Business data processing system, method, equipment, medium and product

Abstract

The embodiment of the invention discloses a business data processing system, method, equipment, medium and product, and the system comprises a model management module which stores the model configuration information and basic business information of a project data model, and a data obtaining module which obtains the configuration information, the basic business information and a model dependency relationship through data, obtaining at least one target business data model and at least one piece of target business data, and constructing a project data model by a data assembly module by utilizing the at least one target business data model and the at least one piece of target business data based on the model dependency relationship and the basic business data, and the business data corresponding to the project data model is transmitted to the project processing system, so that a large number of complex codes do not need to be written manually, the development difficulty and cost are remarkably reduced, the code quantity is effectively reduced, and the system stability is enhanced.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to data processing technology, and in particular, to a business data processing system, method, device, medium and product. BACKGROUND

[0002] In the industry-finance integration scenario, the industry-finance system undertakes the core function of business data transfer and processing, and a typical data flow process thereof is: receiving business data notified by an upstream business system, and then transmitting the business data to a downstream financial system. However, the industry-finance data synchronized by the upstream business system to the industry-finance system is usually only a part of the data required for the business system to complete the entry operation. In order to realize complete collection of entry data, the industry-finance system needs to query and supplement the remaining required business data from multiple upstream business systems, and then push the complete business data to the financial system.

[0003] In this process, due to the diverse types of business systems, and the need to develop specific data extraction codes for each type of business system for business system connection, data query and extraction, developers need to write a large amount of complex and redundant codes, which not only causes the code amount to increase explosively and the development cost to rise, but also significantly increases the difficulty of system maintenance. SUMMARY

[0004] To solve the above technical problems, the embodiments of the present disclosure provide a business data processing system, method, device, medium and product.

[0005] In one aspect of the embodiments of the present disclosure, a business data processing system is provided, which comprises a model management module, a data acquisition module and a data assembly module; the model management module is configured to store model configuration information of a project data model and basic business information, the model configuration information comprises model dependency relationship and data acquisition configuration information, the model dependency relationship is used to represent the relationship between the project data model and a business data model, and the basic business information comprises the business identifier corresponding to each business data of at least one business data used to construct the project data model; the data acquisition module is configured to acquire at least one target business data model and at least one target business data by using the data acquisition configuration information, the basic business information and the model dependency relationship; and the data assembly module is configured to construct the project data model by using at least one target business data model and at least one target business data based on the model dependency relationship and the basic business information, and transmit the business data corresponding to the project data model to a project processing system.

[0006] In another aspect of the embodiments of the present disclosure, a business data processing method is provided, including: obtaining model configuration information of a project data model and basic business information, the model configuration information including model dependency relationships and data acquisition configuration information, the model dependency relationships being used to represent relationships between the project data model and business data models, the basic business information including business identifiers corresponding to each of at least one business data used to construct the project data model; obtaining at least one target business data model and at least one target business data by using the data acquisition configuration information, the basic business information and the model dependency relationships; constructing the project data model by using at least one of the target business data models and at least one of the target business data based on the model dependency relationships and the basic business information, and transmitting business data corresponding to the project data model to a project processing system.

[0007] In yet another aspect of the embodiments of the present disclosure, an electronic device is provided, including a processor and a memory connected to the processor in communication, and further including the business data processing system described above; the memory is used to store executable instructions of the processor; the processor is used to read the executable instructions from the memory to control the business data processing system to implement the business data processing method described above.

[0008] In still another aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, having a computer program stored thereon, the computer program being executed by a processor to implement the business data processing method described above.

[0009] In still another aspect of the embodiments of the present disclosure, a computer program product is provided, including computer program instructions, the computer program instructions being executed by a processor to implement the business data processing method described above.

[0010] According to the embodiments of the present disclosure, the business data processing system is configured to include a model management module, a data acquisition module and a data assembly module, the model management module is used to store model configuration information of a project data model and basic business information, the data acquisition module is used to obtain at least one target business data model and at least one target business data by using the data acquisition configuration information, the basic business information and the model dependency relationships, and the data assembly module is used to construct the project data model by using at least one of the target business data models and at least one of the target business data based on the model dependency relationships and the basic business information, and transmit business data corresponding to the project data model to a project processing system.

[0011] Thus, by pre-abstracting and configuring the model dependency relationship and data acquisition configuration information of the project data model, automatic data calling from inside to outside and layer by layer in the model generation process can be realized, and assembling can be automatically completed based on the model dependency relationship, thereby improving the data acquisition and model assembling efficiency. Relying on the preset configuration information, the business data processing system can automatically acquire the target business data model and the corresponding target business data, without manually writing a large number of complex codes. This significantly reduces the development difficulty and cost, effectively simplifies the code quantity, reduces errors caused by improper code writing, enhances the system stability, and ensures that the project processing system can quickly and accurately provide complete and accurate business data under complex business environment. In addition, when the business demand changes and new business data or data acquisition logic needs to be added or modified, the code does not need to be modified, only the basic business information and data acquisition configuration information need to be updated, which greatly improves the scalability and maintenance efficiency of the system, further enhances the system stability, and ensures that the system can quickly and accurately provide the required project data model under complex business environment.

[0012] The technical solutions of the present disclosure will be further described in detail below with the aid of the accompanying drawings and embodiments. DETAILED DESCRIPTION

[0013] The accompanying drawings, which form a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0014] The present disclosure can be more clearly understood with reference to the following detailed description in conjunction with the accompanying drawings, in which:

[0015] Figure 1 is a structural block diagram of a business data processing system provided by an exemplary embodiment of the present disclosure.

[0016] Figure 2 is a structural block diagram of a data acquisition module provided by an exemplary embodiment of the present disclosure.

[0017] Figure 3 is a structural block diagram of a data acquisition module provided by another exemplary embodiment of the present disclosure.

[0018] Figure 4 is a structural block diagram of a data assembly module provided by an exemplary embodiment of the present disclosure.

[0019] Figure 5 is a structural block diagram of a data assembly module provided by another exemplary embodiment of the present disclosure.

[0020] Figure 6 is a structural block diagram of a business data processing system provided by another exemplary embodiment of the present disclosure.

[0021] Figure 7is a schematic diagram of a service data processing method provided by an example embodiment of the present disclosure.

[0022] Figure 8 is a structural block diagram of an application embodiment of the electronic device of the present disclosure. DETAILED DESCRIPTION

[0023] Various example embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. Note that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments are not limiting to the scope of the present disclosure unless specifically stated otherwise.

