Enterprise data integrity evaluation index generation method and system based on metadata

Abstract

The invention discloses a metadata-based enterprise data integrity evaluation index generation method and system. The method comprises the following steps: in response to storage of metadata information associated with enterprise data, performing normal marking of data integrity on the metadata information; in response to the data integrity evaluation instruction, generating a plurality of sub-tasks based on an evaluation demand; for the dynamic marking task, performing dynamic marking of data integrity on the metadata in the preset evaluation range according to the business change demand and the evaluation demand; for the data extraction task, querying a metadatabase based on the evaluation intention and the data integrity tag; extracting target metadata information according to the query result; and for the evaluation index generation task, converting the target metadata information into an integrity evaluation index, and generating an integrity evaluation index scheme according to the integrity evaluation index. According to the marking mode for continuously matching the full life cycle of the data, the integrity label can be dynamically adjusted, and the generated evaluation index has better adaptability and timeliness.

Description

Technical Field

[0001] This invention relates to the field of data evaluation technology, and in particular to a method and system for generating enterprise data integrity evaluation indicators based on metadata. Background Technology

[0002] Data integrity, as a crucial element ensuring data quality, refers to the agreement that key attribute fields cannot be empty when data entities are generated. This ensures data usability, such as mandatory fields like customer addresses for e-commerce companies and customer identification documents for telecommunications companies, which directly relate to business compliance and service continuity. As enterprises deepen their digital transformation, data types are becoming increasingly diverse and their sources more widespread. To comprehensively understand the current state of their data, enterprises need to conduct regular or ad-hoc data quality assessments, and data integrity assessment is a vital component of this process.

[0003] Currently, the design and implementation of indicators for data integrity assessment in the industry mainly rely on manual operation. That is, technical or business personnel judge the mandatory attributes of data, define the assessment scope, and manually design assessment indicators based on their experience.

[0004] However, enterprises have diverse data types, significantly different storage methods and processing logics, and data integrity assessments initiated at different times often have different assessment objectives and scopes. Therefore, manually designing assessment indicators makes it difficult to form universal standards. Moreover, due to the limitations of the designers' subjective judgment and understanding of the data, it is prone to problems such as missing indicator definitions and insufficient targeting, failing to take into account the integrity requirements of various types of data and failing to meet the assessment needs of different data types.

[0005] Therefore, existing technologies still need improvement and development. Summary of the Invention

[0006] The technical problem to be solved by this invention is to provide a method and system for generating enterprise data integrity assessment indicators based on metadata, in order to address the above-mentioned deficiencies of the prior art. This aims to solve the problem that existing data integrity assessment methods are difficult to adapt to the diverse data scenarios and dynamically changing assessment needs of enterprises.

[0007] The technical solution adopted by this invention to solve the problem is as follows: In a first aspect, embodiments of the present invention provide a method for generating enterprise data integrity assessment indicators based on metadata, the method comprising: In response to the entry of metadata information associated with enterprise data into the database, routine data integrity tags are applied to the metadata information. In response to the data integrity assessment instruction, several sub-tasks are generated based on the assessment requirements; the several sub-tasks include: dynamic labeling task, data extraction task, and indicator generation task. For the dynamic tagging task, based on business change requirements and evaluation requirements, dynamic tagging of data integrity is performed on the metadata within the preset evaluation scope; For the data extraction task, the metadata database is queried based on the evaluation intent and data integrity tags; the target metadata information is extracted based on the query results. For the task of generating the evaluation indicators, the target metadata information is transformed into integrity evaluation indicators, and an integrity evaluation indicator scheme is generated based on the integrity evaluation indicators.

[0008] In one implementation, the normal marking method includes: For each entity table attribute defined in the metadata information entered into the database, determine whether the entity table attribute meets preset conditions; wherein, the preset conditions include: the entity table attribute is a primary key identifier, and / or the required field corresponding to the entity table attribute is a preset character; If the preset conditions are met, the entity table attributes are tagged with data integrity to achieve the normal compliance.

