Method and system for automatically identifying, classifying and grading data

Abstract

The embodiment of the present invention discloses a method and system for automatically identifying, classifying and grading data. The method for automatically identifying, classifying and grading data includes: responding to an input request of a data source, inputting a data source; preprocessing the data source to determine a data resource; identifying the data resource based on a recognition model to obtain recognition data; classifying the recognition data based on a classification model to obtain classification data; grading the classification data based on a grading model to obtain graded data; establishing a label for each graded data to obtain label data; and cataloging the label data. The method for automatically identifying, classifying and grading data solves the problem in the prior art that data cannot be accurately and automatically identified, classified and graded.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method, system, electronic device and storage medium for automatically performing data identification, classification and grading. Background Art

[0002] Data is one of the important means of production in social activities. Comprehensively mastering the organization's data assets has become a top priority. Among them, conducting data identification, data classification, and data grading are the basic prerequisites for comprehensively mastering the organization's data assets.

[0003] However, the method of manually combing through asset management ledgers often has the problem of low efficiency and is difficult to cope with changes in personnel positions; the method of connecting to information system interfaces often has the problem of too extensive asset collection, which can easily lead to incomplete data resources or complicated data usage processes; simply combining the method of manually combing through asset management ledgers with the method of connecting to information system interfaces can easily lead to excessive energy and cost invested in data identification, classification and grading.

[0004] Among them, the identification of trade secret data requires the integration of industry characteristics. For example, it is necessary to comprehensively consider data with multiple industry attributes, such as technical patents, R&D results, product designs, business plans, sales strategies, market research reports, customer lists, supply chain information, etc.

[0005] Business secret data exhibits more complex data attributes than personal information, which has always led to inefficient data identification, classification and grading, and may cause the organization's data asset management to become a castle in the air, which in turn may cause the organization to lack an overall understanding of the data asset situation, and even put the data asset preservation and appreciation in trouble.

[0006] Therefore, there is an urgent need for a method that can accurately and automatically identify, classify and grade data. Summary of the invention

[0007] The purpose of the embodiments of the present invention is to provide a method, system, electronic device and storage medium for automatically identifying, classifying and grading data, so as to solve the problem that the prior art cannot accurately automatically identify, classify and grade data.

[0008] To achieve the above objectives, an embodiment of the present invention provides a method for automatically identifying, classifying and grading data, the method specifically comprising:

[0009] In response to an input request from a data source, inputting a data source;

[0010] Preprocessing the data source to determine data resources;

[0011] Identify the data resource based on the identification model to obtain identification data;

[0012] Classifying the identification data based on the classification model to obtain classification data;

[0013] Classifying the classified data based on a classification model to obtain classified data;

[0014] Creating a label for each of the classified data to obtain label data;

[0015] Perform directory management on the tag data.

[0016] On the basis of the above technical solution, the present invention can also be improved as follows:

[0017] Furthermore, the preprocessing of the data source to determine the data resource includes:

[0018] Extract one or more data sources including real-time business tracking data, offline imported data, and batch aggregated data;

[0019] Convert voice data into text data and convert video data into image data;

[0020] One or more data sources including original files and index values ​​are stored.

[0021] Furthermore, the recognition model is a deep learning model that incorporates convolutional neural networks and recurrent neural networks;

[0022] Extracting the overall features and local features of the data resource through the recognition model;

[0023] Identify the data resource based on automatic data identification rules, automatic data identification strategies and samples;

[0024] The automatic data recognition rules include keyword recognition, regular expressions, digital fingerprint technology and natural language processing;

[0025] The automatic data identification strategy includes accurate data identification, fingerprint document identification and vector machine classification identification;

[0026] The sample includes structured data, unstructured data and micro-feature data.

[0027] Furthermore, the classifying the identification data based on the classification model to obtain the classified data includes:

[0028] dividing the identification data into static data and dynamic data based on data attributes;

[0029] Inject static data into the tree structure area to judge the constraints. If the constraints are not met, the data is discarded. If the constraints are met, the data flows into the classification process.

