlation anomalies, the spatiotemporal correlation matrix is ​​analyzed to evaluate the spatiotemporal correlation of the spatiotemporal data of the digital archives;

[0107] Digital archive management module: A digital archive management model is established based on the degree of discontinuity or abnormal jumps in time data, the degree of abnormal deviation or missingness in spatial data, and the spatiotemporal correlation of digital archive spatiotemporal data to manage digital archives.

[0108] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0109] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0110] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0111] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0112] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0113] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0114] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0115] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0116] Finally: 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, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A digital archive management method, characterized in that: The following steps are involved: Step S1: Analyze the continuity of the time data in the digital archives to evaluate the degree of discontinuity or abnormal jump of the time data; Step S2: Conduct integrity analysis on the spatial data in the digital archives to assess the degree of abnormal deviation or missingness of the spatial data; Step S3: based on the evaluation results of the time data and the space data, screen out the digital archives with abnormal time and space correlation; Step S4: Based on the digital archives with abnormal spatiotemporal correlation, a spatiotemporal matrix is ​​constructed to analyze the spatiotemporal correlation of the spatiotemporal data of the digital archives and evaluate the degree of distortion of the spatiotemporal data of the digital archives; Step S5: Establish a digital archive management model for managing digital archives based on the degree of discontinuity or abnormal jump of time data, the degree of abnormal deviation or missing of spatial data, and the spatiotemporal correlation of spatiotemporal data of digital archives.

2. A digital archive management method according to claim 1, characterized in that: In step S1, the continuity analysis of the time data in the digital archive is performed to evaluate the degree of discontinuity or abnormal jump of the time data, specifically: Define the historical time series T in the digital archive h ={t h1 ,t h2 ,...,t hn } and the current time series T c ={t c1 ,t c2 ,...,t cm }; where t hn and t cm Respectively represent the timestamp data of historical archives and current archives; The dynamic warping algorithm is used to calculate the minimum alignment distance between the historical time series and the current time series. The expression is: DTW=min{DTW(n-1,m),DTW(n,m-1),DTW(n-1,m-1)+d(t hn ,the cm )}; Among them, DTW represents the minimum alignment distance between the historical time series and the current time series; DTW(n-1,m) means aligning the n-1th time point in the historical time series with the m-th time point in the current time series; DTW(n,m-1) means aligning the n-1th time point in the historical time series with the m-1th time point in the current time series; DTW(n-1,m-1) means aligning the n-1th time point in the historical time series with the m-1th time point in the current time series; d(t hn ,t cm ) represents the time point t in the historical time series hn and time point t in the current time series hn The distance between.

3. A digital archive management method according to claim 2, characterized in that: In step S2, the integrity analysis of the spatial data in the digital archive is performed to evaluate the abnormal deviation or missing degree of the spatial data, specifically: Assuming that the digital archive contains spatial coordinates (x, y) representing the geographic coordinates of the spatial data; x represents the longitude of the geographic coordinates; y represents the latitude of the geographic coordinates, the calculation formula for the spherical distance is: D=R*arccos(sin(x1)*sin(x2)+cos(x1)*cos(x2)*cos(y1-y2)); Where D is the spherical distance between two geographic coordinates; (x1, y1) and (x2, y2) are the geographic coordinates of the first spatial data and the second spatial data respectively; In order to detect abnormal deviations or missing spatial data, the geographic integrity of each spatial data point is evaluated. By comparing the spatial position of each spatial data point with the adjacent spatial data points, the spatial integrity coefficient is calculated. The expression is: Among them, C s represents the spatial integrity coefficient; N represents the number of other spatial data points adjacent to the current spatial data point; D i Represents the spherical distance between the current spatial data point and the i-th adjacent spatial data point; Represents the average spherical distance between the current spatial data point and all other adjacent spatial data points.

4. A digital archive management method according to claim 3, characterized in that: In step S3, based on the evaluation results of the time data and the space data, the digital archives with abnormal time and space correlation are screened out, specifically: Set a minimum alignment distance threshold and compare the minimum alignment distance with the minimum alignment distance threshold: When the minimum alignment distance is less than or equal to the minimum alignment distance threshold, it is considered that the current time series matches the historical time series well, and there is no break or abnormal jump in the time data in the digital archives; When the minimum alignment distance is greater than the minimum alignment distance threshold, it is considered that the matching degree between the current time series and the historical time series is low, and there are breaks or abnormal jumps in the time data in the digital archives; Set the space integrity coefficient threshold and compare the space integrity coefficient with the space integrity coefficient threshold: When the spatial integrity coefficient is greater than or equal to the spatial integrity coefficient threshold, it is considered that the spatial data in the digital archive has good integrity and no abnormal deviation or missing occurs; When the spatial integrity coefficient is less than the spatial integrity coefficient threshold, it is considered that the spatial data integrity in the digital archive is poor, with abnormal deviations or missing data; When the minimum alignment distance is greater than the minimum alignment distance threshold and the spatial integrity coefficient is less than the spatial integrity coefficient threshold, the corresponding digital archives with spatiotemporal correlation anomalies are screened out based on the time data with breaks or abnormal jumps and the spatial data with abnormal deviations or missing data.

