An automated employee data archiving and destruction processing system

Abstract

The application relates to the field of enterprise employee data processing, in particular to an employee data archiving and destroying automatic processing system, which comprises a document estimation module, a distribution analysis module and a correlation analysis module. The document estimation module is used to determine characteristic category parameters and characteristic distribution parameters of each target processing document, and to determine whether to perform characteristic distribution analysis and information correlation analysis on the target processing document according to the characteristic category parameters. The distribution analysis module is used to perform characteristic distribution analysis on the target processing document. The correlation analysis module is used to perform information correlation analysis on the target processing document, and to determine whether to perform coverage area optimization on each coverage processing field according to a retention correlation coefficient. A core execution module is used to perform storage and updating tasks of an information field classification table, an information field processing strategy table and an information field processing task table. The application can ensure the encryption effect of sensitive information in the document and improve the overall readability of the processed employee-related document.

Description

Technical Field

[0001] This invention relates to the field of enterprise employee data processing, and in particular to an automated system for archiving and destroying employee data. Background Technology

[0002] In the process of enterprise human resource management, the data of departing employees during their employment period needs to be processed accordingly. During the archiving process, sensitive information in the data related to departing employees needs to be processed to avoid the loss of key data or the leakage of sensitive employee data. However, in actual processing, the data documents often contain multiple information fields representing different information categories. The distribution of different information fields and the information content they represent will affect the processing effect of the data document. In actual processing, the distribution of sensitive information data in the data document varies greatly. When the distribution of different sensitive information data is relatively concentrated, overwriting and destroying character information within a fixed range can easily affect the overall readability of the document content. However, blindly reducing the scope of overwriting and destruction will affect the encryption effect of the data document. How to balance the encryption effect of sensitive information with ensuring the overall readability of the processed data document is a problem that urgently needs to be solved by those skilled in the art.

[0003] Chinese Patent Publication No. CN118863795A discloses a digital employee file management method and system, belonging to the field of digital employee file management technology. It includes acquiring paper files, automatically identifying and extracting file information from the paper files through an intelligent scanning and recognition module, extracting keywords based on the scanned data, and classifying and archiving the data into a database according to preset classification rules. Chinese Patent Publication No. CN118504015A discloses an efficient human resource management system integrating business process outsourcing. This efficient human resource management system includes an information management module, a payroll management module, a data analysis module, an integration extension module, and a data encryption module. The information management module is used for electronic storage of employee information; the payroll management module is used for automatically generating employee payroll reports; the data analysis module is used for visual data analysis of human resources; the integration extension module is used for providing API interfaces to different systems; and the data encryption module is used for encrypted storage of sensitive data. However, the above solutions have the following drawbacks: they fail to make targeted adjustments to the document processing process based on the actual sensitive information contained in the processed employee data documents, resulting in the processing results of employee-related documents not effectively balancing the encryption effect of sensitive information and the overall readability of the processed employee-related documents. Summary of the Invention

[0004] To address this issue, the present invention provides an automated processing system for employee data archiving and destruction, which overcomes the problem in the prior art that fails to make targeted adjustments to the information processing process based on the actual sensitive information contained in the processed employee data documents, resulting in the processing results of employee-related documents failing to effectively balance the encryption effect of sensitive information and the overall readability of the processed employee-related documents.

[0005] To achieve the above objectives, the present invention provides an automated system for archiving and destroying employee data, comprising:

[0006] The document prediction module is used to obtain the feature categories corresponding to each information field in the target document, in order to determine the feature category parameters and feature distribution parameters of each target document, and to determine whether to perform feature distribution analysis and information correlation analysis on the target document based on the feature category parameters.

[0007] The distribution analysis module, which is connected to the document prediction module, includes a clustering execution unit and a uniform execution unit, for performing feature distribution analysis on the target document.

[0008] The clustering execution unit is used to determine the set of feature fields for each information field based on the page distribution index or feature correlation index, and to determine whether to optimize the processing strategy for each information field based on the set reference distribution parameter or set reference distribution parameter.

[0009] The uniform execution unit is used to determine whether to optimize the processing strategy for the information characters in each processed document area based on the regional distribution parameters.

[0010] The correlation analysis module is connected to the document prediction module and the distribution analysis module respectively, and is used to perform information correlation analysis on the target processed document and determine whether to optimize the coverage area for each coverage processing field based on the retention correlation coefficient.

[0011] The core execution module is connected to the document analysis module, the distribution analysis module, and the related analysis module, respectively, and is used to perform storage and update tasks for the information field classification table, the information field processing strategy table, and the information field processing task table.

[0012] Furthermore, if the feature category parameter of a target document is greater than the preset feature category parameter, then it is determined that feature distribution analysis will be performed on the target document.

