Business model configuration method, data processing method and system

Abstract

The invention provides a service model configuration method and a data processing method and system, and relates to the technical field of data management. According to the specific implementation scheme, in response to a detected configuration instruction for a target business model in a business model library, a model configuration page of the target business model is displayed in a user interface; generating configuration information corresponding to the target business model according to a pre-stored data dictionary and input information in the model configuration page; and configuring the target service model according to the configuration information corresponding to the target service model. According to the scheme of the invention, the business model used for carrying out data verification on different businesses can be configured in the data management system, the configuration modification of the business model is supported through the data dictionary, and adaptive adjustment can be carried out on business changes without manually writing corresponding codes again; the data quality control capability of the database on multiple service lines is optimized, and the data management efficiency and the data circulation reliability are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data management, and in particular to a business model configuration method, a data processing method and system. BACKGROUND

[0002] In a big data scenario, a data warehouse needs to receive business data from different business systems, and needs to verify the received business data before storing the received business data. For different types of business data, corresponding verification codes need to be configured in advance in the data warehouse, and each time a new business system is accessed or the original business data is changed, the corresponding verification codes need to be re-written, and it is not flexible to configure to realize dynamic changes of verification. SUMMARY

[0003] Embodiments of the present application provide a business model configuration method, a data processing method and system to solve the technical problem that the verification method of the data warehouse for business data in the prior art is difficult to change flexibly.

[0004] In a first aspect, embodiments of the present application provide a business model configuration method for a data management system, comprising: in response to detecting a configuration instruction for a target business model in a business model library, displaying a model configuration page of the target business model in a user interface, wherein the business model library includes a plurality of business models, each business model is one-to-one bound to each business in an external business system, the business model is used to verify the business data of the bound business, and the business model stores metadata corresponding to the bound business, the metadata is used to describe the business data of the bound business; generating configuration information corresponding to the target business model according to a pre-stored data dictionary and input information in the model configuration page, wherein the data dictionary records data encoding corresponding to data types and data description information respectively; and configuring the target business model according to the configuration information corresponding to the target business model.

[0005] In a possible implementation, in response to detecting a configuration instruction for a target field in a model configuration page, displaying a sub-field configuration page of the target field in the model configuration page, wherein the sub-field configuration page includes at least one selected field generated according to a data dictionary and a field attribute column corresponding to each selected field.

[0006] According to the pre-stored data dictionary and the input information in the model configuration page, the configuration information corresponding to the target business model is generated, comprising: generating the configuration information corresponding to the target business model by using the data dictionary according to the input information in the sub-field configuration page, and the configuration information is used to define the data structure of the target field.

[0007] In a possible implementation, the method further includes: in response to detecting the business model adding instruction, displaying a basic information page and a field definition page of the added business model in the user interface, wherein the basic information to be supplemented in the basic information page includes at least a model name, a model ID, a belonging external business system, and a belonging business, the field definition page includes at least one addable field generated according to the data dictionary and a field attribute column corresponding to each addable field, and the field attribute column includes at least a field name, a data type, and a field level; generating configuration information corresponding to the added business model by using the data dictionary according to the input information detected in the basic information page and the field definition page; generating a target business model according to the configuration information corresponding to the added business model, and storing the target business model in the business model library.

[0008] In a possible implementation, the method further includes: in response to detecting a version control instruction for a target business model in the business model library, displaying a version control page of the target business model in the user interface, wherein the version control page includes at least one historical version of the target business model; generating version control information corresponding to the target business model according to the input information detected in the version control page; and configuring the target business model according to the version control information corresponding to the target business model, wherein the version control information includes switching information of a currently effective version of the target business model and / or editing information of a historical version of the target business model.

[0009] In a second aspect, the embodiments of the present application provide a data processing method based on a business model, for a data management system, including: in response to detecting that any external business system calls a data interface, obtaining, based on the data interface, business data to be processed pushed by the external business system; determining a target business to which the business data to be processed belongs, based on a business identifier in the business data to be processed; determining, in a business model library, a target business model bound to the target business, wherein the target business model stores metadata corresponding to the target business, and the metadata is used to describe business data of the target business; generating a data verification rule according to the metadata in the target business model, and verifying the business data to be processed based on the data verification rule.

[0010] In a possible implementation, the data verification rule includes a field verification rule and a cross-field verification rule, the data verification rule is generated according to metadata in the target business model, and the data verification rule is used to verify the to-be-processed business data, including: generating the field verification rule according to at least one field and a field attribute of the field defined in the metadata, wherein the field attribute at least includes a field name, a data type, and a field level, and the field verification rule includes a verification rule corresponding to each field in at least one field; generating the cross-field verification rule according to an association relationship between fields defined in the metadata; performing field verification on a target field in the to-be-processed business data based on the field verification rule matched with the field name of the target field, wherein the field verification includes mandatory item verification, data type verification, and field level verification; and extracting a plurality of fields corresponding to the cross-field verification rule from the to-be-processed business data, and performing cross-field verification on the plurality of fields according to the cross-field verification rule.

[0011] In a possible implementation, performing the field verification on the target field in the to-be-processed business data based on the field verification rule matched with the field name of the target field includes: performing mandatory item verification on the target field, stopping the verification on the target field if the mandatory item verification fails, performing data type verification on the target field if the mandatory item verification passes, stopping the verification on the target field if the data type verification fails, and performing subfield verification on the target field if the data type verification passes.

[0012] In a possible implementation, performing the subfield verification on the target field includes: in response to detecting that the target field has a subfield, obtaining, for any subfield in the target field, a field verification rule matched with a field name of the subfield; and performing the subfield verification on the subfield based on the field verification rule matched with the field name of the subfield.

[0013] In a possible implementation, after the verification on the to-be-processed business data based on the data verification rule, the method further includes: performing secondary verification on the to-be-processed business data based on an extended rule defined by a user, and generating a verification report corresponding to the to-be-processed business data, wherein the verification report records a verification problem detected in a verification process of the to-be-processed business data, and is used for problem tracing on a field that fails to pass the verification in the to-be-processed business data; generating a storage index for the to-be-processed business data that passes the verification, and storing the to-be-processed business data in an associated storage area of the target business model according to the storage index.

[0014] In a third aspect, an embodiment of the present application provides a data management system, comprising: a page display module configured to display a model configuration page of a target business model in a user interface in response to detecting a configuration instruction for the target business model in a business model library, wherein the business model library comprises a plurality of business models, each of which is bound to a business in an external business system one by one, the business model is used to verify business data of the bound business, and the business model stores metadata corresponding to the bound business, and the metadata is used to describe the business data of the bound business; an information generation module configured to generate configuration information corresponding to the target business model according to pre-stored data dictionary and input information in the model configuration page, wherein the data dictionary records data coding corresponding to data type and data description information respectively; and a model configuration module configured to configure the target business model according to the configuration information corresponding to the target business model.

[0015] Based on the method of the embodiment of the present application, the business model bound to different businesses can be configured in the data management system, so that the business model bound to the business is used for data verification when the business data is stored in the database. Meanwhile, the data management system can support configuration and modification of the business model through the pre-stored data dictionary, without the need of manually writing corresponding codes, and the adaptability to business changes can be adjusted, the data quality control ability of the database for multiple business lines is optimized, and the data management efficiency and data flow reliability are improved.