[0030] Dynamic data is injected into the graph structure area for attribute condition judgment. When the attribute conditions are not met, it is discarded. When the attribute conditions are met, it flows into the grading process.

[0031] Further, the categorized data is classified based on the classification model to obtain the classified data, including:

[0032] Determine whether the classified data meets the policy judgment conditions. If the classified data does not meet the policy judgment conditions, discard the classified data. If the classified data meets the policy judgment conditions, execute one or more grading strategies to complete the deviation analysis. The grading strategies include comparison method, benchmarking method and calculation method.

[0033] Further, the step of grading the classified data based on the grading model to obtain graded data further includes:

[0034] K is calculated by formula (1):

[0035]

[0036] In the formula, i is the proportion of sub-item elements, j is the sub-item score, and K is the data level.

[0037] Furthermore, the step of establishing a label for each of the hierarchical data to obtain label data includes:

[0038] Extracting data attributes of the data resource through a classification model and the data classification model;

[0039] Predicting data labels for the data attributes, wherein the data labels include common labels, attribute labels, and trade secret labels;

[0040] A label is established for each of the hierarchical data at least through a common label and an attribute label, and a tag field or a mapping table is selected in combination with a business scenario to obtain label data.

[0041] A system for automatically identifying, classifying and grading data, comprising:

[0042] A data source acquisition module, used to input a data source in response to an input request of a data source;

[0043] A preprocessing module, used for preprocessing the data source to determine the data resource;

[0044] A data identification module, used to identify the data resource based on the identification model to obtain identification data;

[0045] A data classification module, used to classify the identification data based on a classification model to obtain classified data;

[0046] A data classification module, used for classifying the classified data based on a classification model to obtain classified data;

[0047] A label establishment module is used to establish a label for each of the classified data to obtain label data;

[0048] The directory management module is used to perform directory management on the tag data.

[0049] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the steps of the method are implemented when the processor executes the computer program.

[0050] A non-transitory computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0051] The embodiments of the present invention have the following advantages:

[0052] The method for automatically identifying, classifying and grading data in the present invention responds to an input request from a data source, inputs a data source; pre-processes the data source to determine data resources; identifies the data resources based on a recognition model to obtain recognition data; classifies the recognition data based on a classification model to obtain classified data; grades the classified data based on a grading model to obtain graded data; establishes a label for each graded data to obtain label data; and performs catalog management on the label data. This solves the problem in the prior art that data cannot be accurately and automatically identified, classified and graded. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the implementation methods of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the implementation methods or the description of the prior art. Obviously, the drawings in the following description are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0054] The structures, proportions, sizes, etc. illustrated in this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with the technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantial technical significance. Any structural modification, change in proportion or adjustment of size shall still fall within the scope of the technical contents disclosed in the present invention without affecting the effects and purposes that can be achieved by the present invention.

[0055] Figure 1 A flow chart of the method for automatically performing data identification, classification and grading according to the present invention;

[0056] Figure 2 An architectural diagram of a system for automatically performing data identification, classification and grading according to the present invention;

[0057] Figure 3 This is a schematic diagram of the preprocessing module of the present invention;

[0058] Figure 4 It is a working diagram for feature extraction of the recognition model of the present invention;

[0059] Figure 5 A flow chart for classifying data of the present invention;

[0060] Figure 6 A flow chart for data classification of the present invention;

[0061] Figure 7 This is a diagram for realizing the hierarchical model of the present invention;

[0062] Figure 8 Generating working graphs for the classification model and hierarchical model data labels of the present invention;

[0063] Fig. 9 This is a schematic diagram of the physical structure of the electronic device provided by the present invention.

