Data hierarchical processing method, device, equipment and storage medium

Abstract

The present application provides a data classification processing method, device, equipment and storage medium. The method includes: performing word segmentation processing on the newly added fields through natural language processing to obtain at least one first data label; obtaining the classification level of each first data label according to the classification level corresponding to the second data label in the data classification rule library; determining the classification level of the newly added field according to the classification level of at least one first data label; treating the newly added field as a new data label, and storing the new data label and the classification level corresponding to the new data label in the data classification rule library. This embodiment reduces the degree of manual intervention in data classification, reduces the situation where personal subjective factors are mixed into the classification content, improves the usability and automation of the process, improves the classification accuracy, and realizes the automatic expansion of the data classification rule library.

Description

Technical Field

[0001] The present application relates to the field of information security, and in particular to a data hierarchical processing method, apparatus, device and storage medium. Background Art

[0002] With the rapid development of cloud computing and big data, governments and businesses are collecting vast amounts of data through various channels for analysis and decision-making. This sensitive data includes personal privacy, financial information, and basic corporate information. Data leaks and misuse can cause losses to both users and owners of the data. Therefore, improving the security of data usage is a crucial issue in the data security field.

[0003] Data classification and grading are prerequisites for data security. Accurately and efficiently defining classification levels is the foundation for data sharing and openness. Only after the classification level is confirmed can the sharing and openness level be defined, thereby ensuring the security of data circulation. Current classification technology mainly starts from the field content. That is, according to the actual data content of the field, such as "ID number", "mobile phone number", "name", etc., regular expressions are written to match the data content.

[0004] However, in reality, most fields do not contain actual data content, which makes data classification very difficult. Summary of the Invention

[0005] The present application provides a data classification processing method, apparatus, device and storage medium to solve the defect in the prior art that it is difficult to classify fields that do not contain actual data content.

[0006] In one aspect, the present application provides a data classification processing method, comprising:

[0007] Perform word segmentation processing on the newly added field through natural language processing to obtain at least one first data label;

[0008] Obtaining a classification level for each first data tag according to the classification level corresponding to the second data tag in the data classification rule library, wherein the classification level is used to indicate the sensitivity of the first data tag;

[0009] Determining a hierarchical level of the newly added field according to the hierarchical level of the at least one first data tag;

[0010] The newly added field is used as a new data tag, and the new data tag and the classification level corresponding to the new data tag are stored in the data classification rule library.

[0011] Optionally, obtaining the classification level of each first data tag according to the classification level corresponding to the second data tag in the data classification rule base includes:

[0012] If there is a second data tag that is the same as the first data tag in the data classification rule library, the classification level of the second data tag is used as the classification level of the first data tag.

[0013] Optionally, obtaining the classification level of each first data tag according to the classification level corresponding to the second data tag in the data classification rule base includes:

[0014] If there is no second data tag identical to the first data tag in the data classification rule library, the first preset classification level is used as the classification level of the first data tag.

[0015] Optionally, before using the first preset classification level as the classification level of the first data tag, the method further includes:

[0016] Obtain an average value of classification levels corresponding to a preset number of second data tags in the data classification rule library;

[0017] The first preset grading level is obtained according to the average value.

[0018] Optionally, determining the hierarchical level of the newly added field according to the hierarchical level of the at least one first data tag includes:

[0019] A weighted average is performed on the hierarchical level data of the at least one first data tag to obtain the hierarchical level of the newly added field.

[0020] Optionally, before storing the new data tag and the classification level corresponding to the new data tag in the data classification rule library, the method further includes:

[0021] If the newly added field is a marked newly added field;

[0022] Determining whether the hierarchical level of the newly added field is less than a second preset hierarchical level;

[0023] If it is less, updating the hierarchical level of the newly added field to the second preset hierarchical level;

[0024] Correspondingly, storing the new data tag and the classification level corresponding to the new data tag in the data classification rule library includes:

[0025] The new data tag and the second preset classification level corresponding to the new data tag are stored in the data classification rule library.