[0016] The above description is only a summary of the technical scheme of the present application, in order to more clearly understand the technical means of the present application, the description can be implemented according to the content of the specification, and in order to make the above and other purposes, characteristics and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings are used to better understand the present application, and do not constitute a limitation of the present application. Among them:

[0018] Figure 1 is a flowchart of a business model configuration method provided by an exemplary embodiment of the present application Figure 1 ;

[0019] Figure 2 is a flowchart of a business model configuration method provided by an exemplary embodiment of the present application Figure 2 ;

[0020] Figure 3 is a schematic diagram of a field adding area in a model configuration page provided by an exemplary embodiment of the present application

[0021] Figure 4 is a flowchart of a business model configuration method provided by an exemplary embodiment of the present applicationFigure 3 ;

[0022] Figure 4 is a model management page of a new service model provided by an exemplary embodiment of the present application Figure 5 ;

[0023] Figure 5 is a flowchart of a configuration method of a service model provided by an exemplary embodiment of the present application

[0024] Figure 6 is a version control page of a service model provided by an exemplary embodiment of the present application

[0025] Figure 7 is a flowchart of a data processing method based on a service model provided by an exemplary embodiment of the present application

[0026] Figure 8 is a verification logic of data verification provided by an exemplary embodiment of the present application

[0027] Figure 9 is a schematic diagram of a data management system provided by an exemplary embodiment of the present application Figure 10 ;

[0028] Figure 1 is a schematic diagram of a data management system provided by an exemplary embodiment of the present application Figure 11 ;

[0029] Figure 2 is a block diagram of an electronic device for implementing an embodiment of the present application. DETAILED DESCRIPTION

[0030] In the following, only certain exemplary embodiments are simply described. As those skilled in the art can recognize, the described embodiments can be modified in various different ways without departing from the concept or scope of the present application. Therefore, the drawings and the description are considered to be exemplary in nature, rather than limiting.

[0031] In order to facilitate understanding of the technical solutions of the embodiments of the present application, the related technologies of the embodiments of the present application are described below. The following related technologies can be combined with the technical solutions of the embodiments of the present application in any manner as optional solutions, and all of them belong to the protection scope of the embodiments of the present application.

[0032] Figure 12

[0033] In enterprise data management, a data management system is often configured for an enterprise database to check, store and maintain the data in the database. With the explosive growth of data in the information age, new external business systems are constantly connected to the enterprise database, and the business data from the new external business systems needs to be checked. At the same time, the data structure and data content of the original business data in each external business system may also change.

[0034] In the prior art, when the enterprise database needs to access new business data types or the original business data types change, the corresponding verification code needs to be re-written to form the corresponding business data verification rules. This not only cannot achieve flexible configuration, but also when manually writing new code, the rule definition for different fields in the business data is prone to input errors.

[0035] In addition, the business data faced by the enterprise database is increasingly complex, and the nested data structure refers to a data element (field) containing another complete data structure, thereby forming a hierarchical data organization method. For some business data with nested structure, the existing data verification means lack hierarchical verification capability and can only verify the upper field, and cannot effectively verify the nested data structure contained in the field, resulting in limited management capability for the data in the database.

[0036] Therefore, the present application aims to provide a business model configuration method, a data processing method and a system, which can configure corresponding business models for different businesses in the data management system, thereby defining metadata for describing the corresponding business data in the business model, and generating the verification rules corresponding to the business data through the analysis of the matched business model when the business data is accessed to verify the business data.

[0037] When the data structure and data content of the business data change, dynamic configuration of the business model can be realized on the configuration page of the business model, which can flexibly adapt to the changes of the business data and avoid constant manual writing of verification code.

[0038] In addition, the data dictionary is pre-stored in the data management system, and the newly added field can be configured by using the field-related code definition stored in the data dictionary, and the newly added field can be supported as a sub-field of the defined field in the business model metadata. In essence, the verification rule definition of the infinite nested data structure is realized, thereby supporting effective verification of the nested data structure contained in the business data field.

[0039] Application Scenario

[0040] Example Method 1is a flowchart of a configuration method of a business model provided by an exemplary embodiment of the present application. The method of the present embodiment can be applied to a data management system of a database, such as Figure 1 The method includes the following steps:

[0041] In step S110, in response to detecting a configuration instruction for a target business model in a business model library, a model configuration page of the target business model is displayed in a user interface. The business model library includes a plurality of business models, each of which is bound to a business in an external business system one by one. The business model is used to verify the business data of the bound business. The metadata corresponding to the bound business is stored in the business model. The metadata is used to describe the business data of the bound business.

[0042] It can be understood that in the external business system connected to the database, each external business system can have at least one business. For the business data of different businesses, the data structure is different and contains different data fields. In the data management system of the database, a business model library is set. The business model library can be preconfigured with a business model bound to a business one by one. The metadata corresponding to the bound business is stored in the business model. The metadata is used to describe the business data of the bound business. Therefore, when the incoming business data of the external business system is detected, the data verification rule can be generated based on the business model corresponding to the business of the incoming business data in the business model library. The data verification rule can at least include the definition of the data structure (such as which fields are included, the arrangement order between the fields, etc.) and the data type of each field in the business data, thereby supporting effective verification of the business data.

[0043] For example, if the business data of the original business in the external business system connected to the database changes, the business model bound to the changed business (hereinafter referred to as the target business model) in the business model library needs to be adjusted accordingly. In this case, the user can input a configuration instruction for the target business model in the business model library to adjust the target business model. When the business management system detects the configuration instruction, the model configuration page of the target business model can be displayed in the user interface for the user to configure and modify.

[0044] In step S120, the configuration information corresponding to the target business model is generated according to the pre-stored data dictionary and the input information in the model configuration page. The data dictionary records the data coding corresponding to the data type and the data description information, respectively.

[0045] In step S130, the target business model is configured according to the configuration information corresponding to the target business model.

[0046] Here, the data dictionary pre-stores data encodings corresponding to data types and description information of various common data types, the data types can include String, Int, Float, Object, Time, etc., and the description information of the data can be understood as the attribute definition of the data of this type.

[0047] Therefore, after receiving the input information of the model configuration page, the data management system can generate the encoding corresponding to the input information based on the data encoding stored in the data dictionary, so that the user can configure the original business model through the input in the model configuration page, and the configured business model can continue to correspond to the changed business to which it is bound, supporting the use of the configured business model for the verification of the business data of the bound business.

[0048] The method of the embodiment can configure the business model bound to different businesses in the data management system, so that the business model bound to the business is used for data verification when the business data is stored in the database. At the same time, the data management system can support the configuration and modification of the business model through the pre-stored data dictionary, and can adapt to the changes of the business without manually writing corresponding codes, thereby optimizing the data quality control ability of the database for multiple business lines, improving the data management efficiency and data flow reliability.

[0049] In a possible implementation, the model configuration page of the target business model can include a field editing area and a field adding area, as shown in Figure 1 As shown in FIG. 1, in S110, in response to detecting the configuration instruction for the target business model in the business model library, the model configuration page of the target business model is displayed in the user interface, including:

[0050] In S201, according to the metadata stored in the target business model, the field editing area is displayed in the model configuration page.

[0051] Here, the field editing area is used to edit the fields already defined in the target business model, and the field editing area can include at least one editable field and a field attribute column corresponding to each editable field. The field attribute column includes at least a field name, a data type, and a field level, wherein the field name can be used as an identifier of the field, the data type refers to the data type definition of the field, for example, the data type can be String, Int, Float, Object, and Time, etc., the data type of a certain field can be specified as one or several specific types, and the field level indicates the level of the field in the corresponding business data, which can be used to locate the field.

