Method and device for generating data model and storage medium
By using AI big models to determine the data level and classification catalog, personalized data models are generated and encrypted, the existing storage systems are solved, and efficient classification and hierarchical storage of personal information is achieved.
Patent Information
- Application Number
- CN202510333969.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-20
- Publication Date
- 2025-07-18
AI Technical Summary
The existing personal information storage system lacks targetedness, resulting in insufficient data security and inability to effectively protect personal information.
By obtaining text data and entering AI big model, determining the data level and classification directory, filtering pending fields, generating personalized data models based on business needs, and encrypting and permission configuration in the database.
It realizes personalized classification and hierarchical storage of personal information, improving data security.
Smart Images
Figure CN120337312A_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of computer technology, and particularly to a method, apparatus, and storage medium for generating a data model. Background Art
[0002] In modern economies, as countries pay increasing attention to the protection of personal information, regulatory authorities in various countries have introduced laws and regulations on personal information protection to regulate the correct use of personal information. The operations of many enterprises involve a large amount of personal information data of employees, customers, suppliers, etc. However, the current collection, use, storage, etc. of personal information are all achieved by backend developers and frontend technicians writing relevant codes to generate a general storage system and directly using it in business systems. The existing storage system is generated using general codes, and the differences in personal information of different members are not considered during the generation process. As a result, the use and storage of existing personal information lack pertinence and data security is insufficient. Summary of the Invention
[0003] The purpose of the embodiments of this application is to provide a method, apparatus, and storage medium for generating a data model to solve the problem in the prior art that the storage system is generalized and cannot store personal information pertinently.
[0004] To achieve the above purpose, the first aspect of this application provides a method for generating a data model, and the method includes:
[0005] Obtain multiple text data;
[0006] Input the multiple text data into an AI large model to obtain the interpretation result of each text data, where the interpretation result includes the preliminary classification of personal information and the target compliance strategy of personal information;
[0007] Determine the data level and data classification directory for personal information according to the interpretation result;
[0008] Obtain the metadata information of the business system, and screen out the fields to be processed containing personal information from the metadata information;
[0009] Determine the target level of the field to be processed according to the data level and the target type of the field to be processed according to the data classification directory;
[0010] Process the field to be processed according to the target level and target type to obtain the data standard corresponding to the field to be processed;
[0011] Obtain the business requirements of the user;
[0012] Generate a data model according to the business requirements and the data standard.
[0013] In an embodiment of the present application, generating a data model based on business requirements and data standards includes: when the business requirements include user personal information, determining the current data level corresponding to the business requirements based on the data level and determining the current data type corresponding to the business requirements based on the data classification directory; generating a requirement development specification based on the current data level, the current data type and the business requirements; collecting required data information based on the requirement development specification; extracting the target data standard corresponding to the data information from the data standard; and generating a data model using the target data standard.
[0014] In an embodiment of the present application, generating a requirement development specification based on a current data level, a current data type, and business requirements includes: inputting the current data level, the current data type, and business requirements into a writing model to obtain a requirement development specification.
[0015] In an embodiment of the present application, the method also includes: deploying a data model in a database; identifying personal data written into the database; and, when the personal data is sensitive personal information, using the data model to encrypt and configure permissions for the personal data.
[0016] In an embodiment of the present application, the personal information of the field to be processed includes business standards and management standards, and the field to be processed is processed according to the target level and the target type to obtain the data standard corresponding to the field to be processed, including: when the target type is sensitive personal information, the data items in the business standard are graded using the target level and the data items in the business standard are classified using the target type, and the target compliance policy is configured to the data items in the management standard; the personal information of the field to be processed is updated according to the processed business standard and the processed management standard to obtain the data standard.
[0017] In an embodiment of the present application, processing the field to be processed according to the target level and the target type to obtain the data standard corresponding to the field to be processed includes: when the target type is general personal information, using the target level to grade the data items in the business standard and using the target type to classify the data items in the business standard; obtaining the general compliance policy and configuring the general compliance policy to the data items in the management standard; updating the personal information of the field to be processed according to the processed business standard and the processed management standard to obtain the data standard.
