Method and system for intelligently generating form

Abstract

The invention relates to the technical field of artificial intelligence, and particularly provides a method and system for intelligently generating a form, and the method comprises the following steps: S1, selecting and training a large model; s2, demand analysis and input processing; s3, generating a form; s4, performing form optimization and verification; and S5, user feedback and iteration. Compared with the prior art, the method has the advantages that the time and cost for developing and configuring the form can be greatly reduced, the development difficulty is reduced, and the efficiency and quality of form development are improved.

Description

Technical Field

[0001] The present invention relates to the field of artificial intelligence technology, and specifically provides a method and system for intelligently generating a form. Background Art

[0002] In the field of modern software development, forms are important tools for data collection, user interaction and information management, and their creation and management process plays a pivotal role. Traditionally, the development of forms involves tedious manual coding, including defining the form structure, setting data validation rules, writing front-end display logic and back-end data processing code, etc. This process is not only time-consuming and laborious, but also prone to errors due to human negligence, increasing maintenance costs and development cycles.

[0003] With the emergence of low-code development platforms, form development has been simplified to a certain extent. These platforms reduce the requirements for developers' programming skills by providing graphical interfaces and pre-built component libraries, allowing non-professional developers to participate in form construction. However, although low-code platforms reduce some coding work, their use still requires a certain programming foundation and understanding ability, and there is still a certain threshold for users with no technical background at all. Summary of the invention

[0004] The present invention aims at solving the above-mentioned deficiencies of the prior art and provides a method for intelligently generating forms with strong practicability.

[0005] A further technical task of the present invention is to provide a system for intelligently generating forms that is rationally designed, safe and applicable.

[0006] The technical solution adopted by the present invention to solve its technical problem is:

[0007] A method for intelligently generating a form comprises the following steps:

[0008] S1. Selection and training of large models;

[0009] S2, demand analysis and input processing;

[0010] S3, form generation;

[0011] S4, form optimization and verification;

[0012] S5. User feedback and iteration.

[0013] Furthermore, in step S1, a suitable pre-trained large model is selected, JSON data of the form structure, fields and validation rules are collected and annotated, the form data is converted into a format suitable for model training, a training set is generated, and the large model is allowed to learn and train through a supervised learning mode, and the performance of the fine-tuned model is evaluated.

[0014] Furthermore, in step S2, the user inputs the form requirements through natural language, the trained large model accepts the user's natural language description, extracts the form requirements through natural language processing technology, identifies the overall structure of the form, and then converts the corresponding JSON data format.

[0015] Furthermore, in step S3, the JSON data generated by the large model is imported into the low-code platform, and the low-code platform parses and generates corresponding components and configures corresponding attribute rules according to the data standard;

[0016] In step S4, the user optimizes the generated form through natural language and adjusts the layout and style of the form;

[0017] Perform form validation to check whether there are syntax errors in the intelligently generated or user-written code to ensure that the form functions normally.

[0018] Further, in step S5, the user is allowed to provide feedback on the generated form, and improvement suggestions and opinions from the user are collected, and the user feedback is analyzed to identify common problems and improvement points;

[0019] Based on user feedback, the model is adjusted and improved to improve the quality and accuracy of generated forms. The form generation process is optimized through user feedback and model iteration.

[0020] A system for intelligently generating forms. First, a suitable pre-trained large model is selected, and the user inputs the form requirements through natural language. Then, the form is generated. After the form is generated, the form is optimized and verified. Finally, the user is allowed to provide feedback on the generated form.

[0021] Furthermore, we select a suitable pre-trained large model, collect JSON data of form structure, fields and validation rules, and annotate the data, convert the form data into a format suitable for model training, generate a training set, and use the supervised learning model to let the large model learn and train, and evaluate the performance of the fine-tuned model.

[0022] Furthermore, the user inputs the form requirements through natural language, and the trained large model accepts the user's natural language description, extracts the form requirements through natural language processing technology, identifies the overall structure of the form, and then converts it to the corresponding JSON data format.