[0013] The feature category parameter is determined based on the feature category and field coverage index corresponding to the information fields existing in the target document.

[0014] Furthermore, if the reference distribution parameter of the target processing document is greater than the preset reference distribution parameter, the clustering execution unit determines the feature field set for each information field in the target processing document and performs feature analysis on each feature field set.

[0015] Based on the feature distribution parameters, determine the set reference coverage index or set reference distribution parameters for each feature field set for detection;

[0016] The reference feature parameters are the average value of the feature distribution parameters corresponding to each information field contained in the target document.

[0017] Furthermore, the clustering coefficient of each information field in the feature field set of any information field with respect to the corresponding information field is greater than the preset clustering coefficient;

[0018] The clustering coefficients are set according to the feature distribution parameters corresponding to each information field;

[0019] If the feature distribution parameter of an information field is greater than the preset feature distribution parameter, then the clustering coefficient of each information field for that information field is determined based on the page distribution index;

[0020] If the feature distribution parameter of an information field is less than or equal to the preset feature distribution parameter, then the clustering coefficient of each information field with respect to that information field is determined based on the feature correlation index.

[0021] Furthermore, if the feature distribution parameter of an information field is greater than the preset feature distribution parameter, then the processing strategy optimization for that information field is determined based on the set reference coverage index.

[0022] If the set reference coverage index of the feature field set of an information field is greater than the preset set reference coverage index, it is determined that the processing strategy for that information field should be optimized, and the coverage ratio index of that information field should be reduced according to the set reference coverage parameter.

[0023] The set reference coverage index is the average of the coverage percentage indices of each information field within the feature field set.

[0024] Furthermore, if the feature distribution parameter of an information field is less than or equal to the preset feature distribution parameter, then it is determined whether to optimize the processing strategy for that information field based on the set reference distribution parameter of the feature field set of that information field.

[0025] If the set reference distribution parameter of the feature field set of an information field is greater than the preset set reference distribution parameter, then it is determined that the processing strategy for that information field should be optimized, and the coverage ratio index of that information field should be increased according to the set reference distribution parameter.

[0026] The reference distribution parameter of the set is the average value of the feature distribution parameters of each information field in the feature field set.

[0027] Furthermore, if the reference distribution parameter of the target document is less than or equal to the preset reference distribution parameter, then the processing strategy optimization for the information characters in each processing document area is determined based on the regional distribution parameter.

[0028] If the regional distribution parameter of the processed document area is greater than the preset regional distribution parameter, it is determined that the processing strategy for each information field in the processed document area should be optimized, and the coverage ratio index of each information character in the processed document should be reduced according to the regional distribution parameter.

[0029] The decrease in the coverage ratio index is positively correlated with the regional distribution parameter.

[0030] Furthermore, if there is a target processing document whose feature category parameter is less than or equal to the preset feature category parameter, or if there is a target processing document whose feature category parameter is greater than the preset feature category parameter and whose feature distribution analysis has been completed, then it is determined that information correlation analysis will be performed on the target processing document.

[0031] Furthermore, if the retention correlation coefficient of a covered field is greater than a preset retention correlation coefficient, then it is determined that coverage area optimization should be performed on that covered field.

[0032] The coverage ratio index of the relevant information fields for this coverage processing field is increased based on the retention correlation coefficient;

[0033] The priority replacement coefficient for each character in each relevant information field is determined based on the relevant coverage index.

[0034] Furthermore, the information field classification table is used to store the sensitivity level corresponding to the information fields of each feature category;

[0035] The information field processing strategy table is used to store the processing strategies corresponding to the information fields of each feature category;

[0036] The information field processing task table is used to store the processing tasks set for each information field existing in each target processing document.

[0037] Compared with the prior art, the beneficial effects of the present invention are that the technical solution of the present invention detects the feature category parameters and feature distribution parameters of different target processing documents, so as to make targeted adjustments to the encryption process of sensitive information in each target processing document, making the actual processing process more adapted to the distribution of information fields contained in the target processing document and the existence of corresponding feature categories. The present invention improves the overall readability of the processed employee-related documents while ensuring the encryption effect of sensitive information in the document.

[0038] Furthermore, in this invention, the determination of whether to perform feature distribution analysis on each target processing document is based on the feature category parameter. The feature category parameter characterizes the distribution range of information fields contained in the target processing document and the richness of the feature categories of the information fields, and determines whether to perform feature distribution analysis on the target processing document. This ensures that when the feature category parameter is large, the processing decision for each information field can take into account the specific circumstances of its surrounding or related content. This invention improves the readability of the processed target document and also takes into account the quality of encryption processing of information fields.