[0018] In the embodiments of the present application, determining the data level and data classification directory for personal information according to the interpretation result includes: when the preliminary classification of personal information is general personal information, determining the data level as the first control level, and determining the data classification directory as the first preset first-level directory and at least one first preset second-level directory belonging to the first preset first-level directory; when the preliminary classification of personal information is sensitive personal information, determining the data level as the second control level, and determining the data classification directory as the second preset first-level directory and at least one second preset second-level directory belonging to the second preset first-level directory, where the second control level is higher than the first control level.
[0019] In the embodiments of the present application, determining the target level and target type of the field to be processed according to the data level and data classification directory respectively includes: determining the semantic similarity between the field name of the field to be processed and the data classification directory, and determining the data classification directory with the highest semantic similarity as the candidate target type of the field to be processed; determining the matching degree between the personal information included in the field to be processed and the control content of the first control level and determining the matching degree between the personal information included in the field to be processed and the control content of the second control level; determining the maximum value of the matching degree of the first control level and the matching degree of the second control level as the candidate target level of the field to be processed; sending the candidate target type and the candidate target level to the user for review, and when the user's review is passed, determining the candidate target type and the candidate target level as the target level and target type of the field to be processed.
[0020] A second aspect of the present application provides an apparatus for generating a data model, including:
[0021] A memory configured to store instructions;
[0022] A processor configured to call instructions from the memory and capable of implementing the above-mentioned method for generating a data model when executing the instructions.
[0023] A third aspect of the present application provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the above-mentioned method for generating a data model.
[0024] Through the above technical solution, multiple text data are obtained; the multiple text data are input into the AI large model to obtain the interpretation results of each text data, and the interpretation results include the preliminary classification of personal information and the target compliance strategy of personal information; the data level and data classification directory for personal information are determined according to the interpretation results; the metadata information of the business system is obtained, and the fields to be processed containing personal information are screened out from the metadata information; the target level of the fields to be processed is determined according to the data level and the target type of the fields to be processed is determined according to the data classification directory; the fields to be processed are processed according to the target level and the target type to obtain the data standard corresponding to the fields to be processed; the business requirements of the user are obtained; a data model is generated according to the business requirements and the data standard to obtain a data model that can directly classify and grade personal information, which is convenient for personalized storage of personal information and improves data security.
[0025] Other features and advantages of the embodiments of the present application will be described in detail in the subsequent specific implementation section. Brief Description of the Drawings
[0026] The drawings are used to provide a further understanding of the embodiments of the present application, and constitute a part of the specification. Together with the following specific implementation, they are used to explain the embodiments of the present application, but do not constitute a limitation to the embodiments of the present application. In the drawings:
[0027] Figure 1 Schematically shows a flowchart of a method for generating a data model according to an embodiment of the present application;
[0028] Figure 2 Schematically shows a schematic diagram of a method for generating a data model according to an embodiment of the present application;
[0029] Figure 3 Schematically shows the internal structure diagram of a computer device according to an embodiment of the present application. Detailed Description of the Embodiments
[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. It should be understood that the specific implementation described here is only used to illustrate and explain the embodiments of the present application, and is not used to limit the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.
[0031] It should be noted that if there are directional indications (such as up, down, left, right, front, back, etc.) involved in the embodiments of this application, then such directional indications are only used to explain the relative positional relationship, movement conditions, etc. between components in a specific posture (as shown in the drawings). If this specific posture changes, then the directional indications will also change accordingly.
[0032] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of this application, then such descriptions of "first", "second", etc. are only for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In addition, the technical solutions between various embodiments can be combined with each other, but it must be based on the ability of those of ordinary skill in the art to implement. When the combination of technical solutions results in contradictions or cannot be implemented, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection required by this application.
[0033] Figure 1 Schematically shown is a flowchart of a method for generating a data model according to an embodiment of the present application. As Figure 1 shown, the embodiments of the present application provide a method for generating a data model, and the method may include the following steps.
[0034] Step 101: Obtain a plurality of text data.
[0035] Step 102: Input the plurality of text data into an AI large model to obtain the interpretation results of each text data, and the interpretation results include the preliminary classification of personal information and the target compliance policies of personal information.
[0036] Step 103: Determine the data level and data classification directory for personal information according to the interpretation results.