[0009] In one implementation, the step of generating several sub-tasks based on the evaluation requirements includes: Generate task configuration information based on the assessment requirements; The task is decomposed based on the task configuration information to obtain the several sub-tasks.

[0010] In one implementation, the task configuration information includes: the preset evaluation range, dynamic labeling requirements, and execution time.

[0011] In one implementation, the step of dynamically tagging metadata within a preset assessment scope for data integrity based on business change requirements and the assessment requirements includes: Configure a dynamic tagging strategy for data integrity based on the aforementioned business change requirements and assessment requirements; Customized data integrity tags are generated based on the dynamic tagging strategy, and the metadata of the preset evaluation range is dynamically tagged with data integrity tags.

[0012] In one implementation, the customized data integrity label generated based on the dynamic tagging becomes invalid after this evaluation.

[0013] In one implementation, the steps of converting the target metadata information into integrity assessment metrics and generating an integrity assessment metric scheme based on the integrity assessment metrics include: The target metadata information is transformed into integrity assessment indicators through a pre-set artificial intelligence model. By summarizing all the integrity assessment indicators, a set of integrity assessment indicators is obtained. A completeness assessment index scheme is generated based on the completeness assessment index set.

[0014] Secondly, embodiments of the present invention also provide a system for generating enterprise data integrity assessment indicators based on metadata, the system comprising: The routine tagging module is used to perform routine data integrity tagging on the metadata information in response to the input of enterprise data association metadata information; The task decomposition module is used to generate several sub-tasks based on the assessment requirements in response to the data integrity assessment instruction; the several sub-tasks include: dynamic labeling task, data extraction task, and indicator generation task. The dynamic tagging module is used to dynamically tag the metadata within a preset evaluation scope for data integrity in accordance with business change requirements and evaluation requirements for the dynamic tagging task. The data extraction module is used to query the metadata database based on the evaluation intent and data integrity tags for the data extraction task; and extract the target metadata information based on the query results. The indicator generation module is used to convert the target metadata information into integrity assessment indicators for the assessment indicator generation task, and generate an integrity assessment indicator scheme based on the integrity assessment indicators.

[0015] Thirdly, embodiments of the present invention also provide a terminal, the terminal including a memory and one or more processors; the memory stores one or more programs; the programs include instructions for executing the metadata-based enterprise data integrity assessment index generation method as described above; the processor is used to execute the programs.

[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium having stored thereon a plurality of instructions adapted to be loaded and executed by a processor to implement the steps of the metadata-based enterprise data integrity assessment index generation method as described above.

[0017] The beneficial effects of this invention are as follows: In this embodiment, in response to the input of metadata information associated with enterprise data into the database, the metadata information is routinely tagged for data integrity. In response to data integrity assessment instructions, several sub-tasks are generated based on assessment requirements. These sub-tasks include: dynamic tagging tasks, data extraction tasks, and indicator generation tasks. For the dynamic tagging task, based on business change requirements and the assessment requirements, metadata within a preset assessment scope is dynamically tagged for data integrity. For the data extraction task, the metadata database is queried based on the assessment intent and data integrity tags. Target metadata information is extracted based on the query results. For the indicator generation task, the target metadata information is transformed into integrity assessment indicators, and an integrity assessment indicator scheme is generated based on these indicators. This invention's continuous matching tagging mode throughout the entire data lifecycle allows tags to remain synchronized with the dynamic adjustments of data generation, usage scenarios, and the organization's internal and external environment. The resulting assessment indicators can solve the problems of insufficient adaptability, timeliness, and systematization in traditional assessment indicator design. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart illustrating the method for generating enterprise data integrity assessment indicators based on metadata, as provided in this embodiment of the invention.

[0020] Figure 2 This is a schematic diagram of the enterprise data integrity assessment index generation device based on metadata provided in an embodiment of the present invention.