[0026] Optionally, the method further includes:

[0027] Establishing an original data set, wherein the original data set includes a second data tag with the smallest granularity;

[0028] determining a hierarchical level for each of the second data tags;

[0029] The data classification rule base is obtained according to each second data tag and the corresponding classification level.

[0030] In a second aspect, the present application provides a data hierarchical processing device, comprising:

[0031] A processing module, configured to perform word segmentation processing on the newly added field through natural language processing to obtain at least one first data label;

[0032] The processing module is further configured to obtain a classification level of each first data tag based on a classification level corresponding to the second data tag in a data classification rule base, wherein the classification level is used to indicate a sensitivity of the first data tag;

[0033] a determination module, configured to determine a hierarchical level of the newly added field according to the hierarchical level of the at least one first data tag;

[0034] The storage module is configured to use the newly added field as a new data tag, and store the new data tag and the classification level corresponding to the new data tag in the data classification rule library.

[0035] Optionally, the processing module is specifically configured to use the classification level of the second data tag as the classification level of the first data tag if there is a second data tag that is the same as the first data tag in the data classification rule base.

[0036] Optionally, the processing module is further specifically configured to use the first preset classification level as the classification level of the first data tag if there is no second data tag identical to the first data tag in the data classification rule library.

[0037] Optionally, the processing module is further configured to obtain an average value of classification levels corresponding to a preset number of second data tags in the data classification rule library;

[0038] The first preset grading level is obtained according to the average value.

[0039] Optionally, the determination module is specifically configured to perform weighted averaging on the hierarchical level data of the at least one first data tag to obtain the hierarchical level of the newly added field.

[0040] Optionally, the data classification processing device further includes: a judgment module and an update module,

[0041] If the newly added field is a marked newly added field, the judging module is configured to judge whether the classification level of the newly added field is less than a second preset classification level;

[0042] The updating module is configured to update the hierarchical level of the newly added field to the second preset hierarchical level when the hierarchical level of the newly added field is lower than the second preset hierarchical level;

[0043] The storage module is further configured to store the new data tag and the second preset classification level corresponding to the new data tag in the data classification rule library.

[0044] Optionally, the processing module is further configured to establish an original data set, wherein the original data set includes a second data tag of minimum granularity;

[0045] The determination module is further configured to determine a classification level of each second data tag, and obtain the data classification rule base according to each second data tag and the corresponding classification level.

[0046] In a third aspect, the present application provides a data hierarchical processing device, comprising:

[0047] Memory;

[0048] processor;

[0049] wherein the memory stores computer-executable instructions;

[0050] The processor executes the computer-executable instructions stored in the memory to implement the data classification processing method as described in the first aspect and various possible implementations of the first aspect.

[0051] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement the data hierarchical processing method as described in the first aspect and various possible implementations of the first aspect.

[0052] The data classification processing method provided in this application, based on the data classification rule library, uses natural language processing technology to segment the newly added fields and compare them with the data labels in the data classification rule library, so as to determine the classification level of the newly added fields, reduce the degree of human intervention in data classification, reduce the situation where personal subjective factors are mixed into the classification content, improve the usability and automation of the process, improve the classification accuracy, and realize the automatic expansion of the data classification rule library. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0054] Figure 1 The process of the data classification processing method provided for this application Figure 1 ;

[0055] Figure 2 The process of the data classification processing method provided for this application Figure 2 ;

[0056] Figure 3 The process of the data classification processing method provided for this application Figure 3 ;

[0057] Figure 4 A schematic diagram of the structure of the data hierarchical processing device provided in this application;

[0058] Figure 5 This is a structural diagram of the data hierarchical processing device provided in this application.

[0059] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0060] To make the objectives, technical solutions, and advantages of this application more clear, the technical solutions in this application will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0061] The terms "first," "second," "third," "fourth," and so forth (if any) in the description and claims of the present invention and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequential sequence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can, for example, be implemented in sequences other than those illustrated or described herein.

[0062] With the rapid development of cloud computing and big data, governments and businesses are collecting vast amounts of data through various channels for analysis and decision-making. This sensitive data includes personal privacy, financial information, and basic corporate information. Data leaks and misuse can cause losses to both users and owners of the data. Therefore, improving the security of data usage is a crucial issue in the data security field.

