Electronic archive data management system control method and related equipment

Abstract

The invention discloses an electronic archive data management system control method and related equipment, and relates to the field of archive management, and the method comprises the steps: obtaining electronic archive input data; classifying the electronic archive input data according to a preset classification rule to form a conventional storage archive; classifying the electronic archive input data according to historical query information to form a temporary storage archive; and querying in the temporary storage archive and the conventional storage archive in sequence based on user query information to obtain archive query information.

Description

Technical Field

[0001] The present invention relates to the field of archive management, and more specifically, to an electronic archive data management system control method and related equipment. Background Art

[0002] With the rapid development of informatization, electronic archive management systems are increasingly used in various organizations. These systems achieve efficient management and convenient query of archives by digitizing paper archives and storing them in electronic systems. However, as time goes by, the number of electronic archives continues to increase, and how to efficiently classify, store and retrieve these archives has become an important technical challenge.

[0003] In the prior art, the management method of electronic archives usually includes classifying all archive data according to certain rules and storing them in a unified storage space. When a user makes a query, the system searches all stored archives to obtain archives that meet the query conditions. However, this method is efficient when the number of archives is small, but when the number of archives is large, the query efficiency and system response speed will drop significantly, especially when facing archives with high frequency queries. The system needs to repeatedly search a large amount of data, resulting in a waste of resources and a decline in user experience. In addition, the existing system lacks the ability to deeply analyze and predict user query behavior, and cannot effectively distinguish between high-frequency access archives and low-frequency access archives, resulting in the query process of all archives being treated equally, further increasing the burden on the system. Summary of the invention

[0004] A series of simplified concepts are introduced in the Summary of the Invention section, which will be further described in detail in the Detailed Description of the Invention section. The Summary of the Invention section of this application does not mean to attempt to define the key features and essential technical features of the claimed technical solution, nor does it mean to attempt to determine the scope of protection of the claimed technical solution.

[0005] In a first aspect, the present application proposes a method for controlling an electronic archive data management system, comprising:

[0006] Obtain electronic archive input data;

[0007] Classify the electronic archive input data according to the preset classification rules to form a regular storage archive;

[0008] Classify electronic archive input data according to historical query information to form a temporary storage archive;

[0009] Based on the user query information, the temporary storage archive and the regular storage archive are queried in sequence to obtain archive query information.

[0010] In a feasible implementation manner, the electronic archive input data is classified according to a preset classification rule to form a conventional storage archive, including:

[0011] Performing a preprocessing operation on the electronic archive input data to obtain preprocessed electronic archive data;

[0012] Calculate the TF-IDF value of each word in the preprocessed electronic archive data to obtain the top N keywords;

[0013] Obtaining priority information of the top N keywords;

[0014] Adjusting the order of the top N keywords based on the priority information and the frequency information of the top N keywords;

[0015] Calculate the matching scores between the top N reordered keywords and each of the preset classification rules;

[0016] A target classification rule is determined based on the matching score to form a conventional storage archive.

[0017] In a feasible implementation, the historical query information includes the inquirer's identity feature information and archive query information;

[0018] The electronic archive input data is classified according to the historical query information to form a temporary storage archive, including:

[0019] Constructing a mapping relationship between identity classification information and query preference information according to the inquirer identity feature information and the archive query information;

[0020] The electronic archive input data is classified based on the mapping relationship between the identity classification information and the query preference information to form a temporary storage archive.

[0021] In a feasible implementation manner, the constructing a mapping relationship between identity classification information and query preference information according to the inquirer identity feature information and the archive query information includes:

[0022] Grouping the inquirer identity feature information based on a K-means algorithm to form identity classification information;

[0023] Analyzing the user query data using the FP-Growth algorithm on the archive query information to identify the query preference information;

[0024] The identity classification information and the query preference information are associated to construct a mapping relationship between the identity classification information and the query preference information.