[0021] Figure 3 This is a schematic diagram of the core process of the method for generating enterprise data integrity assessment indicators based on metadata provided in this embodiment of the invention.

[0022] Figure 4 This is a schematic diagram of metadata entry into the database provided in an embodiment of the present invention.

[0023] Figure 5 This is a schematic diagram of the processing of the artificial intelligence model provided in the embodiment of the present invention.

[0024] Figure 6 This is a schematic diagram of the terminal provided in the embodiment of the present invention. Detailed Implementation

[0025] This invention discloses a method and system for generating enterprise data integrity assessment indicators based on metadata. To make the objectives, technical solutions, and effects of this invention clearer and more explicit, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and are not intended to limit the invention.

[0026] Those skilled in the art will understand that, unless specifically stated otherwise, the singular forms “a,” “an,” “the,” and “the” used herein may also include the plural forms. It should be further understood that the term “comprising” as used in this specification means the presence of the stated features, integers, steps, operations, elements, and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof. It should be understood that when we say an element is “connected” or “coupled” to another element, it can be directly connected or coupled to the other element, or there may be intermediate elements. Furthermore, “connected” or “coupled” as used herein can include wireless connections or wireless coupling. The term “and / or” as used herein includes all or any units and all combinations of one or more associated listed items.

[0027] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art, and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.

[0028] To address the aforementioned shortcomings of existing technologies, this invention provides a method for generating enterprise data integrity assessment indicators based on metadata, such as... Figure 1 As shown, the method specifically includes the following steps: Step S100: In response to the entry of metadata information associated with enterprise data into the database, perform routine data integrity tagging on the metadata information.

[0029] Specifically, a prerequisite for the method in this embodiment is the establishment of a metadata database. Metadata is data that interprets data and is used to manage and determine how the data is used. The role of the metadata database is to store and manage metadata. It can collect metadata information corresponding to all data of an enterprise in real time, and this information comprehensively covers the business and technical attributes of the data. Metadata accurately describes the definition, source, and other elements of enterprise data through this collected information, providing a data foundation for subsequent integrity determination.

[0030] In practical applications, the system first implements comprehensive metadata management for all enterprise data. Leveraging complete information collected from the metadata database, it clearly defines the business background, technical characteristics, and relationships of each type of data, ensuring accurate interpretation of key information such as the definition and source of all data. Once the metadata information associated with the enterprise data is collected and formally entered into the database, routine data integrity tagging is triggered (e.g., ...). Figure 2 (As shown). Routine tagging refers to the automatic generation of data integrity tags based on general strategies during the generation of corresponding metadata at each stage of the data lifecycle.

[0031] For example, the information collected in real time by the metadata database includes: the business scenario, subject domain, subject domain definition, business object, business object definition, entity table, entity table definition, entity table attributes, primary key identifier, whether it is required, foreign key related tables, data storage location, etc. for each piece of data in the customer management module, and generates corresponding records to be stored in the metadata database.

[0032] In one implementation, the method for normal labeling includes: For each entity table attribute defined in the metadata information entered into the database, determine whether the entity table attribute meets preset conditions; wherein, the preset conditions include: the entity table attribute is a primary key identifier, and / or the required field corresponding to the entity table attribute is a preset character; If the preset conditions are met, the entity table attributes are tagged with data integrity to achieve normal compliance.

[0033] Throughout the entire lifecycle of enterprise data, various types of business data generate corresponding metadata. When this metadata is collected and formally stored in the metadata database, the system's routine data integrity tagging capability is triggered. Specifically, the system checks each entity table attribute defined in each piece of metadata against pre-defined routine data integrity tagging rules to determine whether it meets preset conditions. Pre-defined conditions include two scenarios: first, the entity table attribute is explicitly marked as a primary key identifier; second, the "required / unrestricted" field corresponding to the entity table attribute is a preset character. If either of these scenarios is met, a data integrity tag is added to the entity table attribute to complete the routine tagging. For example, as business expands into new markets, the requirement for customer identification data in customer information changes from optional to required. In the metadata, the [Customer Identification] field of the entity table [Customer Basic Information] will be tagged with a data integrity label. This means that if a customer identification information record in the production data is empty, the data integrity requirement is not met.