[0037] The processor can obtain multiple text data, which can be laws and regulations of various countries and regions. After obtaining the multiple text data, the processor can input the multiple text data into an AI large model (artificial intelligence large model) to obtain the interpretation results of each text data. The interpretation results include the preliminary classification of personal information and the target compliance strategy for personal information. After obtaining the interpretation results, the processor can determine the data level and data classification directory for personal information according to the interpretation results. For example, it can be determined that the data level for personal information can be internal control and strict internal control according to the preliminary classification of general personal information and sensitive personal information, and the data classification directory for personal information can include the first-level directory of general personal information and the first-level directory of sensitive personal information, as well as the second-level directory of geographical location information, the second-level directory of biogenetic information, the second-level directory of bank account information, etc. respectively under these two first-level directories.
[0038] In the embodiment of the present application, determining the data level and data classification directory for personal information according to the interpretation results includes: in the case where the preliminary classification of personal information is general personal information, determining the data level as the first control level, and determining the data classification directory as the first preset first-level directory and at least one first preset second-level directory belonging to the first preset first-level directory; in the case where the preliminary classification of personal information is sensitive personal information, determining the data level as the second control level, and determining the data classification directory as the second preset first-level directory and at least one second preset second-level directory belonging to the second preset first-level directory, where the second control level is higher than the first control level.
[0039] The processor can determine the data level and data classification directory for personal information according to the interpretation results. Specifically, the processor can determine whether the preliminary classification of personal information is general personal information or sensitive personal information. In the case where the preliminary classification of personal information is general personal information, the processor can determine the data level as the first control level, and determine the data classification directory as the first preset first-level directory and at least one first preset second-level directory belonging to the first preset first-level directory. In the case where the preliminary classification of personal information is sensitive personal information, the processor can determine the data level as the second control level, and determine the data classification directory as the second preset first-level directory and at least one second preset second-level directory belonging to the second preset first-level directory, where the second control level is higher than the first control level. For example, the second control level is strict internal control, and the first control level is internal control.
[0040] Step 104: Obtain the metadata information of the business system, and screen out the fields to be processed containing personal information from the metadata information.
[0041] Step 105: Determine the target level of the field to be processed according to the data level and determine the target type of the field to be processed according to the data classification directory.
[0042] The processor can obtain the metadata information of the business system and screen out the fields to be processed that contain personal information from the metadata information. After obtaining the fields to be processed that contain personal information, the processor can determine the target level of the fields to be processed according to the data level and determine the target type of the fields to be processed according to the data classification directory.
[0043] In the embodiment of the present application, determining the target level and target type of the fields to be processed according to the data level and the data classification directory respectively includes: determining the semantic similarity between the field name of the field to be processed and the data classification directory, and determining the data classification directory with the highest semantic similarity as the candidate target type of the field to be processed; determining the matching degree between the personal information included in the field to be processed and the controlled content of the first control level and determining the matching degree between the personal information included in the field to be processed and the controlled content of the second control level; determining the maximum value between the matching degree of the first control level and the matching degree of the second control level as the candidate target level of the field to be processed; sending the candidate target type and the candidate target level to the user for review, and when the user's review is passed, determining the candidate target type and the candidate target level as the target level and target type of the field to be processed.
[0044] The processor can determine the target level and target type of the fields to be processed according to the data level and the data classification directory respectively. Specifically, the processor can determine the semantic similarity between the field name of the field to be processed and the data classification directory. After obtaining the semantic similarity between the field name of the field to be processed and each data classification directory, the processor can determine the data classification directory with the highest semantic similarity and determine the data classification directory with the highest semantic similarity as the candidate target type of the field to be processed. The processor can determine the matching degree between the personal information included in the field to be processed and the controlled content of the first control level, and determine the matching degree between the personal information included in the field to be processed and the controlled content of the second control level. After determining the matching degree corresponding to the first control level and the matching degree corresponding to the second control level, the processor can determine the maximum value between the matching degree corresponding to the first control level and the matching degree corresponding to the second control level, and determine the maximum value between the matching degree of the first control level and the matching degree of the second control level as the candidate target level of the field to be processed. After obtaining the candidate target type and the candidate target level, the processor can send the candidate target type and the candidate target level to the user for review. The processor can obtain the review result of the user. When the user's review is passed, the processor can determine the candidate target type and the candidate target level as the target level and target type of the field to be processed.
[0045] Step 106: Process the fields to be processed according to the target level and target type to obtain the data standard corresponding to the fields to be processed.
