Data classification and grading processing method and device

Abstract

The one or more embodiments of the specification disclose a data classification and grading processing method and device. The method first acquires target data to be classified and graded; secondly acquires target configuration information matched with the target data, the target configuration information being used for data content recognition of the target data in the process of classification and grading processing, and classification and grading processing of the target data based on the recognized data content and other data elements in the target data except the recognized data content; then recognizes the data content of the target data according to the storage type of the target data and the target configuration information, to obtain the recognized data content of the target data; finally acquires other data elements related to the classification and grading of the target data according to the target configuration information, and determines the classification and grading processing result of the target data according to the recognized data content of the target data, the acquired other data elements and the target configuration information.

Description

TECHNICAL FIELD

[0001] The present document relates to the technical field of data asset processing, and particularly relates to a data classification and grading processing method and device. BACKGROUND

[0002] In the technical field of data asset processing, with the increasing attention of industry supervision requirements and users to private data, the demand for data security processing is increasingly valued. Before the data is processed safely, the data needs to be classified and processed safely.

[0003] In the related art, the method for classifying and grading data usually only depends on the data content, and cannot support the classification and grading of data of different storage types. However, in actual application, the data elements affecting data classification and security level are not limited to data content. Therefore, it is necessary to provide a more efficient data classification and grading processing method considering multiple data elements and adapting to different storage types. SUMMARY

[0004] In one aspect, one or more embodiments of the present specification provide a data classification and grading processing method, comprising: obtaining target data to be classified and graded; obtaining target configuration information matched with the target data, the target configuration information being used for classification and grading processing of the target data, and the target data being subjected to data content recognition in the process of classification and grading processing by the target configuration information, and the target data being subjected to classification and grading processing based on the recognized data content and other data elements in the target data except the recognized data content; identifying the data content of the target data according to the storage type of the target data and the target configuration information, to obtain the recognized data content of the target data; obtaining the other data elements related to the classification and grading of the target data according to the target configuration information, and determining the classification and grading processing result of the target data according to the recognized data content of the target data, the obtained other data elements, and the target configuration information.

[0005] In another aspect, one or more embodiments of the present specification provide a data classification and grading processing apparatus, comprising: a data acquisition module that acquires target data to be classified and graded; a target configuration information determination module that acquires target configuration information matching the target data, the target configuration information being used for classification and grading processing of the target data, and the target data being subjected to data content recognition in the process of classification and grading processing by the target configuration information, and the target data being subjected to classification and grading processing based on the recognized data content and other data elements in the target data other than the recognized data content; a data content recognition module that recognizes data content of the target data according to a storage type of the target data and the target configuration information, to obtain the recognized data content of the target data; and a classification and grading processing module that acquires the other data elements related to classification and grading of the target data according to the target configuration information, and determines a classification and grading processing result of the target data according to the recognized data content of the target data, the acquired other data elements, and the target configuration information.

[0006] In another aspect, one or more embodiments of the present specification provide an electronic device, comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, enable the processor to: acquire target data to be classified and graded; acquire target configuration information matching the target data, the target configuration information being used for classification and grading processing of the target data, and the target data being subjected to data content recognition in the process of classification and grading processing by the target configuration information, and the target data being subjected to classification and grading processing based on the recognized data content and other data elements in the target data other than the recognized data content; recognize data content of the target data according to a storage type of the target data and the target configuration information, to obtain the recognized data content of the target data; and acquire the other data elements related to classification and grading of the target data according to the target configuration information, and determine a classification and grading processing result of the target data according to the recognized data content of the target data, the acquired other data elements, and the target configuration information.

[0007] In still another aspect, one or more embodiments of the present specification provide a storage medium for storing a computer program, which can be executed by a processor to implement the following process: obtaining target data to be classified; obtaining target configuration information matched with the target data, the target configuration information being used for classifying the target data, and identifying data content of the target data in the process of classifying the target data by using the target configuration information, and classifying the target data based on the identified data content and other data elements in the target data other than the identified data content; identifying the data content of the target data according to a storage type of the target data and the target configuration information, to obtain the identified data content of the target data; obtaining the other data elements related to the classification of the target data according to the target configuration information, and determining a classification result of the target data according to the identified data content of the target data, the obtained other data elements, and the target configuration information. BRIEF DESCRIPTION OF DRAWINGS

[0008] In order to more clearly illustrate the technical solutions of one or more embodiments of the present specification or the prior art, the drawings needed in the embodiment or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present specification, and other drawings can be obtained by those skilled in the art without creative labor.

[0009] Figure 1 is a schematic flow chart of a data classification method according to an embodiment of the present specification;

[0010] Figure 2 is a schematic flow chart of another data classification method according to an embodiment of the present specification;

[0011] Figure 3 is a schematic flow chart of another data classification method according to an embodiment of the present specification

[0012] Figure 4 is a schematic diagram of the implementation principle of data classification according to an embodiment of the present specification;

[0013] Figure 5 is a schematic block diagram of a data classification device according to an embodiment of the present specification;

[0014] Figure 6 is a schematic block diagram of an electronic device according to an embodiment of the present specification. DETAILED DESCRIPTION

[0015] The one or more embodiments of the specification provide a data classification and grading processing method and device.

