Intelligent application generation method and device

Abstract

The invention relates to an intelligent application generation method and device, and belongs to the technical field of application generation, and the method comprises the steps: generating an answer outline or a solution framework after a big model receives a question of a user; judging whether the large model receives a request satisfying the answer outline or the solution framework or not; if the judgment result is that the satisfactory request is received, the large model fines the content of the answer outline or the solution framework and sends the formatted output content to the low-code engine; and the low-code engine configures a data model, interface elements and business logic corresponding to the application according to logic and data requirements of the output content so as to generate the application. According to the system composed of the large model and the low-code engine, the comprehensive and efficient intelligent application generation method is achieved, the threshold for entering the field of intelligent application development is lowered, non-professional developers can also rapidly operate, and the overall efficiency of application from design to deployment is greatly improved by optimizing the development process.

Description

Technical Field

[0001] The present invention belongs to the technical field of application generation, and in particular relates to an intelligent application generation method and device. Background Art

[0002] In the current application generation technology context, although the application of large models shows great potential, especially in improving the level of system intelligence, its implementation process faces considerable challenges. Generally, the deployment and optimization of large models require deep professional knowledge, involving complex model selection, parameter tuning and system integration, which is a major obstacle for most developers and limits the widespread popularization and application of large model technology. At the same time, as data becomes the "oil" of the new era, the data-driven development model is gradually becoming the cornerstone for promoting business innovation and decision optimization. However, how to efficiently mine the value of data and transform it into smart decisions in practical applications is still a major problem. This requires developers not only to be proficient in data processing technology, but also to have the ability to transform data insights into business logic. The whole process is often complicated and time-consuming. In addition, large-screen visualization technology is favored because it can intuitively display complex data and assist enterprises in making quick decisions. Under the traditional development method, building such a large-screen interface is costly and time-consuming. Even if you turn to a low-code development platform, although the coding requirements are greatly reduced, users still need to invest time to learn platform-specific tools and interface operations, which invisibly increases the threshold for adopting new technologies. Summary of the invention

[0003] In view of the above-mentioned deficiencies in the prior art, the purpose of the invention is to provide an intelligent application generation method and device. The system composed of a large model and a low-code engine realizes a comprehensive and efficient intelligent application generation method, which not only lowers the threshold for entering the field of intelligent application development, allowing non-professional developers to quickly get started, but also greatly improves the overall efficiency of the application from design to deployment by optimizing the development process.

[0004] In a first aspect of the present invention, an intelligent application generation method is proposed, which is applied to a system including a large model and a low-code engine, including:

[0005] After receiving the user's question, the big model generates an answer outline or solution framework;

[0006] Determine whether the big model has received requests that satisfy the answer outline or solution framework;

[0007] If the judgment result is that the big model receives a request that satisfies the answer outline or solution framework, the big model refines and formats the answer outline or solution framework content and sends the output content to the low-code engine;

[0008] The low-code engine configures the corresponding data model, interface elements and business logic of the application based on the logic and data requirements of the output content to generate the application.

[0009] Furthermore, in the above-mentioned intelligent application generation method, after receiving the user's question, the big model generates an answer outline or a solution framework, including:

[0010] Users ask questions to the big model through the user interface or API interface;

[0011] After receiving the user's question, the big model analyzes the user's question and understands the user's true intention;

[0012] Generate an answer outline or solution framework.

[0013] Furthermore, the above-mentioned intelligent application generation method further includes:

[0014] Determine whether the big model has received requests that satisfy the answer outline or solution framework;

[0015] If the result of the determination is that the big model receives a request that is not satisfactory to the answer outline or solution framework, the user provides additional guidance or preferred feedback information to the big model through the user interface or API interface;

[0016] After receiving the user's feedback, the big model generates an answer outline or solution framework;

[0017] Repeat the above steps until you receive a request that you are satisfied with the answer outline or solution framework.

[0018] Furthermore, in the above-mentioned intelligent application generation method, the big model refines and formats the answer outline or solution framework content, including:

[0019] Expand the answer outline or solution framework into complete, detailed text or code based on user needs;

[0020] The expanded and complete and detailed text or code is formatted according to the preset formatting specifications.