[0025] In a feasible implementation manner, the querying in the temporary storage archive and the regular storage archive in sequence based on the user query information to obtain archive query information includes:

[0026] Firstly, based on the user query information, an initial query is performed in the temporary storage archive to obtain initial query information;

[0027] And feed back the initial query results to the query user;

[0028] In the case where the query user gives a deep query instruction again, a deep query is performed in the conventional storage archive based on the deep query execution to obtain supplementary query information;

[0029] The archive query information is determined according to the initial query information and the deep query information.

[0030] In a feasible implementation manner, the initial query is performed based on a cache query method, and the deep query is performed based on a natural language processing method.

[0031] In a feasible implementation, it also includes:

[0032] The life cycle T of the temporary storage archive is determined based on the following formula: temp :

[0033]

[0034] T temp The life cycle of temporary storage files; T base is the basic retention time; α is the access frequency adjustment coefficient; F is the access frequency of the archive; F max is the maximum access frequency of all files in the system; β is the adjustment coefficient of the most recent access time; t last The number of days between the last time the file was accessed and the current time; t max = is the preset maximum time span; γ is the archive importance adjustment coefficient; I is the archive importance level; δ is the specified requirement adjustment coefficient; R is the specified requirement retention time extension.

[0035] In a second aspect, the present application proposes an electronic archive data management system control device, comprising:

[0036] A first acquisition unit, used to acquire electronic file input data;

[0037] A first classification unit is used to classify the electronic archive input data according to a preset classification rule to form a conventional storage archive;

[0038] The second classification unit is used to classify the electronic archive input data according to the preset classification history query information to form a temporary storage archive;

[0039] The second acquisition unit is used to query the temporary storage archive and the regular storage archive in sequence based on the user query information to obtain archive query information.

[0040] In a third aspect, an electronic device comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the electronic archive data management system control method as described in any one of the above-mentioned first aspects when executing the computer program stored in the memory.

[0041] In a fourth aspect, the present application further proposes a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the electronic archive data management system control method of any one of the first aspects is implemented.

[0042] In summary, the electronic archive data management system control method proposed in the embodiment of the present application includes: obtaining electronic archive input data; classifying the electronic archive input data according to preset classification rules to form a conventional storage archive; classifying the electronic archive input data according to historical query information to form a temporary storage archive; querying in the temporary storage archive and the conventional storage archive in sequence based on user query information to obtain archive query information. This solution forms two types of storage methods, namely conventional storage archives and temporary storage archives, by classifying the electronic archive input data according to preset classification rules and historical query information respectively. Conventional storage archives are used to manage all archive data in an orderly manner, while temporary storage archives are specifically used to store high-frequency access archives predicted based on historical query information. In this way, the system can first perform a quick query in the temporary storage archive, which greatly improves the query efficiency and reduces the dependence on the full amount of data. By classifying archives according to historical query information, this solution effectively reduces the repeated search time and system resource consumption of frequently accessed archives. Compared with the traditional unified storage method, this method can dynamically adjust the storage strategy according to the actual frequency of use of the archive, avoid repeated retrieval of low-frequency archives, save storage resources and reduce the system burden. This solution adopts a hierarchical query strategy. When a user initiates a query, the system first performs a quick query in the temporary storage archives, and only enters the regular storage archives for a more comprehensive search when necessary. This design significantly shortens the query response time and improves the user experience. In addition, users can further trigger deep queries as needed to obtain more comprehensive archive information. This solution intelligently classifies archives based on the user's historical query behavior, allowing the system to predict and prepare in advance the archives that the user may need. This intelligent classification mechanism based on user behavior makes the system more adaptable and flexible, and can better meet the personalized needs of different users. The temporary storage archives of this solution are dynamically generated and adjusted, and the system can re-evaluate the storage method of archives regularly or under specific trigger conditions. This dynamic adjustment mechanism ensures that the temporary storage archives always contain the most relevant and most likely to be queried archives, maintaining the timeliness of the data. Through the above improvements, this solution significantly improves the efficiency and user experience of the electronic archive management system, solves the problems of slow query speed, waste of resources and lack of pertinence in the existing technology, and enables the system to better cope with the challenges of large-scale electronic archive management. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present specification. Also, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings:

[0044] Figure 1 A schematic diagram of a process flow of an electronic archive data management system control method provided in an embodiment of the present application;

[0045] Figure 2 A schematic diagram of the structure of a control device for an electronic archive data management system provided in an embodiment of the present application;

[0046] Figure 3 A schematic diagram of the electronic device structure of an electronic archive data management system control method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0047] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments.