[0024] Those skilled in the art can understand that the terms "first", "second", and the like in the embodiments of the present disclosure are only used to distinguish different steps, devices, or modules, and do not represent any specific technical meaning, nor do they represent a necessary logical sequence between them.

[0025] It should also be understood that in the embodiments of the present disclosure, "multiple" can mean two or more, and "at least one" can mean one, two, or more.

[0026] It should also be understood that for any component, data, or structure mentioned in the embodiments of the present disclosure, unless specifically limited or given a contrary implication by the context or prior art, it can generally be understood as one or more.

[0027] In addition, the term "and / or" in the present disclosure is only a description of the association relationship between the associated objects, which means that there can be three relationships, for example, A and / or B can mean that A exists alone, A and B exist together, and B exists alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the front and rear associated objects.

[0028] It should also be understood that the description of various embodiments of the present disclosure focuses on the differences between the various embodiments, and the same or similar parts can be referred to each other, and for the sake of brevity, will not be repeated.

[0029] At the same time, it should be understood that in order to facilitate description, the size of each part shown in the drawings is not drawn in accordance with the actual proportional relationship.

[0030] The following description of at least one example embodiment is merely illustrative in nature and does not in any way limit the disclosure and its application or uses.

[0031] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail, but where appropriate, the described techniques, methods, and devices should be considered as part of the specification.

[0032] It should be noted that like reference numerals and characters refer to like elements throughout the following description and the claims attached hereto. Therefore, once any certain element is defined in one drawing, it is not necessary to discuss it further in the following drawings.

[0033] Embodiments of the present disclosure can be applied to terminal devices, computer systems, servers and other electronic devices, which can operate with many other general-purpose or special-purpose computing system environments or configurations. Examples of well-known terminal devices, computing systems, environments, and / or configurations suitable for use with terminal devices, computer systems, servers and other electronic devices include, but are not limited to, personal computer systems, server computer systems, thin clients, thick clients, handheld or laptop devices, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers, minicomputer systems, mainframe computer systems, and distributed cloud computing technology environments that include any of the above systems, and the like.

[0034] Terminal devices, computer systems, servers and other electronic devices can be described in the general context of computer system-executable instructions, such as program modules, being executed by a computer system. Generally, program modules can include routines, programs, objects, components, logic, data structures, and the like, which perform particular tasks or implement particular abstract data types. Computer systems / servers can be implemented in a distributed cloud computing environment, in which tasks are performed by remote processing devices that are linked through a communications network. In a distributed cloud computing environment, program modules can be located in local or remote computer system storage media, including storage devices.

[0035] In the business scenario of industry and finance integration, the data flow interaction of the industry and finance system with the upstream business system and the downstream financial system follows the following process: when the business data of the upstream business system flows to the preset node, the node data will be synchronized to the industry and finance system through technical means such as message pushing and interface calling, and then processed by the industry and finance system, and the business data meeting the specification will be pushed to the financial system. However, the business data synchronized by the upstream business system to the industry and finance system is only a subset of the data required for the industry and finance system to complete the entry operation. In order to realize the complete collection of entry data, the industry and finance system needs to query and supplement the remaining necessary business data from multiple upstream business systems with a specific business number as the association identifier, and then push to the financial system after integration.

[0036] In this process, due to the diverse types of upstream business systems, the corresponding data sources present significant diversity in form (for example, internal databases, external third-party interfaces, file systems, etc.), and each data source has essential differences in connection protocols, query syntax, data formats, and access control mechanisms. Developers need to develop a dedicated connection adaptation module, query execution logic, and data extraction code for each data source. This mode directly leads to a large amount of complex and redundant code in the system, which in turn causes a series of problems: first, the explosive growth of code lengthens the system construction period and significantly increases development costs; second, the adaptation code logic of each data source is fragmented and independent, and needs to be adjusted one by one in later iterations, which not only increases modification costs but also increases maintenance difficulty; third, minor changes in upstream data sources (such as interface field adjustment, permission policy update, etc.) can trigger a chain reaction, leading to data extraction failure, seriously affecting the continuity and accuracy of business data flow. In addition, when the business requirements change and new data sources need to be added or data acquisition logic needs to be modified, the existing code needs to be adjusted, and code modification often triggers a chain reaction, requiring a lot of time and effort for debugging and testing, which not only seriously reduces system development efficiency but also significantly increases maintenance costs.

[0037] Figure 1 is a structural block diagram of a business data processing system provided by an example embodiment of the present disclosure. The present embodiment can be applied in electronic devices such as Figure 1 As shown in the figure, the business data processing system 100 includes a model management module 110, a data acquisition module 120, and a data assembly module 130.

[0038] The model management module 110 is used to store model configuration information and basic business information of a project data model.

[0039] The project data model is used to manage the relevant data of a project. The project data model may, for example, include model configuration information and basic business information. The model configuration information may include model dependency relationships and data acquisition configuration information. The model dependency relationships are used to represent the relationship between the project data model and the business data model. The relationship between the project data model and the business data model may, for example, include a containing relationship, i.e., the project data model contains the business data model. The data acquisition configuration information represents the relevant information required to obtain the business data and the business data model used to build the project data model. For example, the data acquisition configuration information may include a data source address, an application programming interface (API), and the like. The basic business information includes a business identifier corresponding to each of at least one business data used to build the project data model. Specifically, the business data corresponding to the business identifier in the basic business information is used to build the project data model. The business identifier is used to abstractly describe the business data. For example, the business identifier corresponding to a business data may be the name of the business data. For example, assuming that the project corresponding to the project data model is a purchase order, the basic business information may be abstracted from the purchase order. The basic business information may include, for example, a purchase time (business identifier), a warehouse (business identifier), a commodity (business identifier), and the like.

