File grouping management method and device

Abstract

The invention discloses a file grouping management method and device. The method comprises the steps of obtaining all files from a database, determining multi-dimensional attribute features and content features of each file, calculating the association degree between every two files based on the attribute features and the content features, determining associated files of each file, and defining file tags according to the attribute features of the files and the associated files thereof; determining a plurality of preset target attribute dimensions, and combining into a plurality of grouping tags according to the attribute features under each target attribute dimension; all the files are clustered through the grouping labels and the attribute characteristics of the files, and a plurality of clustering clusters corresponding to different grouping labels are formed; and creating a file index table for each grouping tag, wherein the file index table comprises the file tag of each file in the cluster corresponding to the grouping tag. According to the method and the device, the technical problem that the follow-up retrieval efficiency is affected due to lack of effective classification and management for massive files in related schemes is solved.

Description

Technical Field

[0001] The present application relates to the technical field of file management, and in particular to a file grouping management method and device. Background Art

[0002] With the booming development of the digital information industry, enterprises and organizations have accumulated a wide variety of files and data in their daily operations, including documents, emails, reports, and multimedia files. These files are not only huge in number, but also stored in scattered locations, and even exist in distributed systems, which poses severe challenges to efficient file management and retrieval. Current file grouping and retrieval systems often group and retrieve files based on single attribute features such as file paths. This method fails to fully consider the frequency of file use and the correlation between files, resulting in low efficiency when facing massive files and unable to meet users' complex retrieval needs. In addition, some retrieval methods are only applicable to specific types of files, such as music files, and lack support for diverse file types. Therefore, there is an urgent need for a high-performance, highly flexible massive file grouping retrieval system that can handle multiple file features and types to meet this challenge.

[0003] To address the above-mentioned problems, no effective solution has been proposed yet. Summary of the invention

[0004] The embodiments of the present application provide a file grouping management method and device to at least solve the technical problem in related solutions of lack of effective classification and management of massive files, which affects the subsequent retrieval efficiency.

[0005] According to one aspect of an embodiment of the present application, a file grouping management method is provided, comprising: acquiring all files in a database, and determining multi-dimensional attribute features and content features corresponding to each file; determining the correlation between every two files based on the attribute features and content features of each file, and determining the associated file corresponding to each file based on the correlation, wherein, for each file, a file tag of the file is determined based on the attribute features and content features of the file and the attribute features of the corresponding associated file; determining multiple preset target attribute dimensions from all attribute dimensions, and determining multiple grouping tags based on multiple attribute features corresponding to each of the target attribute dimensions, wherein each of the grouping tags is composed of a combination of attribute features under each of the target attribute dimensions; clustering all files based on the multiple grouping tags and the attribute features of each file to obtain multiple cluster clusters, wherein each of the cluster clusters corresponds to one grouping tag; determining a file index table corresponding to each of the grouping tags, wherein the file index table includes the file tags of each file in the cluster cluster corresponding to the grouping tag.

[0006] Optionally, the attribute characteristics include: file path, and the method further includes: displaying multiple file index tables in an interactive interface; in response to a selection instruction of the target object in the interactive interface, determining a target file index table corresponding to the selection instruction; in response to a search keyword input by the target object in the interactive interface, determining a target file tag matching the search keyword from the target file index table; obtaining a target file corresponding to the file path information in the target file tag and a target associated file corresponding to the target file, and feeding back the target file and the target associated file in the interactive interface.

[0007] Optionally, all files in a database are obtained, and multi-dimensional attribute features and content features corresponding to each file are determined, including: obtaining all files in the database; extracting multi-dimensional attribute features corresponding to each file, wherein the dimensions of the attribute features include at least one of the following: file name, file path, file type, file size, file priority, creation time, modification time, access time, and access frequency; extracting multi-dimensional content features corresponding to each file, wherein the dimensions of the content features include at least one of the following: keywords, themes, word frequency, color, texture, object recognition, voice, and scene description; performing data preprocessing on the attribute features and content features corresponding to all files, wherein the data preprocessing method includes at least one of the following: data cleaning, data denoising, and normalization.

[0008] Optionally, the correlation between every two files is determined based on the attribute features and content features of each file, and the associated files corresponding to each file are determined based on the correlation, including: performing weighted calculation on the attribute features and content features of each file based on a first weight coefficient corresponding to different preset attribute dimensions and content dimensions to obtain a first fused feature vector for each file; for every two files, determining the cosine similarity between the first fused feature vectors of the two files, and if the cosine similarity is greater than a preset similarity threshold, determining that the two files are mutually associated files.

