System for realizing intelligent service application construction based on AIGC

Abstract

The invention relates to a system for realizing intelligent service application construction based on AIGC, and the system comprises a service semantic processing module which is used for processing an input service text or voice, and extracting a service entity, a service operation and a service rule in service semantics; the AIGC module is used for recommending data entities from a large model layer and a special model layer for the functional module; the business application knitting module is used for knitting according to the specific business application; the business application operation support module is used for providing operation support for the generated business application codes; and the service application continuous delivery module is used for providing continuous integration, medium management and continuous release components for the woven service application. By the adoption of the system for achieving intelligent service application construction based on the AIGC, intelligent service application generation can be achieved based on service semantics, the workload of software developers is relieved, the development efficiency is improved, non-technical personnel can easily construct and deploy service applications, and popularization and innovation of digital applications are promoted.

Description

Technical Field

[0001] The present invention relates to the field of computers, and in particular to the field of intelligent and automated construction of business applications, and specifically refers to a system for implementing intelligent business application construction based on AIGC. Background Art

[0002] With the rapid advancement of digitalization, the wave of digitalization is sweeping all walks of life around the world, and more business personnel will participate in the construction of enterprise digital applications. However, traditional digital application development faces many challenges. It requires a large number of developers, testers, product personnel, etc. with basic computer knowledge, and has high requirements for personnel. Traditional software development methods also have problems such as low efficiency, long development cycle, and easy errors. At the same time, digital transformation requires a large number of non-computer professional business personnel to participate in the construction of digital applications. Therefore, we urgently need an intelligent and semantic development model that can automatically build digital applications. A system and method for building intelligent business applications based on AIGC came into being. This method supports the intelligent extraction of business entities, business operations, and business rules from business texts or voices input by business personnel, and intelligently recommends data models and generates data models, thereby fully weaving the business forms, business logic, business processes, business reports, internationalization, exception handling and other code logic required by digital applications. The application of AIGC technology provides an efficient and economical solution for digital transformation. It reduces the burden of professional developers and gives business personnel the ability to build and deploy applications, thereby promoting the widespread popularization and continuous innovation of digital applications.

[0003] For digital application construction, there are currently two common development methods: high-code development and low-code development. High-code development usually refers to the manual coding programming method, which requires developers to have professional programming knowledge and skills, and directly use programming languages ​​such as Java, Python, C, etc. to code to implement business functions; the advantage of the high-code method is that it is relatively flexible, can realize highly customized applications, and has a large space for performance optimization; the corresponding disadvantages are long development cycles, high costs, and high technical requirements for developers. Low-code development usually refers to simplifying the development process through graphical interfaces and preset modules, allowing users to build applications by dragging and dropping components, so that non-professional developers can also participate in application development; the advantages of low-code development are fast development speed, relatively low cost, and easy maintenance; however, low-code development may have functional limitations, and may not be flexible enough for applications that require complex logic and high customization. Although the low-code development model simplifies development requirements, it still requires business personnel to have certain development capabilities, and business personnel still have great difficulties in using low-code development. To sum up, neither high-code development nor low-code development can quickly provide business personnel with the ability to quickly build digital applications. There is an urgent need to provide an efficient and economical solution for digital transformation. The use of AIGC-based intelligent business application construction can reduce the burden on professional developers and empower business personnel to build and deploy applications, thereby promoting the widespread popularization and continuous innovation of digital applications.

[0004] The following is an analysis of the shortcomings of the above technical implementation methods:

[0005] Disadvantage 1: High technical requirements: Both high-code and low-code development have certain technical barriers. High-code development requires professional programming knowledge and skills, and although low-code development reduces technical requirements, it still requires business personnel to have certain development capabilities.

[0006] Disadvantage 2: Lack of flexibility: Although high-code development is highly flexible, it has a long development cycle and high costs. Although low-code development is fast and relatively low-cost, it has functional limitations and is not flexible enough for complex logic and highly customized applications.

[0007] Disadvantage 3: Low development efficiency: Although high-code development can achieve highly customized applications, it has a long development cycle and high costs. Although low-code development has a fast development speed and relatively low costs, it may have functional limitations and is not flexible enough for applications that require complex logic. It requires a large amount of high-code development to implement, which also leads to low development efficiency. Summary of the invention

[0008] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art and provide a system for building intelligent business applications based on AIGC that is efficient, low-cost and has a wide range of applications.

[0009] In order to achieve the above objectives, the system for implementing intelligent business application construction based on AIGC of the present invention is as follows:

[0010] The main features of the system for implementing intelligent business application construction based on AIGC are as follows:

[0011] The business semantics processing module is used to process the input business text or voice, extract the business entities, business operations and business rules in the business semantics, and return structured data;

[0012] The AIGC module is connected to the business semantic processing module and is used to create a functional module of the business application according to the business entity recommendation needs, and recommend data entities from the big model layer and the proprietary model layer, as well as the association relationship between the data entities for the functional module;

[0013] A business application weaving module, connected to the business semantic processing module and the AIGC module, for weaving according to specific business applications;

[0014] A business application operation support module, connected to the business application weaving module, for providing operation support for the generated business application code;

[0015] The business application continuous delivery module is connected to the business application operation support module and is used to provide continuous integration, media management, and continuous release components for the woven business applications.

[0016] Preferably, the operation process of the business semantic processing module specifically includes the following steps:

[0017] (1.1) Accept input business text or voice and convert voice into business text;

[0018] (1.2) Preprocess the text, clean and segment the business documents;

[0019] (1.3) Identify business entities, identify business entities in business documents;

[0020] (1.4) Identify business operations: Identify business operations in business texts;

[0021] (1.5) Identify business processes and business process links in business documents;

[0022] (1.6) Identify business rules and extract business rules from business texts;

[0023] (1.7) Identify business reports and extract business report requirements from business texts;

[0024] (1.8) Output the results identified and proposed in (1.3), (1.4) and (1.5) in a structured form.