[0039] Furthermore, in this invention, for target processing documents with large reference distribution parameters, the reference distribution parameters can indicate the distribution and cross-distribution of information fields of different feature categories in the target processing document. A feature field set is set for each information field, and a judgment is made on the optimization requirements of the processing strategy for the information field based on the determined feature field set. This allows the processing decisions for each information field to take into account the specific circumstances of its surrounding or related content. The feature distribution parameters determine how to obtain the feature field set for each information field and how to determine whether processing strategy optimization is needed, making the optimization behavior for the processing strategy of each information field more in line with actual needs. This invention improves the overall readability of the processed employee-related documents while ensuring the encryption effect of sensitive information within the document.

[0040] Furthermore, in this invention, for target processing documents with small reference distribution parameters, the distribution of information fields of different feature categories in such target processing documents is relatively dispersed and the degree of cross-distribution is relatively small. In this case, the target processing document is uniformly divided, and the processing strategy is optimized for each information field based solely on the richness of the information fields existing in each processing document region, so as to ensure the validity of the content corresponding to the processing document region with a large regional distribution parameter after processing.

[0041] Furthermore, in this invention, for target processing documents where the feature category parameter is less than or equal to the preset feature category parameter, or where the feature category parameter is greater than the preset feature category parameter and feature distribution analysis has been completed, since the information fields in such target processing documents do not require optimization of the processing strategy or have already been optimized, but the information fields of different feature categories that have been processed in the target processing document may be able to mutually verify the information content covered during the processing, the retention correlation coefficient characterizes whether there is a risk of the information content corresponding to the covered characters being analyzed by other information fields, and when the above risk is high, the coverage area is optimized. This invention ensures the encryption level of sensitive information in the target processing document. Attached Figure Description

[0042] Figure 1 This is a module connection diagram of the automated employee data archiving and destruction system of the present invention;

[0043] Figure 2 This is a module structure diagram of the distribution analysis module of the present invention;

[0044] Figure 3 This is a flowchart illustrating the present invention's method for determining whether to perform feature distribution analysis and information correlation analysis on the target document based on feature category parameters;

[0045] Figure 4 This is a flowchart illustrating how the clustering coefficients are determined based on the feature distribution parameters corresponding to each information field, as per the present invention. Detailed Implementation

[0046] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0047] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0048] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0049] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0050] Please see Figures 1 to 4 As shown, the present invention provides an automated processing system for employee data archiving and destruction, comprising:

[0051] The document prediction module is used to obtain the feature categories corresponding to each information field in the target document, in order to determine the feature category parameters and feature distribution parameters of each target document, and to determine whether to perform feature distribution analysis and information correlation analysis on the target document based on the feature category parameters.

[0052] The distribution analysis module, which is connected to the document prediction module, includes a clustering execution unit and a uniform execution unit, for performing feature distribution analysis on the target document.

[0053] The clustering execution unit is used to determine the set of feature fields for each information field based on the page distribution index or feature correlation index, and to determine whether to optimize the processing strategy for each information field based on the set reference distribution parameter or set reference distribution parameter.

[0054] The uniform execution unit is used to determine whether to optimize the processing strategy for the information characters in each processed document area based on the regional distribution parameters.

[0055] The correlation analysis module is connected to the document prediction module and the distribution analysis module respectively, and is used to perform information correlation analysis on the target processed document and determine whether to optimize the coverage area for each coverage processing field based on the retention correlation coefficient.

[0056] The core execution module is connected to the document analysis module, the distribution analysis module, and the related analysis module, respectively, and is used to perform storage and update tasks for the information field classification table, the information field processing strategy table, and the information field processing task table.

[0057] This invention is used to process data documents related to the employment period of departing employees. During the processing, data related to company business is archived, while data containing sensitive information about departing employees is destroyed or encrypted. In this invention, the data documents to be processed are designated as target processing documents, and the departing employees corresponding to the target processing documents are designated as the target executors for each target processing document. The information fields and corresponding feature categories within the target processing documents are obtained based on the identification features of information fields of different feature categories. The feature categories of the information fields include, but are not limited to: employee's name, gender, date of birth, nationality, ethnicity, ID card number, registered address, current address, contact number, and email address.

[0058] This invention utilizes several document processing records. Each document processing record contains at least one analysis and processing of the feature category parameters, reference feature parameters, clustering coefficients, set reference coverage index, set reference distribution parameters, and retention correlation coefficients of the feature data contained in the target document. Each document processing record also has a corresponding qualification mark, which records whether the processing effect of the target document for departing employees meets the user's needs. The processing effect of the target document includes the degree of encryption of sensitive information and the overall readability of the processed employee-related documents.

[0059] Specifically, if the feature category parameter of a target document is greater than the preset feature category parameter, then it is determined that feature distribution analysis should be performed on the target document.

[0060] The feature category parameter is determined based on the feature category and field coverage index corresponding to the information fields existing in the target document.