[0034] For example, as shown in Table 1, the "Entity Table Attributes" in the "Entity Table Definition" of the metadata are routinely labeled with "Data Integrity Tags".

[0035] When a data entity table is defined in the metadata database, the metadata database performs routine data integrity tagging on the attributes of each entity table defined in the data entity table according to the set "normal tagging rules".

[0036] Example: The standard labeling rules are as follows: Tagging rule 1: In the entity table structure definition, if the entity table attribute is [primary key identifier], or the table attribute [required] value is T, the table attribute must be marked with "integrity tag" as T.

[0037] Tagging rule 2: In the entity table structure definition, when all entity tables have the table attribute "region identifier", the "integrity tag" must be set to T.

[0038] Tagging results: As you can see, the customer identifier, customer name, and region identifier attributes in the [Customer Basic Information] entity table are tagged with "Integrity". This indicates that in the production data, each piece of customer basic information generated by the system must contain the customer identifier, customer name, and region identifier information. If the relevant records are empty, the data integrity requirement is not met.

[0039] Table 1. Examples of Standard Marking

[0040] Step S200: In response to the data integrity assessment instruction, generate several sub-tasks based on the assessment requirements; the several sub-tasks include: dynamic labeling task, data extraction task, and indicator generation task.

[0041] When the system receives a data integrity assessment instruction, indicating that a data integrity assessment is to be initiated, the system will break down the overall assessment work into several sub-tasks based on the assessment requirements to ensure the assessment process proceeds in an orderly manner. Specifically, after the assessment work is started, it will proceed according to a preset process: first, it will enter the integrity assessment indicator generation task configuration stage, where relevant tasks will be configured and issued according to the assessment requirements. For example... Figure 2 As shown, the system then proceeds to the task decomposition and scheduling phase for data integrity-related metadata extraction. On one hand, there is task decomposition: the system breaks down the overall evaluation task into multiple sub-tasks based on task configuration information, including but not limited to: dynamic tagging tasks (also known as customized integrity tagging tasks), data extraction tasks (also known as related metadata extraction tasks), and indicator generation tasks. On the other hand, there is task scheduling: the system calls the corresponding functional modules respectively. For example... Figure 3 As shown, the "Data Integrity Assessment Dynamic Tagging Task Execution Module" can be scheduled to handle dynamic tagging tasks, and the "Data Integrity Tag Extraction Metadata Task Execution Module" can be scheduled to handle data extraction tasks.

[0042] In one implementation, the steps of generating several sub-tasks based on the evaluation requirements include: Generate task configuration information based on the assessment requirements; The task is decomposed based on the task configuration information to obtain the several sub-tasks.

[0043] Furthermore, the task configuration information includes: the preset evaluation range, dynamic labeling requirements, and execution time.

[0044] Specifically, the data integrity assessment requirements are first transformed into quantifiable task configuration information. This task configuration information includes key elements for assessment execution: a preset assessment scope (the scope of metadata to be assessed); dynamic tagging requirements (also known as customized indicator tagging requirements), which specify the types of integrity tags to be added based on the specific business rules or temporary requirements of this assessment; and execution time, which are the relevant time nodes for the task. Then, based on the generated task configuration information, the task is decomposed, breaking down the overall data integrity assessment task into multiple sub-tasks, such as dynamic tagging tasks and data extraction tasks, ensuring that each sub-task corresponds to the specific requirements in the configuration information.

[0045] Step S300: For the dynamic tagging task, based on the business change requirements and the evaluation requirements, perform dynamic tagging of the metadata within the preset evaluation scope to ensure data integrity.