[0025] Preferably, the operation process of the AIGC module specifically includes the following steps:

[0026] (2.1) Based on the business entity identified by the business semantic processing module, recommend the business function module under the business entity from the big model layer;

[0027] (2.2) Select the functional modules that need to be generated for business applications;

[0028] (2.3) Recommend data entities of functional modules from enterprise data assets in the proprietary model;

[0029] (2.4) Recommend data entities of functional modules from the big model;

[0030] (2.5) Select appropriate data entities from the data entities in (2.3) and (2.4) as the data entity model for business application modeling;

[0031] (2.6) Define the relationship between data entities;

[0032] (2.7) Generate business application skeleton: Generate the application skeleton based on the selected functional modules and data entities, and simultaneously generate the external interface module, implementation module, service provision module, and data entity model module.

[0033] Preferably, the operation process of the business application weaving module specifically includes the following steps:

[0034] (3.1) Generate data entity model definition for the data entities identified in the AIGC module and generate the corresponding database initialization script;

[0035] (3.2) According to the data entity determined in the AIGC module, weave the business logic corresponding to the data entity;

[0036] (3.3) Design the layout and fields of the business form according to the woven data entity, and weave the business form;

[0037] (3.4) Weaving process definitions based on the business processes identified by the business semantic processing module;

[0038] (3.5) Based on the business rules identified by the business semantic processing module, weave the business rules through the rule engine and business logic;

[0039] (3.6) Define the global exception handling mechanism and global exception coding, and allocate an exception code segment to each module. Use the exception capture mechanism in the business logic to capture and handle exceptions.

[0040] (3.7) Define the global internationalization processing framework and weave internationalization resource files for each module;

[0041] (3.8) Design report styles based on business report requirements identified by the business semantic processing module, and obtain report data through business logic;

[0042] (3.9) Define the calling relationship between business logics and arrange services according to sequence or conditional combinations;

[0043] (3.10) Define the API interface of the business application and map it to the specific business logic through dependency annotation.

[0044] Preferably, the business application operation support module provides business application operation support for form execution, process execution, report execution, rule execution, service orchestration execution, internationalization, and exception handling capabilities.

[0045] Preferably, the continuous integration is specifically to provide automated construction for business applications; the media management is specifically to manage the media generated during the construction of business applications; the continuous release is specifically to support automated deployment of business applications, quickly and automatically deploy the code of business applications to the target environment, and support advanced deployment strategies such as grayscale release and blue-green deployment.

[0046] The system for building intelligent business applications based on AIGC adopting the present invention can realize intelligent generation of business applications based on business semantics, providing an efficient and low-cost solution for digital transformation. It not only reduces the workload of software developers and improves development efficiency, but also enables non-technical personnel to easily build and deploy business applications, thus promoting the popularization and innovation of digital applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 This is a schematic diagram of the structure of a system for implementing intelligent business applications based on AIGC according to the present invention.

[0048] Figure 2 This is a flowchart of the operation steps of the business semantic processing module of the system for implementing intelligent business application construction based on AIGC of the present invention.

[0049] Figure 3 The present invention is a flowchart of the operation steps of the AIGC module of the system for implementing intelligent business application construction based on AIGC.

[0050] Figure 4This is a flow chart of the operation steps of the business application weaving module of the system for implementing intelligent business application construction based on AIGC of the present invention. DETAILED DESCRIPTION

[0051] In order to more clearly describe the technical content of the present invention, further description is given below in conjunction with specific embodiments.

[0052] The system for implementing intelligent business application construction based on AIGC of the present invention includes:

[0053] The business semantics processing module is used to process the input business text or voice, extract the business entities, business operations and business rules in the business semantics, and return structured data;

[0054] The AIGC module is connected to the business semantic processing module and is used to create a functional module of the business application according to the business entity recommendation needs, and recommend data entities from the big model layer and the proprietary model layer, as well as the association relationship between the data entities for the functional module;

[0055] A business application weaving module, connected to the business semantic processing module and the AIGC module, for weaving according to specific business applications;

[0056] A business application operation support module, connected to the business application weaving module, for providing operation support for the generated business application code;

[0057] The business application continuous delivery module is connected to the business application operation support module and is used to provide continuous integration, media management, and continuous release components for the woven business applications.

[0058] As a preferred embodiment of the present invention, the operation process of the business semantic processing module specifically includes the following steps:

[0059] (1.1) Accept input business text or voice and convert voice into business text;

[0060] (1.2) Preprocess the text, clean and segment the business documents;

[0061] (1.3) Identify business entities, identify business entities in business documents;

[0062] (1.4) Identify business operations: Identify business operations in business texts;

[0063] (1.5) Identify business processes and business process links in business documents;

[0064] (1.6) Identify business rules and extract business rules from business texts;

[0065] (1.7) Identify business reports and extract business report requirements from business texts;

[0066] (1.8) Output the results identified and proposed in (1.3), (1.4) and (1.5) in a structured form.

[0067] As a preferred embodiment of the present invention, the operation process of the AIGC module specifically includes the following steps:

[0068] (2.1) Based on the business entity identified by the business semantic processing module, recommend the business function module under the business entity from the big model layer;

[0069] (2.2) Select the functional modules that need to be generated for business applications;

[0070] (2.3) Recommend data entities of functional modules from enterprise data assets in the proprietary model;

[0071] (2.4) Recommend data entities of functional modules from the big model;

[0072] (2.5) Select appropriate data entities from the data entities in (2.3) and (2.4) as the data entity model for business application modeling;

[0073] (2.6) Define the relationship between data entities;

[0074] (2.7) Generate business application skeleton: Generate the application skeleton based on the selected functional modules and data entities, and simultaneously generate the external interface module, implementation module, service provision module, and data entity model module.