[0021] Furthermore, in the above-mentioned intelligent application generation method, the low-code engine configures the corresponding data model, interface elements and business logic of the application according to the logic and data requirements of the output content to generate the application, and also includes:

[0022] Use data-driven methods to ensure that the generated applications meet the functional requirements and user experience in specific scenarios.

[0023] Furthermore, the above-mentioned intelligent application generation method further includes:

[0024] After receiving the user's request to preview the application, the low-code engine previews the application;

[0025] After receiving the user's operation to perform functional testing and debugging on the application, the low-code engine performs functional testing and debugging on the application.

[0026] Furthermore, the above-mentioned intelligent application generation method further includes:

[0027] After receiving the user's operation of improving the application through dragging and dropping or code, improve the application.

[0028] The second aspect of the present invention further proposes an intelligent application generation device, which is applied to a system including a large model and a low-code engine, including:

[0029] Receiving module: used for the big model to generate an answer outline or solution framework after receiving the user's question;

[0030] Judgment module: used to judge whether the big model has received a request that satisfies the answer outline or solution framework;

[0031] Sending module: if the judgment result is that the big model receives a request that satisfies the answer outline or solution framework, the big model refines and formats the output content of the answer outline or solution framework and sends it to the low-code engine;

[0032] Generation module: used by the low-code engine to configure the corresponding data model, interface elements and business logic of the application according to the logic and data requirements of the output content to generate the application.

[0033] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor and a memory;

[0034] The processor is used to execute any one of the above intelligent application generation methods by calling the program or instruction stored in the memory.

[0035] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute any one of the above intelligent application generation methods.

[0036] The beneficial effects of the present invention are as follows: after receiving the user's question through the big model, the present invention generates an answer outline or a solution framework; determines whether the big model has received a request that satisfies the answer outline or the solution framework; if the judgment result is that a satisfactory request has been received, the big model refines and formats the answer outline or the solution framework content and sends the output content to the low-code engine; the low-code engine configures the corresponding data model, interface elements and business logic of the application according to the logic and data requirements of the output content to generate an application. The system composed of the big model and the low-code engine in the present invention realizes a comprehensive and efficient intelligent application generation method, lowers the threshold for entering the field of intelligent application development, allows non-professional developers to quickly get started, and greatly improves the overall efficiency of the application from design to deployment by optimizing the development process. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings are only used to illustrate specific embodiments and are not considered to limit the present invention. In the entire drawings, the same reference symbols represent the same components. Obviously, the drawings described below are only some embodiments recorded in the embodiments of the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0038] Figure 1 An intelligent application generation method provided by an embodiment of the present invention Figure 1 ;

[0039] Figure 2 A diagram of a method for generating an answer outline or a solution framework provided by an embodiment of the present invention;

[0040] Figure 3 An intelligent application generation method provided by an embodiment of the present invention Figure 2 ;

[0041] Figure 4 A diagram of a method for refining and formatting the content of an answer outline or a solution framework using a large model provided by an embodiment of the present invention;

[0042] Figure 5 An intelligent application generation method provided by an embodiment of the present invention Figure 1 ;

[0043] Figure 6 A schematic block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0044] In order to enable those skilled in the art to better understand the technical solutions in the embodiments of the present invention, the technical solutions of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. It should be understood that these descriptions are only exemplary and are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in the field without making creative work should fall within the scope of protection of the present invention.

[0045] Furthermore, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily obscuring the concepts disclosed in the present invention.

[0046] In the description of the present invention, the terms "first", "second", and "third" are used for descriptive purposes only and should not be understood as indicating or implying relative importance. The terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be internal communication between two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0047] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of methods and systems consistent with some aspects of the present invention as detailed in the appended claims.

[0048] The present invention proposes an intelligent application generation method, device, electronic device and storage medium. The system composed of a large model and a low-code engine realizes a comprehensive and efficient intelligent application generation method, which not only lowers the threshold for entering the field of intelligent application development, allowing non-professional developers to quickly get started, but also greatly improves the overall efficiency of the application from design to deployment by optimizing the development process.