[0046] After obtaining the target level and target type, the processor can process the field to be processed according to the target level and target type to obtain the data standard corresponding to the field to be processed, that is, based on the target level and target type, the processor can convert the field to be processed into the data standard.
[0047] In an embodiment of the present application, the personal information of the field to be processed includes business standards and management standards, and the field to be processed is processed according to the target level and the target type to obtain the data standard corresponding to the field to be processed, including: when the target type is sensitive personal information, the data items in the business standard are graded using the target level and the data items in the business standard are classified using the target type, and the target compliance policy is configured to the data items in the management standard; the personal information of the field to be processed is updated according to the processed business standard and the processed management standard to obtain the data standard.
[0048] The personal information of the field to be processed includes business standards and management standards. The processor can process the field to be processed according to the target level and the target type to obtain the data standard corresponding to the field to be processed. Specifically, the processor can determine whether the target type is sensitive personal information. In the case where the target type is sensitive personal information, the processor can use the target level to grade the data items in the business standard and use the target type to classify the data items in the business standard, and configure the target compliance policy to the data items in the management standard. After grading the data items in the business standard, classifying the data items in the business standard, and configuring the target compliance policy to the data items in the management standard, the processor can update the personal information of the field to be processed according to the processed business standard and the processed management standard to obtain the data standard.
[0049] In an embodiment of the present application, processing the field to be processed according to the target level and the target type to obtain the data standard corresponding to the field to be processed includes: when the target type is general personal information, using the target level to grade the data items in the business standard and using the target type to classify the data items in the business standard; obtaining the general compliance policy and configuring the general compliance policy to the data items in the management standard; updating the personal information of the field to be processed according to the processed business standard and the processed management standard to obtain the data standard.
[0050] The personal information of the field to be processed includes business criteria and management criteria. The processor can process the field to be processed according to the target level and target type to obtain the data criteria corresponding to the field to be processed. Specifically, the processor can determine whether the target type is general personal information. In the case where the target type is general personal information, the processor can grade the data items in the business criteria using the target level and classify the data items in the business criteria using the target type. The processor can also obtain the general compliance policy and configure the general compliance policy to the data items in the management criteria. After grading the data items in the business criteria, classifying the data items in the business criteria, and configuring the general compliance policy to the data items in the management criteria, the processor can update the personal information of the field to be processed according to the processed business criteria and the processed management criteria to obtain the data criteria.
[0051] Step 107: Obtain the business requirements of the user.
[0052] Step 108: Generate a data model according to the business requirements and the data criteria.
[0053] The processor can obtain the business requirements of the user. After obtaining the business requirements of the user, the processor can generate a data model according to the business requirements and the data criteria.
[0054] In the embodiments of the present application, generating a data model according to the business requirements and the data criteria includes: in the case where the business requirements include the personal information of the user, determining the current data level corresponding to the business requirements according to the data level and determining the current data type corresponding to the business requirements according to the data classification directory; generating a requirements development specification according to the current data level, the current data type, and the business requirements; collecting the required data information according to the requirements development specification; extracting the target data criteria corresponding to the data information from the data criteria; and generating a data model using the target data criteria.
[0055] The processor can generate a data model according to business requirements and data standards. Specifically, the processor can determine whether the business requirements include the user's personal information. In the case where the business requirements include the user's personal information, the processor can determine the current data level corresponding to the business requirements according to the data level and determine the current data type corresponding to the business requirements according to the data classification directory. After obtaining the current data level and the current data type corresponding to the business requirements, the processor can generate a requirements development specification according to the current data level, the current data type, and the business requirements. In the embodiments of the present application, the processor can generate a requirements development specification according to the current data level, the current data type, and the business requirements. Specifically, the processor can input the current data level, the current data type, and the business requirements into a writing model to obtain a requirements development specification. After generating the requirements development specification, the processor can collect the required data information according to the requirements development specification. After obtaining the required data information collected by the requirements development specification, the processor can extract the target data standard corresponding to the data information from the data standards and use the target data standard to generate a data model.
[0056] In the embodiments of the present application, the method further includes: deploying the data model in a database; identifying personal data written into the database; and in the case where the personal data is sensitive personal information, performing encryption processing and permission configuration on the personal data using the data model.