[0009] Optionally, multiple preset target attribute dimensions are determined from all attribute dimensions, and multiple grouping labels are determined according to multiple attribute features corresponding to each of the target attribute dimensions, including: determining m preset target attribute dimensions from all attribute dimensions, and determining M corresponding to each of the target attribute dimensions. i attribute features, where m is a preset constant, i∈m; each time, an attribute feature corresponding to each target attribute dimension is combined to obtain a grouping label; all combinations are traversed to obtain k grouping labels in total, where

[0010] Optionally, all files are clustered according to the plurality of grouping labels and the attribute features of each file to obtain a plurality of cluster clusters, including: weighted calculation of the attribute features of each file according to a second weight coefficient corresponding to a preset different attribute dimension to obtain a second fused feature vector for each file, wherein the second weight coefficient of the target attribute dimension is greater than the second weight coefficient of other attribute dimensions; for each grouping label, a file whose attribute features under the target attribute dimension completely match the grouping label is determined as the initial cluster center corresponding to the grouping label; all files are clustered according to the plurality of initial cluster centers and the distance between each file and the initial cluster center to obtain a plurality of clusters, and the sum of square errors is calculated according to the distance between each file and the initial cluster center, wherein the sum of square errors between each file and the initial cluster center is the sum of square errors between each file and the initial cluster center. The distance determination process includes: determining the cosine similarity between the second fused feature vector corresponding to the file and the second fused feature vector corresponding to the initial clustering center, and determining the difference between 1 and the cosine similarity as the distance between the file and the initial clustering center; repeating the following iterative process until the termination condition is met: determining the center of each cluster as a new clustering center, and clustering all files again based on the new clustering center and the distance between each file and the new clustering center to obtain multiple clusters; calculating the sum of square errors based on the distance between each file and the new clustering center, and determining the difference between the sum of square errors obtained by this clustering and the sum of square errors obtained by the previous clustering; if the difference is greater than a preset threshold, continuing the iterative process, if the difference is not greater than the preset threshold, stopping the iterative process, and determining that the clustering result of this clustering is the final clustering result.

[0011] Optionally, the attribute characteristics include: access frequency, determining the file index table corresponding to each of the grouping labels, including: for each of the grouping labels, determining each file in the cluster corresponding to the grouping label, and sorting the files from large to small according to the corresponding access frequency; combining the file labels of the files according to the sorting result to obtain the file index table corresponding to the grouping label.

[0012] Optionally, multiple file index tables are displayed in the interactive interface, including: for each file index table, determining the index name of the file index table as a combination of characteristic values ​​of each attribute feature in the grouping label corresponding to the file index table; and displaying the index names of multiple file index tables in the interactive interface.

[0013] According to another aspect of the embodiment of the present application, a file grouping management device is also provided, including: an acquisition module, used to acquire all files in a database, and determine the multi-dimensional attribute features and content features corresponding to each file; a correlation determination module, used to determine the correlation between every two files based on the attribute features and content features of each file, and determine the associated file corresponding to each file based on the correlation, wherein, for each file, the file label of the file is determined based on the attribute features and content features of the file and the attribute features of the corresponding associated file; a grouping label determination module, used to determine a plurality of preset target attribute dimensions from all attribute dimensions, and determine a plurality of grouping labels based on a plurality of attribute features corresponding to each of the target attribute dimensions, wherein each of the grouping labels is composed of a combination of attribute features under each of the target attribute dimensions; a clustering module, used to cluster all files based on the plurality of grouping labels and the attribute features of each file, to obtain a plurality of cluster clusters, wherein each of the cluster clusters corresponds to one grouping label; an index determination module, used to determine a file index table corresponding to each of the grouping labels, wherein the file index table includes the file labels of each file in the cluster cluster corresponding to the grouping label.

[0014] According to another aspect of an embodiment of the present application, a computer program product is further provided, the computer program product comprising: a computer program, wherein when the computer program is executed by a processor, the above-mentioned file grouping management method is implemented.

[0015] According to another aspect of an embodiment of the present application, an electronic device is further provided, the electronic device comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the above-mentioned file grouping management method through the computer program.

[0016] In the embodiment of the present application, by identifying the multi-dimensional attribute characteristics and content characteristics of the file, the correlation between the files is calculated based on the file attributes and content characteristics, the related files are determined, and a comprehensive label including its own characteristics and the characteristics of the related files is generated for each file, so that the retrieval of the file can take into account its relevance in logic and usage context, and enhance the relevance and richness of the retrieval results. Multiple preset target attribute dimensions and specific attribute characteristics under these dimensions are selected from all attribute dimensions to form a variety of grouping labels, so that the files can be finely classified according to their characteristics in multiple dimensions. Based on the grouping labels, all files are automatically clustered into different clusters, each cluster corresponds to a grouping label, and a file index table is generated for each cluster cluster, which contains the comprehensive label information of the files in the cluster. This method optimizes the file retrieval process. Users can quickly access and retrieve files of the corresponding cluster cluster by selecting specific grouping labels, thereby significantly improving the efficiency and accuracy of the retrieval. The user interface displays a set of file index tables corresponding to the grouping labels. Users can select index tables according to their needs to obtain comprehensive label information of files in the cluster, achieving fast and accurate file positioning and retrieval. In addition, the search results will include directly matching files and related files determined by the degree of association, providing a more complete and personalized search experience, thereby solving the technical problem of lack of effective classification and management of massive files in related solutions, which affects the efficiency of subsequent retrieval. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 is a flowchart of an optional file grouping management method according to an embodiment of the present application;

[0019] Figure 2 is a schematic diagram of an optional file retrieval process according to an embodiment of the present application;

[0020] Figure 3 is a schematic structural diagram of an optional file grouping management device according to an embodiment of the present application;