[0052] Exemplarily, the field attribute column can further include a field length, whether it is a mandatory item, field sorting, and field description, etc. The field length can be understood as a limitation on the data length of a certain field. Whether it is a mandatory item can be understood as defining whether a certain field is necessary in the business data. The field sorting can represent the position of the field in the business data, facilitating extraction of the field for checking. The field description can be understood as a note explanation for the field, for example, the creator of a certain business model makes note explanations for each field, facilitating other users to view and understand according to needs.

[0053] In step S202, according to the data dictionary, a field adding area is displayed in the model configuration page.

[0054] Here, the field adding area is used to add a new field to the target business model and define it. The field adding area includes at least one addable field and a field attribute column corresponding to each addable field. Each field attribute in the field attribute column can be consistent with that in the field editing area. The difference is that in the field attribute column of each editable field in the field editing area, there is already defined value (the non-mandatory field attribute can also be empty), while in the field attribute column of each addable field in the field adding area, each field attribute is empty, and the user needs to input to define the field.

[0055] Based on the input information of the user in the field editing area and / or the field adding area, the configuration information corresponding to the target business model is generated according to the pre-stored data dictionary and the input information in the model configuration page in S120, including: based on the input information of the field editing area and / or the field adding area, the configuration information corresponding to the target business model is generated by using the data dictionary.

[0056] It can be understood that the user can only edit the original field in the target business model, or add a new field to the target business model by using the field adding area. The editable and modifiable field attributes in the field editing area, the data type of the addable field in the field adding area, etc. can be limited to the content that can be supported by the existing data code in the data dictionary, so that when the user adds a new field to the target business model or edits the existing field in the target business model, the configuration information can be generated according to the input information of the user by using the data dictionary.

[0057] Figure 2 is a schematic diagram of the field adding area in the model configuration page provided by an exemplary embodiment of the present application, as Figure 3As shown, the field adding area can include field attribute columns such as "field name", "Chinese name", "data type", "length", "whether mandatory", "description" and "sorting", the user can input the name of the newly added field, when the field name is not a Chinese name, the Chinese name corresponding to the field name can also be supplemented, the data type field attribute column can display the data type of the existing data code in the data dictionary in the drop-down box for selection, the user can select the data type of the newly added field through the drop-down box, and the user can also define the field length, whether mandatory, field sorting and the like of the newly added field in the field adding area, so that after the editing of the newly added field is completed, the "OK" button is clicked to make the data management system detect the input information in the model configuration page, and generate configuration information for the selected business model according to the detected input information.

[0058] It should be noted that the above example of the field attributes of the business data is to better explain the verification of the business data by configuring the business model in the embodiment, and in specific applications, the types of field attributes can be changed according to actual conditions, and the present application is not limited thereto.

[0059] By using the method of the embodiment, the business model can be added with fields and the attribute of the original field can be edited through the pre-stored data dictionary, the user can realize the reconfiguration of the existing model through the input in the model configuration page, and it is not necessary to re-write the verification code when the business is changed, so that the zero code access to the new business is realized in the data management system.

[0060] In a possible implementation, in response to detecting the configuration instruction for the target business model in the business model library in S110, the model configuration page of the target business model is displayed in the user interface, and further comprising: in response to detecting the sub-field configuration instruction for the target field in the model configuration page, the sub-field configuration page of the target field is displayed in association in the model configuration page, wherein the sub-field configuration page includes at least one selected field generated according to the data dictionary and the field attribute column corresponding to each selected field.

[0061] It can be understood that the data type of the field in the business data can include basic data types such as string, integer, float, time, and composite data types such as object and array. Based on the different data types, the field can have a multi-layer structure. For example, the field with an array data type can have a nested structure such as "[product 1, product 2, …]", and each element or subfield (such as product 1 as an element or subfield) in the field needs to be checked. For another example, the field with an object data type can have a multi-layer nested structure such as "user {address [province, city]}", and each element or subfield at each level needs to be checked layer by layer.

[0062] Therefore, based on the business needs of the nested structure, in some cases, it is also necessary to configure a subfield for a field (target field) defined in the business model metadata, thereby editing and defining the data structure of the target field. The user can select an existing field in the model configuration page to configure the subfield, so that the data management system can display the subfield configuration page of the target field in the model configuration page of the target business model when detecting a subfield configuration instruction for the target field in the model configuration page. The subfield configuration page can have the same or similar form as the field addition area, and the user can define the field properties of the subfield in the subfield configuration page in combination with the options provided by the data dictionary.

[0063] Based on this, in S120, the configuration information corresponding to the target business model is generated according to the pre-stored data dictionary and the input information in the model configuration page, including: generating the configuration information corresponding to the target business model by using the data dictionary according to the input information in the subfield configuration page.

[0064] The configuration information includes the field properties of the newly configured subfield for the target field, which is used to define the data structure of the target field, so that the target field and the newly configured subfield form a nested structure after the target business model is configured based on the configuration information.

[0065] It should be noted that when the field is selected in the model configuration page of the target business model for subfield configuration, the selected field can be a field at the top level of the data, or a subfield of another field, thereby supporting the construction of a multi-layer nested structure to meet the hierarchical verification needs of the business data.

[0066] By using the method of the embodiment, the subfield is configured for the field in the business model by using the pre-stored data dictionary, thereby effectively defining the nested data structure in the metadata of the business model, supporting the verification of the business data containing the nested data structure, and improving the data quality detection capability.

[0067] In a possible implementation, as shown in Figure 3 the method for configuring a service model also includes:

[0068] At step S401, in response to detecting a service model addition instruction, a basic information page and a field definition page of an added service model are displayed in a user interface, wherein the basic information to be supplemented in the basic information page includes at least a model name, a model ID, a belonging external service system, and a belonging service, and the field definition page includes at least one addable field generated according to a data dictionary and a field attribute column corresponding to each addable field.

[0069] At step S402, configuration information corresponding to the added service model is generated by using the data dictionary according to input information in the detected basic information page and field definition page.

[0070] At step S403, a target service model is generated according to the configuration information corresponding to the added service model, and the target service model is stored in a service model library.

[0071] It can be understood that when there is an added service (for example, a newly accessed external service system or a new service generated in an original service system), a service model bound to the added service needs to be created in a service model library of a data management system. When a service model addition instruction input by a user is detected, the data management system can display a basic information page and a field definition page of an added service model in a user interface. The basic information page is used to supplement basic information of the added service model, and the field definition page is used to select fields to be added in the added service model and input corresponding field attributes.

[0072] In the basic information page, the user can fill in a model name to identify the added service model, a model ID can be automatically generated by the system or manually input by the user, a belonging external service system is used to associate a specific service source corresponding to the model, and a belonging service is used to explicitly indicate a service type served by the model. These basic information will serve as a core identifier of the service model in the model library, facilitating subsequent management and calling. In the field definition page, the user can select fields suitable for the added service from candidate fields provided by the data dictionary, and define field names, data types, lengths, whether to be filled in, and other attributes in corresponding field attribute columns. If the added service involves a field of a composite data type, a nested structure can be further defined through a subfield configuration function. After all configurations are completed, the system generates complete configuration information of the added service model based on the basic information and field definition information input by the user, in combination with the data dictionary, and stores the complete configuration information in the service model library, thereby realizing quick access of the added service and automatic generation of data verification rules.