[0057] After generating the data model, the processor can deploy the data model in a database. The processor can identify the personal data written into the database to determine the target type of the written personal data. In the case where the target type of the personal data is sensitive personal information, the processor can perform encryption processing and permission configuration on the personal data using the data model.
[0058] In the embodiments of the present application, as Figure 2As shown, the processor can input laws and regulations into the AI large model to extract core data items, formulate classification criteria, and formulate grading criteria through the AI large model. After formulating the classification criteria and grading criteria, the processor can send the classification criteria and grading criteria to the data compliance platform. In the data compliance platform, the processor can classify and grade data according to the classification criteria and grading criteria. The processor can also configure compliance policies based on the interpretation of laws and regulations by the AI large model and combine the compliance policy configuration during sensitive type management. Specifically, the processor can identify sensitive data. When determining that personal data is sensitive personal information, the processor can configure the compliance policy for this personal information, which can include whether it can be collected, whether it can be stored, whether the storage is encrypted, and whether it can be transmitted across borders, etc. The processor can perform data modeling based on sensitive type management and data classification and grading, and perform data annotation management based on sensitive type management. After the processor identifies sensitive data, it can perform metadata management. Specifically, the processor can filter out the fields in the metadata that store personal information and bind the compliance policy to the fields. After completing the field sorting, the processor can sort out the sorted fields to determine the level and type of the personal information in the field. The processor can generate a security compliance plugin based on metadata management and data modeling to collect, store, and calculate personal information through the security compliance plugin, thereby realizing cross-border data transmission. Specifically, the processor can collect data, store data, and transmit data for the data of the business system through the security compliance plugin.
[0059] In the embodiment of the present application, the processor can obtain laws and regulations in multiple regions. After obtaining the laws and regulations in multiple regions, the processor can send the laws and regulations to the platform side of the embodied intelligence to train the AI large model with the laws and regulations in multiple regions on the platform side, obtain the trained AI large model, and store the trained AI large model on the platform side. Input the current laws and regulations into the trained AI large model to extract core data items, formulate classification criteria, and formulate grading criteria through the trained AI large model, and send the classification criteria and grading criteria to the robot. The robot can classify and grade data according to the classification criteria and grading criteria. The robot can also obtain the interpretation result of the laws and regulations by the AI large model, configure the compliance policy according to the interpretation result, and finally the robot can manage personal data based on the compliance policy.
[0060] Through the above technical solutions, it is possible to directly classify and grade personal information, facilitate personalized storage of personal information, and improve data security.
[0061] Figure 1 It is a schematic flowchart of a method for generating a data model in an embodiment. It should be understood that although Figure 1The steps in the flowchart are shown in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figure 1 At least a part of the steps in
[0062] Embodiments of the present application also provide an apparatus for generating a data model, including:
[0063] A memory configured to store instructions;
[0064] A processor configured to call instructions from the memory and capable of implementing the above method for generating a data model when executing the instructions.
[0065] Embodiments of the present application also provide a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the above method for generating a data model.
[0066] In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as Figure 3 shown. The computer device includes a processor A01, a network interface A02, a memory (not shown in the figure), and a database (not shown in the figure) connected through a system bus. Among them, the processor A01 of the computer device is used to provide computing and control capabilities. The memory of the computer device includes an internal memory A03 and a non-volatile storage medium A04. The non-volatile storage medium A04 stores an operating system B01, a computer program B02, and a database (not shown in the figure). The internal memory A03 provides an environment for the operation of the operating system B01 and the computer program B02 in the non-volatile storage medium A04. The database of the computer device is used to store data such as text data, interpretation results, data levels, data classification directories, personal information, metadata information, target types, target levels, data standards, and business requirements. The network interface A02 of the computer device is used to communicate with an external terminal through a network connection. When the computer program B02 is executed by the processor A01, it implements a method for generating a data model.
[0067] Those skilled in the art can understand, Figure 3The structure shown is only a block diagram of some structures related to the solution of this application, and does not constitute a limitation on the computer device to which the solution of this application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.