[0061] For a single target processing document, the feature category parameter is the sum of the feature category proportion index and the field coverage index. The feature category proportion index = the number of feature categories belonging to personnel information features in the feature categories corresponding to the information fields in the target processing document / the number of feature categories corresponding to the information fields in the target processing document. The field coverage index = the total number of characters contained in each information field in the target processing document whose corresponding feature category is personnel information / the total number of characters in the target processing document. If the feature category parameter of the target processing document is greater than the preset feature category parameter, it indicates that there are many information fields of feature categories in the content of the target processing document that need to be processed to different degrees and that the proportion of information fields that need to be processed is large. In the process of processing such target processing documents, since there are many information fields that need to be encrypted or destroyed to different degrees, it is easy to lead to low continuity and integrity of the completed target processing document. By performing feature distribution analysis on the target processing document, the processing decision for each information field takes into account the specific situation of its surrounding or related content, so as to improve the effectiveness of the completed target processing document and take into account the processing quality of sensitive information.

[0062] The value of the preset feature category parameter can be determined by the user according to the actual work scenario. For example, the user can set it according to the document processing records. The higher the user's requirements for the processing effect of the target document of the departing employee, the smaller the value of the preset feature category parameter. A method for setting the value of the preset feature category parameter is provided, in which the document processing records that perform feature distribution analysis on the target document are recorded as feature reference records, and the minimum value of the feature category parameter of the target document in the feature reference records that meets the user's requirements for the processing effect of the target document of the departing employee is recorded as the preset feature category parameter.

[0063] Specifically, if the reference distribution parameter of the target processing document is greater than the preset reference distribution parameter, the clustering execution unit determines the feature field set for each information field in the target processing document and performs feature analysis on each feature field set;

[0064] Based on the feature distribution parameters, determine the set reference coverage index or set reference distribution parameters for each feature field set for detection;

[0065] The reference feature parameters are the average value of the feature distribution parameters corresponding to each information field contained in the target document.

[0066] Specifically, for any information field existing within a single target document, the feature distribution parameter is the sum of the feature richness index and the range richness index. The feature richness index = the number of feature categories corresponding to the information field within the distribution analysis range of the information field / the number of feature categories corresponding to the information field within the target document. The range richness index = the number of information fields within the distribution analysis range of the information field / the total number of information fields within the target document. The distribution analysis range is a rectangular area with the length of the side of the target document parallel to the extension direction of the information field as the longer side and a preset distribution analysis distance as the shorter side. The line connecting the midpoint of the shorter side of the distribution analysis range to the midpoint of the coverage of the information field intersects the information field. The extension direction is parallel. If the information field does not have line breaks in the distribution of the target processing document, the center point of the area covered by the information field is recorded as the coverage midpoint of the information field. If the information field has line breaks in the distribution of the target processing document, the midpoint of the line segment perpendicular to the extension direction of the information field and located in the area covered by the text line of the information field is recorded as the coverage midpoint of the information field. The value of the preset distribution analysis distance can be determined by the user according to the actual work scenario. The higher the user's requirements for the processing effect of the target processing document of the departing employee, the larger the value of the preset distribution analysis distance. A preset distribution analysis distance value is provided, which is 5% of the side length of the longer side of the corresponding page of the target processing document.

[0067] For a single target document, if the reference distribution parameter of the target document is greater than the preset reference distribution parameter, it indicates that the information fields of different feature categories in the target document are densely distributed and have a heavy cross-distribution. Therefore, it is necessary to conduct further analysis based on the processing strategy of each information field and the feature categories corresponding to the surrounding information fields. By setting the feature field set for the target document and determining the targeted processing process for different feature field sets, the processing result of the target document can ensure that the degree of processing of the document content meets the actual needs while taking into account the relevance of the processed document content.

[0068] The value of the preset reference distribution parameter can be determined by the user according to the actual work scenario. For example, the user can set it according to the document processing records. The higher the user's requirements for the processing effect of the target documents of departing employees, the smaller the value of the preset reference distribution parameter. A method for determining the value of the preset reference distribution parameter is provided, in which the document processing records that are clustered and divided for the target documents are recorded as the distribution reference records, and the average value of the reference feature parameters of the target documents in the distribution reference records that meet the user's requirements for the processing effect of the target documents of departing employees is recorded as the preset reference distribution parameter.

[0069] Specifically, the clustering coefficient of each information field in the feature field set of any information field is greater than the preset clustering coefficient for the corresponding information field;

[0070] The clustering coefficients are set according to the feature distribution parameters corresponding to each information field;

[0071] If the feature distribution parameter of an information field is greater than the preset feature distribution parameter, then the clustering coefficient of each information field for that information field is determined based on the page distribution index;

[0072] If the feature distribution parameter of an information field is less than or equal to the preset feature distribution parameter, then the clustering coefficient of each information field with respect to that information field is determined based on the feature correlation index.