[0048] See also Figure 1 , is a flow chart of a control method for an electronic archive data management system provided by an embodiment of the present application, which may specifically include:

[0049] S110, obtaining electronic file input data;

[0050] Exemplarily, these input data can be obtained through various channels, such as user upload, electronic files generated by scanning paper files, or data imported from other systems. The electronic file input data obtained includes but is not limited to digital files in various formats such as text files, image files, audio files or video files.

[0051] S120, classifying the electronic file input data according to the preset classification rules to form a regular storage file;

[0052] Exemplarily, after obtaining the input data of the electronic archive, the system classifies the data according to the preset classification rules and stores the classified data in the regular storage archive. The preset classification rules can be based on the attributes of the archive such as type, date, author, file size, etc. For example, all contract documents are classified into the "contract" category, and archives with dates in the past five years are classified into the "recent archives" category. The regular storage archive formed in this way facilitates the orderly management and retrieval of large-scale data.

[0053] S130, classifying the electronic archive input data according to the historical query information to form a temporary storage archive;

[0054] Exemplarily, in addition to conventional storage, the system also classifies the electronic archive input data according to historical query information to form a temporary storage archive. The historical query information may include the identity characteristics information of the inquirer, the query frequency, the query time, etc. Based on this historical query information, the system determines which archives may be queried again and classifies these archives into temporary storage archives. The purpose of temporary storage archives is to speed up the response time to frequently accessed data and improve query efficiency.

[0055] S140: Based on the user query information, query the temporary storage archive and the regular storage archive in sequence to obtain archive query information.

[0056] Exemplarily, when a user issues a query request, the system first searches the temporary storage archives based on the user query information. If the required information cannot be found in the temporary storage archives, the system will continue to search in the regular storage archives. The user query information may include keywords, query time period, file type, etc. The system first retrieves relevant information in the temporary storage archives because these archives are pre-classified based on historical query information and are usually more relevant and timely. If no matching archives are found, the system will further perform a query in the regular storage archives to ensure that the user can obtain complete and accurate query results.

[0057] In summary, this solution forms two types of storage methods: conventional storage archives and temporary storage archives by classifying the input data of electronic archives according to preset classification rules and historical query information. Conventional storage archives are used to manage all archive data in an orderly manner, while temporary storage archives are specifically used to store high-frequency access archives predicted based on historical query information. In this way, the system can first perform a quick query in the temporary storage archives, which greatly improves the query efficiency and reduces the dependence on the full amount of data. By classifying archives according to historical query information, this solution effectively reduces the repeated search time and system resource consumption of frequently accessed archives. Compared with the traditional unified storage method, this method can dynamically adjust the storage strategy according to the actual frequency of use of archives, avoid repeated retrieval of low-frequency archives, save storage resources and reduce the burden on the system. This solution adopts a hierarchical query strategy. When a user initiates a query, the system first performs a quick query in the temporary storage archives, and only enters the conventional storage archives for a more comprehensive search when necessary. This design significantly shortens the query response time and improves the user experience. In addition, users can further trigger deep queries as needed to obtain more comprehensive archive information. This solution intelligently classifies archives according to the user's historical query behavior, so that the system can predict and prepare archives that users may need in advance. This intelligent classification mechanism based on user behavior makes the system more adaptable and flexible, and can better meet the personalized needs of different users. The temporary storage archives of this solution are dynamically generated and adjusted, and the system can re-evaluate the storage method of archives regularly or under specific trigger conditions. This dynamic adjustment mechanism ensures that the temporary storage archives always contain the most relevant and most likely to be queried archives, maintaining the timeliness of the data. Through the above improvements, this solution significantly improves the efficiency and user experience of the electronic archive management system, solves the problems of slow query speed, waste of resources and lack of pertinence in the existing technology, and enables the system to better cope with the challenges of large-scale electronic archive management.