[0063] Data classification and grading are the prerequisites for data security. Accurate and efficient definition of grading levels is the basis for data sharing and openness. Only when the grading level of the data is confirmed can the sharing and openness level be defined, thereby ensuring the security of data circulation.

[0064] Existing classification technology mainly starts from the field content, that is, based on the actual data content of the field, such as "ID number", "mobile phone number", "name", etc., by writing regular expressions and searching the data content through the matching rules of regular expressions.

[0065] A regular expression is a logical formula for string manipulation. It uses a predefined set of specific characters, or a combination of specific characters and strings, to form a regular string. This regular string expresses a filtering logic for the string being processed. The regular expression determines whether the string matches the filtering logic of the regular expression and retrieves a specific portion of the string. For example, the regular string "^once" can be used to retrieve strings that begin with "once" within the string being processed.

[0066] However, when searching for data content through regular expression matching rules, personnel need to spend a lot of time sorting out the rules for sensitive data. This is not only inefficient, but also because most fields in reality do not contain actual data content. Therefore, during the search, only data content with obvious patterns can be searched. Data content with less obvious patterns is often missed, making data classification more difficult.

[0067] The present application provides a data grading processing method. Since most data fields contain Chinese descriptions of the fields, and the Chinese descriptions of the fields can provide a rough understanding of the field content, the present application uses natural language processing technology to segment the Chinese descriptions, and compare them with the data labels in the rule library, calculate the level of each segmentation label, and then calculate the grading level of the field, thereby improving the degree of automation and accuracy of grading.

[0068] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0069] Figure 1 The process of the data classification processing method shown in the embodiment of this application Figure 1 .like Figure 1 As shown, the data classification processing method provided in this embodiment includes:

[0070] S101: Perform word segmentation processing on the newly added field through natural language processing to obtain at least one first data label.

[0071] Natural language processing refers to the technology that uses the natural language used by humans to communicate with machines. Through human processing of natural language, computers can read and understand it.

[0072] Newly added fields are fields not included in the data classification rule base. For example, a newly added field can include multiple data tags, such as "ID Card Type." "ID Card Type" can be split into "ID Card" and "Type," making it impossible to directly determine the classification level of this newly added field.

[0073] Fields that cannot be further segmented are called data labels. For example, data labels could be "ID card" or "number". Both "ID card" and "number" are data labels with the smallest granularity that cannot be split a second time.

[0074] By using natural language processing technology to segment the newly added fields, the Chinese description of the newly added fields can be parsed, thereby shielding the rigid requirement of identifying the field content.

[0075] S102: Obtain a classification level of each first data tag according to a classification level corresponding to the second data tag in a data classification rule library, where the classification level is used to indicate a sensitivity level of the first data tag.

[0076] The data classification rule base stores second data tags of minimum granularity and the classification levels corresponding to the second data tags. After obtaining at least one first data tag, the classification level of each first data tag can be determined based on the classification level corresponding to the second data tag stored in the data classification rule base. This application does not impose any special restrictions on the specific implementation method of obtaining the classification level of each first data tag based on the classification level corresponding to the second data tag in the data classification rule base.

[0077] Exemplarily, for example, each first data tag may be matched with a second data tag in a data classification rule library, and the classification level of each first data tag may be determined based on the matching result.

[0078] The first data tag may be the same as or different from the second data tag.

[0079] For example, if a new field is added called "ID card type," after word segmentation, it will have two first data tags: "ID card" and "type." If the second data tag stored in the data classification rule library includes "type" but not "ID card," then the first data tag "type" is the same as the second data tag "type," and the first data tag "ID card" is different from the second data tag.

[0080] S103: Determine the hierarchical level of the newly added field according to the hierarchical level of the at least one first data tag.

[0081] After obtaining the hierarchical level of each first data tag, the hierarchical level of the newly added field can be determined based on the hierarchical level of at least one first data tag. For example, the highest or lowest hierarchical level of at least one first data tag can be determined as the hierarchical level of the newly added field. This application does not impose any particular restrictions on the specific implementation method for determining the hierarchical level of the newly added field.