[0073] Specifically, for a single information field, if the feature distribution parameter of that information field is greater than the preset feature distribution parameter, it indicates that there are a large number of information fields and corresponding feature categories within the distribution analysis range of that information field. Analysis is performed on the set of feature fields determined by the page distribution index to determine the processing status of that information field. The focus is primarily on ensuring the integrity of the information content within the distribution analysis range of that information field. The page distribution index between each information field in the target processing document and this information field is detected and normalized. How to perform the normalization process is already known to those skilled in the art and will not be elaborated here. The clustering coefficient of each information field in the target processing document for this information field is positively correlated with the page distribution index after normalization. For any two information fields within the target document, the page distribution index is the sum of the extension interval index and the vertical interval index. The extension interval index = the minimum value of the extension interval parameter between the two information fields / the length of one side of the page of the target document parallel to the text distribution extension direction. The vertical interval index = the minimum value of the vertical interval parameter between the two information fields / the length of one side of the page of the target document perpendicular to the text distribution extension direction. For each of the two information fields, a character is obtained. The extension interval parameter is the shortest distance between the two characters located on the two straight lines perpendicular to the text distribution extension direction. The vertical interval parameter is the shortest distance between the two characters located on the two straight lines parallel to the text distribution extension direction.

[0074] For a single information field, if the feature distribution parameter of the information field is less than or equal to the preset feature distribution parameter, it indicates that the number of information fields and the number of corresponding feature categories within the distribution analysis range of the information field are small. For the feature field set determined by the feature correlation index, the analysis mainly focuses on the processing of information fields that can represent the same information category to determine the processing status of the information field. The feature correlation index between each information field and the information field within the distribution analysis range of the information field is detected and normalized. The clustering coefficient of each information field for the information field is positively correlated with the feature correlation index after normalization. For any two information fields in the target processing document, the feature correlation index = the number of information categories that both information fields correspond to / the number of different information categories that both information fields correspond to.

[0075] The value of the preset clustering coefficient can be determined by the user based on the actual work scenario. For example, the user can set it based on the document processing records. The higher the user's requirements for the processing effect of the target documents of departing employees, the larger the value of the preset clustering coefficient. A method for determining the value of the preset clustering coefficient is provided, which takes the minimum value of the clustering coefficient of each information field in each feature field set in the document processing records that meets the user's requirements for the processing effect of the target documents of departing employees as the preset clustering coefficient.

[0076] Specifically, if the feature distribution parameter of an information field is greater than the preset feature distribution parameter, then the processing strategy optimization for that information field is determined based on the set reference coverage index.

[0077] If the set reference coverage index of the feature field set of an information field is greater than the preset set reference coverage index, it is determined that the processing strategy for that information field should be optimized, and the coverage ratio index of that information field should be reduced according to the set reference coverage parameter.

[0078] The set reference coverage index is the average of the coverage percentage indices of each information field within the feature field set.

[0079] Specifically, for information fields where a single feature distribution parameter is greater than a preset feature distribution parameter, if the set reference coverage index of the feature field set of the information field is greater than the preset set reference coverage index, it indicates that the processing requirements of each information field within the feature field set of the information field have a significant impact on the target document. Therefore, it is necessary to reduce the coverage ratio index of the information field. The reduction value of the coverage ratio index is positively correlated with the set reference coverage parameter. For a single information field, the coverage ratio index = the number of covered characters set by the processing strategy corresponding to the feature category of the information field / the number of characters contained in the information field. If the coverage ratio index of an information field needs to be reduced, the updated retained characters of the information field are replaced. The updated retained characters and the character content used for replacement are randomly selected. The updated retained characters are the covered characters set by the processing strategy corresponding to the feature category of the information field, but the characters are retained due to the reduction adjustment of the coverage ratio index of the information field.

[0080] The value of the preset set reference coverage index can be determined by the user based on the actual work scenario. For example, the user can set it based on document processing records. The higher the user's requirements for the processing effect of the target documents of departing employees, the smaller the value of the preset set reference coverage index. A method for determining the value of the preset set reference coverage index is provided, in which the document processing record whose coverage ratio index of information fields is reduced according to the set reference coverage parameter is recorded as the first adjustment reference record, and the average value of the set reference coverage index of the feature field set of information fields in the first adjustment reference record that meets the user's requirements for the processing effect of the target documents of departing employees is recorded as the preset set reference coverage index.

[0081] Specifically, if the feature distribution parameter of an information field is less than or equal to the preset feature distribution parameter, then it is determined whether to optimize the processing strategy for that information field based on the set reference distribution parameter of the feature field set of that information field.