[0075] As a preferred embodiment of the present invention, the operation process of the business application weaving module specifically includes the following steps:

[0076] (3.1) Generate data entity model definition for the data entities identified in the AIGC module and generate the corresponding database initialization script;

[0077] (3.2) According to the data entity determined in the AIGC module, weave the business logic corresponding to the data entity;

[0078] (3.3) Design the layout and fields of the business form according to the woven data entity, and weave the business form;

[0079] (3.4) Weaving process definitions based on the business processes identified by the business semantic processing module;

[0080] (3.5) Based on the business rules identified by the business semantic processing module, weave the business rules through the rule engine and business logic;

[0081] (3.6) Define the global exception handling mechanism and global exception coding, and allocate an exception code segment to each module. Use the exception capture mechanism in the business logic to capture and handle exceptions.

[0082] (3.7) Define the global internationalization processing framework and weave internationalization resource files for each module;

[0083] (3.8) Design report styles based on business report requirements identified by the business semantic processing module, and obtain report data through business logic;

[0084] (3.9) Define the calling relationship between business logics and arrange services according to sequence or conditional combinations;

[0085] (3.10) Define the API interface of the business application and map it to the specific business logic through dependency annotation.

[0086] As a preferred embodiment of the present invention, the business application operation support module provides business application operation support for form execution, process execution, report execution, rule execution, service orchestration execution, internationalization, and exception handling capabilities.

[0087] As a preferred implementation mode of the present invention, the continuous integration specifically provides automated construction for business applications; the media management specifically manages the media generated during the construction of business applications; the continuous release specifically supports automated deployment of business applications, quickly and automatically deploys the code of business applications to the target environment, and supports advanced deployment strategies such as grayscale release and blue-green deployment.

[0088] In a specific embodiment of the present invention, Figure 1 As shown, the present invention is divided into a business semantic processing module, an AIGC module, a business application weaving module, a business application operation support module, and a business application continuous delivery module.

[0089] The business semantic processing module is responsible for processing the input business text or voice, and extracting business entities, business operations, business rules, etc. from the business semantics; the AIGC module recommends functional modules for business applications, recommends data entities for functional modules, and the associations between data entities; the business application weaving module intelligently generates business forms, business logic, business processes, business reports, business rules, service orchestration, internationalization, exceptions and other code logic based on data entities; the business application operation support module provides form execution, process execution, report execution, rule execution, service execution, internationalization, and exception handling capabilities, and provides operation support for the generated business application code; the business application continuous delivery module provides business application continuous integration, media management, continuous release and other capabilities, and supports the intelligent automated release, deployment, and dynamic expansion capabilities of business applications.

[0090] The business semantic processing module, AIGC module, business application weaving module, business application operation support module, and business application continuous delivery module are described in detail below.

[0091] 1: Business semantic processing module

[0092] The business semantics processing module is responsible for processing the input business text or voice, preprocessing the input text, extracting business entities, business operations, business rules, etc. in the business semantics, and returning structured data.

[0093] The steps of operation are as follows Figure 2 The steps are as follows:

[0094] Step 1: Input processing: accept the input business text or voice and convert the voice into business text;

[0095] Step 2: Text preprocessing: cleaning and word segmentation of business documents;

[0096] Step 3: Business entity identification: Identify the business entities in the business document;

[0097] Step 4: Business operation identification: Identify the business operations in the business text;

[0098] Step 5: Business process identification: Identify the business process links in the business text;

[0099] Step 6: Business rule identification: extract business rules from business text;

[0100] Step 7: Business report identification: extract business report requirements from business texts;

[0101] Step 8: Process output: Output the results identified and proposed in steps 3, 4, and 5 in a structured form.

[0102] 2. AIGC module

[0103] The AIGC module encapsulates the large model layer and the proprietary model layer for internal access. It is responsible for creating functional modules of business applications based on the business entity recommendation needs, and recommends data entities from the large model layer and the proprietary model layer for the functional modules, as well as the association relationships between data entities.

[0104] The steps of operation are as follows Figure 3 The steps are as follows:

[0105] Step 1: Recommend functional modules: Based on the business entity identified in "Step 3" in the "Business Semantic Processing Module", recommend the business functional modules under the business entity from the big model layer;

[0106] Step 2: Select function module: The developer selects the function module that needs to be generated for the business application from "Step 1";

[0107] Step 3: Recommend data entities from the proprietary model: Recommend data entities of functional modules from the enterprise data assets in the proprietary model. The recommended data entities are closer to the actual business status of the enterprise.

[0108] Step 4: Recommend data entities from the big model: recommend data entities of functional modules from the big model, which have better versatility;

[0109] Step 5: Select data entity: The developer selects the appropriate data entity from "Step 3" and "Step 4" as the data entity model for business application modeling;

[0110] Step 6: Define the association relationship of data entities: Define the association relationship for the data entities selected in "Step 5", including "one-to-one, one-to-many, many-to-one". The large model and proprietary model will give recommended association relationships. Ultimately, the developer specifies the association relationship of the data entity.

[0111] Step 7: Generate business application skeleton: Generate the application skeleton according to the "functional module" and "data entity" selected in the above steps. When generating the application skeleton, generate the external interface module (API module), implementation module (IMPL module), service provision module (SPI module), and data entity model module (Model module).

[0112] 3: Business application weaving module

[0113] The business application weaving module is responsible for weaving specific business applications. It weaves the business logic of corresponding business entity management according to the data entities determined in the AIGC module, mainly including basic business logic such as adding, deleting, modifying and querying data entities; it weaves the corresponding business forms based on the data entities, including adding forms, editing forms and querying forms; it weaves the corresponding business process definitions according to the business processes determined in the business semantic processing module; it weaves the business rules determined in the business semantic processing module according to the flow rules of the process; in order to enhance the robustness of business applications, it weaves exception handling, internationalization and other codes for business applications; it weaves the business reports required by business applications and the trend data of statistical entities; it weaves service orchestration for business logic to realize cross-module business integration capabilities; and finally, it weaves an external API interface layer for business applications.

