Data classification and grading method and device

Abstract

The invention relates to the technical field of computers, in particular to a data classification and grading method and device, and the method comprises the steps: obtaining target data corresponding to a target file, and an application scene of the target file; performing label matching on the target data according to a preset matching rule corresponding to the application scene to obtain a label set; and classifying and grading the label set according to a preset classification and grading rule corresponding to the application scene and the sensitivity level set to obtain a classification and grading result corresponding to the target file. The problem that an existing classification and grading method is greatly limited to application scenes is solved, and the flexibility of data classification and grading of the application scenes is improved.

Description

Technical Field

[0001] The present application relates to the field of computer technology, and in particular to a method and device for data classification and grading. Background Art

[0002] With the development of social informatization, data has become an increasingly important production factor. There is no shortage of different sensitive data in different scenarios. Along with the need to manage sensitive data and non-sensitive data differently, data classification and grading have also emerged. Automated data classification and grading is an important part of modern data governance. It helps organizations better manage their data assets, ensure that sensitive data is properly protected, and meet and specify requirements.

[0003] Current data classification and grading methods are mostly aimed at data in specific scenarios, but such data classification and grading methods can only be used in a single scenario. However, the sensitive data and the levels of sensitive data in different scenarios are different. In other words, the traditional data classification and grading methods are obviously insufficient in scalability and difficult to meet the needs of multiple scenarios, and have great limitations on the application scenarios of the methods. Summary of the invention

[0004] The embodiments of the present application provide a data classification and grading method and device to at least solve the problem that the existing classification and grading methods are greatly limited in application scenarios.

[0005] According to one aspect of an embodiment of the present application, a data classification and grading method is provided, including: obtaining target data corresponding to a target file, and an application scenario of the target file; performing label matching on the target data according to a preset matching rule corresponding to the application scenario to obtain a label set; classifying and grading the label set according to a preset classification and grading rule and a sensitivity level set corresponding to the application scenario to obtain a classification and grading result corresponding to the target file.

[0006] According to another aspect of an embodiment of the present application, a data classification and grading device is also provided, including: an acquisition unit, used to acquire target data corresponding to a target file, and an application scenario of the target file; a label matching unit, used to perform label matching on the target data according to a preset matching rule corresponding to the application scenario, and obtain a label set; a classification and grading unit, used to classify and grade the label set according to the preset classification and grading rules and the sensitivity level set corresponding to the application scenario, and obtain a classification and grading result corresponding to the target file.

[0007] Optionally, the above-mentioned classification and grading unit includes: an acquisition subunit, used to acquire a preset classification rule set, a preset grading rule set, and a sensitivity level set corresponding to the preset grading rule set, wherein the preset classification and grading rules include a preset classification rule set and a preset grading rule set; a classification unit, used to classify the label set according to the preset classification rule set to obtain a classification rule set; a grading unit, used to grade the classification rule set according to the preset grading rule set to obtain a grading rule set; and a determination unit, used to determine the classification and grading results based on the sensitivity level set and the grading rule set.

[0008] Optionally, the above-mentioned determination unit includes: a first determination bullet element, used to determine a reference sensitivity level set corresponding to a grading rule set from a sensitivity level set; a first acquisition module, used to obtain a file category of a target file, wherein the file category includes a structured file category and an unstructured file category; and a second determination sub-unit, used to determine a classification and grading result based on the reference sensitivity level set and the file category.

[0009] Optionally, the above-mentioned second determination subunit includes: a second acquisition module, used to obtain the priority corresponding to each grading rule in the grading rule set; a first determination module, used to determine the target sensitivity level according to multiple priorities and a reference sensitivity level set; a second determination module, used to determine the target sensitivity level as a classification and grading result when the file category of the target file is an unstructured file category; when the file category of the target file is a structured file category, determine the target sensitivity level and multiple reference sensitivity levels in the reference sensitivity level set as the classification and grading result.

[0010] Optionally, the above-mentioned label matching unit includes: an acquisition submodule, used to obtain preset matching rules, wherein the preset matching rules include multiple matching methods, and the multiple matching methods include: keyword matching, similarity matching, named entity recognition, regular matching, and metadata matching; a matching module, used to use multiple matching methods to perform label matching on the target data to obtain a label list; a determination submodule, used to determine the label set according to the label list.