[0016] In order for those skilled in the art to better understand the technical solutions in the one or more embodiments of the specification, the technical solutions in the one or more embodiments of the specification will be clearly and completely described below in conjunction with the drawings in the one or more embodiments of the specification. Obviously, the described embodiments are only part of the embodiments of the specification, not all. Based on the one or more embodiments of the specification, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present document.

[0017] Data security management is an important link in the data asset management process. In order to protect user privacy and achieve data security management, combined with the data management and use requirements of the industry field, different types of data need to be classified and graded according to the security level, that is, data classification and grading processing. The embodiments of the specification provide a data classification and grading processing method based on a rule engine. The rule engine is a kind of business rule management system, which can receive input data, interpret business rules, and make business decisions according to business rules. Its working principle is to separate business decisions from application program code, so as to ensure independent maintenance and dynamic update of business logic. In the embodiments of the specification, the rule engine is mainly used for parallel operation of batch rules under large-scale data and acceleration of text rule matching.

[0018] As shown in Figure 1 The embodiments of the specification provide a data classification and grading processing method. The execution subject of the method can be a terminal device or a server. The terminal device can be a computer device such as a notebook computer or a desktop computer. The server can be an independent server, or a server cluster composed of multiple servers. The server can be a background server of a financial service or an online shopping service, or a background server of an application program. In the embodiments, the server is taken as an example for detailed description. For the execution process of the terminal device, please refer to the related content below, which will not be repeated here. The method can specifically include the following steps:

[0019] In step S102, target data to be classified and graded is obtained.

[0020] In implementation, the target data to be classified can be obtained according to the obtained data classification request. The data refers to any record form of information in electronic or other ways, and the data type of the target data can be text data, image data, etc., which can be in the form of plain text, rich text, etc. Among them, for the rich text, there can be a rich text format, i.e. RTF (Rich Text Format) format, which is a cross-platform document format that can facilitate text and graphic document formats for viewing on different devices and systems. The rich text can include text, images, videos and other elements, and the text and image elements can contain various fonts, colors, sizes, links, tables, etc., and the rich text can better present the structure and hierarchy of the text.

[0021] In step S104, target configuration information matching the target data is obtained, the target configuration information is used for classifying and grading the target data, and the data content of the target data is identified in the process of classifying and grading the target data by the target configuration information, and the target data is classified and graded based on the identified data content and other data elements in the target data except the identified data content.

[0022] In implementation, a configuration information set for classifying and grading data can be generated in advance, and the configuration information set contains at least two kinds of configuration information: the first kind of configuration information is used for data content identification of different types of data, for example: configuration information for identifying whether the current data is text data or image data, configuration information for identifying whether the current data contains sensitive information such as identity information and home address information, and the number of sensitive information contained. The second kind of configuration information is configuration information for classifying and grading based on the identified data content and other data elements. Among the above two kinds of configuration information, the configuration information for identifying data content focuses on the configuration management of the detection process of a single data in a file, and the configuration information for classifying and grading based on data content and other data elements focuses on the configuration management of the security level decision of the entire file.

[0023] Taking identity information as an example, the first kind of configuration information is used to identify which identity information is contained in the current target data, and the second kind of configuration information is comprehensive information obtained based on the detected data content and other data elements. If 10 certificate information and 100 certificate information are detected in two documents according to the first kind of configuration information, it can be known according to the second kind of configuration information that the security levels of the two documents are different. If one document contains 10 certificate information and the other document contains 10 certificate information and other identity information besides 15 certificate information according to the first kind of configuration information, it can be known according to the second kind of configuration information that the security levels of the two documents are different.

[0024] After obtaining the target data, first determine the target configuration information matched with the target data from the pre-generated configuration information set. The target configuration information is used for classifying and processing the target data. Correspondingly, since the target configuration information comes from the configuration information set, the target configuration information is used for data content recognition of the target data, and is also used for classifying and processing the target data based on the data content and other data elements of the recognized target data.

[0025] The other data elements can be one or more of data volume (also referred to as data size), data group label, data processing logic, and data desensitization encryption method. Among them, the data group label refers to a label that can identify the group characteristics corresponding to the current target data, which can be a user group label using the target data, for example: a shopping platform sets up a middle-end consumption label, a high-end consumption label, a high-frequency consumption label, and a low-frequency consumption label for a user group according to the user group's consumption interval, occupation, shopping preference and other factors. The data processing logic refers to the processing method of the target data, for example: the field combination form in the target data. The data desensitization encryption method refers to the privacy protection processing method of the data, for example: disk encryption, registry encryption, compressed package encryption, and specific encryption methods such as AES (Advanced Encryption Standard) encryption, etc.

[0026] In step S106, the data content of the target data is recognized according to the storage type of the target data and the target configuration information, and the recognized data content of the target data is obtained.