[0114] The steps of operation are as follows Figure 4 The steps are as follows:

[0115] Step 1: Weaving data entities: Generate data entity model definitions for data entities determined in the AIGC module, including fields, field types, field constraints, field descriptions, etc., and generate corresponding database initialization scripts. Different types of database scripts are generated for different databases.

[0116] Step 2: Weaving business logic: According to the data entity determined in the AIGC module, weaving the interface class and implementation class of the business logic corresponding to the data entity. The operations included in the business logic include adding logic, deleting logic, modifying logic, querying logic, etc. of the data entity;

[0117] Step 3: Weaving business forms: Based on the data entities woven in "Step 1", design the layout and fields of the business form, use the front-end page components to realize the front-end interface display of the form, and use data binding to synchronize the relationship between the form components and the data entities; the forms include adding forms, editing forms (including editing and deleting), query forms, etc.

[0118] Step 4: Weaving business processes: Weaving process definitions based on the business processes identified by the business semantic processing module, mainly including manual processing activities, automatic activities, participants, process flow rules, participant rules, and associated business forms;

[0119] Step 5: Weaving business rules: Based on the business rules identified by the business semantic processing module, weave business rules through the rule engine and the business logic in "Step 2" to form the flow rules and participant rules of the process;

[0120] Step 6: Weaving exception handling: define the global exception handling mechanism and global exception code, and assign an exception code segment to each module. Use the exception capture mechanism in the business logic to capture and process the exception, and feedback the exception code and exception description to the user.

[0121] Step 7: Weaving internationalization: Define the global internationalization processing framework, weave internationalization resource files for each module, and realize the internationalization of resources such as comments, codes, exception fragments, internationalization fragments, and pictures under business applications through resource extraction;

[0122] Step 8: Weaving business reports: Designing report styles based on the business report requirements identified by the business semantic processing module, obtaining report data through business logic, and providing report preview, viewing, and download capabilities;

[0123] Step 9: Weaving service orchestration: define the calling relationship between business logics, and choreograph services according to sequence or conditional combinations;

[0124] Step 10: Weave API interface: Define the API interface of the business application, use the RESTful style definition, and map it to the specific business logic through dependency annotations.

[0125] 4. Business application operation support module

[0126] The business application operation support module provides the core technical components for business application operation, including form execution, process execution, report execution, rule execution, service orchestration execution, internationalization, exception handling capabilities, etc. Form execution mainly includes form status maintenance, page rendering, interaction with back-end business logic, data permission verification, etc.; process execution provides business process startup, task management, process monitoring, process optimization, etc.; report execution provides generation and display of business reports, and supports output in multiple report formats; rule execution is responsible for the management of the business rule library, as well as the parsing and execution of process flow rules and participant rules; service orchestration execution provides service orchestration scheduling and provides reliability assurance for cross-business system execution; internationalization provides an international operation framework, providing different local resource presentations for different languages; exceptions provide an exception handling framework, converting exceptions caused by business application errors into easy-to-understand exception messages and feeding them back to business application users.

[0127] Five: Business application continuous delivery module

[0128] The business application continuous delivery module provides continuous integration, media management, and continuous release components for woven business applications. Continuous integration provides automated construction for business applications to achieve rapid iteration and quality assurance of business application code; media management manages the media (e.g., binary files, container images) generated during the business application construction process to ensure version control and traceability of business applications; continuous release supports automated deployment of business applications, quickly and automatically deploys the business application code to the target environment (e.g., development environment, test environment, pre-release environment, production environment, etc.), and supports advanced deployment strategies such as grayscale release and blue-green deployment to reduce deployment risks; monitors the operating performance and business load of business applications, and supports dynamic adjustment of resource allocation based on business requests to optimize application performance and responsiveness.

[0129] The present invention provides the following business semantics: "Develop a vehicle management system to implement basic maintenance such as adding, deleting, modifying and checking the company's operating vehicles, and provide process approval capabilities for vehicle application, maintenance and accident handling. Vehicle applications are initiated by various business departments, and business department leaders and vehicle management departments are responsible for step-by-step approval; and provide trend charts for vehicle use and vehicle maintenance." The business application requires the use of Java language in the back end and VUE framework in the front end to generate business applications.

[0130] Next, according to the method of the present invention, a system and method for building intelligent business applications based on AIGC is adopted. Through simple interaction, a "vehicle management" business system is intelligently built, and the convenience advantages of the present invention will be clearly felt.

[0131] 1: Business semantic processing module

[0132] According to the description of the above scenario, this example uses the business text input method. The business text used is: "Develop a vehicle management system to implement basic maintenance of the company's operating vehicles such as addition, deletion, modification and query, and provide process approval capabilities for vehicle application, maintenance, and accident handling. Vehicle applications are initiated by various business departments, and business department leaders and vehicle management departments are responsible for step-by-step approval; and provide trend charts for vehicle use and vehicle maintenance." The input text is preprocessed to extract business entities, business operations, business rules, etc. in business semantics, and return structured data.

[0133] The operation steps of the business semantic processing module are as follows:

[0134] 1. Input processing

[0135] This step accepts the business text described in the above scenario as input;

[0136] 2. Text Preprocessing

[0137] For the business text entered in "Step 1", use regular expressions to remove irrelevant characters and punctuation marks in the text, and use it for word segmentation (for example, use Python's "jieba" library);

[0138] 3. Business Entity Identification

[0139] Use named entity recognition (NER) technology to identify business entities in the text. In this example, the business entities identified mainly include: "vehicle, business department, vehicle management department";

[0140] 4. Business operation identification

[0141] Identify business operations in business texts. In this example, the business operations identified mainly include: "add, delete, modify, check, apply, maintain, and approve";

[0142] 5. Business process identification

[0143] Identify the business process links in the business text. In this example, the identified business processes mainly include: "vehicle application process, vehicle maintenance process, and vehicle accident handling process";