[0064] The accompanying drawings are denoted as follows:

[0065] Data source acquisition module 10, preprocessing module 20, extraction module 201, conversion module 202, storage module 203, data identification module 30, data classification module 40, data grading module 50, label establishment module 60, directory management module 70, electronic device 80, processor 801, memory 802, bus 803. DETAILED DESCRIPTION

[0066] The following is a description of the implementation of the present invention by specific embodiments. People familiar with the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0067] Figure 1 The flowchart of the method embodiment of the present invention for automatically performing data identification, classification and grading is as follows: Figure 1 As shown, a method for automatically identifying, classifying and grading data provided by an embodiment of the present invention includes the following steps:

[0068] S101, in response to an input request of a data source, input a data source;

[0069] S102, preprocessing the data source to determine the data resource;

[0070] Specifically, extract one or more data sources including real-time business tracking data, offline imported data, and batch aggregated data;

[0071] Among them, batch aggregation of data includes one or more methods of traffic analysis, port scanning, and interface transmission;

[0072] Business tracking includes using page tracking, function tracking, and resident tracking to customize the starting conditions of the business system to achieve application behavior or user behavior data resource collection and management;

[0073] Offline import includes the use of big data component processing import, database table import, script automatic import, data file import, and the collection and management of application behavior or user behavior data resources;

[0074] Batch aggregation includes the use of databases or big data components to aggregate, collate, analyze, and import heterogeneous data, and to collect and manage application behavior or user behavior data resources;

[0075] Using PAI model compression and MNN inference engine speech recognition method, voice data is converted into text data, and using video stripping method and video frame extraction method, video data is converted into image data;

[0076] One or more data sources including original files and index values ​​are stored.

[0077] S103, identifying data resources based on the identification model to obtain identification data;

[0078] Specifically, automatic data recognition is performed through a recognition model, wherein the recognition model at least includes one or more external parameters, internal training and proprietary identification; the automatic data recognition process at least includes rules, strategies, and samples;

[0079] like Figure 4 The recognition model is a deep learning model (Transformer) that integrates convolutional neural network (CNN) and recurrent neural network (RNN). Transformer is used to implement filling and filling, and then CNN (database table) or RNN (text file) is integrated into Transformer, where I is training data, Y is a predefined label, and X is a weighting parameter to extract overall features and local features.

[0080] Extracting the overall features and local features of the data resource through the recognition model;

[0081] Identify the data resource based on automatic data identification rules, automatic data identification strategies and samples;

[0082] The automatic data recognition rules include keyword recognition, regular expressions, digital fingerprint technology and natural language processing;

[0083] The automatic data identification strategy includes accurate data identification, fingerprint document identification and vector machine classification identification;

[0084] The sample includes structured data, unstructured data and micro-feature data.

[0085] S104, classifying the recognition data based on the classification model to obtain classified data;

[0086] Specifically, the classification model includes at least sample data, a learning algorithm, and data abstraction; the data abstraction includes at least one or more of: attribute clustering analysis, similarity analysis;

[0087] The learning algorithms are preset regression algorithm and decision tree algorithm;

[0088] According to the preset Euclidean distance and cosine similarity, the target data features are measured to achieve attribute clustering analysis; the hash method is used to achieve similarity analysis;

[0089] like Figure 5 As shown, the identification data is divided into static data and dynamic data based on data attributes;

[0090] Inject static data into the tree structure area to judge the constraints. If the constraints are not met, the data is discarded. If the constraints are met, the data flows into the classification process.

[0091] Dynamic data is injected into the graph structure area for attribute condition judgment. When the attribute conditions are not met, it is discarded. When the attribute conditions are met, it flows into the grading process.

[0092] S105, classifying the classified data based on the classification model to obtain classified data;

[0093] Specifically, Figure 6 and Figure 7 As shown, it is determined whether the classified data meets the policy judgment conditions. When the policy judgment conditions are not met, the classified data is discarded. When the policy judgment conditions are met, one or more grading strategies are executed to complete the deviation analysis; wherein the grading strategies include comparison method, benchmarking method and calculation method.

[0094] Combined with the business scenario, the grading comparison method is implemented according to the preset checklist; combined with industry specifications or regulatory requirements, the data level is classified according to the preset discrimination method; combined with the data identification and classification process, the key parameter method is used for grading, and K is calculated by formula (1):

[0095]

[0096] In the formula, i is the proportion of sub-item elements, j is the sub-item score, and K is the data classification level. In practical applications, K can be positively reduced.