[0073] Figure 4is a schematic diagram of a model management page of a newly-added business model provided by an example embodiment of the present application, as shown in Figure 5 The model management page can include a basic information page, which includes a model name, a professional (business) to which the model belongs, a business system to which the model belongs, and a model description (a model ID is not shown). The information filled in the page information page can bind the newly-added business model with a specific business in a specific business system, so that the data management system can call a business model to generate a verification rule based on the binding relationship, and then verify the business data of the specific business through the verification rule, when receiving the business data of the specific business in the database.

[0074] The model management page also includes an "add" function button. Based on the "add" function button, a field definition page can be associated and displayed. The user can add one or more fields based on business requirements. The field definition page can be similar to the form of the field addition area in Figure 5 , which will not be described here again.

[0075] In a possible implementation, as shown in Figure 3 , the basic information page in the model management page also includes "template download" and "template import" function buttons. In addition to inputting information to make the data management system configure a business model using a data dictionary, the user can also download a model template stored in the data management system or import a model template from the outside world. The model template can have at least partial field definitions. In the case that the newly-added business model and the existing definitions in the model template partially coincide, the model template can be used to quickly build a business model.

[0076] By using the method of the present embodiment, a pre-stored data dictionary can be used to quickly generate a business model matching a newly-added business in the case of adding a new business, and the generated business model is stored in the business model library and bound to the newly-added business. The verification rule configuration for the newly-added business can be implemented without manually writing codes, thereby reducing the development difficulty of the data quality detection function of the data management system.

[0077] In a possible implementation, as shown in Figure 5 , the configuration method of the business model provided by the present embodiment further includes:

[0078] Step S601, in response to detecting a version control instruction for a target business model in the business model library, displaying a version control page of the target business model in a user interface, wherein the version control page includes at least one historical version of the target business model.

[0079] Step S602, generating version control information corresponding to the target business model according to the input information in the detected version control page.

[0080] Step S603: Configure the target business model according to the version control information corresponding to the target business model. The version control information includes the switching information of the currently effective version of the target business model and / or the editing information of the historical versions of the target business model.

[0081] It is understandable that some external business systems may experience unsatisfactory results after adjusting the form of business data, requiring a callback. Therefore, the business models bound to the business in the business model library also need corresponding callbacks. It is evident that configuring the callback function of the business models in the business model library is necessary.

[0082] Figure 6 This is a schematic diagram of a version control page for a business model provided in an exemplary embodiment of this application. Users can select to view historical versions of any business model in the model management interface. This operation can trigger the generation of version control instructions for the business model. The data management system will display at least one historical version of the business model in the user interface based on the version control instructions.

[0083] like Figure 7 As shown, the version control page for the business model named "Test" includes four versions: V2, V3, V4, and V5. V5 is the currently active version, while V2, V3, and V4 are historical versions. Users can use this version control page to change other historical versions to the current active version. In this case, the generated version control information is the switching information for the currently active version. Users can also modify or delete historical versions on the version control page. In this case, the generated version control information is the editing information for the historical version.

[0084] In some embodiments, the version control page can present all historical versions of the target business model from creation to date, with version number, creation time, update content summary, and operator, etc. key information marked for each historical version, facilitating users to quickly trace the model configuration at different stages. When the user needs to switch the current effective version to a historical version, they only need to select the target historical version in the version control page and trigger the switching instruction, and the system will check the integrity and availability of the historical version, automatically replace the current configuration of the target business model with the configuration information of the selected historical version after passing the check, and update the effective identification of the model. If the user wants to modify based on a historical version, they can select the corresponding historical version in the version control page and initiate the editing instruction, and the system will load the complete configuration data of the historical version to the model configuration page. The modifications made by the user during the editing process will be saved as a new version, and the original historical version will remain unchanged, thereby forming a new version record chain. In addition, the version control page also supports adding version notes to historical versions. Users can input note information such as design ideas, applicable scenarios, or change reasons for the version when creating a new version or editing a historical version, facilitating other members in team collaboration to understand the background of version iteration, and further improving the standardization and traceability of business model management.

[0085] The method of the present embodiment can increase the version management capability for each business model in the data management system, facilitate flexible version switching according to business data adjustment of external business systems, and improve the data quality control capability of the database for multiple business lines.

[0086] Figure 7

[0087] As an example, the application also provides a data processing method based on a business model, Example Method 2 is a flowchart of a data processing method based on a business model provided by an example embodiment of the application. The method of the present embodiment can be applied to the data management system of a database, as shown in Figure 8 includes the following steps:

[0088] Step S810, in response to detecting that any external business system calls the data interface, the data interface is used to obtain the business data to be processed pushed by the external business system.

[0089] Step S820, based on the business identifier in the business data to be processed, the target business to which the business data to be processed belongs is determined.

[0090] Step S830, the target business model bound with the target business is determined in the business model library, wherein the target business model stores the metadata corresponding to the target business, and the metadata is used to describe the business data of the target business.

[0091] In step S840, a data verification rule is generated according to the metadata in the target business model, and the to-be-processed business data is verified based on the data verification rule.

[0092] Here, the data management system can create a unified data interface to receive the to-be-processed business data sent by the external business system. The to-be-processed business data can carry a business identifier, and the data management system can determine the target business to which the to-be-processed business data belongs based on the business identifier, so as to determine the target business model bound to the target business in the business model library. The concept of the business model and the principle of generating the corresponding data verification rule based on the business model can refer to the related explanations in the example method one, and will not be described in detail here.

[0093] In a possible implementation, before the to-be-processed business data is verified based on the data verification rule in step S840, the to-be-processed business data can also be standardized to convert the to-be-processed business data into data in a preset format.

[0094] It can be understood that, due to different external business systems, the original formats of the data pushed by different business systems can be different, for example, XML, form data, custom protocol, etc. Preferably, the business model in the present application can store metadata for describing business data based on JSON data form, so that the data verification rule in JSON data form can be generated based on the business model. Before the to-be-processed business data is verified by using the data verification rule, the to-be-processed business data can be standardized to JSON data format, so as to ensure the consistency of subsequent verification and comparison. In addition, after the to-be-processed data is converted into JSON data format, the data is presented in the form of key-value pair, which can facilitate accurate comparison at the field level.

[0095] In some embodiments, the data verification rule can include a field verification rule and a cross-field verification rule. In step S830, the data verification rule is generated according to the metadata in the target business model, and the to-be-processed business data is verified based on the data verification rule, including: generating a field verification rule according to at least one field and its field attribute defined in the metadata; generating a cross-field verification rule according to the association relationship between the fields defined in the metadata; performing field verification on the target field in the to-be-processed business data based on the field verification rule matched with the field name of the target field; extracting a plurality of fields corresponding to the cross-field verification rule in the to-be-processed business data, and performing cross-field verification on the plurality of fields according to the cross-field verification rule.

[0096] The field attribute includes at least a field name, a data type, and a field level, and the field check rule includes at least one check rule corresponding to each field in the field. It can be understood that the to-be-processed business data can include more than one field, and the data type of each field can be different. The business model metadata includes field attribute definitions for different fields (the field attributes for different fields can also refer to the description of the business model configuration stage in the example method one), so as to generate corresponding field check rules based on the field attributes of different fields.