[0082] S104: Use the newly added field as a new data tag, and store the new data tag and the classification level corresponding to the new data tag in the data classification rule library.

[0083] Since the newly added field includes at least one first data tag of the smallest granularity, the new data tag may also include at least one first data tag of the smallest granularity.

[0084] For example, the newly added field "ID card type" is used as a new data label, and the "ID card type" and the corresponding classification level are stored in the data classification rule library.

[0085] Storing new data labels and the classification levels corresponding to the new data labels in the data classification rule base can realize automatic expansion and updating of the data classification rule base, thereby gradually improving the data classification rule base.

[0086] The purpose of adding a new field as a new data label here is to enable searching for data with unclear patterns, thereby avoiding data omissions. For example, if the new field is "ID Card Type", in this step, the new field "ID Card Type" is used as the new data label "ID Card Type". This allows subsequent searches for the field "ID Card Type" to directly search for the grading level of the new data label "ID Card Type", improving the automation of the grading process and reducing manual workload.

[0087] The data classification processing method provided in this embodiment includes: performing word segmentation processing on the newly added fields through natural language processing to obtain at least one first data label; obtaining the classification level of each first data label according to the classification level corresponding to the second data label in the data classification rule library, and the classification level is used to indicate the sensitivity of the data label; determining the classification level of the newly added field according to the classification level of the at least one first data label; treating the newly added field as a new data label, and storing the new data label and the classification level corresponding to the new data label in the data classification rule library. Based on the data classification rule library, this embodiment uses natural language processing technology to perform word segmentation on the newly added fields and compares them with the data labels in the data classification rule library to determine the classification level of the newly added field, thereby reducing the degree of manual intervention in data classification, reducing the situation where personal subjective factors are mixed into the classification content, improving the usability and automation of the process, improving the classification accuracy, and realizing the automatic expansion of the data classification rule library.

[0088] Figure 2 The process of the data classification processing method shown in the embodiment of this application Figure 2 .like Figure 2 As shown, the data classification processing method provided in this embodiment includes:

[0089] S201: Establishing an original data set, where the original data set includes a second data tag with the smallest granularity.

[0090] The minimum granularity data tag refers to the identifiable minimum granularity field, and the field cannot be split twice. The original data set can be established by presetting the second data tag of the minimum granularity.

[0091] S202: Determine the classification level of each second data tag.

[0092] The classification level of the second data tag may be determined according to relevant rules, business scenarios, and expert experience, for example. This embodiment does not impose any special restrictions on the determination of the classification level.

[0093] S203: Obtain a data classification rule library according to each second data tag and the corresponding classification level.

[0094] The data classification rule library includes a plurality of second data tags and a classification level corresponding to each second data.

[0095] For example, the data classification rule base includes a data rule set, which is formed by the second data tags and corresponding classification levels.

[0096] As can be understood, since the second data tags in the original data set are all data tags with the smallest granularity, they are relatively easy to find during a search. Based on the original data set with obvious patterns and the classification level corresponding to each second data tag in the original data set, a data classification rule library is generated, which facilitates the subsequent classification of data with less obvious patterns.

[0097] S204: Perform word segmentation processing on the newly added field through natural language processing to obtain at least one first data label.

[0098] Step S204 is similar to the above-mentioned step S101 and will not be described in detail here.

[0099] S205: Determine whether there is a second data tag identical to the first data tag in the data classification rule library; if so, execute step S206; if not, execute step S207.

[0100] S206: Using the classification level of the second data label as the classification level of the first data label.

[0101] Among them, if there is a second data label that is the same as the first data label in the data classification rule library, it means that the first data label obtained after the newly added field segmentation has entered the data classification rule library when the data classification rule library is obtained. At this time, the classification level of the second data label in the data classification rule library can be used as the classification level of the first data label.

[0102] For example: after segmenting the newly added field "ID card type", the first data label "type" is obtained. There is also a second data label "type" in the data classification rule library, and its classification level is level 2. Then the classification level of the first data label "type" can be set to the classification level of the second data label "type": level 2, thereby obtaining the classification level of the first data label "type".