[0021] Figure 4 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution 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, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0023] It should be noted that the terms "first", "second", etc. in the specification, claims and drawings of the present application 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 of the present application described herein can be implemented in an order other than those 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 comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0024] Example 1

[0025] According to an embodiment of the present application, a file grouping management method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0026] Figure 1 is a flowchart of a file grouping management method provided according to an embodiment of the present application, such as Figure 1 As shown, the method comprises the following steps:

[0027] Step S102, obtaining all files in the database, and determining multi-dimensional attribute features and content features corresponding to each file;

[0028] Step S104, determining the correlation between every two files based on the attribute characteristics and content characteristics of each file, and determining the associated file corresponding to each file based on the correlation degree, wherein for each file, the file tag of the file is determined based on the attribute characteristics and content characteristics of the file and the attribute characteristics of the corresponding associated file;

[0029] Step S106, determining a plurality of preset target attribute dimensions from all attribute dimensions, and determining a plurality of grouping labels according to a plurality of attribute features corresponding to each target attribute dimension, wherein each grouping label is composed of a combination of an attribute feature under each target attribute dimension;

[0030] Step S108, clustering all files according to the multiple grouping labels and the attribute characteristics of each file to obtain multiple clusters, wherein each cluster corresponds to a grouping label;

[0031] Step S110 : determining a file index table corresponding to each grouping label, wherein the file index table includes file labels of each file in the cluster corresponding to the grouping label.

[0032] After completing the file feature analysis, grouping and clustering, and building the file index table through the above process, you can Figure 2 The steps shown implement the application at the user interaction level, and the steps are as follows:

[0033] Step S202, displaying multiple file index tables in the interactive interface;

[0034] Step S204, in response to a selection instruction of the target object in the interactive interface, determining a target file index table corresponding to the selection instruction;

[0035] Step S206, in response to the search keyword input by the target object in the interactive interface, determining the target file tag matching the search keyword from the target file index table;

[0036] Step S208, obtaining the target file corresponding to the file path information in the target file tag and the target associated file corresponding to the target file, and feeding back the target file and the target associated file in the interactive interface.

[0037] The following describes each step of the file grouping management method in conjunction with a specific implementation process.

[0038] First, obtain all files in the database and determine the multi-dimensional attribute features and content features corresponding to each file. This process can be carried out in the following steps:

[0039] Acquire all files in the database; extract multi-dimensional attribute features corresponding to each file, wherein the dimensions of the attribute features include at least one of the following: file name, file path, file type, file size, file priority, creation time, modification time, access time, and access frequency; extract multi-dimensional content features corresponding to each file, wherein the dimensions of the content features include at least one of the following: keywords, themes, word frequency, color, texture, object recognition, voice, and scene description; perform data preprocessing on the attribute features and content features corresponding to all files, wherein the data preprocessing methods include at least one of the following: data cleaning, data denoising, and normalization.

[0040] For example, the file information collection device traverses the database to collect the basic information of all files. In the Python code example, the os.walk() function is used to traverse the file system to obtain a list of files. This step ensures the integrity and accuracy of subsequent processing, because it is necessary to understand the existence of all files in the database, which is the starting point for building a data set. Next, the attribute feature collection device extracts multiple attributes of each file, including but not limited to file name, file path, file type, file size, file priority, creation time, modification time, access time, access frequency, etc. In the Python code, multiple methods of the AttributeFeatureCollector class (such as getName(), getPath(), getType(), getFrequency(), etc.) are used to extract this information respectively, and the results are stored in a dictionary data. This process provides basic data for subsequent file grouping. In addition to attribute features, the system will further extract content features, which include keywords, themes, word frequency, color, texture, object recognition, voice, scene description and other dimensions in the file. For text and image files, natural language processing and image recognition technologies may be used to extract keywords, themes, and visual features; for audio and video files, speech recognition and scene analysis technologies may be used. In practice, this step may be achieved by calling a special API (Application Programming Interface) or using a pre-trained deep learning model. After the extraction of attribute features and content features, these feature data are preprocessed by a data processing device. The preprocessing methods include data cleaning, data denoising, and normalization. Data cleaning is used to remove possible errors or inconsistent information, data denoising eliminates possible noise in the data, and improves the accuracy of file features. Normalization ensures that all features have the same weight and comparison scale in subsequent algorithm processing, which is especially important for machine learning algorithms. In the Python code example, data preprocessing is achieved through multiple methods of the DataPreProcessor class (such as vectorization(), cleanout(), denoise(), normalization()).

[0041] After determining the multi-dimensional attribute characteristics and content characteristics corresponding to each file, the correlation between every two files is determined based on the attribute characteristics and content characteristics of each file, and the associated files corresponding to each file are determined based on the correlation degree. For each file, the file label of the file is determined based on the attribute characteristics and content characteristics of the file and the attribute characteristics of the corresponding associated file.

[0042] As an optional implementation, the correlation between every two files is determined based on the attribute features and content features of each file, and the associated files corresponding to each file are determined based on the correlation. The following method can be used: the attribute features and content features of each file are weighted according to the first weight coefficient corresponding to the preset different attribute dimensions and content dimensions to obtain the first fused feature vector of each file; for every two files, the cosine similarity between the first fused feature vectors of the two files is determined, and if the cosine similarity is greater than the preset similarity threshold, it is determined that the two files are mutually associated files.