[0011] Optionally, the above-mentioned acquisition unit includes: a first acquisition sub-unit, used to obtain the file category of the target file, wherein the file category includes a structured file category and an unstructured file category; a second acquisition sub-unit, used to obtain the file name, file remarks, field name, and field remarks when the file category of the target file is a structured file category; and to obtain the file size and file type when the file category of the target file is an unstructured file.

[0012] According to another aspect of the embodiments of the present application, a computer-readable storage medium is provided, characterized in that the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the above data classification and grading method.

[0013] According to another aspect of an embodiment of the present application, there is also provided an electronic device, including a memory and a processor, characterized in that the memory stores a computer program, and the processor is configured to execute the above data classification and grading method through the computer program.

[0014] The data classification and grading method of the present application solves the problem that the existing classification and grading methods are greatly limited in application scenarios, and improves the flexibility of data classification and grading application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some implementation methods of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 is a schematic diagram of a hardware environment of an optional data classification and grading method according to an embodiment of the present invention; Figure 2 is a flow chart of an optional data classification and grading method according to an embodiment of the present invention; Figure 3 is a schematic diagram of an optional data classification and grading method according to an embodiment of the present invention; Figure 4 is a schematic diagram of another optional data classification and grading method according to an embodiment of the present invention; Figure 5 is a schematic diagram of another optional data classification and grading method according to an embodiment of the present invention; Figure 6 is a schematic structural diagram of an optional data classification and grading device according to an embodiment of the present invention; Figure 7 It is a schematic diagram of the structure of an optional electronic device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0017] 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.

[0018] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0019] It should be noted that the embodiments in this application and all the required data in the embodiments are obtained and used by legitimate means with the consent of the user and relevant personnel, and the user's information security will be guaranteed during the acquisition and use process.

[0020] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0021] In order to solve the problem that the existing classification and grading methods are limited to application scenarios, the present application provides a data classification and grading method. As an optional implementation, the above data classification and grading method can be but is not limited to being applied to Figure 1 The data classification and grading system shown in FIG. 1 is composed of a terminal device 102 and a server 104. Figure 1 As shown, the server 104 is connected to the terminal device 102 through the network 116. The above network may include but is not limited to: wired network, wireless network, wherein the wired network includes: local area network, metropolitan area network and wide area network, and the wireless network includes: Bluetooth, WIFI and other networks that realize wireless communication. The above terminal device may include but is not limited to at least one of the following: mobile phone (such as Android mobile phone, iOS mobile phone, etc.), laptop computer, tablet computer, PDA, MID (Mobile Internet Devices, mobile Internet device), PAD, desktop computer, smart TV, etc. A client runs on the above terminal device, and an application program may be installed in the client.

[0022] The terminal device 102 is also provided with a display 106, a processor 108 and a memory 110. The display 106 can be used to display the program interface of the application program. The processor 108 can be used to process the acquired data, such as encryption, compression, filtering, etc. The memory 110 is used to store the data to be processed and the processed data or application programs. It can be understood that the terminal device 102 sends a data classification and grading request to the server 104 through the network 116. When the server 104 receives the data classification and grading request, it performs data classification and grading according to the target file carried in the data classification and grading request. The terminal device 102 can receive the classification and grading results returned by the server 104 through the network 116.

[0023] The server 104 may be a single server, or a server cluster consisting of multiple servers (e.g., a server cluster for performing multiple different tasks), or a cloud server. The server includes a database 112 and a processing engine 114. The processing engine 114 is used to process the data classification and grading request; the database 112 is used to store the data classification and grading request, the target file, the target data, the application scenario of the target file, and the classification and grading result corresponding to the target file.

[0024] According to one aspect of an embodiment of the present invention, the data classification and grading system may further perform the following steps: the terminal device 102 performs step S102 and sends a data classification and grading request to the server 104 via the network 116; the server 104 performs steps S104 to S108 to obtain the target data corresponding to the target file and the application scenario of the target file; performs tag matching on the target data according to the preset matching rules corresponding to the application scenario to obtain a tag set; performs classification and grading on the tag set according to the preset classification and grading rules and sensitivity level set corresponding to the application scenario to obtain a classification and grading result corresponding to the target file. Then, the server 104 performs S110 and sends the classification and grading result to the terminal device 102 via the network 116.

