Mapping method, device and equipment of data model and data standard, and medium

Abstract

The application discloses a data model and data standard mapping method, device, equipment and medium, wherein the data model and data standard mapping method comprises: obtaining data standard information of metadata corresponding to each data type through a standard source end; based on a data standard template corresponding to the data type, using the data standard information to define governance attributes of the metadata corresponding to the data type, so as to generate preliminary data standards corresponding to all the metadata; a review end returns public data standards corresponding to the metadata based on the preliminary standards; any data model is obtained, the semantic distance between the data model and the public data standards is calculated, and based on the semantic distance, the mapping relationship between the data model and the public data standards is confirmed. The method can improve the efficiency of extracting general standards for different data models offline in advance in the process of formulating data standards, and intelligently establishes the associated mapping relationship between the data model and the data standards.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data governance, and in particular to a data model and data standard mapping method, device, equipment and medium. BACKGROUND

[0002] With the development of enterprise business, the construction of information system is accelerated, and the data scale is gradually expanded. At the same time, poor quality data also comes along, which seriously affects the quality of data use and causes serious trouble to information. Improving the quality and usability of data, and the importance of data governance have become the industry consensus.

[0003] The processing object of data governance may be data distributed in various systems. The data of different systems often have differences, such as different data codes, data formats and data identifiers, and there may even be incorrect data. Therefore, it is necessary to establish a standardized system to evaluate whether the data meets the expected quality requirements from multiple dimensions such as accuracy, completeness, consistency, integrity, rationality, timeliness and effectiveness by establishing data quality evaluation standards and management specifications.

[0004] Currently, in the process of formulating data standards, it is necessary to extract general standards for different data models offline in advance, which becomes a tedious work in data governance. SUMMARY

[0005] The embodiments of the present application provide a data model and data standard mapping method, device, equipment and medium to solve the problem that in the process of formulating data standards, it is necessary to extract general standards for different data models offline in advance, which becomes a tedious work in data governance.

[0006] A data model and data standard mapping method comprises:

[0007] Obtain the data standard information of the metadata corresponding to each data type through the standard source end;

[0008] Based on the data standard template corresponding to the data type, define the governance attribute of the metadata corresponding to the data type by using the data standard information, so as to generate the preliminary data standard corresponding to all metadata;

[0009] Send the preliminary data standard to the review end, so that the review end returns the public data standard corresponding to the metadata based on the preliminary standard; obtain any data model, calculate the semantic distance between the data model and the public data standard, and confirm the mapping relationship between the data model and the public data standard based on the semantic distance.

[0010] A data model and data standard mapping device comprises:

[0011] The standard information acquisition module is configured to acquire data standard information of metadata corresponding to each data type from a standard source end.

[0012] The data standard generation module is configured to generate preliminary data standards corresponding to all metadata based on data standard templates corresponding to data types, and adopt the data standard information to define governance attributes of the metadata corresponding to the data types, so as to generate the preliminary data standards.

[0013] The data standard return module is configured to send the preliminary data standards to an evaluation end, so that the evaluation end returns public data standards corresponding to the metadata based on the preliminary standards.

[0014] The mapping relationship confirmation module is configured to acquire any data model, calculate semantic distances between the data model and the public data standards, and confirm mapping relationships between the data model and the public data standards based on the semantic distances.

[0015] In some embodiments, the data model and data standard mapping device is further configured to maintain the governance attributes of the metadata, generate preliminary data standards corresponding to the metadata, and send the preliminary data standards to an evaluation end; acquire evaluation data standards returned by the evaluation end after the evaluation based on the preliminary data standards, take the evaluation data standards as public data standards corresponding to the data types, and publish the evaluation data standards.

[0016] In some embodiments, the data model and data standard mapping device is further configured to maintain the governance attributes by using a task dimension and a metadata model dimension.

[0017] In some embodiments, the data model and data standard mapping device is further configured to, if the semantic distance is similar, establish a mapping relationship between the data model and the public data standards, and establish a data verification rule for the data model; if the semantic distance is a matching failure, sort model data involved in the data model according to distance closeness, and return a sorting result and a matching failure prompt information.