[0043] After determining the associated files and file labels corresponding to each file, multiple preset target attribute dimensions are determined from all attribute dimensions, and multiple grouping labels are determined according to multiple attribute features corresponding to each target attribute dimension, wherein each grouping label is composed of an attribute feature under each target attribute dimension. The process may include the following steps: determining m preset target attribute dimensions from all attribute dimensions, and determining M corresponding to each target attribute dimension. i attribute features, where m is a preset constant, i∈m; each time, an attribute feature corresponding to each target attribute dimension is combined to obtain a grouping label; traverse all combinations to obtain a total of k grouping labels, where

[0044] For example, first, m preset target attribute dimensions are determined from all possible attribute dimensions. These dimensions are pre-selected by the system based on experience, application requirements, or user preferences. For example, file type, size, priority, access frequency, etc. are selected as target attribute dimensions. m is a constant, representing how many attribute dimensions will be used to create labels. In the Python code example, this step is implemented by the SearchFeatureSelector.select() method, which selects m from all features as grouping features to prepare for subsequent label generation. For each target attribute dimension, determine the corresponding M under that dimension. i For example, if file type is a target attribute dimension, then M iIt may represent the number of different file types that have been identified in the system. In this way, the system creates a set of attribute features for each target attribute dimension. Next, a combination method is used to combine one attribute feature under each target attribute dimension at a time to generate a grouping label. For example, if the target attribute dimension includes file type and file size, PDF and Large may be selected as a combination at a time to generate a label. All possible combinations are traversed to pair one attribute feature under each target attribute dimension with the attribute features of other dimensions. In this way, k grouping labels can be generated, where k is the product of the number of attribute features under all target attribute dimensions. This means that if m = 3 (for example, file type, size, priority), there are M_1 = 5 file types, M_2 = 3 size classifications, and M_3 = 2 priority classifications, then 5×3×2 = 30 different grouping labels will be generated.

[0045] After determining multiple grouping labels, all files are clustered, i.e., adaptively grouped, according to the multiple grouping labels and the attribute characteristics of each file to obtain multiple clusters, wherein each cluster corresponds to a grouping label. The specific steps of implementing the process may be the following process;

[0046] The attribute features of each file are weightedly calculated according to the second weight coefficients corresponding to the preset different attribute dimensions to obtain the second fused feature vector of each file, wherein the second weight coefficient of the target attribute dimension is greater than the second weight coefficients of other attribute dimensions, which means that in clustering, the system will pay more attention to the features related to the target attribute dimension, and these weights reflect the importance of each attribute in the clustering process. The second here is distinguished from the first fused feature vector in the previous step. The first fused feature vector takes into account the content features, while the second fused feature vector is more based on attribute features.

[0047] For each grouping label, determine a file whose attribute characteristics under the target attribute dimension completely match the grouping label as the initial cluster center corresponding to the grouping label. This step ensures that each cluster has a representative center point whose attribute characteristics are closest to the grouping label.

[0048] Clustering all files according to multiple initial cluster centers and the distances between each file and the initial cluster center to obtain multiple clusters, and calculating the sum of square errors according to the distances between each file and the initial cluster center, wherein the process of determining the distance between each file and the initial cluster center includes: determining the cosine similarity between the second fused feature vector corresponding to the file and the second fused feature vector corresponding to the initial cluster center, and determining the difference between 1 and the cosine similarity as the distance between the file and the initial cluster center;

[0049] Repeat the following iterative process until the termination condition is met: determine the center of each cluster as the new cluster center, and cluster all files again based on the new cluster center and the distance between each file and the new cluster center to obtain multiple clusters; calculate the sum of square errors based on the distance between each file and the new cluster center, and determine the difference between the sum of square errors obtained by this clustering and the sum of square errors obtained by the previous clustering; if the difference is greater than the preset threshold, continue the iterative process; if the difference is not greater than the preset threshold, stop the iterative process and determine that the clustering result of this clustering is the final clustering result.

[0050] This process uses weighted fusion features, cosine similarity and iterative clustering algorithms to ensure that files are divided into clusters that best match them in the target attribute dimension. At the same time, by iteratively optimizing the clustering results, the final grouping is more reasonable and efficient. Such a grouping strategy can help users quickly locate the file groups they may be interested in, especially when dealing with a large number of files. This method can significantly improve retrieval efficiency and accuracy.

[0051] After obtaining a plurality of clusters corresponding to different grouping labels through the above process, a file index table corresponding to each grouping label is determined, wherein the file index table includes the file labels of each file in the cluster corresponding to the grouping label.

[0052] As an optional implementation, when the attribute characteristics include access frequency, the file index table corresponding to each grouping label can be determined based on the access frequency, so that subsequent retrieval is more efficient. Specifically: for each grouping label, determine the individual files in the cluster corresponding to the grouping label, and sort the individual files from large to small according to the corresponding access frequency; combine the file labels of each file based on the sorting result to obtain the file index table corresponding to the grouping label.