[0144] 6. Business rule identification

[0145] Identify business rules in business text. In this example, the business rules identified mainly include: "Business department leaders and vehicle management departments are responsible for approval at each level";

[0146] 7. Business report identification

[0147] Identify business report requirements in business texts. In this example, the business reports identified mainly include: "Vehicle Usage Trend Chart, Vehicle Maintenance Trend Chart";

[0148] 8. Processing Output

[0149] The results of recognition and extraction in steps 3, 4, 5, 6, and 7 are output in a structured manner. In this example, the output is in the form of TCM JSON format. For specific output content, see the table below:

[0150] The processed output is as follows:

[0151] {

[0152] "Entities": Vehicle, Business Department, Vehicle Management Department,

[0153] “Operations”: add, delete, modify, check, apply, maintain, approve,

[0154] “Processes”: vehicle application process, vehicle maintenance process, vehicle accident handling process,

[0155] "Rules": Business department leaders and vehicle management departments are responsible for approval at each level.

[0156] “Reports”: Vehicle usage trend chart, vehicle maintenance trend chart

[0157] }

[0158] 2. AIGC module

[0159] This module recommends functional modules, data entities, and relationships between data entities for the business application "Vehicle Management". Business personnel select the functional modules, data entities, and relationships that need to be generated, and generate the code skeleton for the "Vehicle Management" application.

[0160] The operation steps of AIGC module are as follows:

[0161] 1. Recommended function module

[0162] By interacting with the AIGC module, access the large model (such as ChatGPT or the domestic general large model) to generate the required functional modules. The recommended functional modules in this example include: "vehicle registration management, vehicle use application, vehicle maintenance management, vehicle dispatch management, vehicle monitoring and tracking, vehicle safety management, vehicle cost management, vehicle status monitoring, vehicle scrapping management, vehicle insurance management" and other modules;

[0163] Examples of recommended functional module contents include: vehicle registration management module; vehicle use application module; vehicle maintenance management module; vehicle dispatch management module; vehicle monitoring and tracking module; vehicle safety management module; vehicle expense management module; vehicle status monitoring module; vehicle scrapping management module; and vehicle insurance management module.

[0164] 2. Select functional modules

[0165] The business developer selects the functional modules that need to be generated for the business application from "Step 1". In this scenario, three modules are selected for generation: vehicle registration management module, vehicle use application module, and vehicle maintenance management module.

[0166] 3. Recommend data entities from proprietary models

[0167] This module connects to the enterprise's proprietary model and recommends data entities for the functional modules selected in "Step 2" from the enterprise's data assets. In this example, data entities are not recommended from the proprietary model, but from the big model.

[0168] 4. Recommending Data Entities from Large Models

[0169] This module recommends data entities for the functional modules "Vehicle Registration Management Module, Vehicle Use Application Module, Vehicle Maintenance Management Module" from the big model (e.g. ChatGPT), including the field name, field type, field length, whether it is a primary key, whether it is required, etc. of the data entity; the recommended data entities include: "Vehicle Entity, Vehicle Application Entity, Maintenance Record Entity";

[0170] The data entity contents recommended from the big model are as follows:

[0171] VehicleEntity

[0172] Vehicle ID (VehicleID): `VARCHAR(20)`, length 20, primary key, required

[0173] License Plate: `VARCHAR(10)`, length 10, required

[0174] Vehicle Type: `VARCHAR(50)`, length 50, required

[0175] Vehicle brand (Brand): `VARCHAR(50)`, length 50, required

[0176] Vehicle model (Model): `VARCHAR(50)`, length 50, required

[0177] Purchase Date: `DATE`, required

[0178] Vehicle status (Status): `VARCHAR(50)`, length 50, required

[0179] Department: `VARCHAR(50)`, length 50, required

[0180] Vehicle location (Location): `VARCHAR(100)`, length 100, optional

[0181] InsuranceInfo: `VARCHAR(255)`, length 255, optional

[0182] InspectionInfo: `VARCHAR(255)`, length 255, optional

[0183] Vehicle Application Entity

[0184] Application ID: `VARCHAR(20)`, length 20, primary key, required

[0185] Applicant: `VARCHAR(100)`, length 100, required

[0186] Application time: `DATETIME`, required

[0187] Application status (Status): `VARCHAR(50)`, length 50, required

[0188] Reason for application (Reason): `VARCHAR(255)`, length 255, required

[0189] ExpectedUsageTime: `DATETIME`, required

[0190] Approver: `VARCHAR(100)`, length 100, optional

[0191] Approval Result: `VARCHAR(50)`, length 50, optional

[0192] Vehicle ID (VehicleID): `VARCHAR(20)`, length 20, foreign key associated with vehicle registration management module, required

[0193] Maintenance record entity (VehicleMaintenanceRecordEntiey)

[0194] Record ID: VARCHAR(20), length 20, primary key, required

[0195] Vehicle ID (VehicleID): `VARCHAR(20)`, length 20, foreign key associated with vehicle registration management module, required

[0196] Maintenance Date (MaintenanceDate): `DATE`, required

[0197] Maintenance type (Type): `VARCHAR(50)`, length 50, required

[0198] Maintenance content (Content): `TEXT`, unlimited length, required

[0199] Maintenance cost (Cost): `DECIMAL(10,2)`, up to 10 digits, 2 decimal places, optional

[0200] Maintainer: `VARCHAR(100)`, length 100, required

[0201] Maintenance result (Result): `VARCHAR(255)`, length 255, required

[0202] Notes: `TEXT`, unlimited length, optional

[0203] 5. Select data entity

[0204] In this scenario, select the three data entities recommended in "Step 4": "Vehicle Entity, Vehicle Application Entity, Maintenance Record Entity";

[0205] 6. Define the relationship between data entities

[0206] In this scenario, it is recommended that the relationship between the vehicle entity and the vehicle application entity be one-to-many; the relationship between the vehicle entity and the maintenance record entity be one-to-many, and the relationship between data entities is agreed upon by means of primary and foreign keys;

[0207] 7. Generate business application skeleton

[0208] According to the function modules selected in the above steps: "Vehicle Registration Management Module, Vehicle Use Application Module, Vehicle Maintenance Management Module", data entities: "Vehicle Entity, Vehicle Application Entity, Maintenance Record Entity", generate the application skeleton of the business application "Vehicle Management".