[0018] In some embodiments, the data model and data standard mapping device is further configured to acquire metadata based on a data model template, import the metadata into the data model template, generate a preliminary model, maintain the preliminary model, generate a data model, and define a data input format for each metadata in the data model.

[0019] In some embodiments, the data model and data standard mapping device is further configured to acquire a target data input format corresponding to each target metadata and a template metadata in the data model, acquire a target governance attribute corresponding to the target metadata in the public data standards, and calculate semantic similarity between a template data input format corresponding to each template metadata and the target governance attribute, and take a statistical result as a semantic distance between the data model and the public data standards.

[0020] In some embodiments, the data model and data standard mapping device is further configured to acquire all data models that have established a mapping relationship with the public data standard as associated data models; if at least one metadata in the public data standard is updated as a governance attribute corresponding to the synchronization metadata, an input format update prompt is sent to the data model associated with the synchronization metadata.

[0021] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the mapping method of the aforementioned data model and data standard.

[0022] A computer-readable medium storing a computer program that, when executed by a processor, implements the mapping method of the above-described data model and data standard.

[0023] The aforementioned data model and data standard mapping method, apparatus, equipment, and medium, through the review end returning the publicly available data standard corresponding to the metadata based on the preliminary standard, calculate the semantic distance between the data model and the publicly available data standard, thereby confirming the mapping relationship between the data model and the publicly available data standard. This can improve the efficiency of pre-extracting general standards for different data models offline during the data standard development process, reduce cumbersome configuration, intelligently establish the association mapping relationship between the data model and the data standard, and provide basic data for subsequent quality inspection and quality improvement. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 A schematic diagram illustrating the application environment of the mapping method between data models and data standards in one embodiment of the present invention is shown.

[0026] Figure 2 A first flowchart illustrating the mapping method between data models and data standards in the first embodiment of the present invention is shown.

[0027] Figure 3 A second flowchart illustrating the mapping method between data models and data standards in a second embodiment of the present invention is shown.

[0028] Figure 4 A schematic diagram illustrating a mapping device for data models and data standards in one embodiment of the present invention is shown.

[0029] Figure 5 A schematic diagram of an electronic device according to an embodiment of the present invention is shown. Detailed Implementation

[0030] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] The following are explanations of several terms used in this application:

[0032] Data refers to symbols that record and identify objective events; it consists of physical symbols or combinations of physical symbols that record the nature, state, and interrelationships of objective things. Metadata is data that interprets data.

[0033] A model is an object that uses subjective consciousness to objectively describe the form and structure of an object, whether physical or virtual. A data model is a model that uses data to describe the form and structural characteristics of an objective thing or entity.

[0034] Data standards are objects that define data classification, recording formats, and encoding from the perspective of describing the world with data. Once data standards are established, different developers can define data storage according to unified rules, enabling data exchange and sharing. Metadata standards include metadata structure standards (i.e., what items metadata includes, such as the Dublin Core set and MARC element set), metadata content standards, metadata value standards, and metadata encoding standards (used for the storage and exchange of machine-readable records, such as MARC (Machine Readable Cataloging) and XML). Data standardization improves data universality, sharing, portability, and usability.

[0035] The mapping method for data models and data standards provided in this invention can be applied to, for example... Figure 1 In this application environment, the mapping method between the data model and data standard is applied in a mapping system that includes distributed clients and servers. The distributed clients communicate with the server over a network. A distributed client, also known as a user terminal, is a program that provides local services to the distributed client, corresponding to the server. The server can be implemented using a standalone server or a server cluster consisting of multiple servers.

[0036] In one embodiment, such as Figure 2As shown, a mapping method between a data model and a data standard is provided, which can be applied to... Figure 1 Taking the server in the example, the specific steps are as follows:

[0037] S110. Obtain the data standard information of the metadata corresponding to each data type through the standard source.