[0046] Specifically, during the data integrity assessment process, data integrity tags can be flexibly applied to metadata within a specified assessment scope based on business change requirements and specific assessment needs. Dynamic tagging differs from routine tagging in that it is not a fixed process but rather has specific triggering conditions, such as business change requirements or changes in mandatory data attribute requirements due to the company expanding into new businesses. Dynamic tagging refers to automatically generating specific business-related integrity tags for new data integrity requirements added in the current assessment (e.g., requiring a non-mandatory attribute to not be empty). By combining business change needs with the current assessment requirements to dynamically tag metadata within a specified scope, the data integrity tags of the metadata can better align with the current business reality and assessment needs, solving the problem that static tagging cannot adapt to dynamic business changes and personalized assessment scenarios.

[0047] In one implementation, the step of dynamically tagging metadata within a preset assessment scope for data integrity based on business change requirements and the assessment requirements includes: Configure a dynamic tagging strategy for data integrity based on the aforementioned business change requirements and assessment requirements; Customized data integrity tags are generated based on the dynamic tagging strategy, and the metadata of the preset evaluation range is dynamically tagged with data integrity tags.

[0048] Furthermore, the customized data integrity label generated based on the dynamic tagging became invalid after this evaluation.

[0049] Specifically, firstly, based on the business change requirements and the needs of this assessment, a customized data integrity tagging strategy is configured for a single application, resulting in a dynamic data integrity tagging strategy. This ensures that the strategy both aligns with the current business changes and matches the specific requirements of this assessment. Next, customized data integrity tags are generated according to the configured dynamic tagging strategy, and then dynamic tagging is performed on metadata records within a specified metadata range. It should be noted that these customized data integrity tags are scenario-limited, valid only during this assessment process, and expire after the assessment to avoid temporary tag residues interfering with subsequent assessments.

[0050] For example, dynamic data integrity labels can be applied to the "entity table attributes" within the "entity table definition" in the metadata. Specifically, when a data quality assessment is initiated, a dynamic labeling strategy (i.e., a customized data integrity labeling strategy) is set according to the assessment's objectives. The metadata database then uses this strategy to dynamically label the data entity table attributes based on data integrity. The dynamically generated labels become invalid after the assessment is completed.

[0051] Example: An assessment task involves evaluating the completeness of customer information-related data. Based on the assessment requirements, the dynamic labeling rules are set as follows: Dynamic tagging rule 3: Based on the real-name system requirements for assessment needs, the following attributes in the entity table structure definition are required fields: [Real-name status], [Customer ID], [Contact mobile phone], and [Customer address].

[0052] Dynamic Tagging Rule 4: Based on the credit management requirements of the assessment needs, in the definition of the [Credit Score] entity table structure, the [Credit Score Value] and [Assessment Time] attributes are required fields.

[0053] Tagging Results: As shown in Table 2 below, in addition to the usual tagged data, the attributes [Real-name Status], [Customer Identification Documents], [Contact Phone Number], and [Customer Address] in the [Customer Basic Information] table have been tagged with completeness. This indicates that in the production data, each customer's basic information generated by the system must include their real-name status, identification document, contact phone number, and customer address. If any related record is empty, the data integrity requirement is not met.

[0054] Table 2. Examples of Dynamic Marking

[0055] Step S400: For the data extraction task, query the metadata database based on the evaluation intent and data integrity label; extract the target metadata information based on the query results.