[0023] Furthermore, the JSON data generated by the large model is imported into the low-code platform, which parses the data according to the data standard to generate corresponding components and configure corresponding attribute rules;

[0024] Users can optimize the generated form through natural language and adjust the layout and style of the form;

[0025] Perform form validation to check whether there are syntax errors in the intelligently generated or user-written code to ensure that the form functions normally.

[0026] Furthermore, users are allowed to provide feedback on the generated forms, suggestions and opinions for improvement are collected, user feedback is analyzed, and common problems and improvement points are identified;

[0027] Based on user feedback, the model is adjusted and improved to improve the quality and accuracy of generated forms. The form generation process is optimized through user feedback and model iteration.

[0028] Compared with the prior art, the method and system for intelligently generating a form of the present invention have the following outstanding beneficial effects:

[0029] The present invention uses the natural language processing capabilities of the large model to identify the user's natural language, extract user feature requirements, generate low-code standard JSON data, import it into the low-code platform, and automatically and intelligently generate forms. It greatly reduces the time and cost of developing and configuring forms, reduces the difficulty of development, and improves the efficiency and quality of form development. At the same time, iterative optimization is carried out through user feedback, so that the generated forms are more in line with user needs, further improving the user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0031] Attached Figure 1 The present invention is a flowchart of a method for intelligently generating a form. DETAILED DESCRIPTION

[0032] In order to enable those skilled in the art to better understand the solution of the present invention, the present invention is further described in detail below in conjunction with specific implementation methods. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0033] A best embodiment is given below:

[0034] like Figure 1 As shown, a method for intelligently generating a form in this embodiment has the following steps:

[0035] S1. Selection and training of large models;

[0036] Select a suitable pre-trained large model, such as GPT-4, GLM, etc. Collect a large amount of JSON data containing form structure, fields, validation rules, etc., annotate the data, convert the form data into a format suitable for model training, and generate a training set. Through the supervised learning model, let the large model learn and train, evaluate the performance of the fine-tuned model, ensure the accuracy and practicality of the generated form code, and gradually enable it to understand and generate low-code forms.

[0037] S2, demand analysis and input processing;

[0038] Users input form requirements through natural language. For example, "Create a user registration form that includes username, password, and email address, and add email format validation." The trained large model accepts the user's natural language description, extracts form requirements through natural language processing technology, identifies the overall structure of the form, such as form title, paragraphs, etc., identifies each field in the form and its attributes, such as field name, data type, validation rules, etc., and then converts the corresponding JSON data format.

[0039] S3, form generation;

[0040] Import the JSON data generated by the large model into the low-code platform. The low-code platform parses the data according to the data standards to generate corresponding components and configure corresponding attribute rules.

[0041] S4, form optimization and verification;

[0042] Users can use natural language to optimize the generated form and adjust the layout and style of the form to ensure that it meets user needs and interface design specifications.

[0043] Able to perform form validation, check whether there are syntax errors in the intelligently generated or user-written code, and ensure that the form functions normally.

[0044] S5. User feedback and iteration;

[0045] Allow users to provide feedback on the generated forms, collect users' suggestions and opinions for improvement, analyze user feedback, and identify common problems and improvement points.

[0046] Based on user feedback, the model is adjusted and improved to improve the quality and accuracy of the generated forms. Through continuous user feedback and model iteration, the form generation process is continuously optimized to improve user experience and development efficiency.

[0047] Based on the above method, a system for intelligently generating forms in this embodiment first selects a suitable pre-trained large model, and the user inputs the form requirements through natural language. Then, the form is generated. After the form is generated, the form is optimized and verified. Finally, the user is allowed to provide feedback on the generated form.

[0048] Among them, select a suitable pre-trained large model, collect JSON data of form structure, fields and validation rules, and annotate the data, convert the form data into a format suitable for model training, generate a training set, and use the supervised learning mode to let the large model learn and train, and evaluate the performance of the fine-tuned model.

[0049] Users input form requirements through natural language. The trained large model accepts the user's natural language description, extracts the form requirements through natural language processing technology, identifies the overall structure of the form, and then converts it to the corresponding JSON data format.