[0027] The storage type of the target data in the embodiments of the present specification includes different types of enterprise-level data storage. The specific storage type can be an analysis-oriented relational storage, for example: a big data analysis platform, or a non-relational storage, including structured databases, file storage, object storage, etc.

[0028] The data content of the target data is recognized in the embodiments of the present specification, which includes two stages of collection adaptation processing and detection. The storage types of the target data are different, and the interfaces of the corresponding storage media are different. Before detecting the data content of the target data, the collection adaptation processing is first performed based on the data source characteristics and the target configuration information in the target data. The collection adaptation processing process is equivalent to a routing function. Different collection adaptation processing methods are used according to different storage types. The characteristics of the target data can specifically include the data source, data content, data structure, development language, and the like of the target data. Through the collection adaptation processing, the target data is processed from the state of the original data to the state that the content analysis processing can be performed on the target data. Then, the target data is detected to obtain the recognized data content of the target data.

[0029] The storage types of the target data are different, and the corresponding collection adaptation processing methods are different. In combination with the corresponding target configuration information, the detection methods of the data content of the target data are also different. In actual applications, the specific storage type of the target data can be determined.

[0030] In the target data detection process, a plurality of preset algorithms can be used, such as an NLP (Neuro-Linguistic Programming) algorithm and a text recognition algorithm. The embodiments of the present specification do not limit this.

[0031] In step S108, other data elements related to the classification and grading of the target data are obtained according to the target configuration information, and the classification and grading processing result of the target data is determined according to the recognized data content of the target data, the obtained other data elements, and the target configuration information.

[0032] The target configuration information is obtained through step S104. The target configuration information includes configuration information for classification and grading processing based on the recognized data content and other data elements other than the recognized data content. The data content of the target data is recognized through step S106. Other data elements for classifying the target data are obtained through step S108. Finally, the classification and grading processing result of the target data is determined through step S108.

[0033] The other data elements can be one or more of a data volume (also referred to as a data scale), a data group label, a data processing logic, and a data desensitization encryption method.

[0034] In implementation, when determining the classification and grading processing result of the target data according to the data content of the identified target data, the obtained other data elements and the target configuration information, the classification result of the target data can be first determined according to the data content of the target data and other data elements, and then the security level grading result of the target data can be determined according to the target configuration information. The classification result of the target data can also be determined according to the data content of the target data, and then the security level classification result of the target data can be determined according to the other data elements of the target data and the target configuration information. The classification and grading result of the target data can also be determined according to the data content of the target data, the other data elements and the target configuration information.

[0035] The embodiment of the present specification provides a data classification and grading processing method, which first acquires target data to be classified and graded; secondly acquires target configuration information matched with the target data, the target configuration information being used for classification and grading processing of the target data, and the target data is subjected to data content recognition in the process of classification and grading processing by the target configuration information, and the target data is subjected to classification and grading processing based on the recognized data content and other data elements in the target data except the recognized data content; then the data content of the target data is recognized according to the storage type of the target data and the target configuration information, to obtain the recognized data content of the target data; finally, other data elements related to the classification and grading of the target data are acquired according to the target configuration information, and the classification and grading processing result of the target data is determined according to the recognized data content of the target data, the acquired other data elements and the target configuration information. When determining the classification and grading processing result of the target data, the embodiment of the present specification not only refers to one data element of the data content of the target data, but also refers to other data elements such as data volume, data group label, data processing logic and data desensitization encryption mode, so that the target data can be classified and graded from multiple dimensions, which is beneficial to improve the reliability and accuracy of the classification and grading result of the target data. In addition, the target configuration information used for classification and grading processing of the target data includes at least two parts of content: one part is used for data content recognition of the target data in the process of classification and grading processing by the target configuration information, so as to realize configuration management of the detection processing process of the target data, and the other part is used for classification and grading processing of the target data based on the recognized data content and other data elements in the target data, so as to realize configuration management of the classification and grading decision process of the target data. Corresponding to the target configuration information, the entire data inspection process is decoupled into a detection process of recognizing the data content of the target data and a decision processing process based on the recognized data content, other data elements and target configuration information. This processing mode based on a rule engine can realize parallel operation of data content detection and data decision, and can greatly improve the efficiency of data classification and grading processing, especially in the case of large-scale data volume.

[0036] Further, the target configuration information can further include a data asset range corresponding to the target data and a data type of the target data. The processing of step S108 can be various, and an optional processing manner is provided below, which can be specifically referred to the processing of steps S1082-S1086.

[0037] In step S1082, other data elements related to the classification and grading of the target data are obtained according to the data asset range corresponding to the target data and the data type of the target data.

[0038] Specifically, the data volume can be determined according to the data asset range corresponding to the target data. The data group label, data processing logic, and data desensitization encryption method can be determined according to the data type of the target data in combination with the data content.

[0039] In step S1084, the data security benchmark level and the data security benchmark level upgrade and downgrade rule are determined according to the data type of the target data.