[0038] Specifically, the standard source refers to the main body of various established data standards, such as the data standard publishing ports of various industries or enterprise-specific data standard publishing ports. The data model and data standard mapping system provided in this embodiment can connect to the APIs provided by the aforementioned standard remote end, thereby timely obtaining the data standards corresponding to various types of data published in real time by industries or enterprises.

[0039] S120. Based on the data standard template corresponding to the data type, use data standard information to define governance attributes for the metadata corresponding to the data type, in order to generate the preliminary data standard corresponding to all metadata.

[0040] Among them, the data standard template is the basic architecture used to form data standards. This basic architecture includes at least the governance attributes corresponding to the metadata, such as business attributes, technical attributes, and management attributes.

[0041] Attributes provide a way to describe a product and its characteristics using user-defined fields. For example, computer attributes include memory size, hard drive capacity, and whether it meets energy requirements.

[0042] Attributes are associated with various Commerce entities, such as product categories and channels, and default values ​​can be set for attributes. When an attribute is associated with a product category or channel, the product inherits these attributes and their default values. Default attribute values ​​can be overridden at the individual product level, at the channel level, or in the catalog.

[0043] For example, television products typically have the attributes shown in Table 1 below.

[0044]

[0045]

[0046] Table 1

[0047] The metadata in the table above includes "Category", "Attribute", "Allowed Values", and "Default Values". Based on the characteristics represented by the metadata, this embodiment can classify the metadata into types such as business attributes, technical attributes, and management attributes.

[0048] Business attributes refer to the defined characteristics of a particular industry's business, often used in the communications field, such as bearer services. Bearer services are described by a series of low-level attributes, and their implementation within the network requires only low-level functionality. Examples include "telephone prepaid cards" and "campus cards." The latter possesses the basic business attributes of the former, and adds additional functionality based on the characteristics of students using telephones on campus.

[0049] Technological attributes include both natural and social attributes. The natural attributes of technology explain that its formation and implementation must conform to natural laws and be constrained by natural factors. For example, the principles of mechanical action must conform to physical laws.

[0050] Managing attributes provides a mechanism to include additional attributes that can be shared with applications. These attributes can contain specific information, such as the company name or user attributes obtained from a user's authentication session.

[0051] Data standards define the input or storage format of data, for example:

[0052] Currency - This type supports currency values, which can be constrained (i.e., support a certain range of values) or left open.

[0053] Date and Time - This type supports date and time values, which can have ranges or remain open.

[0054] Decimal - This type supports numeric values ​​including decimal places, as well as units of measurement. It can have ranges or remain open.

[0055] Integer - This type supports numeric values ​​and units of measurement. It can have a range or remain open.

[0056] Text - This type supports text values, and when the pinned list setting is enabled, it also supports a set of predefined possible values.

[0057] Boolean - This type supports binary values ​​(true or false).

[0058] Specifically, the system provided in this application performs NLP (Natural Language Processing) analysis and classification on the data standard information corresponding to a certain type of metadata according to governance attributes, including: data collection, survey and interview information analysis and information evaluation, etc., sorting out the business indicators, data items and codes of the data standard information, so as to generate the standard of input data format for each type of metadata, that is, to generate the preliminary data standard corresponding to the metadata.

[0059] S130. Send the preliminary data standard to the reviewer so that the reviewer can return the public data standard corresponding to the metadata based on the preliminary standard.

[0060] Specifically, the review process includes various business departments and a dedicated review panel. After providing professional review and recommendations on the preliminary data standard, the review panel can publish the standard as a public data standard to ensure its accuracy and reliability.

[0061] S140. Obtain any data model, calculate the semantic distance between the data model and the public data standard, and based on the semantic distance, confirm the mapping relationship between the data model and the public data standard.