[0040] In an embodiment, the project data model can include definition information and description information. The model management module 110 can store the definition information and the description information. Specifically, the definition information and the description information can be pre-configured and stored in the model management module 110 in the form of a database table. The definition information is used to describe the core entities (or objects) of the project data model, the rules and association relationships, the data operation and flow rules, and the model constraints and specifications, etc. For example, the description of the core entities can include the basic data units (such as orders, users, products, etc. in a business scenario) contained in the project data model, and the name, meaning and business attributes of each entity are described. For example, in the project data model corresponding to the house decoration (project), the “decoration order” (entity) needs to be defined as “a unit recording the decoration materials purchased, the decoration personnel hired, the engineering acceptance and the payment information in the house decoration process”, which can include attributes such as decoration order number, type, quantity, amount and purchase time of decoration materials, and personnel name and acceptance time of engineering acceptance. The description of the entity attributes can include the definition of the fields contained in the entity, such as attribute name (such as “order number”, “user ID”, etc.); the data type of the entity object, such as the data type of the entity object being a string, an integer, a date, etc.; the length or value range of the entity object, such as the order number being fixed as an 18-character string; the constraint conditions of the entity object, such as whether the entity object is a mandatory item, whether it is unique, the default value, etc. The description of the rules and association relationships can include the description of the association manner between different entities, for example, the relationship manner can include one-to-one, one-to-many and many-to-many, for example, one-to-one can be one user associated with one user detail, one-to-many can be one user associated with multiple orders, and many-to-many can be multiple goods associated with multiple orders, and the dependency rules of the association manner need to be described, for example, whether to delete the associated orders when deleting the user, etc. The description of the data operation and flow rules can include information describing the generation, update, storage and life cycle of the data in the project data model, for example, the generation of the data can be “order amount” generated by the unit price and quantity of the decoration materials, the update of the data can be “order status” automatically updated to “paid” after payment is completed, and the life cycle of the data can be the archiving or cleaning rules of “expired orders”. The description of the model constraints and specifications can include the description of the technical or business specifications that the project data model needs to comply with, for example: the description of the model constraints and specifications includes naming specifications (for example, the entity name uses English plural form), data consistency rules (for example, “order total amount” needs to be equal to the sum of each “order item amount”), security constraints (for example, “user password” needs to be stored in encrypted form, and cannot be queried in plaintext, etc.) and the like.

[0041] The description information is used to describe the details of the specific content, application scenario and implementation details of the project data model based on the definition information. For example, the description information can include model configuration information, basic business information, data storage information, data source information, project identifier and data identifier, etc. The project identifier is used to identify the project data model, the data storage information includes the storage address of the project data model, the target business data model and the target business data of the project data model, etc. The data source information includes the data source address, data source name, etc. of each target business data and each target business data model.

[0042] The data acquisition module 120 is configured to acquire at least one target business data model and at least one target business data by using the data acquisition configuration information, the basic business information and the model dependency relationship.

[0043] In the embodiments of the present disclosure, the business data model used to construct the project data model is referred to as the target business data model, and the business data used to construct the project data model is referred to as the target business data.

[0044] In one embodiment, the data acquisition module 120 can employ a data acquisition engine, for example. The data acquisition module 120 can acquire the business data model having a relationship with the project data model in the model dependency relationship as the target business data model by using the data acquisition configuration information, and then acquire the business data corresponding to the basic business information as the target business data.

[0045] For example, assuming that the project data model is a procurement data model, the model dependency relationship includes that the procurement data model contains a warehouse data model (business data model) and a commodity data model (business data model), and the model identifier of the warehouse data model and the model identifier of the commodity data model. The basic business information can include warehouse (business identifier of business data), commodity (business identifier of business data), etc. The warehouse data model (target business data model) and the commodity data model (target business data model) are acquired based on the data acquisition configuration information, and then the business data corresponding to the warehouse (target business data) and the business data corresponding to the commodity (target business data) are acquired by using the data acquisition configuration information.

[0046] The data assembly module 130 is configured to construct the project data model by using at least one target business data model and at least one target business data based on the model dependency relationship and the basic business data, and transmit the business data corresponding to the project data model to the project processing system.

[0047] The project processing system is a downstream system of the business data processing system and is configured to process business data of the project data model. In an embodiment, the project data system can further include at least one target business data and at least one target business data model. The data assembling module 130 can be implemented by a data assembling engine, for example.

[0048] The project processing system can be a financial system, and the business data processing system can be a financial and business system. The business data processing system can provide the business data required by the financial system. In an embodiment, the assembling manner of each target business data model can be determined based on the relationship between each target business data model and the project data model in the model dependency relationship. The model assembling can be performed in the assembling manner corresponding to each target business, and the target business data can be loaded into the basic business information to obtain the project data model.

[0049] For example, the model dependency relationship includes that the sales order expansion model (the project data model) includes the sales order basic model (the target business data model). When the sales order expansion model is constructed, the sales order basic model is directly introduced or embedded into the description information of the sales order expansion model as an attribute of the sales order expansion model. The target business data is recorded into the basic business information to obtain the sales order expansion model.

[0050] In the disclosed embodiments, the model dependency relationship and the data acquisition configuration information of the project data model are abstracted and configured in advance. The automatic data calling from inside to outside and layer by layer during the model generation process can be realized, and the model assembling can be automatically completed based on the model dependency relationship. Therefore, the data acquisition and model assembling efficiency is improved. The target business data model and the corresponding target business data can be automatically acquired by the business data processing system based on the preset configuration information, and a large number of complex codes do not need to be manually written. This significantly reduces the development difficulty and cost, effectively simplifies the code quantity, reduces the errors caused by improper code writing, enhances the system stability, and ensures that the project processing system can quickly and accurately provide complete and accurate business data in a complex business environment. In addition, when the business requirement changes and the business data needs to be added or the data acquisition logic needs to be modified, the code does not need to be modified, and only the basic business information and the data acquisition configuration information need to be updated. This mechanism greatly improves the scalability and maintenance efficiency of the system, further enhances the system stability, and ensures that the system can quickly and accurately provide the required project data model in a complex business environment.

[0051] In some optional embodiments, each business identifier corresponds to at least one data identifier of the business data. The model dependency relationship further includes a model identifier of at least one business data model. The data acquisition configuration information includes at least one acquisition manner configuration information. Any acquisition manner configuration information corresponds to at least one model identifier or at least one data identifier.

[0052] The acquisition method configuration information of each business data and each business data model can be configured according to the acquisition method of the data supported by the data source of the business data and the business data model, and the acquisition method of the data can include, for example, third-party interface calling and database query. For example, when the acquisition method of the data is third-party interface calling, the acquisition method configuration information can include a data interface, query parameters and an interface protocol. The data interface can be, for example, an application programming interface (API), and the interface protocol can include, for example, a hypertext transfer protocol (HTTP) or a remote procedure call protocol (RPC). Correspondingly, the data acquisition module 120 is also configured with the interface protocol so that the data acquisition module 110 supports the interface protocol. The query parameters can include, for example, a request header, a query condition, an interface endpoint address, etc. The interface endpoint address can be, for example, a uniform resource locator (URL). When the acquisition method of the data is database query, the acquisition method configuration information can include, for example, data source connection information and a query statement. The data source connection information can include, for example, a host address of the data source, a driver type, a port number, a database name, a username, a password, etc.