[0040] The data security benchmark level refers to an initial reference standard for security grading of the target data. Based on the initial reference standard in combination with the identified data content, other data elements, and other contents of the target configuration information, the target data is finally classified and graded. The data security benchmark level upgrade and downgrade rule is a rule for upgrading and downgrading the security level of the target data. The definition of data type, data security benchmark level, and data security benchmark level upgrade and downgrade rule belong to the data category of the enterprise. According to the data type of the target data, the data security benchmark level and the data security benchmark level upgrade and downgrade rule can be understood as data category management in the data classification and grading process.

[0041] In step S1086, the classification and grading processing result of the target data is determined according to the determined data security benchmark level and data security benchmark level upgrade and downgrade rule, the identified data content of the target data, the obtained other data elements, and the target configuration information.

[0042] According to steps S1084 and S1086 above, the data type of the internal data of the enterprise can be defined through the target configuration information. Then, the data security benchmark level and the data security benchmark level upgrade and downgrade rule are determined based on the data type of the target data. Finally, the preset data management rule is generated based on the data security benchmark level and the data security benchmark level upgrade and downgrade rule. Thus, the classification and grading processing result of the target data is determined based on the preset data management rule, the identified data content of the target data, the obtained other data elements, and the target configuration information.

[0043] Further, the target configuration information can further include a preset data detection period, wherein the preset data detection period is a period for identifying the data content of the target data, so as to realize the scanning scheduling management of the target data and improve the efficiency of the data classification and grading processing.

[0044] The data detection period can be set based on the data update period, and specifically, the data identification (or data detection) can be triggered at each data content change stage. For the same target data, the data content can change, and the identification processing of the data content is triggered at the moment when the data content changes, which can effectively reduce the risk caused by the timeliness lag of sensitive data detection, and is beneficial to improve the accuracy and reliability of the data detection result, thereby improving the accuracy and reliability of the data classification and grading result.

[0045] Further, the target configuration information includes a preset detection rule for obtaining and identifying the target data. The detection rule can be understood as a part of the target configuration information for identifying the data content of the target data in the classification and grading processing. The storage type of the target data can be various, and the processing of step S106 can be various corresponding to different storage types. Three optional processing modes are provided below. In the first processing mode, the storage type of the target data is object storage, and the processing of steps A1 and A2 can be referred to.

[0046] Step A1, reading the target data based on object storage, and parsing the target data to determine the text data and image data contained in the target data.

[0047] Step A2, identifying the data content of the text data and the image data based on the preset detection rule, to obtain the identified data content of the text data and the image data.

[0048] As known from the above steps A1 and A2, by parsing the target data to split it into text data and image data, and identifying the data content of the text data and the image data based on the target configuration information, the difficulty of data content identification can be reduced, and the accuracy of data content identification can be improved, so that the data classification and grading processing method can effectively support the classification and grading processing of the target data of the object storage type.

[0049] In the second processing mode, the storage type of the target data is analysis-oriented relational storage, and step S106 can perform steps B1-B3 as follows:

[0050] Step B1, collecting the target data based on analysis-oriented relational storage, and constructing the data blood relationship of the target data.

[0051] Taking a big data analytics platform as an example, the first step is to collect the target data to be classified and graded on the big data analytics platform. Then, based on the target data, the data lineage relationship of the target data is constructed. A visualized data lineage relationship includes: data flow route, information nodes (main node, data inflow node, data outflow node), transformation rule nodes, data archiving and destruction rule nodes, and cleaning rule nodes.

[0052] Step B2: Determine the processing method for the target data based on the constructed data lineage.

[0053] Due to the hierarchical and traceable characteristics of data lineage, the processing method of target data can be determined through the constructed data lineage, namely: the formation and processing process of target data, processing logic, and other information.

[0054] Step B3: Based on the preset detection rules and the processing method of the target data, the data content of the target data is identified to obtain the identified data content of the target data.

[0055] In the third processing method, the target data is stored in a structured database, and step S106 can execute the following steps C1-C3:

[0056] Step C1: Collect target data based on table field-level data sampling.

[0057] Step C2: Construct a structured array corresponding to the target data based on the target data's metadata.

[0058] A structured array is a text array in a structured form. For target data stored in a structured database, data content recognition first involves data collection using table field-level data sampling. Then, a structured array corresponding to the target data is constructed based on the target data's metadata, and data adaptation processing is performed to transform the collected target data from source data into data that can be directly analyzed and processed.

[0059] Step C3: Based on the preset detection rules, the structured array corresponding to the target data is identified to obtain the data content of the identified target data.

[0060] Furthermore, such as Figure 2 As shown, the data classification and grading processing method in the embodiments of this specification further includes step S1010: correcting the classification and grading processing results of the target data and annotating the asset profile information of the data assets according to the asset information of the data assets corresponding to the target data and the preset data management rules.