[0058] In some examples, the electronic archive input data is classified according to a preset classification rule to form a conventional storage archive, including:

[0059] Performing a preprocessing operation on the electronic archive input data to obtain preprocessed electronic archive data;

[0060] Calculate the TF-IDF value of each word in the preprocessed electronic archive data to obtain the top N keywords;

[0061] Obtaining priority information of the top N keywords;

[0062] Adjusting the order of the top N keywords based on the priority information and the frequency information of the top N keywords;

[0063] Calculate the matching scores between the top N reordered keywords and each of the preset classification rules;

[0064] A target classification rule is determined based on the matching score to form a conventional storage archive.

[0065] Exemplarily, first, the system performs preprocessing operations on the acquired electronic archive input data. Preprocessing operations may include text cleaning (such as removing stop words, punctuation marks, etc.), stem extraction, data format conversion, etc. The purpose of this step is to make the original data more regular and unified, which is convenient for subsequent keyword extraction and classification operations. Through preprocessing, the system can remove irrelevant information, retain data that is meaningful for classification, and obtain more accurate preprocessed electronic archive data.

[0066] The system performs a term frequency-inverse document frequency (TF-IDF) analysis on the preprocessed electronic archive data and calculates the TF-IDF value for each term. The TF-IDF value is a way to measure the importance of a term in a specific document. TF stands for term frequency and IDF stands for inverse document frequency. By calculating the TF-IDF value for each term, the system can identify the most representative terms in the document. Then, based on these values, the system extracts the top N keywords that best reflect the subject and content of the document.

[0067] After extracting the top N keywords, the system obtains the priority information of these keywords. The priority information can be based on factors such as the TF-IDF value of the word, the importance level of the word, or the weight under a specific classification rule. The purpose of obtaining the priority information is to be able to reasonably sort the keywords according to their importance in the subsequent steps, so as to more accurately match the classification rules.

[0068] The system adjusts the ranking of the top N keywords based on the priority information of the keywords and the frequency information of the keywords in the preprocessed electronic archive data. Frequency information refers to the number of times each keyword appears in the electronic archive data. By combining priority information with frequency information, the system can arrange these keywords more reasonably. The adjusted ranking can better reflect the topic structure of the document, making the keywords more representative, thereby improving the accuracy of classification.

[0069] The system matches the top N reordered keywords with the preset classification rules one by one and calculates the matching score. The matching score indicates the relevance between the keyword and the classification rule. The system calculates the matching score based on the similarity between the keyword and each classification rule (such as whether the keyword appears in the definition of the classification rule, the relationship between the keyword and the classification standard). The higher the score, the more the keyword matches a classification rule.

[0070] According to the matching score of each preset classification rule, the system finally determines the most suitable target classification rule and classifies the electronic archive data under the classification rule to form a regular storage archive. By calculating the matching score, the system can automatically identify which classification rule is most suitable for the current electronic archive input data. According to the target classification rule, the system stores the archive data in the corresponding storage location to complete the classification of the regular storage archive.

[0071] The method proposed in the embodiment of the present application ensures accurate classification of electronic archive data through a series of operations such as preprocessing of electronic archive input data, keyword extraction and sorting, and matching score calculation. By extracting keywords using TF-IDF analysis and then sorting keywords in combination with priority information and frequency information, the system can more accurately match the preset classification rules. Finally, the system determines the most appropriate classification rules based on the matching scores, so that electronic archives can be effectively organized and stored, improving the efficiency and accuracy of the archive management system.

[0072] In some examples, the historical query information includes inquirer identity feature information and archive query inquiry information;

[0073] The electronic archive input data is classified according to the historical query information to form a temporary storage archive, including:

[0074] Constructing a mapping relationship between identity classification information and query preference information according to the inquirer identity feature information and the archive query information;

[0075] The electronic archive input data is classified based on the mapping relationship between the identity classification information and the query preference information to form a temporary storage archive.