5. A digital archive management method according to claim 4, characterized in that: In step S4, based on the digitized archives with abnormal spatiotemporal correlation, a spatiotemporal matrix is ​​constructed to analyze the spatiotemporal correlation of the spatiotemporal data of the digitized archives and evaluate the degree of distortion of the spatiotemporal data of the digitized archives, specifically: Perform singular value decomposition on the spatiotemporal correlation matrix, extract the main singular values ​​of the matrix, and calculate the spatiotemporal coherence coefficient based on the main singular values ​​to evaluate the correlation between time data and space data. The expression of the spatiotemporal coherence coefficient is: Among them, P st is the spatiotemporal coherence coefficient; σ j represents the jth singular value of the space-time incidence matrix; k is the number of primary singular values; M is the total number of all singular values ​​of the space-time incidence matrix; Set the spatiotemporal coherence coefficient threshold and compare the spatiotemporal coherence coefficient with the spatiotemporal coherence coefficient threshold: When the spatiotemporal coherence coefficient is greater than or equal to the spatiotemporal coherence coefficient threshold, it indicates that the temporal data and spatial data in the digitized archives with spatiotemporal correlation anomaly are highly correlated, which means that the digitized archives have no spatiotemporal correlation distortion; When the spatiotemporal coherence coefficient is less than the spatiotemporal coherence coefficient threshold, it indicates that the correlation between the time data and the spatial data in the digital archives with spatiotemporal correlation anomalies is weak, which means that there is spatiotemporal correlation distortion in the digital archives.

6. A digital archive management method according to claim 5, characterized in that: In step S5, a digital archive management model is established based on the degree of discontinuity or abnormal jump of time data, the degree of abnormal deviation or missing of spatial data, and the spatiotemporal correlation of spatiotemporal data of digital archives to manage digital archives, specifically: The minimum alignment distance corresponding to the degree of breakage or abnormal jump of time data, the spatial integrity coefficient corresponding to the degree of abnormal deviation or missing of spatial data, and the spatiotemporal coherence coefficient corresponding to the spatiotemporal correlation of digital archive spatiotemporal data are normalized respectively. According to the minimum alignment distance, spatial integrity coefficient and spatiotemporal coherence coefficient obtained after normalization, a digital archive management model is established: OQC=α*DTW+β*(1-C s )+ω*(1-P st ): Among them, OQC is the comprehensive quality score; DTW represents the minimum alignment distance between the historical time series and the current time series; C s Represents the spatial integrity coefficient; P st is the spatiotemporal coherence coefficient; α, β, and ω represent the minimum alignment distance between the historical time series and the current time series, the spatial integrity coefficient, and the weights of the spatiotemporal coherence coefficient in the digital archive management model; Set a comprehensive quality score threshold and compare the comprehensive quality score with the comprehensive quality score threshold: When the comprehensive quality score is greater than or equal to the comprehensive quality score threshold, it means that the comprehensive quality score of the digitized archive is poor; When the comprehensive quality score is less than the comprehensive quality score threshold, it means that the comprehensive quality of the archive is good, with good usability and reliability.

7. A digital archive management system, applied to a digital archive management method according to any one of claims 1 to 6, characterized in that: include: Time data evaluation module: conducts continuity analysis on time data in digital archives and evaluates the degree of discontinuity or abnormal jump of time data; Spatial data evaluation module: conduct integrity analysis on spatial data in digital archives and evaluate the abnormal deviation or missing degree of spatial data; Temporal and spatial correlation anomaly screening module: based on the evaluation results of time data and spatial data, screen out digital archives with temporal and spatial correlation anomalies; Spatiotemporal correlation evaluation module: Based on the digital archives with spatiotemporal correlation anomalies, the spatiotemporal correlation matrix is ​​analyzed to evaluate the spatiotemporal correlation of the spatiotemporal data of the digital archives; Digital archive management module: A digital archive management model is established based on the degree of discontinuity or abnormal jumps in time data, the degree of abnormal deviation or missingness in spatial data, and the spatiotemporal correlation of digital archive spatiotemporal data to manage digital archives.

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