[0061] In actual application, after the target data is classified and processed according to steps S102-S108, the data category (such as the data security benchmark level and the data security benchmark level upgrade and downgrade rule) can be flexibly configured based on the regulatory requirements of the industry or the business situation of the enterprise, so that the preset data management rule is changed. Through step S1010, the classification and processing result of the target data can be updated based on the asset information of the actual data asset corresponding to the target data and the changed data management rule, so as to further improve the accuracy of the data classification and processing result.

[0062] Further, the data classification and processing method in the embodiment of the present specification further includes step S1012: performing data asset management on the data asset corresponding to the target data. The data asset management includes analyzing and filtering the data asset corresponding to the target data, metadata query processing, distribution and trend analysis of the data asset, and distribution and trend analysis of the protection state of the data asset.

[0063] After the target data is classified and processed according to steps S102-S108, the scanning and inspection result of the target data is obtained, which can be used as the data asset tag of the target data. Subsequently, the data asset tag can be used for retrieval, asset portrait query, etc., so as to realize the integration of metadata management and data classification and management, and further realize the synchronous management of metadata processing and classification and processing in the whole stage of the data life cycle.

[0064] Further, as shown in Figure 3 Further, as shown in

[0065] In step S1062, based on the target configuration information, a multi-modal model constructed based on the target configuration information is obtained.

[0066] The multi-modal model is constructed based on the configuration information, and is obtained by model training based on different modal data samples and a preset loss function. The different modal data samples can be text data, image data, table data, etc.

[0067] In step S1064, according to the storage type of the target data and the multi-modal model, the data content of the target data is identified to obtain the identified data content of the target data.

[0068] The implementation principle of the data classification and grading processing method in the embodiments of this specification can be found in [reference needed]. Figure 4 As shown. By Figure 4 As can be seen, the data classification and grading processing process in the embodiments of this specification mainly includes three parts: data classification and grading configuration management, data inspection processing, and data asset operation management. Data classification and grading configuration management mainly includes: data category management, classification and grading rule management, and scan scheduling management. In the classification and grading rule management process, detection rules and decision rules are decoupled, thereby achieving data classification and grading processing based on a rule engine. The data inspection processing process is divided into data acquisition and adaptation and data detection, and the data detection processing is executed according to the detection rules and different storage types of the target data. Data asset operation management mainly involves further revising the data classification and grading results and managing the target data as data assets, thereby achieving the integration of metadata management and data classification and grading management.

[0069] The embodiment of the present specification provides a data classification and grading processing method, which first acquires target data to be classified and graded; secondly acquires target configuration information matched with the target data, the target configuration information being used for classification and grading processing of the target data, and the target data is subjected to data content recognition in the process of classification and grading processing by the target configuration information, and the target data is subjected to classification and grading processing based on the recognized data content and other data elements in the target data except the recognized data content; then the data content of the target data is recognized according to the storage type of the target data and the target configuration information, and the recognized data content of the target data is obtained; finally, other data elements related to the classification and grading of the target data are acquired according to the target configuration information, and the classification and grading processing result of the target data is determined according to the recognized data content of the target data, the acquired other data elements and the target configuration information. In the process of determining the classification and grading processing result of the target data, the embodiment of the present specification not only refers to one data element of the data content of the target data, but also refers to other data elements such as data volume, data group label, data processing logic and data desensitization encryption mode, so that the target data can be classified and graded from multiple dimensions, which is beneficial to improve the reliability and accuracy of the classification and grading result of the target data. In addition, the target configuration information used for classification and grading processing of the target data at least includes two parts of content: one part is used for data content recognition of the target data in the process of classification and grading processing by the target configuration information, so as to realize configuration management of the detection processing process of the target data, and the other part is used for classification and grading processing of the target data based on the recognized data content and other data elements in the target data, so as to realize configuration management of the classification and grading decision process of the target data. Corresponding to the target configuration information, the entire data inspection process is decoupled into the detection process of recognizing the data content of the target data and the decision processing process based on the recognized data content, other data elements and the target configuration information. This processing mode based on the rule engine can realize parallel operation of data content detection and data decision, and can greatly improve the efficiency of data classification and grading processing, especially in the case of large-scale data volume.

[0070] In summary, particular embodiments of the present subject matter have been described. Other embodiments are within the scope of the following claims. In some cases, the actions recited in the claims can be performed in a different order and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve the desired results. In certain implementations, multitasking and parallel processing can be advantageous.

[0071] Based on the same idea, the one or more embodiments of the present specification also provide a data classification and grading processing apparatus, as shown in the following. Figure 5

[0072] The data classification and grading processing apparatus comprises a data acquisition module 210, a target configuration information determination module 220, a data content identification module 230, and a classification and grading processing module 240, wherein:

[0073] The data acquisition module 210 acquires target data to be classified and graded;

[0074] The target configuration information determination module 220 acquires target configuration information matched with the target data, the target configuration information being used for classification and grading processing of the target data, and the target data being subjected to data content identification in the process of classification and grading processing by the target configuration information, and the target data being subjected to classification and grading processing based on the identified data content and other data elements in the target data except the identified data content;

[0075] The data content identification module 230 identifies the data content of the target data according to the storage type of the target data and the target configuration information, and obtains the identified data content of the target data;

[0076] The classification and grading processing module 240 acquires other data elements related to the classification and grading of the target data according to the target configuration information, and determines the classification and grading processing result of the target data according to the identified data content of the target data, the acquired other data elements, and the target configuration information.