[0062] Specifically, this embodiment extracts all semantic features from the data model and compares each semantic feature with the data input format specified in the corresponding metadata of the publicly available data standard to obtain the comparison results. Based on the comparison results, it is determined whether a mapping can be achieved between the data model and the publicly available data standard. It is understood that if a data model and the publicly available data standard cannot be mapped, the data model should be adjusted accordingly to ultimately satisfy the mapping relationship with the publicly available data standard, thereby ensuring that the data meets the expected quality requirements from multiple dimensions such as accuracy, completeness, consistency, integrity, reasonableness, timeliness, and effectiveness.

[0063] This data model and data standard mapping method calculates the semantic distance between the data model and the public data standard by having the review end return the metadata corresponding to the preliminary standard. This confirms the mapping relationship between the data model and the public data standard, which can improve the efficiency of pre-extracting general standards for different data models offline during the data standard development process, reduce cumbersome configuration, and quickly and intelligently establish the association mapping relationship between the data model and the data standard, providing basic data for subsequent quality inspection and quality improvement.

[0064] In one embodiment, step S130 involves sending the preliminary data standard to the reviewer so that the reviewer can return the public data standard corresponding to the metadata based on the preliminary standard. This specifically includes the following steps:

[0065] S131. Maintain the governance attributes of metadata to generate preliminary data standards corresponding to the metadata, and send the preliminary data standards to the review end.

[0066] Preferably, in step S131, which involves maintaining the governance attributes of the metadata, the following steps are specifically included:

[0067] S1311. Governance attributes are maintained using task dimensions and metadata model dimensions.

[0068] S132. Obtain the review data standard returned by the reviewer after reviewing the preliminary data standard, and publish the review data standard as the public data standard corresponding to the data type.

[0069] Specifically, a dimension refers to the angle from which data is observed, including the angle from which the problem (indicator) is analyzed. For example, the sales volume of a certain mobile phone brand in a certain region in 20XX. Sales volume is an indicator, and the dimensions involved in sales volume include time dimension, region dimension, and product dimension.

[0070] The task dimension is the perspective from which a task is observed. For example, tasks can be divided according to their urgency, including: important and urgent, important but not urgent, and neither important nor urgent.

[0071] Metadata model dimensions can include: business model, domain model, logical model, and physical model. Among them, the business model involves business decomposition and programmatic implementation, defining business boundaries and processes; for example, orders and payments are independent business modules.

[0072] Domain model: Abstraction of business functions, grouping, and organization of relationships between groups, such as the business of user shopping.

[0073] Logical Model: The business concepts in the domain model are materialized, and the specific attributes of the entities and the relationships between entities are considered, such as the relationship between orders (order number, payer, etc.) and payments (amount, payment time, etc.).

[0074] Physical model: Solves a series of technical problems related to the practical application development, deployment, and performance.

[0075] In one embodiment, step S140, which involves confirming the mapping relationship between the data model and the public data standard based on semantic distance, specifically includes the following steps:

[0076] S1411. If the semantic distance is semantically similar, then establish a mapping relationship between the data model and the public data standard, and establish data verification rules for the data model.

[0077] S1412. If the semantic distance indicates a match failure, sort the model data involved in the data model according to the distance, and return the sorting results and the matching failure message.

[0078] Specifically, this embodiment generates data validation rules for the established data model and publicly available data standards. Data validation rules are rules that verify whether input data conforms to the input format. For example, if the currency input format is specified as having only two digits after the decimal point, then if the input data is "1.023", then the input data does not conform to the data validation rules.

[0079] This embodiment returns a matching failure message to data models and public data standards that have not established a relationship. It also matches the input format of the corresponding metadata from multiple data models that caused the mismatch and provides this as the problem format for feedback.

[0080] In one embodiment, prior to step S140, i.e., before acquiring any data model, the following steps are further included:

[0081] S4011. Based on the data model template, obtain metadata and import it into the data model template to generate a preliminary model.

[0082] S4012. Maintain the preliminary model and generate a data model, where each metadata element in the data model corresponds to a defined data input format.