[0053] The data identifier is used to identify the business data, and the model identifier is used to identify the business data model. In an embodiment, the model dependency relationship includes the model identifier of the business data model having a relationship with the project data model.

[0054] Correspondingly, Figure 2 is a structural block diagram of a data acquisition module provided by an exemplary embodiment of the present disclosure. As shown in Figure 2 The data acquisition module 120 can include an information calling sub-module 121 and a data acquisition sub-module 122.

[0055] The information calling sub-module 121 is used to call the model configuration information and the basic business information from the model management module 110.

[0056] Exemplarily, the API corresponding to the model configuration information and the API corresponding to the basic business information can be preconfigured and stored in the information calling submodule 121, and the information calling submodule 121 can also store the model identifier of the project data model. The information calling submodule 121 can call the model configuration information from the model management module 110 based on the API corresponding to the model configuration information and the model identifier of the project data model, and call the basic business information from the model management module 110 based on the API corresponding to the basic business information and the model identifier of the project data model.

[0057] The data acquisition submodule 122 is configured to determine the model identifier corresponding to at least one business identifier as a target model identifier based on the model dependency relationship, acquire at least one target business data model based on the target model identifier and by using the acquisition manner configuration information corresponding to the target model identifier, and acquire at least one target business data based on at least one data identifier and by using the acquisition manner configuration information corresponding to the at least one data identifier.

[0058] In an embodiment, for each business identifier in the at least one business identifier, the business data model corresponding to the business identifier can be determined in the model dependency relationship by manual selection, and the model identifier of the business data model is taken as the target model identifier. For example, when the business identifier is a commodity, the model dependency relationship includes a project data model including a commodity data model, and the model identifier of the commodity data model can be acquired as the target model identifier. Alternatively, a corresponding relationship between the data identifier and the type of the business data model can also be preconfigured, for example, the second character in the data identifier can be used to represent the type of the business data model, such as 1 representing a commodity, 2 representing warehousing, 3 representing transportation, etc. The type of the business data model corresponding to the data identifier corresponding to the business identifier can be determined as a commodity, and the model identifier of the business data model of the model type corresponding to the commodity can be found in the model dependency relationship as the target model identifier.

[0059] For each business identifier in the at least one business identifier, the business data corresponding to the business identifier is acquired as the target business data based on the data identifier corresponding to the business identifier and the acquisition manner configuration information corresponding to the data identifier. Exemplarily, assuming that the business identifier is warehousing and the data identifier corresponding thereto is a warehousing code (CC001), the warehousing data (target business data) can be acquired based on CC001 and by using the acquisition manner configuration information corresponding thereto.

[0060] In the embodiments of the present disclosure, the data acquisition configuration information corresponding to each target business data model and each target business data is preconfigured, and no additional code needs to be written for each target business data model and each target business data, thereby effectively reducing the total amount of code.

[0061] In some optional embodiments, in the embodiments of the present disclosure, the data acquisition submodule 122 can acquire the target business data by the following way:

[0062] Based on the data acquisition configuration information and the at least one data identifier, at least one initial business data is acquired, and then each initial business data in the at least one initial business data is subjected to data format conversion and validity verification based on the first preset data conversion rule and the preset verification rule, so as to obtain at least one target business data.

[0063] The first preset data conversion rule is used to convert the data format of the business data, so that the converted initial business data meets the requirements of the business data processing system. For example, the first preset data conversion rule can include a plurality of data format conversion methods, each of which is used to convert one data format into a target data format specified by the business data processing system. For example, the data format conversion method can include a method for converting Comma-Separated Values (CSV) into a target data format JS key-value pair data (Java Script Object Notation, JOSN), a method for converting Extensible Markup Language (XML) into JOSN, etc.

[0064] The preset verification rule is used to verify the validity of the business data. The preset verification rule can include a plurality of data verification methods, each of which is used to verify the validity of one kind of business data. For example, the data verification method can include a method for verifying the validity of date (business data), transaction amount (business data), phone number (business data), etc. For example, when the business data is the price of a commodity, the corresponding data verification method can include that the price cannot exceed the maximum value and the price cannot be negative, and when the business data is the quantity of a commodity, the corresponding data verification method can include that the quantity of the commodity needs to be within a preset quantity range.

[0065] In one embodiment, the initial business data can be acquired based on the data identifier of each target business data and the corresponding data acquisition method in the data acquisition configuration information. For each initial business data, a corresponding data format conversion method is selected in the first preset data conversion rule based on the data format of the initial business data, and the data format of the initial business data is converted into a target data format based on the data conversion method. Then, a corresponding data verification method is obtained in the preset verification rule, and the initial business data is subjected to validity verification by using the data verification method. When the initial business data passes the validity verification, the initial business data is determined as the target business data.

[0066] For example, assuming that the initial business data is a telephone number and the data format of the telephone number is CVS, the data format conversion method for converting CVS into JOSN (the target data format) is obtained in the first preset data conversion rule, the data format of the initial business data is converted into JOSN by using the data format conversion method, and then the data verification method for verifying the validity of the telephone number is obtained in the preset verification rule. Assuming that the data verification method includes comparing the number of digits of the telephone number with a preset number of digits, when the number of digits of the telephone number is not the preset number of digits, it is determined that the telephone number does not pass the validity verification, and the telephone number is deleted, and when the number of digits of the telephone number is the preset number of digits, it is determined that the telephone number passes the validity verification, and the telephone number is determined as the target business data.

[0067] In the embodiments of the present disclosure, the initial business data is subjected to data format conversion through the first preset data conversion rule, the business data processing system is facilitated to process the business data, the initial business data is subjected to validity verification through the preset verification rule, and the incorrect initial business data (the initial business data that does not pass the validity verification) is filtered out, so as to avoid loading the incorrect initial business data into the project data model, and the accuracy of the data of the project data model is ensured.

[0068] Figure 3 FIG. 1 is a structural block diagram of a data acquisition module provided by another exemplary embodiment of the present disclosure. In some optional embodiments, as shown in FIG. 1, the data acquisition module 120 can further include a timeout monitoring submodule 123. Figure 3 The timeout monitoring submodule 123 is configured to monitor a data acquisition duration of the data acquisition submodule 122 for acquiring the target data, acquire a retry number of the data acquisition submodule 122 for reacquiring the target data in response to the data acquisition duration exceeding a preset duration, control the data acquisition submodule 122 to reacquire the target data in response to the retry number being less than a preset retry number, and control the data acquisition submodule 122 to stop acquiring the target data in response to the retry number being greater than or equal to the preset retry number.