[0056] Specifically, the data extraction task does not blindly search the metadata database. Instead, it first identifies the assessment intent for the data integrity. For example, an artificial intelligence model (AI model) can be used to deeply identify the assessment intent. The assessment intent can be for the integrity of customer information after the expansion of new business or the integrity of related data of specific business modules. Based on this, the system combines the identified assessment intent with existing data integrity tags in the metadata database (including data integrity tags generated based on normal tagging when metadata is entered into the database, and customized data integrity tags generated based on dynamic tagging) to form query conditions. Among them, the assessment intent reflects the type and scope of metadata required, while the data integrity tags reflect the data integrity standards that need to be met. The two complement each other. Subsequently, the system searches the metadata database based on this combined query condition. The metadata database stores complete information such as business scenarios, entity table attributes, and relationships of all enterprise data. Finally, it filters out metadata records that match the assessment intent and have data integrity tags from the massive amount of metadata. These records are the target metadata information.

[0057] Step S500: For the evaluation index generation task, the target metadata information is converted into integrity evaluation indexes, and an integrity evaluation index scheme is generated based on the integrity evaluation indexes.

[0058] Further, the steps of converting the target metadata information into integrity assessment metrics and generating an integrity assessment metric scheme based on the integrity assessment metrics include: The target metadata information is transformed into integrity assessment indicators through a pre-set artificial intelligence model. By summarizing all the integrity assessment indicators, a set of integrity assessment indicators is obtained. A completeness assessment index scheme is generated based on the completeness assessment index set.

[0059] Specifically, another prerequisite for the method in this embodiment is the continuous training and deployment of the artificial intelligence model used to generate integrity assessment metrics. First, it is necessary to review past practical experience in integrity metric design and translate it into specific strategies. Then, through fine-tuning and specialized training of the artificial intelligence model, the model gradually acquires the ability to write integrity assessment metrics. Simultaneously, a standard intelligent agent process is constructed to ensure that the artificial intelligence model can accurately complete the metric generation work, providing technical support for the subsequent conversion of target metadata.

[0060] The task of generating evaluation metrics refers to transforming the previously extracted target metadata information into quantifiable integrity evaluation metrics and integrating them into a completeness evaluation metric scheme. This entire process relies on task configuration information (also known as task configuration rules). First, the target metadata information is obtained after identifying the evaluation intent and filtering data integrity tags. The system then transforms this target metadata information into specific integrity evaluation metrics according to the task configuration information. For example, the mandatory attribute "Customer Documents" in the "Customer Basic Information Table" is transformed into quantifiable metrics directly usable for evaluation, such as "Customer Documents Field Completeness Rate" and "Customer Documents Information Non-Null Percentage." This embodiment continuously matches the entire data lifecycle based on metadata management, continuously tagging and tracking data integrity quality requirements. This ensures that the generated integrity evaluation metrics have high matching accuracy, high effectiveness, and high systematization, better supporting the evaluation work. Finally, these integrity evaluation metrics are summarized and integrated by the system to form a clearly structured and logically rigorous integrity evaluation metric scheme, which is then submitted. This integrity evaluation metric scheme can also be output in Excel, Word, or PDF formats according to actual needs.

[0061] For example, in a certain evaluation task, the completeness index of customer information-related data was assessed.

[0062] (1) Identify intent: The scope of the data to be evaluated is all tables associated with the customer’s basic information.

[0063] (2) Filter "Customer Basic Information Table" OR "Foreign Key Related Table = Customer Basic Information", and retrieve metadata based on the "Integrity Tag". The retrieved metadata records are shown in Table 3 below: Table 3. Examples of retrieved metadata records

[0064] Integrity assessment metrics are generated based on the acquired metadata information, and then corresponding assessment metric schemes are generated, as shown in Table 4 below: Table 4. Examples of Evaluation Indicator Schemes

[0065] In one implementation, the step of generating an integrity assessment index scheme based on the integrity assessment index set further includes: receiving the integrity assessment index scheme and managing the generated integrity assessment indexes accordingly, including but not limited to: revising, optimizing, and publishing.