[0025] In the above-mentioned embodiments of the present invention, the preset matching rules, preset classification and grading rules and sensitivity level sets corresponding to the application scenarios of the target files in this application are used to classify and grade the target files, thereby overcoming the problem that the classification and grading methods in related technologies are greatly limited to application scenarios, broadening the application scenarios of the data classification and grading methods, and improving the flexibility of the data classification and grading application scenarios.

[0026] The above is only an example and is not limited in this embodiment.

[0027] As an optional implementation, please refer to Figure 2, which shows a flow chart of a data classification and grading method according to an embodiment of the present application. The method comprises the following steps: S202, obtaining target data corresponding to the target file and an application scenario of the target file; S204, performing label matching on the target data according to a preset matching rule corresponding to the application scenario to obtain a label set; S206, classify and grade the tag set according to the preset classification and grading rules and sensitivity level set corresponding to the application scenario, and obtain the classification and grading result corresponding to the target file.

[0028] It should be noted that the target file in the above S202 can be a structured file or an unstructured file. The structured file is, for example, a structured library table; the unstructured file is, for example, a text file, an image file, an audio file, a video file, a PDF file, an XML file, an HTML file, a report, and other free-format files. When the target file is a structured file, the target data is structured data; when the target file is an unstructured file, the target data is unstructured data. Structured data has a clear data structure and a standardized format, and can be organized and stored through tables and databases; the organization form of unstructured data is relatively free and flexible, and there is no fixed structure and format. The above-mentioned application scenarios (also referred to as data application scenarios) can be understood, but not limited to, as the environment and context in which data is located in actual applications. Application scenarios include but are not limited to data storage (such as data storage location, data storage form, etc.), data query, data analysis, big data applications, cache database applications (for example, cache databases are used to reduce server load and improve query efficiency, such as Redis and Memcached), search engine database applications (used to index and retrieve website content or internal enterprise documents and data, such as Elasticsearch), and big data query system applications (used to store and process large-scale unstructured data, such as Hadoop HDFS and Apache Spark).

[0029] The preset matching rule in S204 is a preset tag matching rule corresponding to the application scenario of the target file. The specific preset matching rule can be understood as, but not limited to, a plurality of reference tags preset according to the application scenario. The plurality of reference tags are used to characterize different data types (for example, personal name, gender, age, etc. are all reference tags). After the preset matching rule is used to match the target data with tags, a plurality of tags that are successfully matched (a plurality of tags included in the above tag set) can be obtained; the above successful matching can be understood as, but not limited to, a data tag corresponding to each specific data in the target data that can be determined according to the preset matching rule. For example, the target data includes "Li XX, male, 2012 / 6 / 6, 12 years old, 412XXXXXX.M", and the plurality of tags stored in the tag set obtained according to the preset matching rule are "name, gender, birthday, age, ID card".

[0030] The above-mentioned tag matching is essentially to convert the target data into a series of tags for subsequent processing. The preset tag matching rules (i.e. the preset matching rules) and multiple tag matching methods constitute the tag library. The preset matching rules can be preset according to business needs, or according to national standards / industry standards / expert experience of various industries.

[0031] The preset classification and grading rules in the above S206 are pre-set classification and grading rules corresponding to the application scenarios of the target files. The preset classification and grading rules specifically include a preset classification rule set and a preset grading rule set. The preset classification rule set includes multiple classification rules, and each classification rule can be understood as corresponding to a label combination but is not limited to; the preset grading rule set includes multiple grading rules, and each grading rule can be understood as corresponding to a category combination but is not limited to; the above sensitivity level set includes multiple sensitivity levels, and each grading rule corresponds to a sensitivity level.

[0032] Through the above-mentioned implementation methods of the present application, the target file can be classified and graded according to the preset matching rules, preset classification and grading rules and sensitivity level sets corresponding to the application scenarios of the target files, so that data classification and grading can be applied to different application scenarios, broadening the application scenarios of data classification and grading.

[0033] As an optional implementation, the tag set is classified and graded according to the preset classification and grading rules and sensitivity level set corresponding to the application scenario to obtain the classification and grading result corresponding to the target file, including: S1, obtaining a preset classification rule set, a preset grading rule set, and a sensitivity level set corresponding to the preset grading rule set, wherein the preset classification and grading rules include the preset classification rule set and the preset grading rule set; S2, classifying the tag set according to a preset classification rule set to obtain a classification rule set; S3, classifying the classification rule set according to the preset classification rule set to obtain a classification rule set; S4, determining the classification and grading result according to the sensitivity level set and the grading rule set.