[0069] The timeout monitoring submodule 123 is configured to monitor a data acquisition duration of the data acquisition submodule 122 for acquiring the target data, acquire a retry number of the data acquisition submodule 122 for reacquiring the target data in response to the data acquisition duration exceeding a preset duration, control the data acquisition submodule 122 to reacquire the target data in response to the retry number being less than a preset retry number, and control the data acquisition submodule 122 to stop acquiring the target data in response to the retry number being greater than or equal to the preset retry number.

[0070] The target data includes a target business data model or target business data. The preset duration and the preset retry number can be set according to actual requirements. For example, the preset duration can be 10 ms, 20 ms, etc., and the preset retry number can be 3 times, etc.

[0071] In an embodiment, the timeout monitoring submodule 123 monitors the data acquisition duration of the target data (acquisition target business data or target business data model) of the data acquisition submodule 122, and when it is monitored that the data acquisition duration exceeds the preset duration, the retry number of reacquiring the target data is acquired, when the retry number is less than the preset retry number, the value of the retry number is increased by one by the data acquisition submodule 122, and the data acquisition submodule 122 is controlled to reacquire the target data, when the retry number is greater than or equal to the preset retry number, the data acquisition submodule 122 is controlled to stop acquiring the target data, and the data acquisition submodule 122 is controlled to clear the current retry number, and the timeout monitoring submodule 123 can also generate an alarm information and perform data acquisition exception alarm.

[0072] In the embodiments of the present disclosure, the timeout monitoring submodule monitors the data acquisition duration of the data acquisition submodule acquiring the target business data and the target business data model, and when the data acquisition duration exceeds the preset duration, it is determined according to the retry number whether to reacquire the target business data and the target business data model, which avoids the data acquisition submodule from being stuck and improves the data acquisition efficiency.

[0073] Figure 4 FIG. 1 is a structural block diagram of a data assembly module provided by an exemplary embodiment of the present disclosure. In some optional embodiments, as shown in FIG. 1, the data assembly module 130 can include a cleaning and conversion submodule 131 and a data assembly submodule 132. Figure 4

[0074] The cleaning and conversion submodule 131 is configured to filter at least one target business data based on a preset data filtering rule to obtain at least one to-be-assembled business data, and convert the at least one to-be-assembled business data by using a second preset data conversion rule to obtain at least one assembled business data.

[0075] The preset filtering rule is used to clean the target business data to filter out the target business data that does not meet the requirements of the project processing system. The preset filtering rule can be set according to actual needs. For example, the preset filtering rule can include filtering out business data before January 1, 2024, filtering out business data of city A and city B, etc.

[0076] ​The second preset data conversion rule is used to convert the data form of the business data into a data form required by the project processing system. For example, assuming that the project processing system is a financial system, the second preset data conversion rule can include: when converting the business data of the number type, separating the integer part every three digits with a comma (such as 10,000), rounding the amount and percentage to two decimal places (such as 0.16 instead of 0.1578), marking the negative number with a “-” sign (such as -100), disabling the bracket replacement, using the percentage format with a % symbol and rounding to two decimal places (such as 15.78%), using the currency unit of “yuan / ten thousand yuan / billion yuan”, using the time unit of “year / month / day”, and the like.

[0077] In an embodiment, the assembled business data can also be normalized.

[0078] The data assembly submodule 132 is configured to load the at least one assembled business data into the basic business information, determine an assembly manner of the at least one target business data model based on the model dependency relationship, and perform model assembly on the at least one target business data model in the corresponding assembly manner to obtain the project data model.

[0079] The assembly manner indicates a manner in which the target business data model is assembled into the project data model.

[0080] In an embodiment, the assembled business data corresponding to the same data identifier can be bound with the business identifier, so that the business identifier corresponds to the assembled business data. For example, assuming that the business identifier is a commodity, the corresponding business identifier is a commodity ID, and the assembled business data is floor decoration, warm color, 30 cm x 30 cm, and 100 pieces, the corresponding business identifier is the commodity ID, and the correspondence between the business identifier and the assembled business data can be established, which can be in the form of commodity: commodity ID, floor decoration, warm color, 30 cm x 30 cm, and 100 pieces.

[0081] The model dependency relationship can include, for example, a containment relationship, an aggregation relationship, and an association relationship. When the relationship between the project data model and the target business data model is a containment relationship, the assembly manner can be determined as a direct embedding manner, that is, the target business data model is directly introduced (embedded) into the description information of the project data model as an attribute of the project data model; when the relationship between the project data model and the target business data model is an aggregation relationship, the assembly manner can be determined as an identifier introduction, that is, the model identifier of the target business data model and the storage address of the target business data model are introduced into the description information; and when the relationship between the project data model and the target business data model is an association relationship, the assembly manner can be determined as a lazy loading injection manner, that is, the model identifier of the target business data model is loaded into the description information in a lazy loading injection manner.

[0082] The assembled business data and the target business data model are stored in a preset storage location, to obtain a storage address of the assembled business data and a storage address of the target business data model, and the storage address of the assembled business data and the storage address of the target business data model are loaded into the description information of the project data model.

[0083] In the embodiments of the present disclosure, the target business data is filtered and converted by using the preset data filtering rule and the second preset data conversion rule, so that the obtained assembled business data is adapted to the downstream project processing system; and the target business data model is reasonably assembled according to the model dependency relationship to form a project business data model, so as to ensure the consistency and accuracy of the data.

[0084] Figure 5 FIG. 13 is a structural block diagram of a data assembly module provided by another exemplary embodiment of the present disclosure. In some optional embodiments, as shown in FIG. 13, the data assembly module 130 can include a cache submodule 133 and a data transmission submodule 134. Figure 5

[0085] The cache submodule 133 is configured to cache the business data corresponding to the project data model.

[0086] The snapshot data can be obtained by taking a snapshot of the business data corresponding to the project data model, and the snapshot data is stored in the cache submodule 133.

[0087] The data transmission submodule 134 is configured to, when receiving a data request sent by the project processing system and the business data of a target project data model in the data request is stored in the cache submodule 133, acquire the business data of the target project data model from the cache submodule 133, and transmit the business data to the project processing system.