[0103] S207: Using the first preset classification level as the classification level of the first data label.

[0104] If a second data tag identical to the first data tag does not exist in the data classification rule base, this indicates that the first data tag obtained after the newly added field segmentation is a minimum-granularity data tag that is not included in the data classification rule base. Since the first data tag is not included in the data classification rule base, the first preset classification level can be used as the classification level of the first data tag.

[0105] The first preset classification level may be, for example, a classification level preset by a user, or may be the maximum, minimum or median classification level in the data classification rule library, and this application does not impose any special restrictions on this.

[0106] As an example, a possible implementation is given here: obtaining an average value of classification levels corresponding to a preset number of second data tags in the data classification rule library; and obtaining the first preset classification level according to the average value.

[0107] The preset number may refer to, for example, all the second data tags in the data classification rule library, or may refer to a plurality of second data tags selected according to the rule, which is not specifically limited in this application.

[0108] Exemplarily, when determining the classification level of the first data tag, the number of repetitions of the first data tag in the newly added field may also be determined, where the number of repetitions is an integer greater than or equal to 1.

[0109] There may be at least two identical first data tags in a newly added field. In this case, it is also necessary to determine the number of repetitions of the first data tag in the newly added field.

[0110] For example: a new field is added, "ID card type actual type". After word segmentation, the first data label "ID card", "type", "actual", "type" is obtained. It can be known that the number of repetitions of the first data label is 2.

[0111] S208: Perform weighted averaging on the hierarchical level data of the at least one first data tag to obtain the hierarchical level of the newly added field.

[0112] In this embodiment, since the newly added fields are stored as new data tags, after obtaining the classification level of each first data tag, the classification level of the newly added fields needs to be obtained. The classification level of the newly added fields can be made more accurate by using a weighted average algorithm.

[0113] For example, if the number of repetitions of the first data tag in the newly added field is also determined in step S207, then the number of repetitions also needs to be taken into account when determining the classification level of the newly added field. For example, the number of repetitions can be used as a parameter to perform weighted averaging to obtain the classification level of the newly added field.

[0114] S209: Use the newly added field as a new data tag, and store the new data tag and the classification level corresponding to the new data tag in the data classification rule library.

[0115] Step S209 is similar to the above-mentioned step S104 and will not be described in detail here.

[0116] It can be understood that after the data classification rule base is expanded by the method of this embodiment, the subsequent data classification rule base can gradually transition from the minimum granularity data label (second data label) to the multi-granularity data label (new field) due to the increase of new data fields.

[0117] The data classification processing method provided in this embodiment improves the accuracy of classification by determining whether there is a second data tag that is identical to the first data tag in the data classification rule library, and adopts different processing methods to obtain the classification level of the first data tag; it also reduces the degree of human intervention in data classification, reduces the situation where personal subjective factors are mixed into the classification content, improves the usability and automation of the process, improves the classification accuracy, and realizes the automatic expansion of the data classification rule library.

[0118] Figure 3 This is the process of the data classification processing method shown in the embodiment of this application Figure 3 The newly added fields in this embodiment are marked fields. The mark indicates that the newly added field is more sensitive. In actual use scenarios, there is often a situation where the classification level of the newly added field is inconsistent with the actual level range. For the above situation, the method shown in this embodiment can be used to solve it. Figure 3 As shown, the data classification processing method provided in this embodiment includes:

[0119] S301: Determine whether the classification level of the marked newly added field is less than the second preset classification level; if not, execute S302; if so, execute S303.

[0120] S302: Use the newly added field as a new data tag, and store the new data tag and the classification level corresponding to the new data tag in the data classification rule library.

[0121] After obtaining the classification level of the newly added field, it is necessary to determine whether the newly added field is marked. If the newly added field is marked, it indicates that the classification level of the newly added field is high (high sensitivity), so it is necessary to determine whether the classification level of the marked newly added field is inconsistent with the actual level range.

[0122] If the classification level of the marked new field is not less than the second preset classification level, it means that the classification level of the new field is large enough and consistent with the level range actually used. Therefore, there is no need to adjust the classification level of the new field. The new field can be directly used as a new data label, and the new data label and the corresponding classification level can be stored.