[0083] Specifically, this embodiment supports importing metadata online via JDBC (Java Database Connectivity) and offline templates. Based on the acquired metadata information, the metadata information of the data model is improved through various methods such as template files, model design files, design documents, and online maintenance, for example, by describing business meanings.

[0084] In one embodiment, step S140, which involves obtaining any data model and calculating the semantic distance between the data model and the public data standard, specifically includes the following steps:

[0085] S1421. Obtain the target data input format corresponding to each target metadata and template metadata in the data model.

[0086] S1422. Obtain the target governance attributes corresponding to the target metadata in the public data standard.

[0087] S1423. Calculate the semantic similarity between the template data input format and the target governance attribute corresponding to each template metadata, and use the statistical results as the semantic distance between the data model and the public data standard.

[0088] Specifically, this embodiment can calculate the distance between the data model and the public data standard by using the target governance attributes of the data model, such as: model name, description information, data type and data standard definition, business meaning, format requirements and set synonym roots, based on corpus, word segmentation and keyword extraction.

[0089] In one embodiment, after step S140, that is, after confirming the mapping relationship between the data model and the public data standard, the following steps are further included:

[0090] S4021. Obtain all data models that establish a mapping relationship with public data standards as associated data models.

[0091] S4022. If at least one metadata in the open data standard is updated as a governance attribute corresponding to the synchronization metadata, then send an input format update prompt to the data model associated with the synchronization metadata.

[0092] Specifically, this embodiment can extract updated content from the updated public data standard, including the governance attributes corresponding to the synchronized metadata, and send the updated content to the associated data model. The associated data model will also update its data input format synchronously to ensure the universality and compatibility of the data.

[0093] This embodiment collects and organizes industry standards and enterprise-specific data standards, inputs them into the system, and automatically associates and maps fields between standards and models. It also automatically sets and generates data validation rules for fields, enabling efficient subsequent data validation. The intelligent data standard mapping is based on NLP semantic comparison, determining the semantic distance between each field and the standard, analyzing and comparing the semantics, and sorting the standards matched to the fields according to semantic distance to quickly establish associations and improve work efficiency.

[0094] The mapping method between the data model and data standards provided in this embodiment, such as Figure 3 As shown, by using the review end to return the publicly available data standard corresponding to the metadata based on the preliminary standard, the semantic distance between the data model and the publicly available data standard is calculated, thereby confirming the mapping relationship between the data model and the publicly available data standard. This can improve the efficiency of pre-extracting common standards for different data models offline during the data standard development process, reduce cumbersome configuration and manpower consumption, and intelligently establish the association mapping relationship between the data model and the data standard. This provides basic data for subsequent quality inspection and quality improvement, and is of great help to IT personnel or professionals in traditional industries who maintain the governance platform in the long term.

[0095] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0096] In one embodiment, a mapping device for data models and data standards is provided, which corresponds one-to-one with the mapping methods for data models and data standards described in the above embodiments. For example... Figure 4As shown, the data model and data standard mapping device includes a standard information acquisition module 110, a data standard generation module 120, a data standard return module 130, and a mapping relationship confirmation module 140. Detailed descriptions of each functional module are as follows:

[0097] The standard information acquisition module 110 is used to acquire the data standard information of the metadata corresponding to each data type through the standard source.

[0098] The data standard generation module 120 is used to define governance attributes for metadata corresponding to data types based on the data standard template corresponding to the data type, and to generate preliminary data standards for all metadata.

[0099] The data standard return module 130 is used to send the preliminary data standard to the review end so that the review end can return the public data standard corresponding to the metadata based on the preliminary standard.

[0100] The mapping relationship confirmation module 140 is used to obtain any data model, calculate the semantic distance between the data model and the public data standard, and confirm the mapping relationship between the data model and the public data standard based on the semantic distance.

[0101] Preferably, the data standard return module 130 includes:

[0102] The data standard sending submodule 131 is used to maintain the governance attributes of metadata, generate preliminary data standards corresponding to the metadata, and send the preliminary data standards to the review end.