[0209] The application skeleton of the business application "Vehicle Management" is as follows:

[0210] com.primeton.vehicle.api:api module

[0211] com.primeton.vehicle.model: data entity model module

[0212] com.primeton.vehicle.impl: Vehicle registration management module

[0213] com.primeton.vehicle.apply.impl: Vehicle application module

[0214] com.primeton.vehicle.maintenance.impl: Vehicle maintenance management module

[0215] com.primeton.vehicle.spi:spi module

[0216] The application skeleton includes 6 functional modules: com.primeton.vehicle.api is the API module; com.primeton.vehicle.model is the data entity model module; com.primeton.vehicle.spi is the SPI module; com.primeton.vehicle.impl is the vehicle registration management module; com.primeton.vehicle.apply.impl is the vehicle use application module; com.primeton.vehicle.maintenance.impl is the vehicle maintenance management module;

[0217] 3: Business application weaving module

[0218] Next, based on the scenario requirements, we will automatically weave the business application "Vehicle Management", including: weaving data entities, weaving business logic, weaving business forms, weaving business processes, weaving business rules, weaving exception handling, weaving internationalization, compiling business reports, weaving service orchestration, weaving API interfaces, etc.; finally, the business code for the business application "Vehicle Management" is intelligently generated.

[0219] The steps for business application weaving module are as follows:

[0220] 1. Weaving data entities

[0221] Create a dataset in the com.primeton.vehicle.model module of the business application "Vehicle Management": com.primeton.vehicle.model.VehicleDataset.datasetsx file, and automatically generate three data entities:

[0222] Vehicle entity: com.primeton.vehicle.model.VehicleDataset.VehicleEntity

[0223] Vehicle application entity: com.primeton.vehicle.model.VehicleDataset.VehicleApplicationEntity

[0224] Maintenance record entity: com.primeton.vehicle.model.VehicleDataset.VehicleMaintenanceRecordEntiey

[0225] Generate database scripts for the above three data entities and automatically initialize the database. Taking MySQL database as an example, the generated database script sample code is as follows:

[0226]

[0227]

[0228] 2. Weaving business logic

[0229] Create business logic for the data entities generated in "Step 1" and provide corresponding interface classes and implementation classes. In this example, Java language is used as an example to automatically weave business logic for the business application "Vehicle Management". The automatically generated interface classes and implementation classes are:

[0230] Interface class:

[0231] / / Service interface corresponding to the vehicle entity

[0232] com.primeton.vehicle.impl.IVehicleService.j ava

[0233] / / Service interface corresponding to the vehicle application entity

[0234] com.primeton.vehicle.apply.impl.IVehicleApplicationService.j ava

[0235] / / Maintain the service interface corresponding to the record entity

[0236] com.primeton.vehicle.maintenance.impl.IVehicleMaintenanceRecordService.

[0237] java

[0238] Implementation class:

[0239] / / Service implementation corresponding to the vehicle entity

[0240] com.primeton.vehicle.impl.VehicleServiceImpl.java

[0241] / / Service implementation corresponding to the vehicle application entity

[0242] com.primeton.vehicle.apply.impl.VehicleApplicationServiceImpl.j ava

[0243] / / Maintain the service implementation corresponding to the record entity

[0244] com.primeton.vehicle.maintenance.impl.VehicleMaintenanceRecordServiceImpl.java

[0245] The corresponding service interface includes the operations of adding, deleting, modifying and checking by default. Taking the service interface com.primeton.vehicle.impl.IVehicleService corresponding to the vehicle entity as an example, the following are the specific operations of the interface:

[0246]

[0247] 3. Weaving business forms

[0248] According to the three data entities woven in "Step 1": "Vehicle Entity, Vehicle Application Entity, Maintenance Record Entity", the corresponding business forms are automatically created. In this example, a new form, an edit form (including edit and delete), and a query form are created. In this example, the front end uses the VUE framework as the basis and uses the AIGC module to interact with the large model to generate a new form (AddVehicle.vue), an edit form (EditVehicle.vue), and a query form (VehicleList.vue); the backend API endpoint is / api / vehicles, and v-model is used for data two-way binding;

[0249] 4. Weaving business processes

[0250] Create a vehicle application process in the module "Vehicle Use Application Module"; create a vehicle maintenance process and a vehicle accident handling process in the module "Vehicle Maintenance Management Module"; the business process and activity definition examples created in this example are as follows:

[0251] ###1. Vehicle use application process

[0252]

[0253] ###2. Vehicle maintenance process

[0254]

[0255] ###3. Vehicle accident handling process

[0256]

[0257] This module automatically converts the above process definition into an executable process definition.