[0088] When the business data of the target project data model is not stored in the cache submodule 133, the model configuration information and the basic business information of the target project data are created, and the model configuration information and the basic business information are stored in the model management module 110, so that the data acquisition module 120 acquires the business data and the business data model of the target project data model based on the model configuration information and the basic business information, and the data assembly module 130 constructs the target project data model and transmits the business data of the target project data model to the project processing system.

[0089] The data transmission submodule 134 receives the data request sent by the project processing system, determines whether the business data of the target project data model is stored in the cache submodule 133, acquires the business data from the cache submodule 133 when the business data is stored in the cache submodule 133, and transmits the business data to the project processing system. ​

[0090] When it is determined that the service data is not stored in the data transmission submodule 134, the model configuration information and the basic service information of the target project data are created, and the model configuration information and the basic service information are stored to the model management module 110. The data acquisition module 120 acquires the service data and the service data model of the target project data model based on the model configuration information and the basic service information. The data assembly module 130 constructs the target project data model based on the basic service information, the service data and the service data model, and transmits the service data of the target project data model to the project processing system. In an embodiment, the data transmission submodule 134 sends the service data corresponding to the project data model to the project processing system when the project data model is monitored.

[0091] In the embodiments of the present disclosure, the cache mechanism is used to cache the service data of the high-frequency accessed project data model to the cache submodule, which can reduce the overhead of the project processing system in acquiring the service data and improve the system stability.

[0092] Figure 6 FIG. 1 is a structural block diagram of a service data processing system provided by another exemplary embodiment of the present disclosure. In some optional embodiments, as shown in FIG. 1, the service data processing system 100 further includes a model monitoring module 140. Figure 6

[0093] The model monitoring module 140 is configured to monitor the data acquisition module 120 and the data assembly module 130, and generate abnormal information when the data acquisition module 120 is abnormal or the data assembly module 130 is abnormal, and update the abnormal information to an error log, and acquire key information of the project data model and update the key information to a key log.

[0094] The model management module 110 is further configured to store the error log and the key log of the project data model. The error log is used to record the error information occurred in the generation of the project data model. The error information may, for example, include the abnormal information of the data acquisition module in acquiring the service data, the abnormal information of the data acquisition module in acquiring the service data model, and the abnormal information of the data assembly module in assembling the project data model. The key information log is used to record the key information of the project data model. The key information may, for example, include the model identifier of the project data model, the model identifier of each target service data model, the model dependency relationship, the data source of each target service data, the data source of each target service data, the query delay and the cache hit rate, etc. The query delay represents the time experienced from initiating a data request (such as a database query, an API call) to receiving a complete response. The cache hit rate represents the proportion of successful requests directly acquired from the cache submodule.

[0095] ​In the embodiments of the present disclosure, the model monitoring module monitors the generation process of the project data model, captures error information and key information therein, and updates the two types of information to error logs and key logs respectively, so as to facilitate subsequent troubleshooting and performance optimization.

[0096] In some optional embodiments, in the embodiments of the present disclosure, the data acquisition module 120 is further configured to acquire updated business data when it is detected that the business data corresponding to the basic business information is updated.

[0097] In one embodiment, when it is detected that the business data corresponding to at least one business identifier in the basic business information is updated, the updated business data can be acquired based on the data identifier corresponding to the business identifier and the data acquisition manner corresponding to the data identifier, and the updated business data is transmitted to the data assembly module 130.

[0098] The data assembly module 130 is further configured to load the updated business data into the project data model.

[0099] The data assembly module 130 loads the updated business data into the basic business information to load the updated business data into the project data model.

[0100] In one embodiment, the data assembly module 130 can also send the updated business data to the project data system.

[0101] In the embodiments of the present disclosure, when the business data corresponding to the basic business information is updated, the data acquisition module and the data assembly module can load the updated business data into the project data model in time, thereby ensuring the accuracy and timeliness of the business data in the project data model.

[0102] Figure 7 FIG. 1 is a schematic diagram of a business data processing method provided by an exemplary embodiment of the present disclosure. The present embodiment can be applied in an electronic device, such as a server, a mobile phone, a tablet computer, a personal computer, a wearable device, etc. Figure 7 As shown in FIG. 1, the business data processing method can include the following steps:

[0103] In step S200, model configuration information and basic business information of a project data model are acquired, the model configuration information including model dependency relationships and data acquisition configuration information, the model dependency relationships being used to represent the relationship between the project data model and a business data model, and the basic business information including the business identifier corresponding to each of at least one business data used to construct the project data model;

[0104] In step S210, at least one target business data model and at least one target business data are acquired by using the data acquisition configuration information, the basic business information, and the model dependency relationships.

[0105] In step S220, the project data model is constructed based on the model dependency relationship and the basic business information by using at least one target business data model and at least one target business data, and the business data corresponding to the project data model is transmitted to a project processing system.

[0106] In some optional embodiments, each business identifier corresponds to a data identifier of at least one business data, the model dependency relationship further includes a model identifier of at least one business data model, and the data acquisition configuration information includes at least one acquisition manner configuration information, any acquisition manner configuration information corresponding to at least one model identifier or corresponding to at least one data identifier.

[0107] Step S210 in the embodiments of the present disclosure can include:

[0108] The model configuration information and the basic business information are called from the model management module.

[0109] Based on the model dependency relationship, a model identifier corresponding to at least one business identifier is determined as a target model identifier, at least one target business data model is acquired based on the target model identifier by using the acquisition manner configuration information corresponding to the target model identifier, and at least one target business data is acquired based on at least one data identifier by using the acquisition manner configuration information corresponding to the data identifier.

[0110] In some optional embodiments, the target business data in the embodiments of the present disclosure can be acquired in the following manner:

[0111] At least one initial business data is acquired based on the data acquisition configuration information and at least one data identifier.

[0112] Each initial business data in at least one initial business data is subjected to data format conversion and validity verification based on a first preset data conversion rule and a preset verification rule, and at least one target business data is obtained.

[0113] In some optional embodiments, the business data processing method in the embodiments of the present disclosure further includes:

[0114] The data acquisition time length of the acquired target data is monitored, the retry number of the data acquisition submodule for reacquiring the target data is acquired in response to the data acquisition time length exceeding a preset time length, reacquiring the target data is controlled in response to the retry number being less than a preset retry number, and stopping acquiring the target data is controlled in response to the retry number being greater than or equal to the preset retry number, the target data including the target business data model or the target business data.