[0053] After the file index table is established through the above process, users can use the file index table to search for files in the following ways:

[0054] In the case where the file attribute features include the file path, retrieval can be performed in the following manner: displaying multiple file index tables in an interactive interface; in response to a selection instruction of the target object in the interactive interface, determining a target file index table corresponding to the selection instruction; in response to a search keyword input by the target object in the interactive interface, determining a target file tag that matches the search keyword from the target file index table; obtaining a target file corresponding to the file path information in the target file tag and a target associated file corresponding to the target file, and feeding back the target file and the target associated file in the interactive interface.

[0055] For example, when a file index table is selected in the interactive interface, the system will recognize this selection instruction and determine which specific grouping tag the user is interested in. This is usually done by the user clicking or selecting a specific index name. The system will interpret this user operation as a retrieval requirement for a specific group of files. Next, when the user enters a search keyword in the selected file index table, the system will search for file tags that match the keywords from the file index table. Since the file tag contains detailed properties of the file, including file name, path, etc., the system can determine which files the user is looking for by comparing the keywords with the file name. The system will read the file path information in the file tags that match the search keywords, as well as other file paths associated with these files. For example, if the user is looking for a financial report and the report is highly associated with a budget spreadsheet, the system will not only provide the file path of the financial report, but also the path of the budget spreadsheet.

[0056] As an optional implementation, multiple file index tables are displayed in an interactive interface, and the following steps can be adopted: for each file index table, determine the index name of the file index table as a combination of characteristic values ​​of each attribute feature in the grouping label corresponding to the file index table; display the index names of multiple file index tables in the interactive interface.

[0057] For example, on the interactive interface, the system will display multiple file index tables, each of which is named by the characteristic value combination of the attribute features in the grouping label. If the attribute feature combination of a grouping label is PDF document, medium size, and high priority, then the corresponding file index table may be named PDF_Medium_HighPriority. This allows users to intuitively understand the contents of the index table and thus more easily select the file group they are interested in.

[0058] In the embodiment of the present application, by identifying the multi-dimensional attribute characteristics and content characteristics of the file, the correlation between the files is calculated based on the file attributes and content characteristics, the related files are determined, and a comprehensive label including its own characteristics and the characteristics of the related files is generated for each file, so that the retrieval of the file can take into account its relevance in logic and usage context, and enhance the relevance and richness of the retrieval results. Multiple preset target attribute dimensions and specific attribute characteristics under these dimensions are selected from all attribute dimensions to form a variety of grouping labels, so that the files can be finely classified according to their characteristics in multiple dimensions. Based on the grouping labels, all files are automatically clustered into different clusters, each cluster corresponds to a grouping label, and a file index table is generated for each cluster cluster, which contains the comprehensive label information of the files in the cluster. This method optimizes the file retrieval process. Users can quickly access and retrieve files of the corresponding cluster cluster by selecting specific grouping labels, thereby significantly improving the efficiency and accuracy of the retrieval. The user interface displays a set of file index tables corresponding to the grouping labels. Users can select index tables according to their needs to obtain comprehensive label information of files in the cluster, achieving fast and accurate file positioning and retrieval. In addition, the search results will include directly matching files and related files determined by the degree of association, providing a more complete and personalized search experience, thereby solving the technical problem of lack of effective classification and management of massive files in related solutions, which affects the efficiency of subsequent retrieval.

[0059] Example 2

[0060] According to an embodiment of the present application, a file grouping management device for implementing the file grouping management method in embodiment 1 is also provided. Figure 3 As shown, the file grouping management device at least includes: an acquisition module 31, a relevance determination module 32, a grouping label determination module 33, a clustering module 34 and an index determination module 35, wherein:

[0061] The acquisition module 31 is used to acquire all files in the database and determine the multi-dimensional attribute characteristics and content characteristics corresponding to each file;

[0062] A correlation determination module 32 is used to determine the correlation between every two files based on the attribute characteristics and content characteristics of each file, and determine the associated file corresponding to each file based on the correlation, wherein for each file, the file label of the file is determined based on the attribute characteristics and content characteristics of the file and the attribute characteristics of the corresponding associated file;

[0063] A grouping label determination module 33 is used to determine a plurality of preset target attribute dimensions from all attribute dimensions, and determine a plurality of grouping labels according to a plurality of attribute features corresponding to each of the target attribute dimensions, wherein each of the grouping labels is composed of a combination of an attribute feature under each of the target attribute dimensions;

[0064] A clustering module 34, configured to cluster all files according to the plurality of grouping labels and attribute characteristics of each file to obtain a plurality of clusters, wherein each cluster corresponds to one of the grouping labels;

[0065] The index determination module 35 is used to determine a file index table corresponding to each of the grouping labels, wherein the file index table includes the file labels of each file in the cluster corresponding to the grouping label.

[0066] The functions of each module of the file grouping management device are described below in conjunction with a specific implementation process.

[0067] First, the acquisition module acquires all files in the database and determines the multi-dimensional attribute features and content features corresponding to each file. This process can be performed using the following steps:

[0068] Acquire all files in the database; extract multi-dimensional attribute features corresponding to each file, wherein the dimensions of the attribute features include at least one of the following: file name, file path, file type, file size, file priority, creation time, modification time, access time, and access frequency; extract multi-dimensional content features corresponding to each file, wherein the dimensions of the content features include at least one of the following: keywords, themes, word frequency, color, texture, object recognition, voice, and scene description; perform data preprocessing on the attribute features and content features corresponding to all files, wherein the data preprocessing methods include at least one of the following: data cleaning, data denoising, and normalization.