[0082] If the set reference distribution parameter of the feature field set of an information field is greater than the preset set reference distribution parameter, then it is determined that the processing strategy for that information field should be optimized, and the coverage ratio index of that information field should be increased according to the set reference distribution parameter.

[0083] The reference distribution parameter of the set is the average value of the feature distribution parameters of each information field in the feature field set.

[0084] Specifically, for information fields whose single feature distribution parameter is less than or equal to the preset feature distribution parameter, if the set reference distribution parameter of the feature field set of the information field is greater than the preset set reference distribution parameter, it indicates that the feature categories corresponding to the information fields within the distribution analysis range of other information fields in the target processing document that are similar to or consistent with the information category reflected by the information field are relatively complex. That is, the processing of other information fields that are similar to or consistent with the information category reflected by the information field needs to be adaptively adjusted, such as reducing the coverage ratio index. In order to ensure the encryption degree of the information category reflected by the information field, the coverage ratio index of the information field is increased. The increase in the coverage ratio index is positively correlated with the set reference distribution parameter.

[0085] The value of the preset set reference distribution parameter can be determined by the user according to the actual work scenario. For example, the user can set it according to the document processing records. The higher the user's requirements for the processing effect of the target documents of departing employees, the smaller the value of the preset set reference distribution parameter. A method for determining the value of the preset set reference distribution parameter is provided. The document processing record that is adjusted by increasing the coverage ratio index of the information field according to the set reference distribution parameter is recorded as the second adjustment reference record. The average value of the set reference distribution parameter of the feature field set of the information field in the second adjustment reference record that meets the user's requirements for the processing effect of the target documents of departing employees is recorded as the preset set reference distribution parameter.

[0086] Specifically, if the reference distribution parameter of the target document is less than or equal to the preset reference distribution parameter, then the processing strategy optimization for the information characters in each processing document area is determined based on the regional distribution parameter.

[0087] If the regional distribution parameter of the processed document area is greater than the preset regional distribution parameter, it is determined that the processing strategy for each information field in the processed document area should be optimized, and the coverage ratio index of each information character in the processed document should be reduced according to the regional distribution parameter.

[0088] The decrease in the coverage ratio index is positively correlated with the regional distribution parameter.

[0089] For a single target processing document, if the reference distribution parameter of the target processing document is less than or equal to the preset reference distribution parameter, it indicates that the distribution of information fields of different feature categories in the target processing document is relatively scattered and the degree of cross-distribution is relatively light. In this case, it is only necessary to divide the target processing document evenly, without considering the impact of the processing of different information fields on the encryption level of sensitive information, and only the readability of the content of the processed target processing document needs to be considered.

[0090] For a single target document, the page of the target document is evenly divided along the text distribution direction to obtain several processing document regions. The number of processing document regions obtained can be determined by the user according to the actual work scenario. The higher the user's requirements for the processing effect of the target document of the departing employee, the more processing document regions are obtained. There is no specific limit on the number of processing document regions obtained. For a single processing document region, the region distribution parameter is the number of information fields present in the processing document region.

[0091] Specifically, if there is a target processing document whose feature category parameter is less than or equal to the preset feature category parameter, or if there is a target processing document whose feature category parameter is greater than the preset feature category parameter and whose feature distribution analysis has been completed, then it is determined that information correlation analysis will be performed on the target processing document.

[0092] Specifically, if the retention correlation coefficient of a covered field is greater than the preset retention correlation coefficient, then it is determined that the coverage area should be optimized for that covered field.

[0093] The coverage ratio index of the relevant information fields for this coverage processing field is increased based on the retention correlation coefficient;

[0094] The priority replacement coefficient for each character in each relevant information field is determined based on the relevant coverage index.

[0095] Specifically, for a single target document, if the feature category parameter of the target document is less than or equal to the preset feature category parameter, it indicates that the information fields in the content of the target document have fewer feature categories and the proportion of information fields that need to be processed is relatively small. In processing such target documents, most information fields are processed according to the processing strategies corresponding to the information fields of each feature category stored in the information field processing strategy table, which is less likely to affect the continuity of the content of the target document after processing. If the feature category parameter of the target document is greater than the preset feature category parameter, but feature distribution analysis has already been completed, targeted processing is performed on each information field to ensure the continuity of the content of the target document after processing. However, it is still necessary to consider whether the information fields of different feature categories processed in the target document can corroborate the information content covered during processing, further ensuring the encryption effect of the target document on the sensitive personal information of its corresponding departing employees. The covered processing field is an information field with covered characters. For a single covered processing field, the... The retention correlation coefficient is the number of related information fields in the target processing document for the covered processing field. The related information fields are information fields that can reflect the information category corresponding to the covered characters in the covered processing field. The increase in the coverage ratio index is positively correlated with the retention correlation coefficient. When adjusting the coverage ratio index of a single related information field, the updated replacement characters are selected according to the priority replacement coefficient. The updated replacement characters are characters within the related information field that meet the coverage ratio index for the increased adjustment and are not within the range of covered characters set by the processing strategy for the feature category corresponding to the related information field. Several updated replacement characters are determined according to the priority replacement coefficient and the coverage ratio index. The character content is randomly selected to replace the content of the updated replacement characters. The characters in the related information field that are not within the range of covered characters set by the processing strategy for the feature category corresponding to the related information field are considered to be updated replacement characters. The priority replacement coefficient is the number of covered processing fields in the target processing document that can reflect the information category corresponding to the character. The larger the priority replacement coefficient, the higher the priority of the character is determined as the updated replacement character.