[0097] Exemplarily, the field check includes mandatory item check, data type check, and field level check. The mandatory item check is used to check whether the to-be-processed business data includes a field marked as mandatory in the target field attribute. If there is a missing, a check exception prompt is triggered. The data type check compares the actual data type of the target field in the to-be-processed business data with the data type (such as string, integer, date, etc.) defined in the field attribute. If they are inconsistent, a type error information is generated. The field level check is for a composite field with a nested structure, verifying whether the position and nesting relationship of each subfield in the data level conform to the definition in the metadata, to ensure the integrity of the complex data structure.

[0098] The cross-field check rule is generated based on the preset logical association between fields in the metadata. For example, the “order amount” field needs to be greater than the calculation result of the “product unit price x quantity” field, or the “start date” field needs to be earlier than the “end date” field. The system extracts the values of the associated multiple fields from the to-be-processed business data, and completes the check by performing the preset logical operation or comparison rule. If the association condition is not met, the detailed reason for the cross-field check failure is returned. By combining the field check and the cross-field check, the business data can be comprehensively detected from multiple dimensions of data integrity, accuracy, and logical rationality, effectively improving the quality threshold before data storage.

[0099] In some embodiments, in addition to the above-mentioned check types, user-defined rule check can also be included. The user-defined rule check can be executed synchronously with the above-mentioned check types, or can be configured to be executed after the completion of other checks.

[0100] Exemplarily, after the to-be-processed business data is checked based on the data check rule, a secondary check is performed on the to-be-processed business data based on the user-defined extension rule, and a check report corresponding to the to-be-processed business data is generated. The check report records the check problems detected in the check process of the to-be-processed business data, and is used for problem tracing of the fields in the to-be-processed business data that do not pass the check. For the to-be-processed business data that passes the check, a storage index is generated, and the to-be-processed business data is stored in the associated storage area of the target business model according to the storage index.

[0101] In the check report, in addition to recording the check problems, auxiliary data such as the statistics of the check pass field and the check time consumption can be included, so that the user can comprehensively understand the data check situation. For the business data that does not pass the check, the data management system can support temporary storage of the data in an exception data queue, and highlight the position of the exception data and the specific problem description in the user interface. The user can modify and re-submit the check of the exception data based on the check report. The generation rule of the storage index can be dynamically determined according to the metadata characteristics of the business model, for example, a unique storage index is generated by combining the model ID, the business identifier and the data generation timestamp, so as to ensure that the subsequent query, update and deletion operations of the business data can quickly locate the target storage area, and the data access efficiency is improved. At the same time, the associated storage area adopts a structured storage manner corresponding to the business model, and the business data is stored according to the hierarchical relationship defined by the field, which further guarantees the orderliness and consistency of the data, and provides a reliable data basis for subsequent advanced data processing functions such as data analysis and report generation based on the business model.

[0102] It can be understood that the user-defined rule can be a custom rule temporarily added by the user when receiving the to-be-processed business data, or the user-defined rule can be an additional rule pre-configured in the binding business model. The user-defined rule can be a limit condition for the value range of a numerical field, for example, a limit on the value of the unit price of goods in the order business data. The user-defined rule can also be a limit condition for the logical relationship between different fields, for example, the total price of goods calculated based on the "goods price" field in the order business data needs to be less than or equal to the order total amount calculated based on the "order amount" field.

[0103] Figure 8 is a check logic diagram of data check provided by an exemplary embodiment of the present application. The following will be described in combination with Figure 9 The content describes how to use the data check rule generated by the business model to check the business data.

[0104] (1) Initialization preparation

[0105] In this step, a blank check record table can be prepared in advance, which is used to record the problems found in the data check process (such as missing fields, mismatched field data types, etc.), and record the specific path of the problem so as to quickly locate the problem data later.

[0106] This step also includes dividing the to-be-processed business data into different check containers, recording the field path of the data in each check container, and extracting the field values that need to be checked.

[0107] For example, for business data with nested structure, it can be split into multiple independent verification containers according to the hierarchical relationship, such as the "order information" container containing "order basic information", "commodity detail list" and other sub-containers, each sub-container corresponding to a specific hierarchical path in the data, ensuring accurate positioning of the location of each field in the subsequent verification process. At the same time, the system will assign a unique identifier to each verification container to facilitate the differentiation of verification results in multi-container parallel verification.

[0108] (2) Traverse field definition

[0109] This step includes traversing the field definition of each field in the specific data verification rule, determining the field verification rule required for the verification in each verification container, and thus distributing the field verification rule to the corresponding verification container for targeted verification.

[0110] For example, if the "user information" module is included in the verification container, the system will extract the definition information of all fields (such as user ID, name, contact information, etc.) under this module from the metadata, including whether the field is required, whether the data type is a string or an integer, length limit and other attributes, and then generate the corresponding field verification rule package and map it to the "user information" verification container, ensuring that each container only executes verification logic related to its own data, avoiding verification confusion caused by rule crossing.

[0111] (3) Field verification

[0112] As shown in Figure 9 , based on the field attributes defined in the field verification rule, multiple verifications can be performed on the field, including but not limited to required item verification (based on whether the field is a required item), data type verification, composite object verification (for multi-layer nested data structure verification), and array collection verification (for array form data verification).

[0113] Exemplarily, for the target field to be verified, required item verification can be performed on the target field, if the required item verification fails, the verification of the target field is stopped; if the required item verification passes, data type verification is performed on the target field; if the data type verification fails, the verification of the target field is stopped; if the data type verification passes, subfield verification is performed on the target field.

[0114] For example, for the case where the required item verification fails, the problem can be recorded in the inspection record table, and the problem type is marked as "missing required item" and the field path; if the required item verification passes, data type verification is continued, the actual type of the target field is strictly matched with the metadata definition type, such as a field defined as "integer" if a string type data is transmitted, it is determined as a verification failure, and the problem of "data type mismatch" and the specific type difference are recorded.

[0115] Exemplarily, the subfield checking on the target field comprises: in response to detecting that the target field has a subfield, obtaining a field checking rule matching the field name of the subfield for any subfield in the target field; and performing subfield checking on the subfield based on the field checking rule matching the field name of the subfield.

[0116] For the checking of the subfield, according to the different nested structures, the checking can be divided into the checking of the object type and the array type. For the field of the object type, the system will recursively check the subfield nested in the field, and when the field of the object type is detected, the field checking rule corresponding to the subfield of the field is obtained based on the field name and other attributes of the subfield, so that the subfield checking is performed on the subfield. Similarly, for the field defined as the object type in the data checking rule, the field attribute of the field further includes the field level, and the field level checking can be performed on the object to determine whether the object completely contains the nested data structure of the required level. For the subfield, if the data type of the subfield is also a composite object, the field level still needs to be checked, and if the data type of the subfield is a basic type, only the data type checking can be performed. For example, whether the subfields of the province / city / district under the delivery address field meet the definition rule, and if any subfield checking fails, the whole is marked as “object structure exception”.

[0117] The array type field can be understood as a field object including a plurality of elements (subfields) of the same level. For these elements, they can have consistent sub-checking rules, and the subfield checking can be performed on the plurality of subfields one by one based on the sub-checking rules. In addition to checking the data type of each element in the array, whether the array length meets the quantity limit in the metadata (such as the minimum length and the maximum length) is checked, and the uniqueness or sorting rule of the array elements is verified to ensure the standardization of the collection data.