[0066] In summary, this invention constructs a method for intelligently and dynamically generating enterprise data integrity assessment indicators through metadata management, routine and dynamic tagging, and artificial intelligence technology. This comprehensively solves the problems of adaptability, timeliness, and systematization deficiencies in traditional assessment indicator design. This tagging model, which continuously matches the entire data lifecycle, ensures that tags remain synchronized with the dynamic adjustments of data generation, usage scenarios, and the organization's internal and external environment, providing accurate foundational data for subsequent indicator generation. When an enterprise initiates an integrity assessment, the artificial intelligence model can intelligently identify the assessment target, locate the required integrity tag range, quickly extract the corresponding metadata information, and transform it into assessment indicators. This overcomes the subjectivity, experience limitations, and indicator gaps caused by insufficient data understanding resulting from manual indicator design, significantly improving the efficiency and accuracy of assessment indicator generation.

[0067] The advantages of this invention are specifically manifested in the following aspects: The generated evaluation metrics have a high degree of matching: because metadata defines all data, regardless of the differences in the source data, each integrity evaluation metric is generated based on the metadata associated with the "data integrity tag." It can be adapted to the evaluation of each data point, and the aggregated metrics represent a complete set of data managed by metadata.

[0068] The generated evaluation metrics are highly effective: Furthermore, the data integrity tags generated from metadata are a continuous process of matching the entire data lifecycle. As data generation and usage scenarios constantly change, metadata management will keep pace with the dynamic adjustments made to the organization's internal and external environments. For example, as business expands into new markets, the requirements for customer identification data in customer information may change from optional to mandatory. In this case, the customer identification field will be tagged with an integrity label in the metadata.

[0069] The generated evaluation metrics are highly systematic: extracting these change tags can form dynamic metrics that adapt to the business needs at the time. Based on metadata for normal / dynamic data integrity tagging, a set of integrity evaluation metrics covering different data types, storage methods, and business scenarios is constructed to achieve a comprehensive quantitative evaluation of organizational data integrity.

[0070] Based on the above embodiments, the present invention also provides a system for generating enterprise data integrity assessment indicators based on metadata, the system comprising: The routine tagging module is used to perform routine data integrity tagging on the metadata information in response to the input of enterprise data association metadata information; The task decomposition module is used to generate several sub-tasks based on the assessment requirements in response to the data integrity assessment instruction; the several sub-tasks include: dynamic labeling task, data extraction task, and indicator generation task. The dynamic tagging module is used to dynamically tag the metadata within a preset evaluation scope for data integrity in accordance with business change requirements and evaluation requirements for the dynamic tagging task. The data extraction module is used to query the metadata database based on the evaluation intent and data integrity tags for the data extraction task; and extract the target metadata information based on the query results. The indicator generation module is used to convert the target metadata information into integrity assessment indicators for the assessment indicator generation task, and generate an integrity assessment indicator scheme based on the integrity assessment indicators.

[0071] Based on the above embodiments, the present invention also provides a terminal, the principle block diagram of which can be as follows: Figure 6 As shown, the terminal includes a processor, memory, network interface, and display screen connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for generating enterprise data integrity assessment indicators based on metadata. The display screen can be an LCD screen or an e-ink screen.

[0072] Those skilled in the art will understand that Figure 6 The schematic diagram shown is merely a partial structural diagram related to the present invention and does not constitute a limitation on the terminal to which the present invention is applied. A specific terminal may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0073] In one implementation, the terminal's memory stores one or more programs, and these programs are configured to be executed by one or more processors. The programs include instructions for generating methods for metadata-based enterprise data integrity assessment metrics.

[0074] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0075] It should be understood that the application of the present invention is not limited to the examples above. Those skilled in the art can make improvements or modifications based on the above description, and all such improvements and modifications should fall within the protection scope of the appended claims.