[0097] S106, creating a label for each classified data to obtain label data;

[0098] Specifically, Figure 8 As shown, the data attributes of the data resource are extracted through the classification model and the grading model;

[0099] Predicting data labels for the data attributes, wherein the data labels include common labels, attribute labels, and trade secret labels;

[0100] A label is established for each of the hierarchical data at least through a common label and an attribute label, and a tag field or a mapping table is selected in combination with a business scenario to obtain label data.

[0101] Ordinary labels include data owner, data size, and data type; attribute labels include data access rights, data sensitivity level, data physical location, data logical location, data metadata, data status, and data security protection requirements; trade secret labels include data category, confidentiality level, confidentiality period, and decryption flag; tag fields include file attribute tags and database expansion field tags.

[0102] S107, performing directory management on the tag data;

[0103] Specifically, label data directory management is implemented through directory management, and data input is provided for the organization's data asset management platform, data security management platform, data risk monitoring system, and data security audit system. External data services are implemented through external service interfaces, presentation layer integration, and data integration.

[0104] The method for automatically identifying, classifying and grading data responds to an input request from a data source and inputs a data source; preprocesses the data source to determine data resources; identifies the data resources based on an identification model to obtain identification data; classifies the identification data based on a classification model to obtain classified data; grades the classified data based on a grading model to obtain graded data; establishes a label for each graded data to obtain label data; and performs catalog management on the label data. The method for automatically identifying, classifying and grading data solves the problem in the prior art that data cannot be accurately and automatically identified, classified and graded, and can provide data input for other important asset management systems of an organization, improve data identification accuracy and the degree of intelligence of classification and grading, and realize scientific and reasonable data identification, classification and grading management.

[0105] Figure 2 The system embodiment architecture diagram of the present invention for automatically performing data identification, classification and grading; Figure 2 As shown, an embodiment of the present invention provides a system for automatically identifying, classifying and grading data, comprising the following steps:

[0106] The data source acquisition module 10 is used to input the data source in response to the input request of the data source;

[0107] A preprocessing module 20, used for preprocessing the data source to determine the data resource;

[0108] like Figure 3 As shown, the preprocessing module 20 includes an extraction module 201, a conversion module 202 and a storage module 203;

[0109] The extraction module 201 is used to extract one or more data sources of real-time business buried data, offline imported data, and batch aggregated data;

[0110] The conversion module 202 is used to convert voice data into text data and convert video data into image data;

[0111] The storage module 203 is used to store one or more data sources in the original file and the index value;

[0112] A data identification module 30, used to identify the data resource based on the identification model to obtain identification data;

[0113] The recognition model is a deep learning model that incorporates convolutional neural networks and recurrent neural networks;

[0114] Extracting the overall features and local features of the data resource through the recognition model;

[0115] Identify the data resource based on automatic data identification rules, automatic data identification strategies and samples;

[0116] The automatic data recognition rules include keyword recognition, regular expressions, digital fingerprint technology and natural language processing;

[0117] The automatic data identification strategy includes accurate data identification, fingerprint document identification and vector machine classification identification;

[0118] The sample includes structured data, unstructured data and micro-feature data.

[0119] A data classification module 40, used to classify the recognition data based on a classification model to obtain classified data;

[0120] The data classification module 40 is also used for:

[0121] dividing the identification data into static data and dynamic data based on data attributes;

[0122] Inject static data into the tree structure area to judge the constraints. If the constraints are not met, the data is discarded. If the constraints are met, the data flows into the classification process.

[0123] Dynamic data is injected into the graph structure area for attribute condition judgment. When the attribute conditions are not met, it is discarded. When the attribute conditions are met, it flows into the grading process.

[0124] A data classification module 50, configured to classify the classified data based on a classification model to obtain classified data;

[0125] The data classification module 50 is also used for:

[0126] Determine whether the classified data meets the policy judgment conditions. If the classified data does not meet the policy judgment conditions, discard the classified data. If the classified data meets the policy judgment conditions, execute one or more grading strategies to complete the deviation analysis. The grading strategies include comparison method, benchmarking method and calculation method.