[0103] The data standard publication submodule 132 is used to obtain the review data standard returned by the reviewer after reviewing the preliminary data standard, and publish the review data standard as the public data standard corresponding to the data type.

[0104] Preferably, the data standard transmission submodule 131 includes:

[0105] The governance attribute maintenance unit 1311 is used to maintain governance attributes using task dimensions and metadata model dimensions.

[0106] Preferably, the mapping relationship confirmation module 140 includes:

[0107] The mapping relationship establishment submodule 1411 is used to establish a mapping relationship between the data model and the public data standard if the semantic distance is semantically similar, and to establish data verification rules for the data model.

[0108] The prompt message return submodule 1412 is used to sort the model data involved in the data model according to the distance if the semantic distance is a match failure, and return the sorting result and the prompt message of the match failure.

[0109] Preferably, the mapping device for the data model and data standard further includes:

[0110] The preliminary model generation module 4011 is used to obtain metadata and import the data model template based on the data model template to generate a preliminary model.

[0111] The data model generation module 4012 is used to maintain the preliminary model and generate the data model. Each piece of metadata in the data model corresponds to a defined data input format.

[0112] Preferably, the mapping relationship confirmation module 140 includes:

[0113] The metadata acquisition submodule 1421 is used to acquire the target data input format corresponding to each target metadata in the data model and the template metadata.

[0114] The governance attribute acquisition submodule 1422 is used to acquire the target governance attributes corresponding to the target metadata in the public data standard.

[0115] The semantic similarity statistics submodule 1423 is used to calculate the semantic similarity between the template data input format and the target governance attribute corresponding to each template metadata, and to use the statistical results as the semantic distance between the data model and the public data standard.

[0116] Preferably, the mapping device for the data model and data standard further includes:

[0117] The associated data model acquisition module 4021 is used to acquire all data models that have established a mapping relationship with the public data standard as associated data models.

[0118] The format update prompt sending module 4022 is used to send an input format update prompt to the data model associated with the synchronization metadata if at least one metadata in the public data standard is updated as a governance attribute corresponding to the synchronization metadata.

[0119] Specific limitations regarding the mapping device for data models and data standards can be found in the limitations on the mapping method for data models and data standards described above, and will not be repeated here. Each module in the aforementioned mapping device for data models and data standards can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device in software form, so that the processor can call and execute the operations corresponding to each module.

[0120] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5As shown, this electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile media and internal memory. The non-volatile media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs on the non-volatile media. The database contains data related to mapping methods for data models and data standards. The network interface is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a mapping method for data models and data standards.

[0121] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the mapping method for the data model and data standard described in the above embodiments, for example... Figure 2 Steps S10 to S40 are shown. Alternatively, when the processor executes the computer program, it implements the functions of each module / unit of the mapping device for the data model and data standard in the above embodiments, for example... Figure 4 The functions of modules 10 to 40 are shown. To avoid repetition, they will not be described again here.

[0122] In one embodiment, a computer-readable medium is provided having a computer program stored thereon. When executed by a processor, the computer program implements the mapping method for the data model and data standard described in the above embodiments, for example... Figure 2 Steps S10 to S40 are shown. Alternatively, when the computer program is executed by a processor, it implements the functions of each module / unit in the data model and data standard mapping device in the above-described device embodiment, for example... Figure 4 The functions of modules 10 to 40 are shown. To avoid repetition, they will not be described again here.

[0123] 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. This computer program can be stored in a non-volatile computer-readable medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments of this application 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 RAMbus dynamic RAM (RDRAM), etc.