[0077] Further, the classification and grading processing module 240 comprises:

[0078] The other data element acquisition unit acquires other data elements related to the classification and grading of the target data according to the data asset range corresponding to the target data and the data type of the target data;

[0079] The data security benchmark level and data security benchmark level upgrade and downgrade rule determination unit determines the data security benchmark level and the data security benchmark level upgrade and downgrade rule according to the data type of the target data;

[0080] The classification and grading unit determines the classification and grading processing result of the target data according to the determined data security benchmark level and data security benchmark level upgrade and downgrade rule, the identified data content of the target data, the acquired other data elements, and the target configuration information.

[0081] Further, the data content identification module 230 comprises:

[0082] ​The first data content recognition unit includes preset detection rules for obtaining and recognizing target data in the target configuration information, the storage type of the target data is object storage, target data based on object storage is read and parsed, text data and image data contained in the target data are determined, the data content of the text data and the image data is identified based on the preset detection rules, and the data content of the identified text data and image data is obtained.

[0083] The second data content recognition unit includes preset detection rules for obtaining and recognizing target data in the target configuration information, the storage type of the target data is an analysis-oriented relational storage, target data based on the analysis-oriented relational storage is collected, a data blood relationship of the target data is constructed, a processing method of the target data is determined based on the constructed data blood relationship, and the data content of the target data is identified according to the preset detection rules and the processing method of the target data, and the data content of the identified target data is obtained.

[0084] The third data content recognition unit includes preset detection rules for obtaining and recognizing target data in the target configuration information, the storage type of the target data is a structured database, and the target data is collected based on a table field level data sampling method; a structured array corresponding to the target data is constructed according to metadata of the target data; and the structured array corresponding to the target data is identified based on the preset detection rules, and the data content of the identified target data is obtained.

[0085] Further, the data classification and grading processing apparatus further includes a correction module that corrects the classification and grading processing result of the target data and labels asset portrait information of the data asset according to asset information of the data asset corresponding to the target data and a preset data management rule.

[0086] Further, the data classification and grading processing apparatus further includes a multi-modal model construction module that constructs a multi-modal model based on the target configuration information, and the multi-modal model is obtained by model training of different modal data samples and a preset loss function. Correspondingly, the data content recognition module 230 includes:

[0087] The model acquisition unit acquires the multi-modal model constructed based on the target configuration information based on the target configuration information.

[0088] The data content recognition unit identifies the data content of the target data according to the storage type of the target data and the multi-modal model, and obtains the data content of the identified target data.

[0089] The embodiment of the present specification provides a data classification and grading processing apparatus, which acquires target data to be classified and graded through a data acquisition module; acquires target configuration information matched with the target data through a target configuration information determination module, the target configuration information being used for classifying and grading the target data, and performing data content recognition on the target data in the process of classifying and grading the target data through the target configuration information, and classifying and grading the target data based on the recognized data content and other data elements in the target data except the recognized data content; recognizes the data content of the target data according to the storage type of the target data and the target configuration information through a data content recognition module, to obtain the recognized data content of the target data; acquires other data elements related to the classification and grading of the target data according to the target configuration information through a classification and grading processing module, and determines the classification and grading processing result of the target data according to the recognized data content of the target data, the acquired other data elements, and the target configuration information. In the process of determining the classification and grading processing result of the target data, the embodiment of the present specification not only refers to one data element, i.e., the data content of the target data, but also refers to other data elements such as data volume, data group label, data processing logic, and data desensitization encryption mode, so that the target data can be classified and graded from multiple dimensions, which is beneficial to improving the reliability and accuracy of the classification and grading result of the target data. In addition, the target configuration information used for classifying and grading the target data includes at least two parts of content: one part is used for recognizing the data content of the target data in the process of classifying and grading the target data through the target configuration information, so as to realize configuration management of the detection processing process of the target data, and the other part is used for classifying and grading the target data based on the recognized data content and other data elements in the target data, so as to realize configuration management of the classification and grading decision process of the target data. Corresponding to the target configuration information, the entire data inspection process is decoupled into a detection process of recognizing the data content of the target data and a decision processing process based on the recognized data content, other data elements, and the target configuration information. This processing mode based on a rule engine can realize parallel operation of data content detection and data decision, and can greatly improve the efficiency of data classification and grading processing, especially in the case of large-scale data volume.

[0090] Those skilled in the art shall understand that the above data classification and grading processing apparatus can be used to realize the data classification and grading processing method described above, and the detailed description thereof shall be similar to the description in the foregoing method part, and will not be described again here to avoid tediousness.