[0127] K is calculated by formula (1):

[0128]

[0129] In the formula, i is the proportion of sub-item elements, j is the sub-item score, and K is the data level. In actual application, rounding or other reductions are performed according to the predefined number of levels.

[0130] A label creation module 60 is used to create a label for each of the classified data to obtain label data;

[0131] The label creation module 60 is also used for:

[0132] Extracting data attributes of the data resource through a classification model and the data classification model;

[0133] Predicting data labels for the data attributes, wherein the data labels include common labels, attribute labels, and trade secret labels;

[0134] A label is established for each of the hierarchical data at least through a common label and an attribute label, and a tag field or a mapping table is selected in combination with a business scenario to obtain label data.

[0135] The directory management module 70 is used to perform directory management on the tag data.

[0136] The system for automatically identifying, classifying and grading data inputs a data source in response to an input request of the data source through a data source acquisition module 10; pre-processes the data source through a pre-processing module 20 to determine data resources; identifies the data resources based on a recognition model through a data identification module 30 to obtain identification data; classifies the identification data based on a classification model through a data classification module 40 to obtain classified data; grades the classified data based on a classification model through a data grading module 50 to obtain graded data; establishes a label for each graded data through a label establishment module 60 to obtain label data; and performs catalog management on the label data through a catalog management module 70. The system for automatically identifying, classifying and grading data solves the problem in the prior art that it is impossible to accurately and automatically identify, classify and grade data.

[0137] Fig. 9 A schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention, such as Fig. 9 As shown, the electronic device 80 includes: a processor 801 (processor), a memory 802 (memory) and a bus 803;

[0138] The processor 801 and the memory 802 communicate with each other via the bus 803;

[0139] The processor 801 is used to call the program instructions in the memory 802 to execute the methods provided by the above-mentioned method embodiments, for example, including: responding to an input request of a data source, inputting a data source; preprocessing the data source to determine a data resource; identifying the data resource based on a recognition model to obtain recognition data; classifying the recognition data based on a classification model to obtain classification data; classifying the classification data based on a classification model to obtain classification data; establishing a label for each of the classified data to obtain label data; and performing catalog management on the label data.

[0140] The present embodiment provides a non-transitory computer-readable storage medium, which stores computer instructions. The computer instructions enable a computer to execute the methods provided by the above-mentioned method embodiments, for example, including: responding to an input request from a data source, inputting a data source; preprocessing the data source to determine a data resource; identifying the data resource based on a recognition model to obtain recognition data; classifying the recognition data based on a classification model to obtain classification data; classifying the classification data based on a classification model to obtain classification data; establishing a label for each of the classified data to obtain label data; and performing catalog management on the label data.

[0141] A person skilled in the art can understand that all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: ROM, RAM, magnetic disk or optical disk, etc., various storage media that can store program codes.

[0142] The device embodiments described above are merely illustrative, wherein 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, i.e., they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. Those of ordinary skill in the art may understand and implement it without creative effort.

[0143] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods of each embodiment or some parts of the embodiment.

[0144] Although the present invention has been described in detail above by general description and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements can be made to the present invention. Therefore, these modifications or improvements made without departing from the spirit of the present invention all belong to the scope of protection claimed by the present invention.