[0124] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0125] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A mapping method between a data model and a data standard, characterized in that, include: Data standard information for obtaining metadata corresponding to each data type through a standard source end, wherein the standard source end is the main end of various established data standards; Based on the data standard template corresponding to the data type, the data standard information is used to define governance attributes for the metadata corresponding to the data type, so as to generate preliminary data standards for all the metadata. The preliminary data standard is sent to the reviewer so that the reviewer can return the public data standard corresponding to the metadata based on the preliminary data standard. Based on the data model template, metadata is obtained and imported into the data model template to generate a preliminary model; The preliminary model is maintained to generate a data model, wherein each metadata element in the data model corresponds to a defined data input format; Obtain any data model, calculate the semantic distance between the data model and the public data standard, and confirm the mapping relationship between the data model and the public data standard based on the semantic distance; The step of obtaining any data model and calculating the semantic distance between the data model and the public data standard includes: Obtain the target data input format corresponding to each target metadata and template metadata in the data model; Obtain the target governance attributes corresponding to the target metadata in the public data standard; The semantic similarity between the template data input format corresponding to each template metadata and the target governance attribute is statistically analyzed, and the statistical results are used as the semantic distance between the data model and the public data standard. The process of confirming the mapping relationship between the data model and the public data standard based on the semantic distance includes: If the semantic distance is semantically similar, then a mapping relationship is established between the data model and the public data standard, and data verification rules are established for the data model; If the semantic distance fails to match, the model data involved in the data model are sorted according to the distance, and the sorting results and matching failure prompts are returned.

2. The mapping method between data models and data standards according to claim 1, characterized in that, Sending the preliminary data standard to the reviewer, so that the reviewer can return the public data standard corresponding to the metadata based on the preliminary data standard, includes: The governance attributes of the metadata are maintained to generate preliminary data standards corresponding to the metadata, and the preliminary data standards are sent to the review end. Obtain the review data standard returned by the reviewer after reviewing the preliminary data standard, and publish the review data standard as the public data standard corresponding to the data type.

3. The mapping method between data models and data standards according to claim 2, characterized in that, Maintaining the governance attributes of the metadata includes: The governance attributes are maintained using both task-based and metadata model-based dimensions.

4. The mapping method between data models and data standards according to claim 1, characterized in that, After confirming the mapping relationship between the data model and the public data standard, the method further includes: All data models that establish a mapping relationship with the publicly available data standard are obtained as associated data models; If at least one metadata in the public data standard is updated as a governance attribute corresponding to the synchronization metadata, an input format update prompt is sent to the associated data model associated with the synchronization metadata.

5. A mapping device for data models and data standards, characterized in that, include: The standard information acquisition module is used to acquire data standard information of metadata corresponding to each data type through the standard source end, which is the main end of various established data standards. The data standard generation module is used to define governance attributes for the metadata corresponding to the data type based on the data standard template corresponding to the data type and using the data standard information to generate preliminary data standards for all the metadata. The data standard return module is used to send the preliminary data standard to the review end, so that the review end can return the public data standard corresponding to the metadata based on the preliminary data standard; The preliminary model generation module is used to obtain metadata based on the data model template and import the data model template to generate a preliminary model; A data model generation module is used to maintain the preliminary model and generate a data model, wherein each piece of metadata in the data model corresponds to a defined data input format; The mapping relationship confirmation module is used to obtain any data model, calculate the semantic distance between the data model and the public data standard, and confirm the mapping relationship between the data model and the public data standard based on the semantic distance. The mapping relationship confirmation module includes: The metadata acquisition submodule is used to acquire the target data input format corresponding to each target metadata and template metadata in the data model. The governance attribute acquisition submodule is used to acquire the target governance attributes corresponding to the target metadata in the public data standard; The semantic similarity statistics submodule is used to calculate the semantic similarity between the template data input format corresponding to each template metadata and the target governance attribute, and to use the statistical results as the semantic distance between the data model and the public data standard. The mapping relationship confirmation module also includes: The mapping relationship establishment submodule is used to establish a mapping relationship between the data model and the public data standard if the semantic distance is semantically similar, and to establish data verification rules for the data model; The prompt message return submodule is used to sort the model data involved in the data model according to the distance if the semantic distance fails to match, and return the sorting result and the prompt message of the matching failure.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the mapping method of the data model and data standard as described in any one of claims 1 to 4.

7. A computer-readable medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the mapping method of the data model and data standard as described in any one of claims 1 to 4.

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