[0068] An embodiment of this application provides a device, which includes a processor, a memory, and a program stored on the memory and executable on the processor. When the processor executes the program, the following steps are implemented: obtaining a plurality of text data; inputting the plurality of text data into an AI large model to obtain an interpretation result for each text data, where the interpretation result includes a preliminary classification of personal information and a target compliance policy for personal information; determining a data level and a data classification directory for personal information according to the interpretation result; obtaining metadata information of the business system, and screening out fields to be processed that contain personal information from the metadata information; determining a target level of the field to be processed according to the data level and a target type of the field to be processed according to the data classification directory; processing the field to be processed according to the target level and the target type to obtain a data standard corresponding to the field to be processed; obtaining the business requirements of the user; generating a data model according to the business requirements and the data standard.
[0069] In one embodiment, generating a data model according to the business requirements and the data standard includes: when the business requirements include the user's personal information, determining a current data level corresponding to the business requirements according to the data level and a current data type corresponding to the business requirements according to the data classification directory; generating a requirements development specification according to the current data level, the current data type, and the business requirements; collecting the required data information according to the requirements development specification; extracting a target data standard corresponding to the data information from the data standard; using the target data standard to generate a data model.
[0070] In one embodiment, generating a requirements development specification according to the current data level, the current data type, and the business requirements includes: inputting the current data level, the current data type, and the business requirements into a writing model to obtain a requirements development specification.
[0071] In one embodiment, the method further includes: deploying the data model in a database; identifying personal data written into the database; and when the personal data is sensitive personal information, encrypting and configuring permissions for the personal data using the data model.
[0072] In one embodiment, the personal information of the field to be processed includes business criteria and management criteria. Processing the field to be processed according to the target level and target type to obtain data criteria corresponding to the field to be processed includes: when the target type is sensitive personal information, grading the data items in the business criteria using the target level and classifying the data items in the business criteria using the target type, and configuring the target compliance policy to the data items in the management criteria; updating the personal information of the field to be processed according to the processed business criteria and the processed management criteria to obtain data criteria.
[0073] In one embodiment, processing the field to be processed according to the target level and target type to obtain data criteria corresponding to the field to be processed includes: when the target type is general personal information, grading the data items in the business criteria using the target level and classifying the data items in the business criteria using the target type; obtaining a general compliance policy and configuring the general compliance policy to the data items in the management criteria; updating the personal information of the field to be processed according to the processed business criteria and the processed management criteria to obtain data criteria.
[0074] In one embodiment, determining the data level and data classification directory for personal information according to the interpretation result includes: when the preliminary classification of the personal information is general personal information, determining the data level as the first control level, and determining the data classification directory as the first preset first-level directory and at least one first preset second-level directory belonging to the first preset first-level directory; when the preliminary classification of the personal information is sensitive personal information, determining the data level as the second control level, and determining the data classification directory as the second preset first-level directory and at least one second preset second-level directory belonging to the second preset first-level directory, where the second control level is higher than the first control level.
[0075] In one embodiment, determining the target level and target type of the field to be processed according to the data level and data classification directory respectively includes: determining the semantic similarity between the field name of the field to be processed and the data classification directory, and determining the data classification directory with the highest semantic similarity as the candidate target type of the field to be processed; determining the matching degree between the personal information included in the field to be processed and the control content of the first control level and determining the matching degree between the personal information included in the field to be processed and the control content of the second control level; determining the maximum value of the matching degree of the first control level and the matching degree of the second control level as the candidate target level of the field to be processed; sending the candidate target type and the candidate target level to the user for review, and when the user's review is passed, determining the candidate target type and the candidate target level as the target level and target type of the field to be processed.
[0076] The present application also provides a computer program product which, when executed on a data processing device, is adapted to execute a program initialized with method steps for generating a data model.
[0077] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system or a computer program product. Therefore, the present application can take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0078] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and the combination of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, such that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the specified functions in Figure 1 one or more flows and / or Figure 1 blocks.
[0079] These computer program instructions can also be stored in a computer-readable memory capable of guiding the computer or other programmable data processing devices to work in a specific manner, such that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the specified functions in Figure 1 one or more flows and / or Figure 1 blocks.
[0080] These computer program instructions can also be loaded onto a computer or other programmable data processing devices, such that a series of operation steps are executed on the computer or other programmable devices to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable devices provide steps for implementing the specified functions in Figure 1 one or more flows and / or Figure 1 blocks.
[0081] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.
[0082] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM) and / or non-volatile memory such as read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.
[0083] Computer-readable media includes permanent and non-permanent, removable and non-removable media that can store information by any method or technology. 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 cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.