[0115] In some optional embodiments, step S120 in the embodiments of the present disclosure can include:

[0116] performing data format conversion and validity verification on each of the initial business data based on a first preset data conversion rule and a preset verification rule, to obtain at least one target business data;

[0117] loading the at least one assembled business data into the basic business information, determining an assembly mode of at least one target business data model based on the model dependency relationship, and performing model assembly on the at least one target business data model in the corresponding assembly mode to obtain the project data model.

[0118] In some optional embodiments, the business data processing method in the embodiments of the present disclosure further includes:

[0119] caching business data corresponding to the project data model;

[0120] when receiving a data request sent by the project processing system and having cached business data of a target project data model in the data request, transmitting the business data to the project processing system, when not having stored the business data in the caching submodule, creating model configuration information of the target project data and the basic business information, and performing acquisition of business data and a business data model of the target project data model based on the model configuration information and the basic business information, construction of the target project data model, and transmission of business data of the target project data model to the project processing system.

[0121] In some optional embodiments, the business data processing method in the embodiments of the present disclosure further includes:

[0122] acquiring error logs and key logs of the project data model;

[0123] in response to an exception in acquisition of business data or project data model or an exception in model assembly, generating corresponding exception information and updating the exception information to error logs of the project data model;

[0124] acquiring key information of the project data model and updating the key information to key logs of the project data model.

[0125] In some optional embodiments, the business data processing method in the embodiments of the present disclosure further includes:

[0126] When detecting that the basic service information corresponds to service data update, obtaining updated service data, and loading the updated service data into the project data model.

[0127] The service data processing method of the embodiments of the present disclosure corresponds to the embodiments of the service data processing system of the present disclosure, and the related content can be mutually referred to, which will not be repeated here.

[0128] The beneficial technical effects of the exemplary embodiments of the service data processing method of the embodiments of the present disclosure can be referred to the corresponding beneficial technical effects of the above-mentioned exemplary method, which will not be repeated here.

[0129] In addition, the embodiments of the present disclosure also provide an electronic device, comprising:

[0130] a memory for storing a computer program;

[0131] a processor for executing the computer program stored in the memory, and when the computer program is executed, the service data processing method of any one of the embodiments of the present disclosure is implemented.

[0132] Figure 8 is a structural block diagram of an application embodiment of the electronic device of the present disclosure. Hereinafter, the electronic device according to the embodiments of the present disclosure will be described with reference to Figure 8 The electronic device can be any one or both of the first device and the second device, or a single device independent of them, which can communicate with the first device and the second device to receive the collected input signals therefrom.

[0133] As shown in Figure 8 The electronic device includes one or more processors and a memory.

[0134] The processor can be a central processing unit (CPU) or other forms of processing units having data processing and / or instruction execution capabilities, and can control other components in the electronic device to perform desired functions.

[0135] The memory can include one or more computer program products, which can include various forms of computer readable storage media, such as volatile memory and / or non-volatile memory. The volatile memory may, for example, include random access memory (RAM), cache memory, and the like. The non-volatile memory may, for example, include read-only memory (ROM), hard disk, flash memory, and the like. One or more computer program instructions can be stored on the computer readable storage medium, and the processor can run the program instructions to implement the service data processing method of the embodiments of the present disclosure and / or other desired functions.

[0136] In one example, the electronic device can further include an input device and an output device, which components are interconnected through a bus system and / or other forms of connection mechanism (not shown).

[0137] In addition, the input device can further include, for example, a keyboard, a mouse, and the like.

[0138] The output device can output various information including the determined distance information, direction information, and the like to the outside. The output device can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, and the like.

[0139] Of course, in order to simplify, Figure 8 In the above-described electronic device, only some of the components related to the present disclosure are shown, and components such as buses, input / output interfaces, and the like are omitted. In addition, the electronic device can further include any other appropriate components according to a specific application.

[0140] In addition to the above-described method and device, an embodiment of the present disclosure can be a computer program product including computer program instructions that, when executed by a processor, cause the processor to perform the steps of the service data processing method according to various embodiments of the present disclosure described in the above-described parts of the specification.

[0141] The computer program product can be written in any combination of one or more programming languages, including an object-oriented programming language such as Java, C++, and the like, and conventional procedural programming languages, such as the "C" programming language, or the like. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server.

[0142] In addition, an embodiment of the present disclosure can also be a computer readable storage medium having stored thereon computer program instructions, which, when executed by a processor, cause the processor to perform the steps of the service data processing method according to various embodiments of the present disclosure described in the above-described parts of the specification.

[0143] The computer readable storage medium can be a combination of one or more computer readable media. The computer readable media can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can include, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium include the following: an electrical connection having one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0144] Those skilled in the art can understand that all or part of the steps of the above-mentioned method embodiments can be completed by program instruction related hardware, and the foregoing program can be stored in a computer readable storage medium. When the program is executed, the steps of the method embodiments are executed; and the foregoing storage medium includes ROM, RAM, magnetic disk or optical disk and various storage media that can store program codes.

[0145] The above describes the basic principles of the present disclosure in combination with specific embodiments. However, it should be noted that the advantages, advantages, effects and the like mentioned in the present disclosure are only examples and are not limiting. These advantages, advantages, effects and the like cannot be considered as necessary for each embodiment of the present disclosure. In addition, the above specific details are only for the purpose of example and for the purpose of understanding, and the above details do not limit the present disclosure to the above specific details.

[0146] Each embodiment in the specification is described in a progressive manner, and each embodiment focuses on the difference from other embodiments. The same or similar parts between each embodiment can be referred to each other. For system embodiments, since they basically correspond to method embodiments, the description is relatively simple, and the relevant parts can be referred to the part of the method embodiment.

[0147] The block diagrams of the devices, apparatuses, equipment, systems involved in the present disclosure are only illustrative examples and are not intended to require or imply the connection, arrangement, configuration shown in the block diagram. As those skilled in the art will recognize, these devices, apparatuses, equipment, systems can be connected, arranged, configured in any way. Words such as "include", "contain", "have" and the like are open-ended words, which mean "include but not limited to", and can be used interchangeably. The words "or" and "and" used herein mean the word "and / or", and can be used interchangeably unless the context clearly indicates otherwise. The word "such as" used herein means the phrase "such as but not limited to", and can be used interchangeably.