[0091] Those skilled in the art shall understand that the above sequence representation extraction apparatus can be used to realize the sequence representation extraction method described above, and the detailed description thereof shall be similar to the description in the foregoing method part, and will not be described again here to avoid tediousness.

[0092] Based on the same idea, one or more embodiments of the present specification also provide an electronic device, such as Figure 6 The electronic device can have a large difference due to different configurations or performances, and can include one or more processors 301 and memories 302, and the memories 302 can store one or more stored applications or data. Among them, the memory 302 can be temporary storage or persistent storage. The application stored in the memory 302 can include one or more modules (not shown in the figure), and each module can include a series of computer executable instructions in the electronic device. Further, the processor 301 can be configured to communicate with the memory 302 and execute a series of computer executable instructions in the memory 302 on the electronic device. The electronic device can also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.

[0093] In particular, in the present embodiment, the electronic device includes a memory and one or more programs, wherein one or more programs are stored in the memory, and one or more programs can include one or more modules, and each module can include a series of computer executable instructions in the electronic device, and the one or more processors are configured to execute the one or more programs include the following computer executable instructions:

[0094] Obtain target data to be classified and graded;

[0095] Obtain target configuration information matched with the target data, the target configuration information is used for classifying and grading the target data, and the target data is identified in the process of classifying and grading the target data by the target configuration information, and the target data is classified and graded based on the identified data content and other data elements in the target data except the identified data content;

[0096] According to the storage type of the target data and the target configuration information, the data content of the target data is identified to obtain the identified data content of the target data;

[0097] According to the target configuration information, obtain other data elements related to the classification and grading of the target data, and according to the identified data content of the target data, the obtained other data elements and the target configuration information, determine the classification and grading result of the target data.

[0098] One or more embodiments of the present specification provide a storage medium for storing computer executable instructions, and the executable instructions are executed by the processor to implement the following processes:

[0099] obtaining target data to be classified and graded;

[0100] obtaining target configuration information matched with the target data, the target configuration information being used for classifying and grading the target data, and the target data being subjected to data content recognition in the process of classifying and grading the target data by using the target configuration information, and the target data being subjected to classifying and grading based on the recognized data content and other data elements in the target data other than the recognized data content;

[0101] recognizing the data content of the target data according to the storage type of the target data and the target configuration information, to obtain the recognized data content of the target data;

[0102] obtaining other data elements related to the classification and grading of the target data according to the target configuration information, and determining the classification and grading result of the target data according to the recognized data content of the target data, the obtained other data elements, and the target configuration information.

[0103] The system, apparatus, module or unit illustrated in the above embodiments can be implemented by a computer chip or entity, or by a product with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.

[0104] For the convenience of description, the above apparatus is described as various units respectively by functions. Of course, the functions of each unit can be implemented in one or more software and / or hardware in implementing one or more embodiments of the present specification.

[0105] Those skilled in the art should understand that one or more embodiments of the present specification can be provided as a method, a system, or a computer program product. Therefore, one or more embodiments of the present specification can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, one or more embodiments of the present specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0106] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0107] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0108] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks.

[0109] In a typical configuration, a computing device includes one or more processors (CPUs), input / output interfaces, network interfaces, and memory.

[0110] The memory can include non-persistent memory, Random Access Memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or Flash memory, among others. The memory is an example of computer-readable media.

[0111] Computer-readable media includes permanent and non-permanent, movable and non-movable media that can be implemented by any method or technology to store information. The information can be computer-readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassette, magnetic tape disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information accessible to a computing device. According to the definition herein, computer-readable media does not include transitory computer-readable media, such as modulated data signals and carriers.

[0112] It should also be noted that the terms "comprising", "containing", or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, article or apparatus that comprises a list of elements does not only include those elements, but also includes other elements not explicitly listed or inherent to such a process, method, article or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.

[0113] One or more embodiments of the specification can be described in the general context of computer-executable instructions being executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. The specification can also be practiced in a distributed computing environment in which tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0114] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other, and each embodiment focuses on the difference from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0115] The above merely provides one or more embodiments of the present specification, and is not intended to limit the present application. One of ordinary skill in the art can make various modifications and changes to the one or more embodiments of the present specification. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the one or more embodiments of the present specification shall be included in the scope of the claims of the one or more embodiments of the present specification.

Claims

1. A data classification and grading processing method, comprising: obtaining target data to be classified and graded; obtaining target configuration information matched with the target data, the target configuration information being used for classification and grading processing of the target data, and data content of the target data being identified in the process of classification and grading processing by the target configuration information, and the target data being classified and graded based on the identified data content and other data elements in the target data other than the identified data content, the target configuration information being used for data content identification of the target data, and being used for classification and grading processing of the target data based on the identified data content and other data elements of the target data, the other data elements including one or more of data volume, data group label, data processing logic, and data desensitization encryption mode; identifying data content of the target data according to a storage type of the target data and the target configuration information, to obtain the identified data content of the target data; obtaining the other data elements related to classification and grading of the target data according to the target configuration information, and determining a classification and grading processing result of the target data according to the identified data content of the target data, the obtained other data elements, and the target configuration information.