[0084] It should also be noted that the term "comprising", "including" or any other variation thereof is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.
[0085] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.
Claims
1. A method for generating a data model, characterized in that, The method includes: Obtain multiple text data; Input the multiple text data into an AI large model to obtain the interpretation results of each text data, where the interpretation results include the preliminary classification of personal information and the target compliance policies for personal information; Determine the data level and data classification directory for personal information according to the interpretation results; Obtain the metadata information of the business system, and screen out the fields to be processed that contain personal information from the metadata information; Determine the target level of the field to be processed according to the data level and determine the target type of the field to be processed according to the data classification directory; Process the field to be processed according to the target level and target type to obtain the data standard corresponding to the field to be processed; Obtain the business requirements of the user; Generate a data model according to the business requirements and the data standard.
2. The method for generating a data model according to claim 1, wherein The generating a data model according to the business requirements and the data standard includes: When the business requirements include the personal information of the user, determine the current data level corresponding to the business requirements according to the data level and determine the current data type corresponding to the business requirements according to the data classification directory; Generate a requirements development specification according to the current data level, the current data type and the business requirements; Collect the required data information according to the requirements development specification; Extract the target data standard corresponding to the data information from the data standard; Generate the data model using the target data standard.
3. The method for generating a data model according to claim 2, wherein The generating a requirements development specification according to the current data level, the current data type and the business requirements includes: Input the current data level, the current data type and the business requirements into a writing model to obtain the requirements development specification.
4. The method for generating a data model according to claim 2, wherein The method further includes: Deploy the data model in a database; Identify the personal data written into the database; When the personal data is sensitive personal information, use the data model to encrypt and configure permissions for the personal data.
5. The method for generating a data model according to claim 1, wherein The personal information of the field to be processed includes business standards and management standards. The processing the field to be processed according to the target level and target type to obtain the data standard corresponding to the field to be processed includes: When the target type is sensitive personal information, grade the data items in the business standards using the target level and classify the data items in the business standards using the target type, and configure the target compliance policies to the data items in the management standards; Update the personal information of the field to be processed according to the processed business standards and processed management standards to obtain the data standard.
6. The method for generating a data model according to claim 5, wherein, The processing the field to be processed according to the target level and target type to obtain the data standard corresponding to the field to be processed includes: When the target type is general personal information, grade the data items in the business standards using the target level and classify the data items in the business standards using the target type; Obtain a general compliance policy and configure the general compliance policy into the data items in the management standard; Update the personal information of the to-be-processed field according to the processed business standard and the processed management standard to obtain the data standard.
7. The method for generating a data model according to claim 1, wherein The determining the data level and data classification directory for personal information according to the interpretation result includes: In the case where the preliminary classification of the personal information is general personal information, determine that the data level is the first control level, and determine that the data classification directory is the first preset first-level directory and at least one first preset second-level directory belonging to the first preset first-level directory; In the case where the preliminary classification of the personal information is sensitive personal information, determine that the data level is the second control level, and determine that the data classification directory is the second preset first-level directory and at least one second preset second-level directory belonging to the second preset first-level directory, wherein the second control level is higher than the first control level.
8. The method for generating a data model according to claim 7, wherein The determining the target level and target type of the to-be-processed field according to the data level and the data classification directory respectively includes: Determine the semantic similarity between the field name of the to-be-processed field and the data classification directory, and determine the data classification directory with the highest semantic similarity as the candidate target type of the to-be-processed field; Determine the matching degree between the personal information included in the to-be-processed field and the control content of the first control level and determine the matching degree between the personal information included in the to-be-processed field and the control content of the second control level; Determine the maximum value of the matching degree of the first control level and the matching degree of the second control level as the candidate target level of the to-be-processed field; Send the candidate target type and the candidate target level to the user for review, and in the case where the user's review is passed, determine the candidate target type and the candidate target level as the target level and target type of the to-be-processed field.
9. An apparatus for generating a data model, characterized in that Includes: A memory configured to store instructions; A processor configured to call the instructions from the memory and capable of implementing the method for generating a data model according to any one of claims 1 to 8 when executing the instructions.
10. A machine-readable storage medium, characterized in that, Instructions are stored on the machine-readable storage medium, and the instructions are used to cause the machine to execute the method for generating a data model according to any one of claims 1 to 8.