[0069] After determining the multi-dimensional attribute characteristics and content characteristics corresponding to each file, the correlation determination module determines the correlation between every two files based on the attribute characteristics and content characteristics of each file, and determines the associated files corresponding to each file based on the correlation. For each file, the file label of the file is determined based on the attribute characteristics and content characteristics of the file and the attribute characteristics of the corresponding associated file.

[0070] As an optional implementation, the correlation between every two files is determined based on the attribute features and content features of each file, and the associated files corresponding to each file are determined based on the correlation. The following method can be used: the attribute features and content features of each file are weighted according to the first weight coefficient corresponding to the preset different attribute dimensions and content dimensions to obtain the first fused feature vector of each file; for every two files, the cosine similarity between the first fused feature vectors of the two files is determined, and if the cosine similarity is greater than the preset similarity threshold, it is determined that the two files are mutually associated files.

[0071] After determining the associated files and file labels corresponding to each file, the grouping label determination module determines a plurality of preset target attribute dimensions from all attribute dimensions, and determines a plurality of grouping labels according to a plurality of attribute features corresponding to each target attribute dimension, wherein each grouping label is composed of a combination of an attribute feature under each target attribute dimension. The process may include the following steps:

[0072] Determine m preset target attribute dimensions from all attribute dimensions, and determine the M corresponding to each target attribute dimension i attribute features, where m is a preset constant, i∈m;

[0073] Each time, an attribute feature corresponding to each target attribute dimension is combined to obtain a grouping label;

[0074] Traverse all combinations and get a total of k grouping labels, among which,

[0075] After determining the multiple grouping labels, the clustering module clusters all the files according to the multiple grouping labels and the attribute characteristics of each file to obtain multiple clusters, wherein each cluster corresponds to a grouping label. The specific steps of implementing the process may be the following process;

[0076] The attribute features of each file are weighted according to the second weight coefficients corresponding to the preset different attribute dimensions to obtain the second fused feature vector of each file, wherein the second weight coefficient of the target attribute dimension is greater than the second weight coefficients of other attribute dimensions, which means that in clustering, the system will pay more attention to the features related to the target attribute dimension, and these weights reflect the importance of each attribute in the clustering process. The "second" here is distinguished from the "first" fused feature vector in the previous step, probably because the first fused feature vector takes into account the content features, while the second fused feature vector is more based on the attribute features;

[0077] For each grouping label, determine a file whose attribute characteristics under the target attribute dimension completely match the grouping label as the initial cluster center corresponding to the grouping label. This step ensures that each cluster has a representative center point whose attribute characteristics are closest to the grouping label.

[0078] Clustering all files according to multiple initial cluster centers and the distances between each file and the initial cluster center to obtain multiple clusters, and calculating the sum of square errors according to the distances between each file and the initial cluster center, wherein the process of determining the distance between each file and the initial cluster center includes: determining the cosine similarity between the second fused feature vector corresponding to the file and the second fused feature vector corresponding to the initial cluster center, and determining the difference between 1 and the cosine similarity as the distance between the file and the initial cluster center;

[0079] Repeat the following iterative process until the termination condition is met: determine the center of each cluster as the new cluster center, and cluster all files again based on the new cluster center and the distance between each file and the new cluster center to obtain multiple clusters; calculate the sum of square errors based on the distance between each file and the new cluster center, and determine the difference between the sum of square errors obtained by this clustering and the sum of square errors obtained by the previous clustering; if the difference is greater than the preset threshold, continue the iterative process; if the difference is not greater than the preset threshold, stop the iterative process and determine that the clustering result of this clustering is the final clustering result.

[0080] After obtaining multiple clusters corresponding to different grouping labels through the above process, the index determination module determines a file index table corresponding to each grouping label, wherein the file index table includes the file labels of each file in the cluster corresponding to the grouping label.

[0081] As an optional implementation, when the attribute characteristics include access frequency, the file index table corresponding to each grouping label can be determined based on the access frequency. The process can adopt the following steps: for each grouping label, determine each file in the cluster corresponding to the grouping label, and sort each file from large to small according to the corresponding access frequency; combine the file labels of each file according to the sorting result to obtain the file index table corresponding to the grouping label.

[0082] After the file index table is established through the above process, the user can use the file index table to perform file retrieval.

[0083] In the case where the file attribute features include the file path, retrieval can be performed in the following manner: displaying multiple file index tables in an interactive interface; in response to a selection instruction of the target object in the interactive interface, determining a target file index table corresponding to the selection instruction; in response to a search keyword input by the target object in the interactive interface, determining a target file tag that matches the search keyword from the target file index table; obtaining a target file corresponding to the file path information in the target file tag and a target associated file corresponding to the target file, and feeding back the target file and the target associated file in the interactive interface.

[0084] As an optional implementation, multiple file index tables are displayed in an interactive interface, and the following steps can be adopted: for each file index table, determine the index name of the file index table as a combination of characteristic values ​​of each attribute feature in the grouping label corresponding to the file index table; display the index names of multiple file index tables in the interactive interface.