[0118] In the field checking process, each checking node generates detailed checking logs, including the checking time, the rule ID and the actual checking value, to provide a basis for subsequent problem tracing.

[0119] By using the method, the data management system can generate the data checking rule based on the business model bound by the specific business when receiving the specific business data, so that the preset type of checking is performed on the specific business data, and the data quality control ability of the database for the multiple business lines is optimized.

[0120] (4) Generating a checking result

[0121] In this step, after completing the multiple checks on the fields, the check results in each check container can be combined to generate a complete check result, which can be recorded in the check record table prepared in step (1) to form a check report. In addition to the check status (pass or fail) of each field, the specific failure reason and the field path, the check report can also mark abnormal problems according to the priority of the check rule, for example, mark the problem that causes the data core logic error as "serious", and mark the problem that the data format is not standardized but does not affect the main process as "warning", so as to facilitate the user to prioritize processing key exceptions. At the same time, the system supports exporting the check report in a structured format (such as JSON, XML) or a visual report form to meet the data quality audit needs in different scenarios. After the check result is generated, if all check items pass, the system will automatically trigger the data storage process, and write the business data into the associated storage area in combination with the storage index generated previously; if there are check items that do not pass, different processing strategies are executed according to the exception level, such as "serious" level exception directly blocking the storage process and locking the data, and "warning" level exception allowing the user to choose forced storage or return to correct, taking into account data strictness and business flexibility. In addition, the check result is also synchronized to the system log module, recording the trigger time, associated business model ID, processing time, and other metadata of this check, providing data support for subsequent optimization of check rules and improvement of system performance.

[0122] In some embodiments, after completing the field check of step (3), a secondary check can also be performed on the data based on user-defined extension rules before generating the check result. The trigger mechanism of the secondary check can be flexibly configured according to business needs, for example, automatically enabled when no serious error is detected in the field check but there is a non-key exception, or manually triggered and executed by the user. The user-defined extension rule supports configuration through a visual rule editor, which provides a drag-and-drop rule combination interface, and users can complete the setting of complex check logic without writing code, for example, by selecting "field A" "greater than" "field B" and associating the "order amount" and "total price" fields, to quickly generate a custom logic check condition.

[0123] The extension rule can also support referencing external data interfaces to dynamically obtain real-time data for comparison during the verification process. For example, when verifying the "customer credit rating" field, the system automatically calls the third-party credit investigation platform interface to compare the real-time credit score corresponding to the customer ID in the to-be-processed business data with the preset threshold. If the real-time credit score is lower than the threshold, it is determined that the verification fails. For problems found in the secondary verification, the system combines them with the field verification results into the same verification report and sorts the abnormal information according to the rule priority to display. The abnormal information of the user-defined rule is marked as "extension rule alarm" to distinguish from the basic verification problems. The introduction of the secondary verification further enhances the flexibility and scene adaptability of data verification, enabling the business model to cope with complex and personalized data quality requirements. At the same time, through the rule reuse mechanism, users can save commonly used custom rules as templates and bind them to multiple business models, effectively reducing the repeated configuration cost and improving the overall ease of use of the data management system.

[0124] For example, the data management system can also be preconfigured with corresponding data processing programs based on different verification results. For example, an alarm information can be generated when the amount of data that fails to pass the verification in the data verification result reaches a preset proportion to prompt the operation and maintenance personnel to handle it. The operation and maintenance personnel can refer to the inspection record table to understand and locate the problems occurring in the verification process. For another example, some key fields can be verified without error to pass the verification. In this case, if there is no problem of key fields failing to pass the verification in the verification result, the business data can be stored in the corresponding position of the database, and the inspection record table recording the verification problems of non-key fields can be kept for future query.

[0125] In addition, the data management system can predefine corresponding storage areas for different businesses in the database, and the storage areas can be associated with the business models bound to the businesses. Therefore, for business data that passes the verification, the data association system can generate an index for the business data and store it in the corresponding associated storage area, so as to quickly call out the batch of business data in the database based on the index in the future.

[0126] By using the method of the embodiment, in addition to the data verification rules generated in the business model, the data management system can obtain user-defined rules and execute them in combination with the original data verification rules, thereby enriching the verification capability of the data management system for business data and optimizing the data quality control capability of the database for multiple business lines.

[0127] The specific settings and implementation modes of the embodiments of the application are described from different angles above. By using the method provided by the above embodiments, a bound business model can be configured for different business configurations in the data management system, so that data checking is performed on the business data using the business model bound with the business when the business data is warehoused. Meanwhile, the data management system can support configuration modification of the business model through the pre-stored data dictionary, and can adapt to business changes without manually re-writing the corresponding code, thereby optimizing the data quality control capability of the database for multiple business lines, improving the data management efficiency and data flow reliability.

[0128] It should be further noted that by configuring the sub-field function for the business model, the effective definition of the nested data structure can be implemented in the metadata of the business model, which can support checking of the business data containing the nested data structure, further improving the coverage of the data quality detection, and meeting the higher standard data quality detection requirements.

[0129] Figure 9

[0130] As an implementation of the above exemplary method one, as shown in Example System 1 The data management system provided by the embodiments of the application comprises:

[0131] The page display module 1001 is configured to display a model configuration page of a target business model in a user interface in response to detecting a configuration instruction for the target business model in the business model library, wherein the business model library comprises a plurality of business models, each business model is bound one-to-one with each business in an external business system, the business model is used for checking the business data of the bound business, and the metadata corresponding to the bound business is stored in the business model, and the metadata is used for describing the business data of the bound business.

[0132] The information generation module 1002 is configured to generate configuration information corresponding to the target business model according to the pre-stored data dictionary and the input information in the model configuration page, wherein the data dictionary records the data coding corresponding to the data type and the data description information respectively.

[0133] The model configuration module 1003 is configured to configure the target business model according to the configuration information corresponding to the target business model.

[0134] Exemplarily, the model configuration page includes a field editing area and a field adding area, and the page display module 1001 is further configured to display the field editing area in the model configuration page according to the metadata stored in the target business model, wherein the field editing area includes at least one editable field and a field attribute column corresponding to each editable field, and the field attribute column includes at least a field name, a data type and a field level; and display the field adding area in the model configuration page according to the data dictionary, wherein the field adding area includes at least one addable field and a field attribute column corresponding to each addable field.

[0135] The information generation module 1002 is further configured to generate configuration information corresponding to the target business model by using the data dictionary based on the input information of the field editing area and / or the field adding area.

[0136] Exemplarily, the page display module 1001 is further configured to, in response to detecting a subfield configuration instruction for a target field in the model configuration page, display a subfield configuration page of the target field in association in the model configuration page, wherein the subfield configuration page includes at least one candidate field generated according to the data dictionary and a field attribute column corresponding to each candidate field.

[0137] The information generation module 1002 is further configured to generate configuration information corresponding to the target business model by using the data dictionary according to the input information in the subfield configuration page, and the configuration information is used to define a data structure of the target field.

[0138] In a possible implementation, the page display module 1001 is further configured to, in response to detecting a business model adding instruction, display a basic information page and a field definition page of an added business model in the user interface, wherein the basic information page to be supplemented includes at least a model name, a model ID, a belonging external business system and a belonging business, and the field definition page includes at least one addable field generated according to the data dictionary and a field attribute column corresponding to each addable field.

[0139] The information generation module 1002 is further configured to generate configuration information corresponding to the added business model by using the data dictionary according to the input information in the basic information page and the field definition page.