[0258] 5. Weaving business rules

[0259] The business rules identified in this example mainly include: "The business department leader and the vehicle management department are responsible for step-by-step approval", which automatically generates "Get the business department leader" and "Get the person in charge of the vehicle management department". These two rules are used in "Step 4" and the AIGC module is used to automatically generate the above two rules. The sample code is as follows:

[0260]

[0261] 6. Weaving exception handling

[0262] In this example, the exception codes are automatically assigned to the modules "Vehicle Registration Management Module, Vehicle Use Application Module, and Vehicle Maintenance Management Module", and the exceptions are captured by "try / catch" in the business logic to throw more friendly exception codes and exception information. The exception code intervals assigned to the three modules are shown in the following table:

[0263] Vehicle registration management module: 10000000-10002000

[0264] Vehicle use application module: 10002001-10004000

[0265] Vehicle maintenance management module: 10004001-10006000

[0266] And create an exception storage file exception.properties for each module, and extract all the extracted exception codes and exception information into this file;

[0267] 7. Weaving Internationalization

[0268] In this example, all Chinese comments, Chinese codes, Chinese exceptions, and Chinese image resources are extracted for the modules "Vehicle Registration Management Module, Vehicle Use Application Module, and Vehicle Maintenance Management Module" and extracted into internationalized resource files (stored in the i18n.properties file under each module);

[0269] 8. Weaving business reports

[0270] In this example, two business reports, vehicle usage trend and vehicle maintenance trend, are generated in the "Vehicle Registration Management Module". JFreeChart components are used for chart display, providing report preview, viewing, and data download functions; Java is used as the backend code to generate statistical data;

[0271] 9. Weaving Service Orchestration

[0272] For the business logic generated in "Step 2", composite services are implemented according to business needs to realize complex business scenarios in business requirements, and service orchestration code is automatically created; in this example, the vehicle usage trend business report in "Step 8" is taken as an example. First, call com.primeton.vehicle.impl.IVehicleService to query all vehicles, then call the usage frequency of each vehicle, and assemble these two steps into a new service to provide a unified interface and transaction management to the outside world;

[0273] 10. Weaving API interface

[0274] Define the external API of the business application "Vehicle Management" and put it in the com.primeton.vehicle.api module. The external API adopts the RESTful style. In this example, the SpringBoot framework is used to add annotations to implement it. The following takes vehicle management as an example to give the automatically generated sample code:

[0275] ```java

[0276] @RestController

[0277] @RequestMapping(" / api / vehicles")

[0278] public class VehicleController{

[0279] @GetMapping

[0280] public List <vehicle>getAllVehicles(){}

[0281] @GetMapping(" / {id}")

[0282] public ResponseEntity <vehicle>getVehicleById(@PathVariable String id){}

[0283] @PostMapping

[0284] public Vehicle createVehicle(@RequestBody Vehicle vehicle){}

[0285] @PutMapping(" / {id}")

[0286] public Vehicle updateVehicle(@PathVariable String id,@RequestBodyVehicle vehicleDetails){}

[0287]

[0288] 4. Business application operation support module

[0289] The business application operation support module provides an operation support environment for the business code woven by the business application "Vehicle Management", and provides operational support for data models, business forms, business processes, business reports, business rules, service orchestration, internationalization, exceptions, and other operations. Form execution provides status maintenance, page rendering, interaction with backend business logic, data permission verification, etc. for "add form, edit form, query form" in the business application "vehicle management"; process execution provides process initiation, task management, process monitoring, process optimization, etc. for "vehicle application process, vehicle maintenance process, vehicle accident handling process" in the business application "vehicle management"; report execution provides display for "vehicle usage trend, vehicle maintenance trend" reports in the business application "vehicle management" and supports output in multiple report formats; rule execution provides rule library management for the "business department leaders and vehicle management department are responsible for step-by-step approval" rule in the business application "vehicle management", as well as parsing and execution of process flow rules and participant rules; service choreography execution provides service choreography scheduling for the service choreography in the business application "vehicle management", and provides reliability guarantee for cross-business system execution; internationalization provides an international operation framework for the business application "vehicle management", provides different local resource presentations for different languages, and realizes the internationalization capability of business applications; exceptions provide an exception handling framework for the business application "vehicle management", converting exceptions caused by errors in business applications into easy-to-understand exception messages and feeding them back to business application users;

[0290] Five: Business application continuous delivery module

[0291] The business application continuous delivery module provides continuous integration, value management, and continuous release components for the business code generated by the business application "Vehicle Management". After the intelligent weaving of the business application "Vehicle Management" code is completed, this module automatically builds the code generated by the business application "Vehicle Management" with Maven, and generates a deployable release package, a binary package with the suffix "vehicle-1.0.0.jar"; and submits the generated binary package "vehicle-1.0.0.jar" to the media warehouse for version management; continuous release automatically deploys the binary package "vehicle-1.0.0.jar" of the business application "Vehicle Management" to the container cloud environment (Docker container is used as an example in this example); now business personnel can access the business application "Vehicle Management" through a browser and perform data maintenance and process processing on the vehicle;

[0292] According to the above steps, based on the business definition provided by the business personnel, the intelligent generation and construction of the business application "Vehicle Management" can be realized intelligently and automatically. Since the business application system can be generated based on natural language support, it provides an efficient and convenient application creation method for digital transformation; at the same time, combined with enterprise data assets, the generated business system is more in line with the business characteristics of the enterprise, accelerating the efficiency of business personnel in building business applications; in addition, this implementation provides continuous delivery capabilities, supports business personnel to support face-to-face operation and maintenance to realize the automated construction, release, and deployment of business applications, and provides media management and version management mechanisms, so that business applications can be quickly browsed and accessed, speeding up the iteration speed of business applications, and supporting the construction of business applications in various technical languages. The use of AIGC-based intelligent business application construction reduces the burden on professional developers and gives business personnel the ability to build and deploy applications, thereby promoting the widespread popularization and continuous innovation of digital applications.

[0293] The present invention is directed to a system and method for implementing intelligent business application construction based on AIGC, and mainly achieves the following objectives:

[0294] (1) Intelligent construction of business applications: This system can support business personnel to realize intelligent creation and code generation of business application systems based on natural language, greatly reducing the cost of business application creation and providing an efficient and convenient application creation method for digital transformation.

[0295] (2) Intelligent publishing of business applications: By providing continuous delivery capabilities, business personnel can realize the automated construction, publishing, and deployment of business applications without maintenance. In addition, media management and version management mechanisms are provided to manage the publishing media, thereby supporting fast browsing and access to business applications and accelerating the iteration speed of business applications.