[0085] It should be noted that each module in the file grouping management device in the embodiment of the present application corresponds one-to-one to each implementation step of the file grouping management method in Example 1. Since a detailed description has been given in Example 1, some details not reflected in this embodiment can be referred to Example 1 and will not be repeated here.

[0086] Example 3

[0087] According to an embodiment of the present application, a computer program product is also provided. The computer program product includes a computer program, wherein when the computer program is executed by a processor, the file grouping management method in Embodiment 1 is implemented.

[0088] According to an embodiment of the present application, a non-volatile storage medium is also provided, which includes a stored computer program, wherein the device where the non-volatile storage medium is located executes the file grouping management method in Example 1 by running the computer program.

[0089] According to an embodiment of the present application, a processor is also provided, which is used to run a computer program, wherein the file grouping management method in Example 1 is executed when the computer program is running.

[0090] According to an embodiment of the present application, an electronic device is also provided, which includes: a memory and a processor, wherein a computer program is stored in the memory, and the processor is configured to execute the file grouping management method in Example 1 through the computer program.

[0091] Specifically, when the computer program is running, the following steps are executed: all files in the database are obtained, and multi-dimensional attribute features and content features corresponding to each file are determined; the correlation between every two files is determined based on the attribute features and content features of each file, and the associated file corresponding to each file is determined based on the correlation, wherein, for each file, the file label of the file is determined based on the attribute features and content features of the file and the attribute features of the corresponding associated file; multiple preset target attribute dimensions are determined from all attribute dimensions, and multiple grouping labels are determined based on multiple attribute features corresponding to each of the target attribute dimensions, wherein each of the grouping labels is composed of a combination of attribute features under each of the target attribute dimensions; all files are clustered based on the multiple grouping labels and the attribute features of each file to obtain multiple cluster clusters, wherein each of the cluster clusters corresponds to one grouping label; and a file index table corresponding to each of the grouping labels is determined, wherein the file index table includes the file labels of each file in the cluster cluster corresponding to the grouping label.

[0092] As an optional implementation, the electronic device may be in the form of a mobile terminal, a computer terminal or a similar computing device. Figure 4 FIG. 1 shows a hardware structure block diagram of an electronic device for implementing a file grouping management method. Figure 4 As shown, the electronic device 40 may include one or more (402a, 402b, ..., 402n are used to illustrate) processors 402 (the processor 402 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 404 for storing data, and a transmission device 406 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It can be understood by those skilled in the art that Figure 4 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 4 More or fewer components as shown, or with Figure 4 Different configurations are shown.

[0093] It should be noted that the one or more processors 402 and / or other data processing circuits described above may generally be referred to herein as "data processing circuits". The data processing circuits may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuit may be a single independent processing module, or may be incorporated in whole or in part into any of the other components in the electronic device 40. As described in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0094] The memory 404 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the file group management method in the embodiment of the present application. The processor 402 executes various functional applications and data processing by running the software programs and modules stored in the memory 404, that is, realizing the vulnerability detection method of the above-mentioned application. The memory 404 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 404 may further include a memory remotely arranged relative to the processor 402, and these remote memories may be connected to the electronic device 40 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0095] The transmission device 406 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the electronic device 40. In one example, the transmission device 406 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 406 can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0096] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the electronic device 40 .

[0097] The serial numbers of the above embodiments are only for description and do not represent the advantages or disadvantages of the embodiments.

[0098] In the above embodiments of the present application, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0099] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of units can be 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 through some interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0100] 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 over multiple units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0101] 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.

[0102] 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, 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, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of each embodiment method of the present application. The aforementioned storage medium includes: U disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), mobile hard disk, disk or optical disk, etc. Various media that can store program codes.

[0103] The above are only preferred implementations of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A file grouping management method, characterized in that: include: Obtain all files in the database and determine the multi-dimensional attribute features and content features corresponding to each file; Determine the degree of association between every two files based on the attribute characteristics and content characteristics of each file, and determine the associated file corresponding to each file based on the association degree, wherein for each file, determine the file tag of the file based on the attribute characteristics and content characteristics of the file and the attribute characteristics of the corresponding associated file; Determine a plurality of preset target attribute dimensions from all attribute dimensions, and determine a plurality of grouping labels according to a plurality of attribute features corresponding to each of the target attribute dimensions, wherein each of the grouping labels is composed of a combination of an attribute feature under each of the target attribute dimensions; Clustering all files according to the plurality of grouping labels and attribute characteristics of each file to obtain a plurality of clusters, wherein each cluster corresponds to one of the grouping labels; A file index table corresponding to each of the grouping labels is determined, wherein the file index table includes the file labels of the respective files in the cluster corresponding to the grouping label.

2. The method according to claim 1, characterized in that: The attribute characteristics include: file path, and the method further includes: Displaying a plurality of the file index tables in an interactive interface; In response to a selection instruction of a target object in the interactive interface, determining a target file index table corresponding to the selection instruction; In response to a search keyword input by the target object in the interactive interface, determining a target file tag matching the search keyword from the target file index table; The target file corresponding to the file path information in the target file tag and the target associated file corresponding to the target file are obtained, and the target file and the target associated file are fed back in the interactive interface.