Claims

1. A method for automatically identifying, classifying and grading data, characterized in that: The method specifically comprises: In response to an input request from a data source, inputting a data source; Preprocessing the data source to determine the data resource includes: Extract one or more data sources including real-time business tracking data, offline imported data, and batch aggregated data; Convert voice data into text data and convert video data into image data; One or more data sources in the original file and index value are stored; Among them, business tracking includes using page tracking, function tracking, and resident tracking; offline import includes using big data component processing import, database table import, script automatic import, and data file import; batch aggregation includes using database or big data components to realize heterogeneous data aggregation, sorting, analysis, and import; Identify the data resource based on the identification model to obtain identification data; Classifying the identification data based on the classification model to obtain classified data includes: dividing the identification data into static data and dynamic data based on data attributes; Inject static data into the tree structure area to judge the constraints. If the constraints are not met, the data is discarded. If the constraints are met, the data flows into the classification process. Inject dynamic data into the graph structure area to judge the attribute conditions. If the attribute conditions are not met, the data will be discarded. If the attribute conditions are met, the data will flow into the classification process. The classified data is classified based on the classification model to obtain classified data, including: Determine whether the classified data meets the policy judgment conditions. When the policy judgment conditions are not met, discard the data. When the policy judgment conditions are met, execute one or more classification strategies to complete the deviation analysis. The classification strategies include comparison method, benchmarking method and calculation method. The comparison method performs data classification based on a preset checklist. The benchmarking method performs data classification based on a preset discrimination method. The calculation method performs data classification based on a key parameter method. K is calculated by formula (1): In the formula, i is the proportion of sub-item elements, j is the sub-item score, and K is the data level; Creating a label for each of the classified data to obtain label data; Perform directory management on the tag data.

2. The method for automatically identifying, classifying and grading data according to claim 1, characterized in that: The recognition model is a deep learning model that incorporates convolutional neural networks and recurrent neural networks; Extracting the overall features and local features of the data resource through the recognition model; Identify the data resource based on automatic data identification rules, automatic data identification strategies and samples; The automatic data recognition rules include keyword recognition, regular expressions, digital fingerprint technology and natural language processing; The automatic data identification strategy includes accurate data identification, fingerprint document identification and vector machine classification identification; The sample includes structured data, unstructured data and micro-feature data.

3. The method for automatically identifying, classifying and grading data according to claim 1, characterized in that: The step of establishing a label for each of the hierarchical data to obtain label data includes: Extracting data attributes of the data resource through a classification model and the data classification model; Predicting data labels for the data attributes, wherein the data labels include common labels, attribute labels, and trade secret labels; A label is established for each of the hierarchical data at least through a common label and an attribute label, and a tag field or a mapping table is selected in combination with a business scenario to obtain label data.

4. A system for automatically identifying, classifying and grading data, characterized in that: include: A data source acquisition module, used to input a data source in response to an input request of a data source; A preprocessing module is used to preprocess the data source to determine the data resource, including: Extract one or more data sources including real-time business tracking data, offline imported data, and batch aggregated data; Convert voice data into text data and convert video data into image data; One or more data sources in the original file and index value are stored; Among them, business tracking includes using page tracking, function tracking, and resident tracking; offline import includes using big data component processing import, database table import, script automatic import, and data file import; batch aggregation includes using database or big data components to realize heterogeneous data aggregation, sorting, analysis, and import; A data identification module, used to identify the data resource based on the identification model to obtain identification data; A data classification module is used to classify the identification data based on a classification model to obtain classified data, including: dividing the identification data into static data and dynamic data based on data attributes; Inject static data into the tree structure area to judge the constraints. If the constraints are not met, the data is discarded. If the constraints are met, the data flows into the classification process. Inject dynamic data into the graph structure area to judge the attribute conditions. If the attribute conditions are not met, the data will be discarded. If the attribute conditions are met, the data will flow into the classification process. A data classification module, used to classify the classified data based on a classification model to obtain classified data, including: Determine whether the classified data meets the policy judgment conditions. When the policy judgment conditions are not met, discard the data. When the policy judgment conditions are met, execute one or more classification strategies to complete the deviation analysis. The classification strategies include comparison method, benchmarking method and calculation method. The comparison method performs data classification based on a preset checklist. The benchmarking method performs data classification based on a preset discrimination method. The calculation method performs data classification based on a key parameter method. K is calculated by formula (1): In the formula, i is the proportion of sub-item elements, j is the sub-item score, and K is the data level; A label establishment module is used to establish a label for each of the classified data to obtain label data; The directory management module is used to perform directory management on the tag data.

5. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 3 are implemented.

6. A non-transitory 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 method according to any one of claims 1 to 3 are implemented.

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