[0140] The model configuration module 1003 is further configured to generate a target business model according to the configuration information corresponding to the added business model, and store the target business model to the business model library.

[0141] In a possible implementation, the page display module 1001 is further configured to display, in response to detecting a version control instruction for a target business model in the business model library, a version control page of the target business model in the user interface, where the version control page includes at least one historical version of the target business model.

[0142] The information generation module 1002 is further configured to generate, according to the detected input information in the version control page, version control information corresponding to the target business model.

[0143] The model configuration module 1003 is further configured to configure the target business model according to the version control information corresponding to the target business model, where the version control information includes switching information of a currently effective version of the target business model and / or editing information of a historical version of the target business model.

[0144] The functions of each unit, module or sub-module in the embodiments can be referred to the corresponding description in the above example method one, and have corresponding beneficial effects, which will not be repeated here.

[0145] Figure 10

[0146] As an implementation of the above example method two, as shown in the method two, the present embodiment further provides a data management system, which includes: Example System 2

[0147] The data acquisition module 1101 is configured to acquire, in response to detecting that any external business system calls a data interface, the to-be-processed business data pushed by the external business system based on the data interface;

[0148] The first determination module 1102 is configured to determine, based on a business identifier in the to-be-processed business data, a target business to which the to-be-processed business data belongs;

[0149] The second determination module 1103 is configured to determine, in the business model library, a target business model bound to the target business, where the target business model stores metadata corresponding to the target business, and the metadata is used to describe business data of the target business.

[0150] The verification module 1104 is configured to generate a data verification rule according to the metadata in the target business model, and verify the to-be-processed business data based on the data verification rule.

[0151] It should be noted that the data management system shown in the method two and the data management system shown in the method one can be one system, Figure 11 Figure 11 Figure 10 and Figure 10 ​​​The modules in the data management system respectively represent functional modules of the data management system in different stages (a model configuration stage and a data verification stage).

[0152] Exemplarily, the data verification rules include field verification rules and cross-field verification rules, and the verification module 1104 is further configured to: generate the field verification rules according to at least one field and field attributes of the field defined in the metadata, wherein the field attributes at least include a field name, a data type and a field level, and the field verification rules include verification rules corresponding to each field in the at least one field; generate the cross-field verification rules according to an association relationship between the fields defined in the metadata; and perform field verification on a target field in the to-be-processed business data based on the field verification rules matched with the field name of the target field, wherein the field verification includes mandatory item verification, data type verification and field level verification; extract a plurality of fields corresponding to the cross-field verification rules from the to-be-processed business data, and perform cross-field verification on the plurality of fields according to the cross-field verification rules.

[0153] Exemplarily, the verification module 1104 is further configured to: perform mandatory item verification on the target field, and if the mandatory item verification fails, stop the verification on the target field; if the mandatory item verification passes, perform data type verification on the target field; if the data type verification fails, stop the verification on the target field; and if the data type verification passes, perform subfield verification on the target field.

[0154] In a possible implementation, the verification module 1104 is further configured to: in response to detecting that the target field has a subfield, for any subfield in the target field, acquire a field verification rule matched with a field name of the subfield; and perform subfield verification on the subfield based on the field verification rule matched with the field name of the subfield.

[0155] Exemplarily, the verification module 1104 is further configured to: perform secondary verification on the to-be-processed business data based on an extended rule defined by a user, and generate a verification report corresponding to the to-be-processed business data, wherein the verification report records verification problems detected in a verification process of the to-be-processed business data, and is used for problem tracing on fields in the to-be-processed business data that fail to pass the verification.

[0156] The data management system further includes a data storage module, which is configured to generate a storage index for to-be-processed business data that passes the verification, and store the to-be-processed business data to an associated storage area of a target business model according to the storage index.

[0157] The functions of each unit, module or submodule in the embodiment can be referred to the corresponding description in the above exemplary method two, with the corresponding beneficial effects, which will not be described here again.

[0158] Figure 11

[0159] Example Electronic Device and Computer-Readable Storage Medium Figure 1 is a block diagram of an electronic device for implementing the embodiments of the present application. As shown in Figure 1, the electronic device includes a memory 1201 and a processor 1202, and the memory 1201 stores a computer program executable on the processor 1202. The processor 1202 implements the method in the above embodiments when executing the computer program. The number of the memory 1201 and the processor 1202 can be one or more. Figure 12

[0160] The electronic device further includes:

[0161] a communication interface 1203 for communicating with external devices and transmitting data.

[0162] If the memory 1201, the processor 1202 and the communication interface 1203 are independently implemented, the memory 1201, the processor 1202 and the communication interface 1203 can be connected to each other through a bus and complete communication therebetween. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc. For the convenience of representation, Figure 12 Figure 12 only one thick line is used in the figure, but it does not mean that there is only one bus or only one type of bus.

[0163] Optionally, in a specific implementation, if the memory 1201, the processor 1202 and the communication interface 1203 are integrated on a chip, the memory 1201, the processor 1202 and the communication interface 1203 can complete communication therebetween through an internal interface.

[0164] The embodiments of the present application also provide a chip, which includes a processor, and the processor is configured to call and execute instructions stored in a memory, so that a communication device installed with the chip executes the method provided by the embodiments of the present application.

[0165] The embodiments of the present application also provide a chip, which includes an input interface, an output interface, a processor and a memory, and the input interface, the output interface, the processor and the memory are connected through an internal connection path. The processor is configured to execute code in the memory, and when the code is executed, the processor is configured to execute the method provided by the embodiments of the present application.

[0166] ​It is to be understood that the above-described processor can be a CPU, and can also be other general-purpose processors, Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), FPGAs or other programmable logic devices, discrete gates or transistor logic, discrete hardware components, or the like. The general-purpose processor can be a microprocessor or any conventional processor, or the like. It is to be noted that the processor can be a processor supporting an Advanced RISC Machines (ARM) architecture.

[0167] Further, the memory can include a read-only memory and a random access memory, optionally. The memory can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memory. The non-volatile memory can include a Read-Only Memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically EPROM (EEPROM), or a flash memory, for example. The volatile memory can include a Random Access Memory (RAM) used as an external cache memory. Many forms of RAM are available by way of example but not limitation. For example, a Static RAM (SRAM), a Dynamic RAM (DRAM), a Synchronous DRAM (SDRAM), a Double Data Rate SDRAM (DDR SDRAM), an Enhanced SDRAM (ESDRAM), a Sync Link DRAM (SLDRAM), and a Direct Rambus RAM (DR RAM) can be used.

[0168] In the above-described embodiments, all or part can be implemented by software, hardware, firmware, or any combination thereof. When implemented by software, all or part can be implemented in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions according to the present disclosure are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another computer-readable storage medium.

[0169] In addition to the above methods and devices, embodiments of the present disclosure can also provide a computer program product including computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the image processing method of various embodiments of the present disclosure described in the above “Exemplary Methods” section.

[0170] The computer program product can be written in any combination of one or more programming languages to perform the operations of embodiments of the present disclosure, including an object-oriented programming language, such as Java, C++, and the like, and a conventional procedural programming language, such as the “C” language or similar programming languages. The program code can be executed entirely on a user computing device, partially on a user device, as an independent software package, partially on a user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0171] In the description of the present specification, the description of the terms “one embodiment”, “some embodiments”, “an example”, “a specific example” or “some examples” and the like means that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present disclosure. Moreover, the specific features, structures, materials or characteristics described can be combined in any appropriate manner in any one or more embodiments or examples. In addition, different embodiments or examples described in the present specification and features of different embodiments or examples can be combined and combined by those skilled in the art without contradiction.