[0076] For example, the identity feature information of the queryer may include the user's identity category, role, position, authority level, etc., while the archive query information refers to the specific query conditions used by the user in the historical query, such as the query keywords, time range, document type, etc. This information provides basic data for subsequent classification.

[0077] The system constructs a mapping relationship between identity classification information and query preference information based on the inquirer's identity feature information and archive query information. The construction of the mapping relationship is one of the core steps of this solution. By analyzing the inquirer's identity feature information, the system can determine the identity category to which the user belongs, such as management, technical personnel, financial personnel, etc. Then, combined with the archive query information used by the user in historical queries, the system can identify the preferences and common patterns of users of these different identity categories when querying archives. For example, management may pay more attention to strategic documents, while technical personnel more often query technical reports and specifications. By mapping identity classification information with query preference information, the system can predict the query behavior of different user categories and form personalized classification standards.

[0078] Based on the mapping relationship between the constructed identity classification information and the query preference information, the system classifies the electronic archive input data to form a temporary storage archive. After having the mapping relationship between the identity classification information and the query preference information, the system can classify the electronic archive input data according to the user's query preference. For example, if users of a certain identity category often query specific types of archives, then the electronic archive input data related to these archive types will be preferentially classified into the temporary storage archive. This classification method enables the system to quickly respond to user query needs and improve query efficiency. At the same time, since temporary storage archives are dynamically generated, the system can regularly update these classifications based on the user's behavior patterns, so that the classification results are always consistent with the user's actual needs.

[0079] The method proposed in the embodiment of the present application analyzes and utilizes historical query information to classify the electronic archive input data according to historical query information to form a temporary storage archive. By combining the inquirer's identity feature information and the archive query information, the system can construct a mapping relationship between identity classification information and query preference information, thereby performing targeted classification management of archive data. Ultimately, the temporary storage archive formed based on these mapping relationships can not only improve the query speed, but also provide a personalized query experience, allowing users to find the required archives faster, optimizing the overall performance and user satisfaction of the electronic archive management system.

[0080] In some examples, constructing a mapping relationship between identity classification information and query preference information based on the inquirer identity feature information and the archive query information includes:

[0081] Grouping the inquirer identity feature information based on a K-means algorithm to form identity classification information;

[0082] Analyzing the user query data using the FP-Growth algorithm on the archive query information to identify the query preference information;

[0083] The identity classification information and the query preference information are associated to construct a mapping relationship between the identity classification information and the query preference information.

[0084] Exemplarily, first, the system processes the identity feature information of the inquirer and groups the inquirers using the K-means algorithm to form identity classification information. The identity feature information of the inquirer may include the user's role, department, position, authority level, etc. Through this grouping, the system can identify different categories of user groups and form identity classification information. For example, the system may identify one group of users as management and another group of users as technical support personnel. Based on these classifications, the system can provide more targeted services.

[0085] Next, the system analyzes the archive query information and uses the FP-Growth algorithm to identify query preference information. Archive query information includes keywords used by users in historical queries, the type of archives queried, time range, etc. Through the FP-Growth algorithm, the system can find patterns and regularities in these query conditions, thereby identifying query preference information for different user groups. For example, the system may find that a group of users frequently query financial statements for a specific time period, while another group of users frequently query technical specification documents.

[0086] Finally, the system associates the identity classification information with the query preference information and constructs a mapping relationship between the identity classification information and the query preference information. Through the first two steps, the system has generated the identity classification information and the query preference information. Next, the system associates the two, that is, matches each user identity category with its corresponding query preference and constructs a mapping relationship. This mapping relationship reflects the query behavior patterns of different user categories. For example, a certain type of user (such as management) tends to query certain specific types of files (such as annual reports), while another type of user (such as technical personnel) may be more concerned with technical documents and operation manuals.

[0087] The method proposed in the embodiment of the present application uses the K-means algorithm and the FP-Growth algorithm to construct a mapping relationship between identity classification information and query preference information through in-depth analysis of the inquirer's identity feature information and archive query information. By using the K-means algorithm to classify the inquirer's identity, the system can identify and manage different user groups; at the same time, by analyzing the user's query behavior through the FP-Growth algorithm, the system can accurately identify the user's query preference. Ultimately, this mapping relationship can be used for personalized archival data classification management, allowing the system to respond to user query requests more efficiently, improving query speed and user experience.