[0049] Method Embodiment

[0050] Figure 1 An intelligent application generation method provided by an embodiment of the present invention Figure 1 .

[0051] In the first aspect of the present invention, an intelligent application generation method is proposed, which is applied to a system including a large model and a low-code engine, combined with Figure 1 , including four steps from S1 to S4:

[0052] S1: After receiving the user's question, the big model generates an answer outline or solution framework.

[0053] Specifically, in the embodiment of the present invention, after the large model receives the user's question, the method for generating an answer outline or a solution framework is described in detail below.

[0054] S2: Determine whether the big model has received a request that satisfies the answer outline or solution framework.

[0055] Specifically, in the embodiment of the present invention, whether the user is satisfied or dissatisfied with the answer outline or solution framework generated by the big model, the user can provide feedback through the interface or instructions provided by the big model.

[0056] For example, two satisfied or dissatisfied buttons can be set, and the user can directly click to select whether he is satisfied. If the user clicks the dissatisfied button, it means that the user is dissatisfied with the answer outline or solution framework. If the user clicks the satisfied button, it means that the user is satisfied with the answer outline or solution framework. It should be understood that the seamless connection between deep learning capabilities and business logic is achieved through the large model, giving the application a core of intelligent decision-making and processing.

[0057] S3: If the judgment result is that the big model receives a request that satisfies the answer outline or solution framework, the big model refines and formats the output content of the answer outline or solution framework and sends it to the low-code engine.

[0058] Specifically, in an embodiment of the present invention, for example, if the user clicks a satisfied button, it indicates that the user is satisfied with the answer outline or solution framework, and the big model receives a request for satisfaction with the answer outline or solution framework. The big model refines and formats the content of the answer outline or solution framework to ensure the clarity and readability of the content, and sends the clear and readable output content to the low-code engine.

[0059] S4: The low-code engine configures the corresponding data model, interface elements and business logic of the application based on the logic and data requirements of the output content to generate the application.

[0060] Specifically, in an embodiment of the present invention, step S4 makes full use of data-driven to ensure that the generated application can meet the functional requirements and user experience in specific scenarios. The low-code engine performs real-time data analysis based on the logic and data requirements of the output content, dynamically optimizes business processes, and ensures that each functional module in the application can respond based on the latest and most accurate data, thereby improving the flexibility and adaptability of the generated application.

[0061] Figure 2 A diagram of a method for generating an answer outline or a solution framework provided in an embodiment of the present invention.

[0062] Furthermore, in the above-mentioned intelligent application generation method, after receiving the user's question, the big model generates an answer outline or a solution framework, combined with Figure 2 , including three steps from S21 to S23:

[0063] S21: The user asks questions to the big model through the user interface or API interface;

[0064] S22: After receiving the user's question, the big model analyzes the user's question and understands the user's true intention;

[0065] S23: Generate an answer outline or solution framework.

[0066] Specifically, in an embodiment of the present invention, a user asks questions or demands to a deployed large-scale language model through a convenient user interface or API interface. After the large-scale language model receives the questions or demands, the large model first parses the user's questions or demands, and uses its powerful natural language processing capabilities to understand the user's true intentions. Subsequently, the large-scale language model automatically generates a detailed answer outline or solution framework.

[0067] Figure 3 An intelligent application generation method provided by an embodiment of the present invention Figure 2 .

[0068] Furthermore, the above intelligent application generation method is combined with Figure 3 , and also includes four steps S31 to S34:

[0069] S31: Determine whether the big model has received a request that satisfies the answer outline or solution framework.

[0070] Specifically, in the embodiment of the present invention, whether the user is satisfied or dissatisfied with the answer outline or solution framework generated by the big model can be fed back through the interface or instructions provided by the big model.

[0071] Exemplarily, two satisfied or dissatisfied buttons may be provided, and the user may directly click to select whether he is satisfied. If the user clicks the dissatisfied button, it indicates that the user is dissatisfied with the answer outline or solution framework. If the user clicks the satisfied button, it indicates that the user is satisfied with the answer outline or solution framework.