[0172] In addition, the terms “first”, “second” are used only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with “first”, “second” can explicitly or implicitly include at least one of the features. In the description of the present application, the meaning of “a plurality of” is two or more, unless otherwise specifically limited.

[0173] Any processes or methods described in the flowcharts or otherwise described herein can be understood as representing code modules, segments, or portions of code which include one or more executable instructions for implementing specific logic functions (or steps) in the processes. Moreover, in some embodiments, the various functions or steps can be performed in the order shown or in a different order.

[0174] The logic and / or steps described in the flowcharts or otherwise described herein, for example, can be embodied in computer-readable instructions, such as software and / or firmware, that are executable by a processing system, e.g., a computer system, a processor, or processor core. Generally, computer-readable instructions include routines or subroutines, programs, applications, software module, scripts, data files, program data, program binaries, machine code, instructions, computer-executable code, and / or the like.

[0175] It should be understood that some or all of the components of the present application can be implemented with hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented with software or firmware stored in memory and executed by a suitable instruction execution system. All or part of the steps of the above-described embodiments can be instructed by a program to complete the relevant hardware, and the program can be stored in a computer-readable storage medium, and when executed, includes one or a combination of steps of the method embodiments.

[0176] In addition, each functional unit in the various embodiments of the present application can be integrated in one processing module, or each unit can be physically present separately, or two or more units can be integrated in one module. The above integrated module can be realized in the form of hardware or in the form of a software function module. When the above integrated module is realized in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. The storage medium can be a read-only memory, a magnetic disk or an optical disk, etc.

[0177] The above description is only exemplary embodiments of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of various changes or replacements within the technical scope disclosed in the present application, and these should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for configuring a business model for a data management system, characterized in that, include: In response to detecting a configuration instruction for a target business model in the business model library, a model configuration page for the target business model is displayed in the user interface. The business model library includes multiple business models, each of which is bound to a business in an external business system. The business model is used to verify the business data of the bound business. The business model stores metadata corresponding to the bound business, and the metadata is used to describe the business data of the bound business. Based on the pre-stored data dictionary and the input information in the model configuration page, configuration information corresponding to the target business model is generated, wherein the data dictionary records the data codes corresponding to the data types and data description information respectively; Configure the target business model according to the configuration information corresponding to the target business model.

2. The method according to claim 1, characterized in that, In response to detecting a configuration instruction for a target business model in the business model library, the model configuration page of the target business model is displayed in the user interface, including: In response to detecting a subfield configuration instruction for a target field in the model configuration page, the subfield configuration page for the target field is displayed in association on the model configuration page, wherein the subfield configuration page includes at least one candidate field generated according to the data dictionary and a field attribute bar corresponding to each candidate field; Based on the pre-stored data dictionary and the input information in the model configuration page, generate the configuration information corresponding to the target business model, including: Based on the input information in the subfield configuration page, the configuration information corresponding to the target business model is generated using the data dictionary. The configuration information is used to define the data structure of the target field.

3. The method according to claim 1, characterized in that, The method further includes: In response to the detection of a new business model instruction, a basic information page and a field definition page for the new business model are displayed in the user interface. The basic information page includes at least the model name, model ID, external business system to which it belongs, and business to which it belongs. The field definition page includes at least one addable field generated according to the data dictionary and a field attribute bar corresponding to each addable field. The field attribute bar includes at least the field name, data type, and field level. Based on the input information detected in the basic information page and the field definition page, the configuration information corresponding to the new business model is generated using the data dictionary; A target business model is generated based on the configuration information corresponding to the newly added business model, and the target business model is stored in the business model library.

4. The method according to claim 1, characterized in that, The method further includes: In response to detecting a version control instruction for a target business model in the business model library, a version control page for the target business model is displayed in the user interface, wherein the version control page includes at least one historical version of the target business model; Based on the input information detected in the version control page, generate version control information corresponding to the target business model; The target business model is configured according to the version control information corresponding to the target business model, wherein the version control information includes switching information for the currently effective version of the target business model and / or editing information for the historical versions of the target business model.

5. A data processing method based on a business model, used in a data management system, characterized in that, include: In response to detecting any external business system calling the data interface, the pending business data pushed by the external business system is obtained based on the data interface; Based on the service identifier in the service data to be processed, determine the target service to which the service data to be processed belongs; In the business model library, a target business model bound to the target business is determined, wherein the target business model stores metadata corresponding to the target business, and the metadata is used to describe the business data of the target business; Data verification rules are generated based on the metadata in the target business model, and the business data to be processed is verified based on the data verification rules.

6. The method according to claim 5, characterized in that, The data validation rules include field validation rules and cross-field validation rules. Data validation rules are generated based on the metadata in the target business model, and the business data to be processed is validated based on these rules, including: Field validation rules are generated based on at least one field and its field attributes defined in the metadata, wherein the field attributes include at least field name, data type and field level, and the field validation rules include validation rules corresponding to each field in the at least one field; Based on the relationships between fields defined in the metadata, generate cross-field validation rules; For the target field in the business data to be processed, field validation is performed on the target field based on the field validation rules that match the field name of the target field. The field validation includes mandatory field validation, data type validation, and field hierarchy validation. Extract multiple fields corresponding to the cross-field validation rule from the business data to be processed, and perform cross-field validation on the multiple fields according to the cross-field validation rule.

7. The method according to claim 6, characterized in that, The step of performing field validation on the target field in the business data to be processed, based on field validation rules that match the field name of the target field, includes: Perform mandatory field validation on the target field. If the mandatory field validation fails, stop the validation of the target field. If the required field validation passes, then perform data type validation on the target field; If the data type validation fails, the validation of the target field will be stopped. If the data type validation passes, then subfield validation is performed on the target field.

8. The method according to claim 6, characterized in that, The sub-field validation performed on the target field includes: In response to the detection that the target field has a subfield, for any subfield in the target field, obtain the field validation rule that matches the field name of the subfield; Subfield validation is performed on the subfield based on the field validation rules that match the field name of the subfield.

9. The method according to any one of claims 5-8, characterized in that, After verifying the business data to be processed based on the data verification rules, the process further includes: The pending business data is subjected to secondary verification based on user-defined extended rules, and a verification report corresponding to the pending business data is generated. The verification report records the verification problems detected during the verification process of the pending business data, which is used to trace the source of the problems in the fields that fail the verification in the pending business data. For the business data to be processed that has passed the verification, a storage index is generated, and the business data to be processed is stored in the associated storage area of ​​the target business model according to the storage index.

10. A data management system, characterized in that, include: The page display module is configured to display the model configuration page of the target business model in the user interface in response to the detection of a configuration instruction for a target business model in the business model library. The business model library includes multiple business models, each of which is bound to a business in an external business system. The business model is used to verify the business data of the bound business. The business model stores metadata corresponding to the bound business, and the metadata is used to describe the business data of the bound business. The information generation module is configured to generate configuration information corresponding to the target business model based on a pre-stored data dictionary and the input information in the model configuration page, wherein the data dictionary records data codes corresponding to data types and data description information respectively; The model configuration module is configured to configure the target business model according to the configuration information corresponding to the target business model.

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