[0050] Import the JSON data generated by the large model into the low-code platform, which parses the data according to the data standard to generate corresponding components and configure corresponding attribute rules;

[0051] Users can optimize the generated form through natural language and adjust the layout and style of the form;

[0052] Perform form validation to check whether there are syntax errors in the intelligently generated or user-written code to ensure that the form functions normally.

[0053] Allow users to provide feedback on the generated forms, collect users' suggestions and opinions for improvement, analyze user feedback, and identify common problems and improvement points;

[0054] Based on user feedback, the model is adjusted and improved to improve the quality and accuracy of generated forms. The form generation process is optimized through user feedback and model iteration.

[0055] The above-mentioned specific implementations are only specific cases of the present invention. The patent protection scope of the present invention includes but is not limited to the above-mentioned specific implementations. Any technical solutions that conform to the above-mentioned specific implementations of the present invention and any appropriate changes or substitutions made by ordinary technicians in the relevant technical field shall fall within the patent protection scope of the present invention.

[0056] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligently generating a form, characterized in that: The steps are as follows: S1. Selection and training of large models; S2, demand analysis and input processing; S3, form generation; S4, form optimization and verification; S5. User feedback and iteration.

2. A method for intelligently generating a form according to claim 1, characterized in that: In step S1, select a suitable pre-trained large model, collect JSON data of form structure, fields and validation rules, and annotate the data. Convert the form data into a format suitable for model training, generate a training set, and use the supervised learning mode to let the large model learn and train, and evaluate the performance of the fine-tuned model.

3. A method for intelligently generating a form according to claim 2, characterized in that: In step S2, the user inputs the form requirements through natural language. The trained large model accepts the user's natural language description, extracts the form requirements through natural language processing technology, identifies the overall structure of the form, and then converts the corresponding JSON data format.

4. A method for intelligently generating a form according to claim 3, characterized in that: In step S3, the JSON data generated by the large model is imported into the low-code platform, and the low-code platform parses and generates corresponding components and configures corresponding attribute rules according to the data standard; In step S4, the user optimizes the generated form through natural language and adjusts the layout and style of the form; Perform form validation to check whether there are syntax errors in the intelligently generated or user-written code to ensure that the form functions normally.

5. A method for intelligently generating a form according to claim 4, characterized in that: In step S5, the user is allowed to provide feedback on the generated form, and suggestions and opinions for improvement are collected, and the user feedback is analyzed to identify common problems and improvement points; Based on user feedback, the model is adjusted and improved to improve the quality and accuracy of generated forms. The form generation process is optimized through user feedback and model iteration.

6. A system for intelligently generating forms, characterized in that: First, select a suitable pre-trained large model, and the user inputs the form requirements through natural language. Then, the form is generated, optimized and verified after the form is generated, and finally, the user is allowed to provide feedback on the generated form.

7. A system for intelligently generating forms according to claim 6, characterized in that: Select a suitable pre-trained large model, collect JSON data of form structure, fields and validation rules, and annotate the data. Convert the form data into a format suitable for model training, generate a training set, and use the supervised learning model to let the large model learn and train, and evaluate the performance of the fine-tuned model.

8. The system for intelligently generating forms according to claim 7, characterized in that: Users input form requirements through natural language. The trained large model accepts the user's natural language description, extracts the form requirements through natural language processing technology, identifies the overall structure of the form, and then converts it to the corresponding JSON data format.

9. The system for intelligently generating a form according to claim 8, characterized in that: Import the JSON data generated by the large model into the low-code platform, which parses the data according to the data standard to generate corresponding components and configure corresponding attribute rules; Users can optimize the generated form through natural language and adjust the layout and style of the form; Perform form validation to check whether there are syntax errors in the intelligently generated or user-written code to ensure that the form functions normally.

10. The system for intelligently generating forms according to claim 9, characterized in that: Allow users to provide feedback on the generated forms, collect users' suggestions and opinions for improvement, analyze user feedback, and identify common problems and improvement points; Based on user feedback, the model is adjusted and improved to improve the quality and accuracy of generated forms. The form generation process is optimized through user feedback and model iteration.

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