[0034] It is understandable that the preset classification and grading rules include a preset classification rule set and a preset grading rule set corresponding to the application scenario, and each grading rule in the preset grading rules corresponds to a sensitivity level, that is, multiple sensitivity levels in the sensitivity level set are pre-set corresponding to the grading rules, indicating the level of data sensitivity. Each preset classification rule in the preset classification rule set is designed as a composite rule of data features + conditions, where the data features are a combination of labels (both data features and conditions are optional).

[0035] The preset matching rules, preset classification rule sets, and preset grading rule sets corresponding to a certain business application scenario of personal information as exemplified in Tables 1 to 3 below (the preset matching rules, preset classification rule sets, and preset grading rule sets corresponding to other business application scenarios of personal information may be different from the specific rules exemplified in Tables 1 to 3 below) are now specifically described for S1 to S4 above: As shown in Table 1 below, the content in the first column is the multiple classification rules included in the above-mentioned preset classification rule set, and the content in the second column is the multiple reference tags (i.e., the above-mentioned preset matching rules) that are preset and correspond to the application scenario. It can be seen from Table 1 below that each classification rule corresponds to a set of reference tags; by matching the target data with tags according to the preset matching rules, multiple successfully matched tags (tags included in the tag set) can be obtained, such as personal name, personal phone number, ID card, fingerprint, more than 1,000 pieces of data, etc., as shown in Table 1 below, and then multiple preset classification rules in the preset classification rule set are used to classify the multiple tags in the tag set. After the classification of the multiple tags in the tag set is completed, a classification rule set including multiple classification rules (i.e., multiple preset classification rules in the preset classification rule set that have been classified) can be obtained, such as the classification rules for personal basic information, personal identity information, three elements of personal information, large data volume, etc. shown in Table 1 below.

[0036] Table 1 It should be noted that each classification rule in the above-mentioned preset classification rule set not only corresponds to a group of labels, but also may correspond to a preset condition, that is, each preset classification rule in the present application is a composite design of data features (label combination) + preset conditions. For example, as shown in Table 2 below, each classification rule corresponds to a group of labels and may also correspond to a preset condition. The "at least three or more hits" shown in Table 2 below can be understood, but is not limited to, as in a group of labels corresponding to the three elements of the individual information obtained after label matching, the number of labels is at least three or more.

[0037] Table 2 After obtaining the classification rule set, the classification rule set is graded using the preset grading rule set (each grading rule corresponds to a set of classification rules), and the final multiple grading rules can be obtained. For example, the multiple grading rules shown in Table 3 below (the multiple grading rules included in the preset grading rule set above) are both designed by composite rules of data features + conditions. The difference is that the data features of the classification rules in the preset classification rule set are label combinations, and the data features of the grading rules in the preset grading rule set are category combinations. Each grading rule also corresponds to a priority and a sensitivity level. The preset grading rule set includes multiple preset grading rules. After grading the classification rule set according to the preset grading rule set, multiple grading rules as shown in Table 3 below can be obtained, such as non-sensitive personal information (non-sensitive personal information), sensitive personal information (sensitive personal information), multi-factors, and non-sensitive personal information with large amounts of data. Each grading rule corresponds to its unique data features, conditions, priorities, and sensitivity levels. Finally, the final classification and grading results can be obtained by comprehensively calculating the sensitivity level (i.e., the above-mentioned sensitivity level set) and priority corresponding to each grading rule.

[0038] Table 3 It should be noted that the specific steps of classification and grading are similar. Figure 3 Take the above classification in S2 or the classification in S3 as an example to explain in detail. Figure 3 As shown, the specific steps include: S302, obtaining classification / grading rules (i.e., the above-mentioned preset classification rule set / preset grading rule set); S304, determining whether the classification / grading rules contain data features; The operation in S304 above can be understood, but is not limited to, as determining whether the preset classification rule set / preset grading rule set includes data features that can classify or grade the data in the previous step (i.e., whether it includes a label combination or a category combination). If not, S306 is executed; if yes, S308 is executed.