[0088] In some examples, the querying in the temporary storage archive and the regular storage archive in sequence based on the user query information to obtain archive query information includes:

[0089] Firstly, based on the user query information, an initial query is performed in the temporary storage archive to obtain initial query information;

[0090] And feed back the initial query results to the query user;

[0091] In the case where the query user gives a deep query instruction again, a deep query is performed in the conventional storage archive based on the deep query execution to obtain supplementary query information;

[0092] The archive query information is determined according to the initial query information and the deep query information.

[0093] Exemplarily, the system first performs an initial query in a temporary storage archive based on the user query information to obtain the initial query information. The temporary storage archive is dynamically generated based on the historical query information and contains data that the user may frequently access or recently query. Therefore, when the user issues a query request, the system preferentially performs the query operation in the temporary storage archive. This step can quickly return the initial query information related to the user query information, because the content of the temporary storage archive is dynamically adjusted based on the user's query preferences and historical query behavior, and is usually more relevant and timely.

[0094] After obtaining the initial query information, the system will feed back the initial query results to the querying user. The initial query results are based on the data in the temporary storage archive that best matches the user's query information. This step allows users to quickly obtain preliminary query results and meet their immediate query needs. Through rapid response, the system improves the user's query experience, especially when users need to obtain frequently accessed archives, the temporary storage archive can provide fast and relevant results.

[0095] In the case where the query user gives a deep query instruction again, a deep query is performed in the conventional storage archive based on the deep query to obtain supplementary query information. If the query user is not completely satisfied with the initial query result and gives a deep query instruction again, the system will perform a query in the conventional storage archive based on the deep query to obtain supplementary query information. Conventional storage archives contain all archived data, and their scope is wider than that of temporary storage archives, covering archives of larger scale and longer time span. When the user needs more comprehensive or specific information, the query operation of the conventional storage archive can be triggered by the deep query instruction. The system will conduct a deeper and more exhaustive search in the conventional storage archive to find the archival data that cannot be found in the temporary storage archive, and then generate supplementary query information.

[0096] The system finally determines the archive query information based on the initial query information and the in-depth query information. The archive query information is the comprehensive result of the system based on the initial query information and the supplementary query information. The system integrates these two parts of the query results to ensure that the query information obtained by the user is comprehensive and accurate. This result reflects the user's query needs. From quick acquisition to in-depth exploration, the system can provide hierarchical query services to meet the user's different query scenario needs.

[0097] The method proposed in the embodiment of the present application achieves a balance between quick response and in-depth query by querying in temporary storage archives and regular storage archives in sequence. Through the initial query based on the user's query information, the system can quickly provide relevant results; and when the user needs more detailed information, through in-depth query, the system provides more comprehensive data support in the regular storage archives. Finally, by combining the initial query information and the in-depth query information, the system can generate accurate and comprehensive archive query information, thereby optimizing query efficiency and improving the user's query experience.

[0098] In some examples, the initial query is performed based on a cache query method, and the deep query is performed based on a natural language processing method.

[0099] Exemplarily, after a user initiates a query request, the system first performs an initial query based on a cache query method to obtain initial query information. The cache query method refers to the system using pre-stored query results or frequently accessed data caches to quickly respond to user query requests. The data in the temporary storage archive may have been cached, and the system can directly extract relevant information from the cache without having to re-retrieve the original data each time. In this way, the system can return the initial query information in a very short time and provide instant query responses, which is especially suitable for frequently queried archive data.

[0100] After the initial query is completed, the system will feed back the initial query results to the querying user. Due to the high efficiency of the cache query method, the initial query results can be quickly provided to users to meet their needs for fast information acquisition. The initial query results usually contain archive data that users frequently access or have recently accessed, because these data have been cached or stored in temporary storage archives. Therefore, this step enables users to obtain relevant archive information in a short time, improving query efficiency and user experience.