3. The method according to claim 1, characterized in that Obtain all files in the database and determine the multi-dimensional attribute characteristics and content characteristics corresponding to each file, including: Get all files in the database; Extracting multi-dimensional attribute features corresponding to each file, wherein the dimensions of the attribute features include at least one of the following: file name, file path, file type, file size, file priority, creation time, modification time, access time, and access frequency; Extracting multi-dimensional content features corresponding to each file, wherein the dimensions of the content features include at least one of the following: keywords, themes, word frequency, color, texture, object recognition, voice, and scene description; Data preprocessing is performed on the attribute features and content features corresponding to all files, wherein the data preprocessing method includes at least one of the following: data cleaning, data denoising, and normalization processing.

4. The method according to claim 1, characterized in that: Determining the correlation between every two files according to the attribute characteristics and content characteristics of each file, and determining the associated file corresponding to each file according to the correlation, including: Performing weighted calculation on the attribute features and content features of each file according to first weight coefficients corresponding to different preset attribute dimensions and content dimensions to obtain a first fused feature vector for each file; For every two files, the cosine similarity between the first fused feature vectors of the two files is determined. If the cosine similarity is greater than a preset similarity threshold, it is determined that the two files are related files.

5. The method according to claim 1, characterized in that Determining a plurality of preset target attribute dimensions from all attribute dimensions, and determining a plurality of grouping labels according to a plurality of attribute features corresponding to each of the target attribute dimensions, including: Determine m preset target attribute dimensions from all attribute dimensions, and determine M corresponding to each of the target attribute dimensions i attribute features, where m is a preset constant, i∈m; Each time, an attribute feature corresponding to each target attribute dimension is taken and combined to obtain a grouping label; Traverse all combinations to obtain k grouping labels, among which, 6. The method according to claim 1, characterized in that All files are clustered according to the plurality of grouping labels and the attribute characteristics of each file to obtain a plurality of clusters, including: Performing weighted calculation on the attribute features of each file according to second weight coefficients corresponding to different preset attribute dimensions to obtain a second fused feature vector of each file, wherein the second weight coefficient of the target attribute dimension is greater than the second weight coefficients of other attribute dimensions; For each grouping label, determine a file whose attribute characteristics under the target attribute dimension completely match the grouping label as the initial cluster center corresponding to the grouping label; Clustering all files according to the multiple initial cluster centers and the distances between each file and the initial cluster centers to obtain multiple clusters, and calculating the sum of square errors according to the distances between each file and the initial cluster centers, wherein the process of determining the distance between each file and the initial cluster center includes: determining the cosine similarity between the second fused feature vector corresponding to the file and the second fused feature vector corresponding to the initial cluster center, and determining the difference between 1 and the cosine similarity as the distance between the file and the initial cluster center; Repeat the following iterative process until the termination condition is met: determine the center of each cluster as a new cluster center, and cluster all files again based on the new cluster center and the distance between each file and the new cluster center to obtain multiple clusters; calculate the sum of square errors based on the distance between each file and the new cluster center, and determine the difference between the sum of square errors obtained by this clustering and the sum of square errors obtained by the previous clustering; if the difference is greater than a preset threshold, continue the iterative process; if the difference is not greater than the preset threshold, stop the iterative process and determine that the clustering result of this clustering is the final clustering result.

7. The method according to claim 1, characterized in that The attribute features include: access frequency, and determining the file index table corresponding to each of the grouping tags includes: For each of the grouping labels, determine each file in the cluster corresponding to the grouping label, and sort the files from large to small according to the corresponding access frequency; The file tags of the respective files are combined according to the sorting result to obtain the file index table corresponding to the grouping tag.

8. The method according to claim 2, characterized in that: Displaying multiple file index tables in the interactive interface includes: For each file index table, determining that the index name of the file index table is a combination of feature values ​​of each attribute feature in the grouping tag corresponding to the file index table; The index names of the plurality of file index tables are displayed in the interactive interface.

9. A file grouping management device, characterized in that: include: The acquisition module is used to acquire all files in the database and determine the multi-dimensional attribute characteristics and content characteristics corresponding to each file; A correlation determination module, used to determine the correlation between every two files based on the attribute characteristics and content characteristics of each file, and determine the associated file corresponding to each file based on the correlation, wherein for each file, the file label of the file is determined based on the attribute characteristics and content characteristics of the file and the attribute characteristics of the corresponding associated file; A grouping label determination module, used to determine a plurality of preset target attribute dimensions from all attribute dimensions, and determine a plurality of grouping labels according to a plurality of attribute features corresponding to each of the target attribute dimensions, wherein each of the grouping labels is composed of a combination of an attribute feature under each of the target attribute dimensions; A clustering module, used for clustering all files according to the plurality of grouping labels and attribute characteristics of each file to obtain a plurality of clusters, wherein each cluster corresponds to one of the grouping labels; The index determination module is used to determine a file index table corresponding to each of the grouping labels, wherein the file index table includes the file labels of each file in the cluster corresponding to the grouping label.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the file grouping management method according to any one of claims 1 to 8 through the computer program.

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