[0072] S32: If the judgment result is that the big model receives a request that is not satisfactory to the answer outline or solution framework, the user provides additional guidance or preferred feedback information to the big model through the user interface or API interface.

[0073] Specifically, in an embodiment of the present invention, if the user clicks an "unsatisfied" button, it indicates that the user is dissatisfied with the answer outline or solution framework, and the big model receives a request for dissatisfaction with the answer outline or solution framework. The user provides additional guidance or preference feedback information to the big model through the user interface or API interface, and the guidance or preference here is more specific guidance and preference.

[0074] S33: After receiving the user's feedback information, the large model generates an answer outline or a solution framework.

[0075] Specifically, in the embodiment of the present invention, after receiving the user's feedback information, the large model is adjusted according to the user's guidance or preference, and regenerates an answer outline or solution framework that better meets the user's needs.

[0076] S34: Repeat the above steps until a request is received that satisfies the answer outline or solution framework.

[0077] Specifically, in an embodiment of the present invention, after generating an answer outline or solution framework that is more in line with user needs, it is determined again whether the user is satisfied or dissatisfied with the answer outline or solution framework generated by the big model. If the user is dissatisfied, the above steps are repeated until a request for satisfaction with the answer outline or solution framework is received. If the user is satisfied, the big model refines and formats the output content of the answer outline or solution framework and sends it to the low-code engine. The low-code engine configures the corresponding data model, interface elements, and business logic of the application based on the logic and data requirements of the output content to generate an application.

[0078] Figure 4 A method diagram of a large model provided in an embodiment of the present invention to refine and format the content of an answer outline or a solution framework.

[0079] Furthermore, in the above-mentioned intelligent application generation method, the big model refines and formats the answer outline or solution framework content, and combines Figure 4 , including two steps from S41 to S42:

[0080] S41: Expand the answer outline or solution framework content into complete and detailed text or code according to user needs;

[0081] S42: The expanded complete and detailed text or code is formatted according to the preset format specifications.

[0082] Specifically, in an embodiment of the present invention, the large model expands the answer outline or solution framework content into complete and detailed text or code according to user needs, and formats the complete and detailed text or code according to preset format specifications to ensure clarity and readability of the content.

[0083] Furthermore, in the above-mentioned intelligent application generation method, the low-code engine configures the corresponding data model, interface elements and business logic of the application according to the logic and data requirements of the output content to generate the application, and also includes:

[0084] Use data-driven methods to ensure that the generated applications meet the functional requirements and user experience in specific scenarios.

[0085] Furthermore, the above-mentioned intelligent application generation method further includes:

[0086] After receiving the user's request to preview the application, the low-code engine previews the application;

[0087] After the low-code engine receives the user's operation to perform functional testing and debugging on the application, it performs functional testing and debugging on the application.

[0088] Specifically, in an embodiment of the present invention, in a low-code engine, applications can be previewed, functionally tested and debugged, which improves the user experience. After debugging is completed, the application can be directly deployed in the target environment, whether it is a cloud server or a local server.

[0089] Furthermore, the above-mentioned intelligent application generation method further includes:

[0090] After receiving the user's operation of improving the application through dragging and dropping or code, improve the application.

[0091] Specifically, in the embodiment of the present invention, the user can improve the application by dragging and dropping or modifying a small amount of code, thereby further improving the user experience.

[0092] It should be understood that the low-code engine provides a visual development environment that is suitable for complex data visualization scenarios, making multi-dimensional information clear at a glance and enhancing decision-making support capabilities.

[0093] Device Embodiment

[0094] Figure 5 An intelligent application generation device diagram is provided for an embodiment of the present invention.

[0095] In a second aspect of the present invention, an intelligent application generation device is provided, which is applied to a system including a large model and a low-code engine, and is combined with Figure 5 ,include:

[0096] Receiving module 51: used for the large model to generate an answer outline or solution framework after receiving the user's question.

[0097] Specifically, in the embodiment of the present invention, after receiving the user's question, the receiving module 51 of the large model generates an answer outline or a solution framework.

[0098] Determination module 52: used to determine whether the large model has received a request that satisfies the answer outline or solution framework.