[0123] S303: Update the hierarchical level of the newly added field to the second preset hierarchical level.

[0124] S304: Use the newly added field as a new data tag, and store the new data tag and the second preset classification level corresponding to the new data tag in the data classification rule library.

[0125] If the classification level of the marked newly added field is lower than the second preset classification level, it indicates that the classification level of the newly added field is relatively small and inconsistent with the actual level range. In this case, the classification level of the newly added field needs to be updated to the second preset classification level to make the classification level of the newly added field consistent with the actual level range. Similarly, when storing, the updated second preset classification level is stored.

[0126] The second preset classification level can be, for example, a classification level pre-set by the user, or it can be determined according to relevant regulations, actual usage scenarios, etc. This embodiment does not impose any special restrictions on the determination of the classification level, and this application does not impose any special restrictions on this.

[0127] The data classification processing method provided in this embodiment updates the classification level of the marked new field when the classification level of the marked new field is lower than the second preset classification level, so that the classification level of the new field is more in line with the actual scenario.

[0128] Figure 4 This is a schematic diagram of the structure of the data classification processing device provided by this application. Figure 4 As shown, the present application provides a data hierarchical processing device, the data hierarchical processing device 300 includes:

[0129] Processing module 301, configured to perform word segmentation processing on the newly added field through natural language processing to obtain at least one first data label;

[0130] The processing module 301 is further configured to obtain a classification level of each first data tag based on the classification level corresponding to the second data tag in the data classification rule base, wherein the classification level is used to indicate the sensitivity of the first data tag;

[0131] A determination module 302, configured to determine a classification level of the newly added field according to the classification level of the at least one first data tag;

[0132] The storage module 303 is configured to use the newly added field as a new data tag, and store the new data tag and the classification level corresponding to the new data tag in the data classification rule library.

[0133] Optionally, the processing module 301 is specifically configured to use the classification level of the second data tag as the classification level of the first data tag if there is a second data tag that is the same as the first data tag in the data classification rule base.

[0134] Optionally, the processing module 301 is further specifically configured to use the first preset classification level as the classification level of the first data tag if there is no second data tag identical to the first data tag in the data classification rule library.

[0135] Optionally, the processing module 301 is further configured to obtain an average value of classification levels corresponding to a preset number of second data tags in the data classification rule library;

[0136] The first preset grading level is obtained according to the average value.

[0137] Optionally, the determination module 302 is specifically configured to perform weighted averaging on the hierarchical level data of the at least one first data tag to obtain the hierarchical level of the newly added field.

[0138] Optionally, the data classification processing device further includes: a judgment module 304 and an update module 305,

[0139] If the newly added field is a marked newly added field, the determining module 304 is configured to determine whether the classification level of the newly added field is less than a second preset classification level;

[0140] The updating module 305 is configured to update the hierarchical level of the newly added field to the second preset hierarchical level when the hierarchical level of the newly added field is lower than the second preset hierarchical level;

[0141] The storage module 303 is further configured to store the new data tag and the second preset classification level corresponding to the new data tag in the data classification rule library.

[0142] Optionally, the processing module 301 is further configured to establish an original data set, wherein the original data set includes a second data tag of minimum granularity;

[0143] The determining module 302 is further configured to determine a classification level of each second data tag, and obtain the data classification rule base according to each second data tag and the corresponding classification level.

[0144] Figure 5 This is a schematic diagram of the structure of the data hierarchical processing device provided by this application. Figure 5 As shown, the present application provides a data hierarchical processing device, which includes a receiver 401 , a transmitter 402 , a processor 403 and a memory 404 .

[0145] Receiver 401, for receiving instructions and data;

[0146] Transmitter 402, used to send instructions and data;

[0147] Memory 404, for storing computer-executable instructions;

[0148] The processor 403 is configured to execute the computer-executable instructions stored in the memory 404 to implement the various steps of the data classification processing method in the above embodiment. For details, please refer to the relevant description in the above embodiment of the data classification processing method.

[0149] Optionally, the memory 404 may be independent or integrated with the processor 403 .