[0039] S306, if the preset classification rule set or the preset grading rule set does not contain the data feature, then take the intermediate result (label / category combination), that is, the label set obtained by label matching or the classification rule set obtained by classification (which can be understood as but not limited to taking the current label / category combination, that is, taking multiple labels in the label set before classification / multiple classification rules in the classification rule set before grading), and then judge the preset conditions, that is, execute S312; S308, if data features are included, obtaining the data features in a preset classification rule set / preset grading rule set; S310, taking the intersection of the intermediate result and the data feature, can be understood as, but not limited to, the above-mentioned classification / grading, and obtaining a classification rule set / grading rule set, that is, determining a classification rule set corresponding to a label set, or determining a grading rule set corresponding to a classification rule set; S312, then determine whether the preset classification rule set / preset grading rule set contains the preset condition, if not, execute S316; if it contains the preset condition, execute S314; S314: Determine whether the preset conditions are met. If the preset conditions are met, execute S318; if the preset conditions are not met, execute S316; S316, determining the general classification / grading rule as a classification rule in a classification rule set / a grading rule in a grading rule set; S318, determine the final classification / grading rule set.

[0040] Through the above-mentioned implementation mode of the present application, the target files can be accurately classified and graded according to the classification and grading rules set for different scenarios, thereby greatly broadening the application scenarios of classification and grading while ensuring accurate classification and grading.

[0041] As an optional implementation, determining the classification and grading result based on the sensitivity level set and the grading rule set includes: determining a reference sensitivity level set corresponding to the grading rule set from the sensitivity level set; obtaining a file category of the target file, wherein the file category includes a structured file category and an unstructured file category; and determining the classification and grading result based on the reference sensitivity level set and the file category.

[0042] It should be noted that after grading (or grading operation), multiple final grading rules can be obtained, and then the reference sensitivity level corresponding to each grading rule is determined from the sensitivity level set, that is, the reference sensitivity level set is determined; then the file category of the target file is obtained, and the file category includes a structured file category and an unstructured file category. The structured file category indicates that the target file is a structured file, and the unstructured file category indicates that the target file is an unstructured file; finally, the classification and grading result is determined based on the multiple reference sensitivity levels and file categories in the reference sensitivity level set.

[0043] The specific steps of determining the classification and grading results based on the reference sensitivity level set and the file category include: obtaining the priority corresponding to each grading rule in the grading rule set, and obtaining multiple priorities, and determining the target sensitivity level based on the multiple priorities and the reference sensitivity level set; when the file category of the target file is an unstructured file category, determining the target sensitivity level as the classification and grading result; when the file category of the target file is a structured file category, determining the target sensitivity level and multiple reference sensitivity levels in the reference sensitivity level set as the classification and grading result.

[0044] The specific steps of determining the target sensitivity level include: when multiple priorities are the same, the highest sensitivity level in the reference sensitivity level set is determined as the target sensitivity level (i.e., the highest settlement principle); when multiple priorities are different, the sensitivity level corresponding to the highest priority among the multiple priorities is determined as the target sensitivity level; thereafter, when the file category of the target file is an unstructured file category (which can be understood as, but not limited to, the target file is an unstructured file), the target sensitivity level can be directly determined as the classification and grading result corresponding to the target file; when the file category of the target file is a structured file category (which can be understood as, but not limited to, the target file is a structured file), each field in the target file corresponds to a reference sensitivity level, and the metadata of the target file (also known as intermediate data, relay data, which is data describing data, mainly information describing data attributes) also corresponds to a reference sensitivity level, as shown in Table 4 below:

[0045] Table 4 At this time, the reference sensitivity level corresponding to each field in the target file and the above target sensitivity level are determined as the classification and grading results corresponding to the target file. Specifically, it can be understood, but not limited to, that each reference sensitivity level is determined as the classification and grading result of each field and metadata in the target file, and the target sensitivity level is determined as the classification and grading result of the entire target file. The above target sensitivity level determination process can be understood, but not limited to, as the overall file rating process (such as the table rating process of structured files). The method for determining the target sensitivity level has been specifically described above and will not be repeated here. The target sensitivity level can be the sensitivity level finally determined according to the priority level corresponding to each grading rule and the reference sensitivity level. For example, the overall sensitivity level of the target file determined according to the information exemplified in Table 4 above (that is, the above target sensitivity level) is L4 (the priority level corresponding to each grading rule is the same, and the highest priority level is taken as the overall sensitivity level of the target file at this time).

[0046] Through the above-mentioned implementation mode of the present application, it is not necessary to adopt different classification and grading methods for different file types, and the classification and grading results of different file types can be accurately determined, which removes the restrictions of the classification and grading method on the file type and improves the overall utilization rate of data classification and grading.