2. The method of claim 1, the target configuration information further comprising: a data asset range corresponding to the target data, a data type of the target data, and a preset data detection period, the preset data detection period being a period for identifying data content of the target data, and the data detection period being set based on a data update period; the obtaining the other data elements related to classification and grading of the target data according to the target configuration information, and the determining the classification and grading processing result of the target data according to the identified data content of the target data, the obtained other data elements, and the target configuration information, comprising: obtaining the other data elements related to classification and grading of the target data according to the data asset range corresponding to the target data and the data type of the target data; determining a data security benchmark level and a data security benchmark level promotion and demotion rule according to the data type of the target data; determining the classification and grading processing result of the target data according to the determined data security benchmark level and data security benchmark level promotion and demotion rule, the identified data content of the target data, the obtained other data elements, and the target configuration information.

3. The method of claim 1, the target configuration information including a preset detection rule used for obtaining and identifying the target data, and the storage type of the target data being object storage, the identifying data content of the target data according to the storage type of the target data and the target configuration information to obtain the identified data content of the target data, comprising: reading target data based on object storage, and performing parsing processing on the target data to determine text data and image data contained in the target data; According to the preset detection rule, data contents of the text data and the image data are respectively identified, and the identified data contents of the text data and the image data are obtained.

4. The method of claim 1, wherein the target configuration information comprises a preset detection rule for obtaining and identifying the target data, and the storage type of the target data is an analytical-oriented relational storage, and wherein the data contents of the target data are identified according to the storage type of the target data and the target configuration information, and the identified data contents of the target data are obtained, including: collecting the target data based on the analytical-oriented relational storage, and constructing a data blood relationship of the target data; determining a processing mode of the target data based on the constructed data blood relationship; and identifying the data contents of the target data according to the preset detection rule and the processing mode of the target data, and obtaining the identified data contents of the target data.

5. The method of claim 1, wherein the target configuration information comprises a preset detection rule for obtaining and identifying the target data, and the storage type of the target data is a structured database, and wherein the data contents of the target data are identified according to the storage type of the target data and the target configuration information, and the identified data contents of the target data are obtained, including: collecting the target data based on a table field level data sampling mode; constructing a structured array corresponding to the target data according to metadata of the target data; and identifying the structured array corresponding to the target data based on the preset detection rule, and obtaining the identified data contents of the target data.

6. The method of claim 2, further comprising: correcting a classification and grading processing result of the target data and labeling asset portrait information of a data asset corresponding to the target data according to asset information of the data asset and a preset data management rule; constructing a multi-modal model based on the target configuration information, wherein the multi-modal model is obtained by model training based on different modal data samples and a preset loss function; and identifying the data contents of the target data according to the storage type of the target data and the target configuration information, and obtaining the identified data contents of the target data, including: obtaining the multi-modal model constructed based on the target configuration information based on the target configuration information; and identifying the data contents of the target data according to the storage type of the target data and the multi-modal model, and obtaining the identified data contents of the target data.

8. A data classification and grading processing apparatus, comprising: a data acquisition module configured to acquire target data to be classified and graded; ​ ​ ​ ​ ​ ​ 7. The method of claim 1, further comprising: ​ ​ ​ ​ ​ ​ The target configuration information determination module obtains target configuration information matched with the target data, the target configuration information being used for classifying and processing the target data, and the target data is subjected to data content identification in the process of classifying and processing the target data by using the target configuration information, and the target data is subjected to classifying and processing based on the identified data content and other data elements in the target data except the identified data content, the target configuration information being used for data content identification of the target data, and being used for classifying and processing the target data based on the identified data content and other data elements of the target data, the other data elements including one or more of data volume, data group label, data processing logic, and data desensitization encryption mode; The data content identification module identifies the data content of the target data according to the storage type of the target data and the target configuration information, and obtains the identified data content of the target data. The classifying and processing module obtains the other data elements related to the classifying and processing of the target data according to the target configuration information, and determines the classifying and processing result of the target data according to the identified data content of the target data, the obtained other data elements, and the target configuration information. 9.An electronic device comprising: a processor; and a memory arranged to store computer executable instructions that, when executed, can cause the processor to: obtain target data to be classified and processed; obtain target configuration information matched with the target data, the target configuration information being used for classifying and processing the target data, and the target data is subjected to data content identification in the process of classifying and processing the target data by using the target configuration information, and the target data is subjected to classifying and processing based on the identified data content and other data elements in the target data except the identified data content, the target configuration information being used for data content identification of the target data, and being used for classifying and processing the target data based on the identified data content and other data elements of the target data, the other data elements including one or more of data volume, data group label, data processing logic, and data desensitization encryption mode; identify the data content of the target data according to the storage type of the target data and the target configuration information, and obtain the identified data content of the target data; obtain the other data elements related to the classifying and processing of the target data according to the target configuration information, and determine the classifying and processing result of the target data according to the identified data content of the target data, the obtained other data elements, and the target configuration information.

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