[0296] (3) Intelligent model recommendation: With the help of big models and enterprise data assets, this system can intelligently recommend proprietary data models that meet the business characteristics of the enterprise from the enterprise data assets, intelligently recommend industry-wide data models from big models, and intelligently recommend the relationship between models, thereby accelerating the efficiency of business personnel in building data models.

[0297] (4) Business application construction supports the implementation of multiple technical languages ​​to support different infrastructures and environments.

[0298] The "system and method for building intelligent business applications based on AIGC" of the invention uses natural language support to generate business application systems, thereby providing an efficient and convenient application creation method for digital transformation; at the same time, combined with enterprise data assets, the generated business system is more in line with the business characteristics of the enterprise, accelerating the efficiency of business personnel in building business applications; in addition, the implementation provides continuous delivery capabilities, supports business personnel-free operation and maintenance to achieve automated construction, release, and deployment of business applications, and provides media management and version management mechanisms, so that business applications can be quickly browsed and accessed, the iteration speed of business applications can be accelerated, and support for the construction of business applications in various technical languages.

[0299] The specific implementation scheme of this embodiment can refer to the relevant description in the above embodiment, which will not be repeated here.

[0300] It can be understood that the same or similar parts of the above embodiments can be referenced to each other, and the contents not described in detail in some embodiments can refer to the same or similar contents in other embodiments.

[0301] It should be noted that, in the description of the present invention, the terms "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying relative importance. In addition, in the description of the present invention, unless otherwise specified, the meaning of "plurality" refers to at least two.

[0302] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0303] The system for building intelligent business applications based on AIGC adopting the present invention can realize intelligent generation of business applications based on business semantics, providing an efficient and low-cost solution for digital transformation. It not only reduces the workload of software developers and improves development efficiency, but also enables non-technical personnel to easily build and deploy business applications, thus promoting the popularization and innovation of digital applications.

[0304] In this specification, the present invention has been described with reference to specific embodiments thereof. However, it is apparent that various modifications and variations may be made without departing from the spirit and scope of the present invention. Therefore, the specification and drawings should be regarded as illustrative rather than restrictive.< / vehicle> < / vehicle>

Claims

1. A system for implementing intelligent business application construction based on AIGC, characterized in that: The system comprises: The business semantics processing module is used to process the input business text or voice, extract the business entities, business operations and business rules in the business semantics, and return structured data; The AIGC module is connected to the business semantic processing module and is used to create a functional module of the business application according to the business entity recommendation needs, and recommend data entities from the big model layer and the proprietary model layer, as well as the association relationship between the data entities for the functional module; A business application weaving module, connected to the business semantic processing module and the AIGC module, for weaving according to specific business applications; A business application operation support module, connected to the business application weaving module, for providing operation support for the generated business application code; The business application continuous delivery module is connected to the business application operation support module and is used to provide continuous integration, media management, and continuous release components for the woven business applications.

2. The system for implementing intelligent business application construction based on AIGC according to claim 1, characterized in that: The operation process of the business semantic processing module specifically includes the following steps: (1.1) Accept input business text or voice and convert voice into business text; (1.2) Preprocess the text, clean and segment the business documents; (1.3) Identify business entities, identify business entities in business documents; (1.4) Identify business operations: Identify business operations in business texts; (1.5) Identify business processes and business process links in business documents; (1.6) Identify business rules and extract business rules from business texts; (1.7) Identify business reports and extract business report requirements from business texts; (1.8) Output the results identified and proposed in (1.3), (1.4) and (1.5) in a structured form.

3. The system for implementing intelligent business application construction based on AIGC according to claim 1, characterized in that: The operation process of the AIGC module specifically includes the following steps: (2.1) Based on the business entity identified by the business semantic processing module, recommend the business function module under the business entity from the big model layer; (2.2) Select the functional modules that need to be generated for business applications; (2.3) Recommend data entities of functional modules from enterprise data assets in the proprietary model; (2.4) Recommend data entities of functional modules from the big model; (2.5) Select appropriate data entities from the data entities in (2.3) and (2.4) as the data entity model for business application modeling; (2.6) Define the relationship between data entities; (2.7) Generate business application skeleton: Generate the application skeleton based on the selected functional modules and data entities, and simultaneously generate the external interface module, implementation module, service provision module, and data entity model module.

4. The system for implementing intelligent business application construction based on AIGC according to claim 1, characterized in that: The operation process of the business application weaving module specifically includes the following steps: (3.1) Generate data entity model definition for the data entities identified in the AIGC module and generate the corresponding database initialization script; (3.2) According to the data entity determined in the AIGC module, weave the business logic corresponding to the data entity; (3.3) Design the layout and fields of the business form according to the woven data entity, and weave the business form; (3.4) Weaving process definitions based on the business processes identified by the business semantic processing module; (3.5) Based on the business rules identified by the business semantic processing module, weave the business rules through the rule engine and business logic; (3.6) Define the global exception handling mechanism and global exception coding, and allocate an exception code segment to each module. Use the exception capture mechanism in the business logic to capture and handle exceptions. (3.7) Define the global internationalization processing framework and weave internationalization resource files for each module; (3.8) Design report styles based on business report requirements identified by the business semantic processing module, and obtain report data through business logic; (3.9) Define the calling relationship between business logics and arrange services according to sequence or conditional combinations; (3.10) Define the API interface of the business application and map it to the specific business logic through dependency annotation.

5. The system for implementing intelligent business application construction based on AIGC according to claim 1, characterized in that: The business application operation support module provides business application operation support for form execution, process execution, report execution, rule execution, service orchestration execution, internationalization, and exception handling capabilities.

6. The system for implementing intelligent business application construction based on AIGC according to claim 1, characterized in that: The continuous integration mentioned above specifically provides automated construction for business applications; the media management mentioned above specifically manages the media generated during the construction process of business applications; the continuous release mentioned above specifically supports automated deployment of business applications, quickly and automatically deploys the code of business applications to the target environment, and supports advanced deployment strategies such as grayscale release and blue-green deployment.

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