[0047] As an optional implementation, the above-mentioned label matching of the target data according to the preset matching rules corresponding to the application scenario to obtain a label set includes: obtaining the preset matching rules, wherein the preset matching rules include multiple matching methods, and the multiple matching methods include: keyword matching, similarity matching, named entity recognition, regular matching, and metadata matching; using multiple matching methods to match the target data respectively to obtain a label list; determining the label set according to the label list.

[0048] The following Figure 4 Taking the example of FIG. 1 as an example, the process of determining the label set is specifically described. The steps of determining the label set include: S402, obtaining a structured database table / unstructured file (i.e., obtaining a target file); S404, data preprocessing; the data preprocessing operation here can be understood as, but not limited to, a process of obtaining target data according to a target file; S406, performing label matching on the target data according to the label library, and specific matching methods include keyword matching, similarity matching, NER matching (i.e. the above-mentioned named entity recognition), regular matching, and metadata matching.

[0049] It can be understood that multiple tags can be obtained through each of the above matching methods, that is, a list of tags can be obtained after each matching method is used. Then, there are duplicate tags among the multiple tag lists. Therefore, it is necessary to remove the duplicate tags to finally obtain a tag set (that is, the process of determining the tag set based on the tag list finally).

[0050] Since the data forms after data preprocessing may be diverse, various forms of matching methods are required to associate the diverse data with tags. This application uses methods such as keyword matching, similarity matching, NER matching, regular matching, and metadata matching. For example, for the "personal name" tag: Keyword matching: Keywords such as "name, xingming, name" can be set for keyword matching; Similarity matching: Since "name" is generally similar to "personal name", "name" can be associated with the "personal name" tag through similarity matching; NER: According to the context semantics, specific names such as "Zhang XX", "Li XX", etc. can be recognized through the NER model and associated with the "personal name" tag; Regular matching: For example, for "ID card", a regular expression "\b[1-9]\d{5}(18|19|20)\d{2}(0[1-9]|1[0-2])(0[1-9]|

[12] \d|3

[01] )\d{3}[\dXx]\b" can be set for the ID card to capture each ID card and associate it with the "ID card" tag; Metadata matching: For example, set "more than 1000 data". Then, by comparing the metadata information of the table, if it is determined that the number of table samples > 1000, the tag "more than 1000 data" is matched.

[0051] Determining the tag set through the above method can ensure that each part of the data in the target data can be tag-matched, avoid missing data during the tag-matching process, and improve the accuracy of tag matching.

[0052] As an optional implementation method, obtaining the target data corresponding to the target file includes: S1. Obtain the file category of the target file, where the file category includes structured file categories and unstructured file categories; S2. When the file category of the target file is a structured file category, obtain the file name, file remarks, field name, and field remarks; when the file category of the target file is an unstructured file, obtain the file size and file type.

[0053] It can be understood that the operations in S1 to S2 above are Figure 4The data preprocessing process shown in S404, that is, for target files of different file categories, the target data obtained is different. The data preprocessing stage is essentially to obtain as detailed data information as possible for subsequent processing. For example, for structured library tables, possible data information includes: table name, table notes, table metadata information (table file size, number of table records), field name, field notes, field sampling data, metadata information of each field (field type, field length, field precision), etc.; for unstructured files, it is mainly to parse and obtain text, as well as metadata information such as file size, file type, etc.

[0054] By obtaining the target data corresponding to the target file in the above manner, detailed data corresponding to different file types can be obtained in a targeted manner, thereby more appropriately classifying and grading the data corresponding to different files.

[0055] The following Figure 5 The above data classification and grading method is generally described by taking the example of FIG. 1 as an example. In general, the present application provides a data classification and grading method applicable to a variety of application scenarios, such as Figure 5 As shown, the specific steps include: S502, (obtaining) a structured database table / unstructured file; that is, obtaining the above-mentioned target file; S504, data preprocessing: that is, performing data preprocessing on the target file to obtain target data corresponding to the target file; S506, label matching; specifically, it can be understood as using a preset label library corresponding to the application scenario (i.e., the preset matching rule) to perform label matching on the target data to obtain a label set; S508, classification; specifically, it can be understood as using a preset classification rule library corresponding to the application scenario (i.e., the preset classification rule set) to classify multiple tags in the tag set to obtain multiple classification rules (i.e., the classification rules included in the classification rule set); S510, grading; specifically, it can be understood as using a preset grading rule library corresponding to the application scenario (i.e., the preset grading rule set) to grade multiple classification rules in the classification rule set to obtain multiple grading rules (i.e., the grading rules included in the grading rule set); S512, comprehensive calculation: that is, the process of performing comprehensive calculation according to the sensitivity level and priority corresponding to each classification rule to obtain the final classification and grading result.