Claims

1. A method for generating enterprise data integrity assessment indicators based on metadata, characterized in that, The method includes: In response to the entry of metadata information associated with enterprise data into the database, routine data integrity tags are applied to the metadata information. In response to the data integrity assessment instruction, several sub-tasks are generated based on the assessment requirements; the several sub-tasks include: dynamic labeling task, data extraction task, and indicator generation task. For the dynamic tagging task, based on business change requirements and evaluation requirements, dynamic tagging of data integrity is performed on the metadata within the preset evaluation scope; For the data extraction task, the metadata database is queried based on the evaluation intent and data integrity tags; the target metadata information is extracted based on the query results. For the task of generating the evaluation indicators, the target metadata information is transformed into integrity evaluation indicators, and an integrity evaluation indicator scheme is generated based on the integrity evaluation indicators.

2. The method for generating enterprise data integrity assessment indicators based on metadata according to claim 1, characterized in that, The methods for routine labeling include: For each entity table attribute defined in the metadata information entered into the database, determine whether the entity table attribute meets preset conditions; wherein, the preset conditions include: the entity table attribute is a primary key identifier, and / or the required field corresponding to the entity table attribute is a preset character; If the preset conditions are met, the entity table attributes are tagged with data integrity to achieve the normal compliance.

3. The method for generating enterprise data integrity assessment indicators based on metadata according to claim 1, characterized in that, The steps for generating several sub-tasks based on the assessment requirements include: Generate task configuration information based on the assessment requirements; The task is decomposed based on the task configuration information to obtain the several sub-tasks.

4. The method for generating enterprise data integrity assessment indicators based on metadata according to claim 3, characterized in that, The task configuration information includes: the preset evaluation range, dynamic labeling requirements, and execution time.

5. The method for generating enterprise data integrity assessment indicators based on metadata according to claim 1, characterized in that, Based on business change requirements and the aforementioned assessment requirements, the steps for dynamically tagging metadata within the preset assessment scope to ensure data integrity include: Configure a dynamic tagging strategy for data integrity based on the aforementioned business change requirements and assessment requirements; Customized data integrity tags are generated based on the dynamic tagging strategy, and the metadata of the preset evaluation range is dynamically tagged with data integrity tags.

6. The method for generating enterprise data integrity assessment indicators based on metadata according to claim 5, characterized in that, The customized data integrity label generated based on the dynamic tagging became invalid after this evaluation.

7. The method for generating enterprise data integrity assessment indicators based on metadata according to claim 1, characterized in that, The steps of converting the target metadata information into integrity assessment metrics and generating an integrity assessment metric scheme based on the integrity assessment metrics include: The target metadata information is transformed into integrity assessment indicators through a pre-set artificial intelligence model. By summarizing all the integrity assessment indicators, a set of integrity assessment indicators is obtained. A completeness assessment index scheme is generated based on the completeness assessment index set.

8. A system for generating enterprise data integrity assessment indicators based on metadata, characterized in that, The system includes: The routine tagging module is used to perform routine data integrity tagging on the metadata information in response to the input of enterprise data association metadata information; The task decomposition module is used to generate several sub-tasks based on the assessment requirements in response to the data integrity assessment instruction; the several sub-tasks include: dynamic labeling task, data extraction task, and indicator generation task. The dynamic tagging module is used to dynamically tag the metadata within a preset evaluation scope for data integrity in accordance with business change requirements and evaluation requirements for the dynamic tagging task. The data extraction module is used to query the metadata database based on the evaluation intent and data integrity tags for the data extraction task; and extract the target metadata information based on the query results. The indicator generation module is used to convert the target metadata information into integrity assessment indicators for the assessment indicator generation task, and generate an integrity assessment indicator scheme based on the integrity assessment indicators.

9. A terminal, characterized in that, The terminal includes a memory and one or more processors; the memory stores one or more programs; the programs contain instructions for executing the metadata-based enterprise data integrity assessment index generation method as described in any one of claims 1 to 7; the processors are used to execute the programs.

10. A computer-readable storage medium storing a plurality of instructions thereon, characterized in that, The instructions are applicable to be loaded and executed by a processor to implement the steps of the metadata-based enterprise data integrity assessment metric generation method as described in any one of claims 1 to 7.

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