[0099] Specifically, in the embodiment of the present invention, the judgment module determines whether the user is satisfied or dissatisfied with the answer outline or solution framework generated by the big model, and feedback can be provided through the interface or instructions provided by the big model.

[0100] For example, two satisfied or dissatisfied buttons can be set, and the user can directly click to select whether he is satisfied. If the user clicks the dissatisfied button, it means that the user is dissatisfied with the answer outline or solution framework. If the user clicks the satisfied button, it means that the user is satisfied with the answer outline or solution framework. It should be understood that the seamless connection between deep learning capabilities and business logic is achieved through the large model, giving the application a core of intelligent decision-making and processing.

[0101] Sending module 53: If the judgment result is that the big model receives a request that satisfies the answer outline or solution framework, the big model sends the output content of the answer outline or solution framework after refining and formatting the content to the low-code engine.

[0102] Specifically, in an embodiment of the present invention, for example, if the user clicks a satisfied button, it indicates that the user is satisfied with the answer outline or solution framework, and the big model receives a request for satisfaction with the answer outline or solution framework. The big model refines and formats the content of the answer outline or solution framework to ensure the clarity and readability of the content, and sends the clear and readable output content to the low-code engine through the sending module 53.

[0103] Generation module 54: used by the low-code engine to configure the corresponding data model, interface elements and business logic of the application according to the logic and data requirements of the output content to generate the application.

[0104] Specifically, in an embodiment of the present invention, the generation module 54 makes full use of data-driven to ensure that the generated application can meet the functional requirements and user experience in specific scenarios. The low-code engine performs real-time data analysis based on the logic and data requirements of the output content, dynamically optimizes business processes, and ensures that each functional module in the application can respond based on the latest and most accurate data, thereby improving the flexibility and adaptability of the generated application.

[0105] According to a third aspect of the present invention, an electronic device is provided, comprising: a processor and a memory;

[0106] The processor is used to execute any one of the above intelligent application generation methods by calling the program or instruction stored in the memory.

[0107] In a fourth aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute any one of the above intelligent application generation methods.

[0108] Figure 6 It is a schematic block diagram of an electronic device provided by an embodiment of the present invention.

[0109] like Figure 6 As shown, the electronic device includes: at least one processor 601, at least one memory 602 and at least one communication interface 603. The various components in the electronic device are coupled together through a bus system 604. The communication interface 603 is used to transmit information between external devices. It can be understood that the bus system 604 is used to achieve connection and communication between these components. In addition to the data bus, the bus system 604 also includes a power bus, a control bus and a status signal bus. However, for the sake of clarity, Figure 6 Various buses are labeled as bus system 604 .

[0110] It can be understood that the memory 602 in this embodiment can be a volatile memory or a non-volatile memory, or can include both volatile and non-volatile memories.

[0111] In some implementations, the memory 602 stores the following elements, executable units or data structures, or a subset thereof, or an extended set thereof: an operating system and application programs.

[0112] The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, etc., which are used to implement various basic services and process hardware-based tasks. The application includes various application programs, such as a media player (Media Player), a browser (Browser), etc., which are used to implement various application services. A program that implements any method of an intelligent application generation method provided in an embodiment of the present invention can be included in the application.

[0113] In an embodiment of the present invention, the processor 601 calls a program or instruction stored in the memory 602, specifically, a program or instruction stored in an application program, and the processor 601 is used to execute the steps of each embodiment of an intelligent application generation method provided in an embodiment of the present invention.

[0114] After receiving the user's question, the big model generates an answer outline or solution framework;

[0115] Determine whether the big model has received requests that satisfy the answer outline or solution framework;

[0116] If the judgment result is that the big model receives a request that satisfies the answer outline or solution framework, the big model refines and formats the answer outline or solution framework content and sends the output content to the low-code engine;

[0117] The low-code engine configures the corresponding data model, interface elements and business logic of the application based on the logic and data requirements of the output content to generate the application.