[0056] The above-mentioned data classification and grading method can obtain the classification and grading results of target files in different application scenarios under different application scenarios of the same data, and broaden the application scenarios of the classification and grading method while ensuring the accuracy of the classification and grading results.

[0057] It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all described as a series of action combinations, but those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present invention.

[0058] According to another aspect of the embodiments of the present invention, a data classification and grading device for implementing the above data classification and grading method is also provided. Figure 6 As shown, the device comprises: An acquisition unit 602 is used to acquire target data corresponding to a target file and an application scenario of the target file; The label matching unit 604 is used to perform label matching on the target data according to a preset matching rule corresponding to the application scenario to obtain a label set; The classification and grading unit 606 is used to classify and grade the tag set according to the preset classification and grading rules and sensitivity level set corresponding to the application scenario, and obtain the classification and grading result corresponding to the target file.

[0059] Optionally, in this embodiment, the embodiments to be implemented by the above-mentioned various unit modules can refer to the above-mentioned various method embodiments, which will not be repeated here.

[0060] According to another aspect of the embodiment of the present invention, an electronic device for implementing the above data classification and grading method is also provided. The electronic device may be Figure 7 The terminal device or server shown in the figure. This embodiment is described by taking the electronic device as a terminal device as an example. Figure 7 As shown, the electronic device includes a memory 702 and a processor 704. The memory 702 stores a computer program, and the processor 704 is configured to execute the steps in any of the above method embodiments through the computer program.

[0061] Optionally, in this embodiment, the electronic device may be located in at least one network device among a plurality of network devices of a computer network.

[0062] Optionally, in this embodiment, the processor may be configured to execute the data classification and grading method through a computer program.

[0063] Alternatively, a person skilled in the art may understand that: Figure 7The structure shown is for illustration only, and the electronic device may also be a smart phone (such as an Android phone, an iOS phone, etc.), a tablet computer, a PDA, a mobile Internet device (Mobile Internet Devices, MID), a PAD, or other terminal devices. Figure 7 The structure of the electronic device is not limited. Figure 7 More or fewer components (such as network interfaces, etc.) as shown in 7, or having Figure 7 Different configurations shown.

[0064] Among them, the memory 702 can be used to store software programs and modules, such as the program instructions / modules corresponding to the data classification and grading method and device in the embodiment of the present invention. The processor 704 executes various functional applications and data processing by running the software programs and modules stored in the memory 702, that is, to implement the above-mentioned data classification and grading method. The memory 702 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 702 may further include a memory remotely located relative to the processor 704, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof. Among them, the memory 702 can be specifically, but not limited to, used to store file information such as target data sets. As an example, such as Figure 7 As shown, the memory 702 may include but is not limited to the acquisition unit 602, the label matching unit 604 and the classification and grading unit 606 in the data classification and grading device. In addition, it may also include but is not limited to other module units in the data classification and grading device, which will not be repeated in this example.

[0065] Optionally, the transmission device 706 is used to receive or send data via a network. Specific examples of the network may include a wired network and a wireless network. In one example, the transmission device 706 includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices and routers via a network cable so as to communicate with the Internet or a local area network. In one example, the transmission device 706 is a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0066] In addition, the electronic device mentioned above further includes: a display 708, and a connection bus 710, which is used to connect various module components in the electronic device mentioned above.

[0067] In other embodiments, the terminal device or server may be a node in a distributed system, wherein the distributed system may be a blockchain system, and the blockchain system may be a distributed system formed by connecting the multiple nodes through network communication. Among them, the nodes may form a peer-to-peer (P2P, Peer To Peer) network, and any form of computing device, such as a server, terminal and other electronic devices, may become a node in the blockchain system by joining the peer-to-peer network.

[0068] According to one aspect of the present application, a computer program product is provided, the computer program product comprising a computer program / instruction, the computer program / instruction comprising a program code for executing the method shown in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part, and / or installed from a removable medium. When the computer program is executed by a central processing unit, various functions provided by the embodiments of the present application are executed.