[0096] The value of the preset retention correlation coefficient can be determined by the user based on the actual work scenario. For example, the user can set it based on document processing records. The higher the user's requirements for the processing effect of the target documents of departing employees, the smaller the value of the preset retention correlation coefficient. A method for determining the value of the preset retention correlation coefficient is provided, which records the document processing records that optimize the coverage area for the coverage processing field are recorded as coverage reference records, and the minimum value of the retention correlation coefficient of the coverage processing field in the coverage reference records that meet the user's requirements for the processing effect of the target documents of departing employees is recorded as the preset retention correlation coefficient.

[0097] Specifically, the information field classification table is used to store the sensitivity level corresponding to the information fields of each feature category;

[0098] The information field processing strategy table is used to store the processing strategies corresponding to the information fields of each feature category;

[0099] The information field processing task table is used to store the processing tasks set for each information field existing in each target processing document.

[0100] In this invention, the target document contains information fields of different feature categories. For personnel information features, each feature category of information field corresponds to a specific identification feature, the information category corresponding to each part of the characters, the sensitivity level, and the processing strategy. Users can determine the identification features, the information category corresponding to each part of the characters, the sensitivity level, and the processing strategy for each feature category of information fields based on the actual situation. This is easily understood by those skilled in the art. For example, if the feature category corresponding to an information field is the ID card number of a departing employee, the identification features could be: the information field contains 18 characters; the first six characters of the information field can be identified as an administrative division code; and the seventh to fourteenth characters of the information field represent the year, month, and date prior to the identification time. The information category reflected by the first six characters of the information field of this feature category... For birthplace information, the information fields in this feature category contain characters that can represent the following information categories: birthplace, date of birth, and gender. The sensitivity level is high sensitivity. The processing strategy is to overwrite the characters representing the date of birth in the information fields of this feature category one year after the employee leaves the company, in order to complete encrypted storage. Specific settings for the identification features, key field ranges, sensitivity levels, and processing strategies of each feature category's information fields are not detailed here. The sensitivity levels include high, medium, and low. The processing strategy includes the time when the overwrite processing is performed and the information category represented by the characters overwritten at that time. For a single information field, the processing task includes the time when the overwrite action has been performed on that information field and the corresponding character content.

[0101] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0102] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. An automated system for archiving and destroying employee data, characterized in that, include: The document prediction module is used to obtain the feature categories corresponding to each information field in the target document to determine the feature category parameters of each target document. If the feature category parameters of a target document are greater than the preset feature category parameters, it is determined that feature distribution analysis will be performed on the target document. If the feature category parameters of a target document are less than or equal to the preset feature category parameters, or if there are target documents with feature category parameters greater than the preset feature category parameters and feature distribution analysis has been completed, it is determined that information correlation analysis will be performed on the target document. The distribution analysis module, which is connected to the document prediction module, includes a clustering execution unit and a uniform execution unit, for performing feature distribution analysis on the target document. The clustering execution unit is used to determine the set of feature fields for each information field based on the page distribution index or feature correlation index, and to determine whether to optimize the processing strategy for each information field based on the set reference coverage index or set reference distribution parameter. The uniform execution unit is used to determine whether to optimize the processing strategy for the information characters in each processed document area based on the regional distribution parameters. The correlation analysis module is connected to the document prediction module and the distribution analysis module respectively. It is used to perform information correlation analysis on the target processed document and determine whether to optimize the coverage area for each covered processing field based on the retention correlation coefficient. When optimizing the coverage area, the coverage ratio index of the relevant information fields of the corresponding covered processing field is adjusted according to the retention correlation coefficient. The core execution module is connected to the document prediction module, the distribution analysis module, and the correlation analysis module, respectively, and is used to perform storage and update tasks for the information field classification table, the information field processing strategy table, and the information field processing task table. For a single target processing document, the feature category parameter is the sum of the feature category proportion index and the field coverage index. The feature category proportion index = the number of feature categories belonging to personnel information features in the feature categories corresponding to the information fields in the target processing document / the number of feature categories corresponding to the information fields in the target processing document. The field coverage index = the total number of characters contained in each information field in the target processing document whose corresponding feature category is personnel information feature / the total number of characters contained in the target processing document. For any two information fields in the target processing document, the page distribution index is the sum of the extension interval index and the vertical interval index, and the feature correlation index is the number of information categories that both of the above information fields correspond to / the number of different information categories that the above two information fields correspond to. The set reference coverage index is the average of the coverage ratio index of each information field in the feature field set, and the set reference distribution parameter is the average of the feature distribution parameters of each information field in the feature field set. For a single processed document region, the region distribution parameter is the number of information fields present within that processed document region.