[0118] Any method in the intelligent application generation method provided in an embodiment of the present invention can be applied to the processor 601, or implemented by the processor 601. The processor 601 can be an integrated circuit chip with signal processing capabilities. In the implementation process, each step of the above method can be completed by an integrated logic circuit of hardware in the processor 601 or an instruction in the form of software. The above-mentioned processor 601 can be a general-purpose processor, a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0119] The steps of any method in the intelligent application generation method provided by the embodiment of the present invention can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software units in the decoding processor. The software unit can be located in a storage medium mature in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 602, and the processor 601 reads the information in the memory 602 and completes the steps of the method in combination with its hardware.

[0120] Those skilled in the art will appreciate that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present invention and to form different embodiments.

[0121] Those skilled in the art will appreciate that the description of each embodiment has its own emphasis, and for parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0122] Although the embodiments of the present invention are described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations all fall within the scope defined by the appended claims. The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of various equivalent modifications or substitutions within the technical scope disclosed by the present invention, and these modifications or substitutions should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

[0123] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed by the present invention, and these modifications or replacements should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. An intelligent application generation method, characterized in that: Applied to systems including: large models and low-code engines, including: After receiving the user's question, the big model generates an answer outline or solution framework; Determine whether the big model has received requests that satisfy the answer outline or solution framework; If the judgment result is that the big model receives a request that satisfies the answer outline or solution framework, the big model refines and formats the answer outline or solution framework content and sends the output content to the low-code engine; The low-code engine configures the corresponding data model, interface elements and business logic of the application based on the logic and data requirements of the output content to generate the application.

2. The intelligent application generation method according to claim 1, characterized in that: After receiving the user's question, the large model generates an answer outline or solution framework, including: Users ask questions to the big model through the user interface or API interface; After receiving the user's question, the big model analyzes the user's question and understands the user's true intention; Generate an answer outline or solution framework.

3. The intelligent application generation method according to claim 1, characterized in that: The method further comprises: Determine whether the big model has received requests that satisfy the answer outline or solution framework; If the result of the determination is that the big model receives a request that is not satisfactory to the answer outline or solution framework, the user provides additional guidance or preferred feedback information to the big model through the user interface or API interface; After receiving the user's feedback, the big model generates an answer outline or solution framework; Repeat the above steps until you receive a request that you are satisfied with the answer outline or solution framework.

4. The intelligent application generation method according to claim 1, characterized in that: The macro model refines and formats the content of the answer outline or solution framework, including: Expand the answer outline or solution framework into complete, detailed text or code based on user needs; The expanded and complete and detailed text or code is formatted according to the preset formatting specifications.

5. The intelligent application generation method according to claim 1, characterized in that: The low-code engine configures the corresponding data model, interface elements and business logic of the application according to the logic and data requirements of the output content to generate the application, and also includes: Use data-driven methods to ensure that the generated applications meet the functional requirements and user experience in specific scenarios.

6. The intelligent application generation method according to claim 1, characterized in that: The method further comprises: After receiving the user's request to preview the application, the low-code engine previews the application; After receiving the user's operation to perform functional testing and debugging on the application, the low-code engine performs functional testing and debugging on the application.

7. The intelligent application generation method according to claim 1, characterized in that: The method further comprises: After receiving the user's operation of improving the application through dragging and dropping or code, improve the application.

8. An intelligent application generation device, characterized in that: Applied to systems including: large models and low-code engines, including: Receiving module: used for the big model to generate an answer outline or solution framework after receiving the user's question; Judgment module: used to judge whether the big model has received a request that satisfies the answer outline or solution framework; Sending module: if the judgment result is that the big model receives a request that satisfies the answer outline or solution framework, the big model refines and formats the output content of the answer outline or solution framework and sends it to the low-code engine; Generation module: used by the low-code engine to configure the corresponding data model, interface elements and business logic of the application according to the logic and data requirements of the output content to generate the application.

9. An electronic device, characterized in that: include: Processor and memory; The processor is used to execute the intelligent application generation method as described in any one of claims 1 to 7 by calling the program or instruction stored in the memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or instruction, and the program or instruction enables a computer to execute an intelligent application generation method as claimed in any one of claims 1 to 7.

Images