[0069] The serial numbers of the above embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0070] According to one aspect of the present application, a computer-readable storage medium is provided, and a processor of a computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions, so that the computer device executes the above-mentioned data classification and grading method.

[0071] Optionally, in this embodiment, the above-mentioned computer-readable storage medium can be configured to store a computer program for executing the above-mentioned data classification and grading method.

[0072] Optionally, in this embodiment, a person of ordinary skill in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by instructing hardware related to the terminal device through a program, and the program may be stored in a computer-readable storage medium, and the storage medium may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0073] If the integrated units in the above embodiments are implemented in the form of software functional units and sold or used as independent products, they can be stored in the above computer-readable storage medium. Based on this understanding, the technical solution of the present invention, 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 enabling one or more computer devices (which can be personal computers, servers or network devices, etc.) to execute all or part of the steps of the above methods of various embodiments of the present invention.

[0074] In the above embodiments of the present invention, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0075] The client disclosed in several embodiments provided in this application can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, units or modules, which can be electrical or other forms.

[0076] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0077] In addition, each functional unit in each embodiment of the present invention 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.

[0078] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A data classification and grading method, characterized in that: include: Obtain target data corresponding to a target file and an application scenario of the target file; Perform label matching on the target data according to a preset matching rule corresponding to the application scenario to obtain a label set; The tag set is classified and graded according to the preset classification and grading rules and sensitivity level set corresponding to the application scenario to obtain the classification and grading result corresponding to the target file.

2. The method according to claim 1, characterized in that Classifying and grading the tag set according to the preset classification and grading rules and sensitivity level set corresponding to the application scenario to obtain the classification and grading result corresponding to the target file, including: Acquire a preset classification rule set, a preset grading rule set, and the sensitivity level set corresponding to the preset grading rule set, wherein the preset classification and grading rules include the preset classification rule set and the preset grading rule set; Classify the tag set according to the preset classification rule set to obtain a classification rule set; Classifying the classification rule set according to the preset classification rule set to obtain a classification rule set; The classification and grading result is determined according to the sensitivity level set and the grading rule set.

3. The method according to claim 2, characterized in that Determining the classification and grading result according to the sensitivity level set and the grading rule set includes: Determining a reference sensitivity level set corresponding to the grading rule set from the sensitivity level set; Acquire the file category of the target file, wherein the file category includes a structured file category and an unstructured file category; The classification and grading result is determined according to the reference sensitivity level set and the file category.

4. The method according to claim 3, characterized in that Determining the classification result according to the reference sensitivity level set and the file category includes: Obtaining the priority corresponding to each grading rule in the grading rule set; Determining a target sensitivity level according to the plurality of priorities and the reference sensitivity level set; In a case where the file category of the target file is the unstructured file category, determining the target sensitivity level as the classification result; In a case where the file category of the target file is the structured file category, the target sensitivity level and a plurality of reference sensitivity levels in the reference sensitivity level set are determined as the classification and grading result.

5. The method according to claim 1, characterized in that Performing label matching on the target data according to the preset matching rule corresponding to the application scenario to obtain a label set includes: Obtaining the preset matching rule, wherein the preset matching rule includes multiple matching modes; Using a plurality of matching methods to perform label matching on the target data to obtain a label list; The tag set is determined according to the tag list.

6. The method according to claim 5, characterized in that The multiple matching methods include: keyword matching, similarity matching, named entity recognition, regular matching, and metadata matching.

7. The method according to claim 1, characterized in that Get the target data corresponding to the target file, including: Acquire the file category of the target file, wherein the file category includes a structured file category and an unstructured file category; When the file category of the target file is the structured file category, obtaining a file name, a file remark, a field name, and a field remark; When the file category of the target file is an unstructured file, the file size and the file type are obtained.

8. A data classification and grading device, characterized in that: include: An acquisition unit, used to acquire target data corresponding to a target file and an application scenario of the target file; A label matching unit, configured to perform label matching on the target data according to a preset matching rule corresponding to the application scenario to obtain a label set; The classification and grading unit is used to classify and grade the label set according to the preset classification and grading rules and sensitivity level set corresponding to the application scenario, and obtain the classification and grading result corresponding to the target file.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the method described in any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that: The memory stores a computer program, and the processor is configured to execute the method according to any one of claims 1 to 7 through the computer program.

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