2. The automated employee data archiving and destruction system according to claim 1, characterized in that, If the reference distribution parameter of the target processing document is greater than the preset reference distribution parameter, the clustering execution unit determines the feature field set for each information field in the target processing document and performs feature analysis on each feature field set. Determine whether to perform detection for the set reference coverage index or set reference distribution parameter of each feature field set based on the feature distribution parameters; The reference distribution parameter is the average value of the feature distribution parameters corresponding to each information field contained in the target document. The feature distribution parameter is the sum of the feature richness index and the range richness index. The feature richness index is the number of feature categories corresponding to the information field within the distribution analysis range of the information field / the number of feature categories corresponding to the information field within the target document. The range richness index is the number of information fields within the distribution analysis range of the information field / the number of information fields within the target document.

3. The automated employee data archiving and destruction system according to claim 2, characterized in that, The clustering coefficient of each information field in the feature field set of any information field is greater than the preset clustering coefficient. The clustering coefficients are set according to the feature distribution parameters corresponding to each information field; If the feature distribution parameter of an information field is greater than the preset feature distribution parameter, then the clustering coefficient of each information field for that information field is determined based on the page distribution index; If the feature distribution parameter of an information field is less than or equal to the preset feature distribution parameter, then the clustering coefficient of each information field with respect to that information field is determined based on the feature correlation index.

4. The automated employee data archiving and destruction system according to claim 3, characterized in that, If the feature distribution parameter of an information field is greater than the preset feature distribution parameter, then the processing strategy optimization for that information field is determined based on the set reference coverage index. If the set reference coverage index of the feature field set of an information field is greater than the preset set reference coverage index, then it is determined that the processing strategy for that information field should be optimized, and the coverage ratio index of that information field should be reduced according to the set reference coverage index. For a single information field, the coverage ratio index = the number of covered characters set by the processing strategy corresponding to the feature category of the information field / the number of characters contained in the information field.

5. The automated employee data archiving and destruction system according to claim 4, characterized in that, If the feature distribution parameter of an information field is less than or equal to the preset feature distribution parameter, then determine whether to optimize the processing strategy for that information field based on the set of feature field set reference distribution parameters. If the set reference distribution parameter of the feature field set of an information field is greater than the preset set reference distribution parameter, then it is determined that the processing strategy for that information field should be optimized, and the coverage ratio index of that information field should be increased according to the set reference distribution parameter. The reference distribution parameter of the set is the average value of the feature distribution parameters of each information field in the feature field set.

6. The automated employee data archiving and destruction system according to claim 1, characterized in that, If the reference distribution parameter of the target document is less than or equal to the preset reference distribution parameter, then determine whether to optimize the processing strategy for the information characters in each processing document area based on the regional distribution parameter. If the regional distribution parameter of the processed document area is greater than the preset regional distribution parameter, it is determined that the processing strategy for each information field in the processed document area should be optimized, and the coverage ratio index of each information character in the processed document should be reduced according to the regional distribution parameter. The decrease in the coverage ratio index is positively correlated with the regional distribution parameter; For a single processed document region, the region distribution parameter is the number of information fields present within that processed document region.

7. The automated employee data archiving and destruction system according to claim 1, characterized in that, If the retention correlation coefficient of a covered field is greater than the preset retention correlation coefficient, then it is determined that the coverage area should be optimized for that covered field. The coverage ratio index of the relevant information fields for this coverage processing field is increased based on the retention correlation coefficient; The priority replacement coefficient of each character in each relevant information field is determined based on the relevant coverage index; The relevant coverage index = the number of characters in the relevant information field that have the same information category as the coverage processing field / the number of characters in the relevant information field.

8. The automated employee data archiving and destruction system according to claim 1, characterized in that, The information field classification table is used to store the sensitivity level corresponding to the information fields of each feature category; The information field processing strategy table is used to store the processing strategies corresponding to the information fields of each feature category; The information field processing task table is used to store the processing tasks set for each information field existing in each target processing document.

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