[0101] If the user is not completely satisfied with the initial query results and issues a deep query instruction, the system will perform a deep query based on natural language processing to obtain supplementary query information. Natural language processing (NLP) is used to understand and process complex query requests from users. This method can parse the natural language queries entered by users and understand the context, intent, and detail requirements of the queries. Based on NLP, the system can perform more complex query operations in conventional storage archives, including processing fuzzy queries, multi-keyword combinations, semantic analysis, etc., to find archive data that better meets the user's complex needs. Through NLP, deep queries can not only find more relevant data, but also return more accurate results based on the meaning of the query statement.

[0102] After completing the deep query, the system integrates the deep query results with the initial query results to form complete archive query information. Based on the initial query results, the deep query results provide users with more comprehensive information coverage. By integrating the query results of these two parts, the system ensures that the archive query information is both timely and comprehensive. In the end, the archive query information obtained by users not only quickly responds to their initial needs, but also deeply mines broader and more accurate data to meet their deep query requirements.

[0103] The method proposed in the embodiment of the present application optimizes the efficiency and accuracy of archive query by combining cache query and natural language processing. The initial query is performed through cache query, which can quickly respond to the user's regular query request and provide timely feedback on relevant results; when the user needs more detailed or complex information, the system performs a deep query through natural language processing to obtain more accurate archive data. Finally, the system integrates the results of the initial query and the deep query to form complete archive query information, which not only improves the query speed but also meets the user's complex query needs.

[0104] In some examples, this also includes:

[0105] The life cycle T of the temporary storage archive is determined based on the following formula: temp :

[0106]

[0107] T temp The life cycle of temporary storage files; T base is the basic retention time; α is the access frequency adjustment coefficient; F is the access frequency of the archive; F max is the maximum access frequency of all files in the system; β is the adjustment coefficient of the most recent access time; t last The number of days between the last time the file was accessed and the current time; t max= is the preset maximum time span; γ is the archive importance adjustment coefficient; I is the archive importance level; δ is the specified requirement adjustment coefficient; R is the specified requirement retention time extension.

[0108] The embodiment of the present application dynamically determines the life cycle of temporary storage archives through the above formula, effectively balancing multiple factors such as access frequency, last access time, archive importance, and regulatory requirements. Through this dynamic adjustment mechanism, the system can optimize the use of storage resources, ensure that high-value archives are properly retained in temporary storage, and reduce unnecessary storage waste.

[0109] like Figure 2 As shown, the present application proposes an electronic archive data management system control device, comprising:

[0110] A first acquisition unit 21 is used to acquire electronic file input data;

[0111] A first classification unit 22 is used to classify the electronic archive input data according to a preset classification rule to form a conventional storage archive;

[0112] The second classification unit 23 is used to classify the electronic archive input data according to the preset classification history query information to form a temporary storage archive;

[0113] The second acquisition unit 24 is used to query the temporary storage archive and the regular storage archive in sequence based on the user query information to obtain archive query information.

[0114] like Figure 3 As shown, an embodiment of the present application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, the steps of any method of the above-mentioned electronic archive data management system control method are implemented.

[0115] Since the electronic device introduced in this embodiment is a device used to implement an electronic archive data management system control device in the embodiment of the present application, based on the method introduced in the embodiment of the present application, technical personnel in this field can understand the specific implementation method of the electronic device of this embodiment and its various variations. Therefore, how the electronic device implements the method in the embodiment of the present application is not introduced in detail here. As long as the equipment used by technical personnel in this field to implement the method in the embodiment of the present application is within the scope of protection of this application.

[0116] In the specific implementation process, when the computer program 311 is executed by the processor, it can achieve Figure 1 Any implementation manner in the corresponding embodiments.

[0117] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and for parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded computer, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0121] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0122] An embodiment of the present application also provides a computer program product, which includes computer software instructions. When the computer software instructions are executed on a processing device, the processing device executes the process of the electronic archive data management system control method in the corresponding embodiment.