[0148] The methods and apparatus of this disclosure can be implemented in a number of ways. For example, the methods and apparatus of this disclosure can be implemented using software, hardware, firmware, or any combination of these methods. The order of any steps described above is merely exemplary and the steps of the methods of this disclosure need not be performed in the order described unless otherwise specified. Furthermore, any steps described above can be combined, reordered, or otherwise altered unless otherwise specified. Moreover, in some embodiments, the disclosure can also be implemented as a program for running on a computer or other programmable apparatus to implement the methods as described above. Thus, the disclosure also covers record mediums storing programs for implementing the methods of this disclosure.

[0149] It is also noted that the methods of this disclosure can be implemented by a computer or other programmable apparatus as a series of steps or instructions to be executed by the computer or other programmable apparatus. The steps or instructions need not be implemented in the order shown but can be implemented in any order. The steps or instructions can be implemented in hardware, software, or a combination of both. The disclosure is not limited to any particular programming language or computer system.

[0150] The above description of disclosed aspects is provided to enable any person skilled in the art to make or use the disclosure. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein can be applied to other aspects without departing from the scope of the disclosure. Thus, the present disclosure is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0151] The above description has been presented for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the disclosure to the forms disclosed herein. Although various example aspects and embodiments have been discussed above, those of skill in the art will recognize certain modifications, permutations, additions, and sub-combinations thereof.

Claims

1. A business data processing system, characterized in that, The system includes: a model management module, a data acquisition module, and a data assembly module; The model management module is used to store model configuration information and basic business information of the project data model. The model configuration information includes model dependency relationships and data acquisition configuration information. The model dependency relationships are used to characterize the relationship between the project data model and the business data model. The basic business information includes the business identifier corresponding to each business data in at least one business data that constructs the project data model. The data acquisition module is used to acquire at least one target business data model and at least one target business data by utilizing the data acquisition configuration information, the basic business information and the model dependency relationship; The data assembly module is used to construct the project data model based on the model dependencies and the basic business information, using at least one target business data model and at least one target business data, and to transmit the business data corresponding to the project data model to the project processing system.

2. The system according to claim 1, characterized in that, Each of the business identifiers corresponds to at least one data identifier for business data. The model dependency relationship also includes at least one model identifier for a business data model. The data acquisition configuration information includes at least one acquisition method configuration information. Any of the acquisition method configuration information corresponds to at least one model identifier or corresponds to at least one data identifier. The data acquisition module includes: The information retrieval submodule is used to retrieve the model configuration information and the basic business information from the model management module; The data acquisition submodule is used to determine at least one model identifier corresponding to the business identifier as a target model identifier based on the model dependency relationship, acquire at least one target business data model based on the target model identifier and using the acquisition method configuration information corresponding to the target model identifier, and acquire at least one target business data based on at least one data identifier and using the acquisition method configuration information corresponding to at least one data identifier.

3. The system according to claim 2, characterized in that, The target business data is acquired and further used for: Based on the data acquisition configuration information and at least one of the data identifiers, at least one initial business data is obtained; Based on the first preset data conversion rule and preset verification rule, the data format of each initial business data in at least one of the initial business data is converted and the validity is verified to obtain at least one target business data.

4. The system according to claim 2 or 3, characterized in that, The data acquisition module further includes: The timeout monitoring submodule is used to monitor the data acquisition time of the data acquisition submodule in acquiring the target data. In response to the data acquisition time exceeding a preset time, it acquires the number of retries for the data acquisition submodule to reacquire the target data. In response to the number of retries being less than a preset number of retries, it controls the data acquisition submodule to reacquire the target data. In response to the number of retries being greater than or equal to the preset number of retries, it controls the data acquisition submodule to stop acquiring the target data. The target data includes the target business data model or the target business data.

5. The system according to any one of claims 1-4, characterized in that, The data assembly module includes: The cleaning and conversion submodule is used to filter at least one of the target business data based on a preset data filtering rule to obtain at least one business data to be assembled, and to convert at least one of the business data to be assembled using a second preset data conversion rule to obtain at least one assembled business data. The data assembly submodule is used to load at least one of the assembled business data into the basic business information, and to determine the assembly method of at least one of the target business data models based on the model dependency relationship, and to perform model assembly on at least one of the target business data models in the corresponding assembly method to obtain the project data model.

6. The system according to any one of claims 1-5, characterized in that, The data assembly module further includes: A caching submodule is used to cache the business data corresponding to the project data model. A data transmission submodule is used to, when receiving a data request sent by the project processing system and the caching submodule stores the business data of the target project data model in the data request, retrieve the business data from the caching submodule and transmit the business data to the project processing system; when the caching submodule does not store the business data, create model configuration information and basic business information for the target project data, and store the model configuration information and basic business information in the model management module, so that the data acquisition module can obtain the business data and business data model of the target project data model based on the model configuration information and basic business information, and the data assembly module can construct the target project data model and transmit the business data of the target project data model to the project processing system.

7. The system according to any one of claims 1-6, characterized in that, The model management module is also used to store error logs and critical logs of the project data model; The business data processing system also includes: The model monitoring module is used to monitor the data acquisition module and the data assembly module, and when the data acquisition module obtains data anomalies or the data assembly module assembles the model anomalies, it generates anomaly information and updates the anomaly information to the error log, and obtains key information of the project data model and updates the key information to the key log.

8. The system according to any one of claims 1-7, characterized in that, Also includes: The data acquisition module is also used to acquire updated business data when an update to the business data corresponding to the basic business information is detected. The data assembly module is also used to load the updated business data into the project data model.

9. A business data processing method, characterized in that, include: Obtain model configuration information and basic business information of the project data model. The model configuration information includes model dependency relationship and data acquisition configuration information. The model dependency relationship is used to characterize the relationship between the project data model and the business data model. The basic business information includes the business identifier corresponding to each business data in at least one business data in the project data model. Using the data, configuration information, basic business information, and model dependencies are obtained to acquire at least one target business data model and at least one target business data. Based on the model dependencies and the basic business information, the project data model is constructed using at least one target business data model and at least one target business data, and the business data corresponding to the project data model is transmitted to the project processing system.

10. An electronic device, characterized in that, include: The processor, and the memory communicatively connected to the processor, further comprising the business data processing system according to any one of claims 1-8; The memory is used to store the processor-executable instructions; The processor is configured to read the executable instructions from the memory to control the business data processing system to implement the business data processing method of claim 9.

11. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the business data processing method described in claim 9.

12. A computer program product comprising computer program instructions, characterized in that, When the computer program instructions are executed by the processor, they implement the business data processing method described in claim 9.

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