[0150] When the memory 404 is independently provided, the electronic device further includes a bus for connecting the memory 404 and the processor 403 .

[0151] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the data hierarchical processing method performed by the above-mentioned data hierarchical processing device is implemented.

[0152] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media generally embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0153] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present application that follow the general principles of the present application and include common knowledge or customary techniques in the art not disclosed herein. The description and examples are to be considered as exemplary only, and the true scope and spirit of the present application are indicated by the following claims.

[0154] It should be understood that the present application is not limited to the exact structure described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present application is limited only by the appended claims.

Claims

1. A data hierarchical processing method, characterized in that: include: Perform word segmentation processing on the newly added field through natural language processing to obtain at least one first data label; Obtaining a classification level for each first data tag according to the classification level corresponding to the second data tag in the data classification rule library, wherein the classification level is used to indicate the sensitivity of the first data tag; Determining a hierarchical level of the newly added field according to the hierarchical level of the at least one first data tag; Using the newly added field as a new data tag, and storing the new data tag and the classification level corresponding to the new data tag in the data classification rule library; Obtaining the classification level of each first data tag according to the classification level corresponding to the second data tag in the data classification rule base includes: If there is a second data tag that is the same as the first data tag in the data classification rule library, the classification level of the second data tag is used as the classification level of the first data tag; If there is no second data tag identical to the first data tag in the data classification rule library, the first preset classification level is used as the classification level of the first data tag; The determining the hierarchical level of the newly added field according to the hierarchical level of the at least one first data tag includes: The hierarchical level data of the at least one first data tag is weighted averaged to obtain the hierarchical level of the newly added field, the hierarchical level data including the number of repetitions of the first data tag in the newly added field, and the number of repetitions is an integer greater than or equal to 1.

2. The method according to claim 1, characterized in that Before using the first preset classification level as the classification level of the first data tag, the method further includes: Obtain an average value of classification levels corresponding to a preset number of second data tags in the data classification rule library; The first preset grading level is obtained according to the average value.

3. The method according to claim 1, characterized in that Before storing the new data tag and the classification level corresponding to the new data tag in the data classification rule library, the method further includes: If the newly added field is a marked newly added field; Determining whether the hierarchical level of the newly added field is less than a second preset hierarchical level; If it is less, updating the hierarchical level of the newly added field to the second preset hierarchical level; Correspondingly, storing the new data tag and the classification level corresponding to the new data tag in the data classification rule library includes: The new data tag and the second preset classification level corresponding to the new data tag are stored in the data classification rule library.

4. The method according to claim 1, wherein The method further comprises: Establishing an original data set, wherein the original data set includes a second data tag with the smallest granularity; determining a hierarchical level for each of the second data tags; The data classification rule base is obtained according to each second data tag and the corresponding classification level.

5. A data hierarchical processing device, characterized in that: include: A processing module, configured to perform word segmentation processing on the newly added field through natural language processing to obtain at least one first data label; The processing module is further configured to obtain a classification level of each first data tag based on a classification level corresponding to the second data tag in a data classification rule base, wherein the classification level is used to indicate a sensitivity of the first data tag; a determination module, configured to determine a hierarchical level of the newly added field according to the hierarchical level of the at least one first data tag; a storage module, configured to use the newly added field as a new data tag, and store the new data tag and the classification level corresponding to the new data tag in the data classification rule library; The processing module is specifically configured to, if a second data tag identical to the first data tag exists in the data classification rule base, use the classification level of the second data tag as the classification level of the first data tag; and if a second data tag identical to the first data tag does not exist in the data classification rule base, use the first preset classification level as the classification level of the first data tag; The determination module is specifically used to perform weighted averaging on the hierarchical level data of the at least one first data tag to obtain the hierarchical level of the newly added field. The hierarchical level data includes the number of repetitions of the first data tag in the newly added field, and the number of repetitions is an integer greater than or equal to 1.

6. A data hierarchical processing device, characterized in that: include: Memory; processor; wherein the memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the data hierarchical processing method according to any one of claims 1 to 4.

7. A computer storage medium, characterized in that The computer storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the data hierarchical processing method according to any one of claims 1 to 4.

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