[0123] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the process or function according to 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 a website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium can be any available medium that a computer can store or a data storage device such as a server or a data center that includes one or more available media integration. 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 (e.g., a solid state drive (SSD)), etc.

[0124] 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 units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0125] 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 units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units 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 an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0126] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0127] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0128] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions to enable 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 various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0129] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for controlling an electronic archive data management system, characterized in that: include: Obtain electronic archive input data; Classify the electronic archive input data according to the preset classification rules to form a regular storage archive; Classify electronic archive input data according to historical query information to form a temporary storage archive; Based on the user query information, the temporary storage archive and the regular storage archive are queried in sequence to obtain archive query information.

2. The electronic archive data management system control method according to claim 1, characterized in that: The electronic archive input data is classified according to a preset classification rule to form a conventional storage archive, including: Performing a preprocessing operation on the electronic archive input data to obtain preprocessed electronic archive data; Calculate the TF-IDF value of each word in the preprocessed electronic archive data to obtain the top N keywords; Obtaining priority information of the top N keywords; Adjusting the order of the top N keywords based on the priority information and the frequency information of the top N keywords; Calculate the matching scores between the top N reordered keywords and each of the preset classification rules; A target classification rule is determined based on the matching score to form a conventional storage archive.

3. The electronic archive data management system control method according to claim 2, characterized in that: The historical query information includes the identity feature information of the queryer and the archive query information; The electronic archive input data is classified according to the historical query information to form a temporary storage archive, including: Constructing a mapping relationship between identity classification information and query preference information according to the inquirer identity feature information and the archive query information; The electronic archive input data is classified based on the mapping relationship between the identity classification information and the query preference information to form a temporary storage archive.

4. The electronic archive data management system control method according to claim 1, characterized in that: The step of constructing a mapping relationship between identity classification information and query preference information based on the inquirer identity feature information and the archive query information includes: Grouping the inquirer identity feature information based on a K-means algorithm to form identity classification information; Analyzing the user query data using the FP-Growth algorithm on the archive query information to identify the query preference information; The identity classification information and the query preference information are associated to construct a mapping relationship between the identity classification information and the query preference information.

5. The electronic archive data management system control method according to claim 1, characterized in that: The step of sequentially searching the temporary storage archive and the regular storage archive based on the user query information to obtain archive query information includes: Firstly, based on the user query information, an initial query is performed in the temporary storage archive to obtain the initial query information; And feed back the initial query results to the query user; In the case where the query user gives a deep query instruction again, a deep query is performed in the conventional storage archive based on the deep query execution to obtain supplementary query information; The archive query information is determined according to the initial query information and the deep query information.

6. The electronic archive data management system control method according to claim 5, characterized in that: The initial query is performed based on a cache query method, and the deep query is performed based on a natural language processing method.

7. The electronic archive data management system control method according to claim 1, characterized in that: Also includes: The life cycle T of the temporary storage archive is determined based on the following formula: temp : T temp The life cycle of temporary storage files; T base is the basic retention time;α is the access frequency adjustment coefficient; F is the access frequency of the file; F max is the maximum access frequency of all files in the system; β is the adjustment factor for the most recent access time; t last The number of days between the last time the file was accessed and the current time; t max = is the preset maximum time span; γ is the file importance adjustment coefficient; I is the file importance level; δ is the adjustment coefficient required by regulations; R is the retention time extension required by regulations.

8. An electronic archive data management system control device, characterized in that: include: A first acquisition unit, used to acquire electronic file input data; A first classification unit is used to classify the electronic archive input data according to a preset classification rule to form a conventional storage archive; The second classification unit is used to classify the electronic archive input data according to the preset classification history query information to form a temporary storage archive; The second acquisition unit is used to query the temporary storage archive and the regular storage archive in sequence based on the user query information to obtain archive query information.

9. An electronic device, comprising: A memory and a processor, wherein the processor is used to implement the steps of the electronic archive data management system control method as described in any one of claims 1 to 7 when executing the computer program stored in the memory.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the electronic archive data management system control method according to any one of claims 1 to 7 are implemented.

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