Low-code platform integration service generation method and device, equipment, medium and product

Abstract

The invention provides a low-code platform integration service generation method and device, equipment, a medium and a product, and relates to the technical field of computers. The method comprises the following steps: obtaining a user interface type keyword; obtaining a first target demand description according to the keyword and a pre-stored keyword list; a correlation matching result is obtained through the pre-training semantic analysis model and the first target demand description; obtaining a second target demand description according to the result; user key information is obtained through the pre-training information extraction model and the second target demand description; when the information comprises a preset number of pre-stored interface attribute fields, generating prompt words according to codes obtained by the information and a pre-training iterative optimization model; generating a model according to the cue word and the pre-training code to obtain a target code; and converting the target code into a specified format code, and when the specified format code satisfies a preset condition, exporting the specified format code to a low code platform to generate an integrated service. Corresponding specified format codes are described through user requirements, adaptive integration services are generated, and the learning threshold of the user is lowered.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a method, apparatus, device, medium and product for generating low-code platform integration services. Background Technology

[0002] Low-code platforms enable rapid system deployment, significantly shortening development cycles and allowing for agile development and iterative optimization of business requirements with minimal cost and maximum efficiency.

[0003] Currently, low-code platform integration services are implemented through manual coding. Users need to write their own code to implement the interface logic and embed it into the platform. This method of manual configuration or coding relies on an understanding of the system's underlying logic, requiring users to have low-level technical knowledge, resulting in a high learning curve and making it difficult for non-technical users to operate. Summary of the Invention

[0004] This application provides a method, apparatus, device, medium, and product for generating low-code platform integration services, which solves the technical problem that the prior art requires users to have underlying technical knowledge, resulting in a high learning threshold for users and difficulty for non-technical personnel to operate.

[0005] In a first aspect, embodiments of this application provide a method for generating low-code platform integration services, including:

[0006] Obtain a user requirement description and process the user requirement description according to a pre-trained natural language processing model to obtain user interface type keywords corresponding to the user requirement description;

[0007] Based on the user interface type keywords and the pre-stored keyword list, the first target requirement description is obtained;

[0008] The first target requirement description is processed according to the pre-trained semantic analysis model to obtain the association matching result between the first target requirement description and the corresponding user interface type keywords;

[0009] The second target requirement description is obtained based on the association matching results;

[0010] Based on the pre-trained information extraction model and the second target requirement description, the user key information corresponding to the second target requirement description is obtained;

[0011] When the user key information includes a preset number of pre-stored interface attribute fields, code generation prompts are obtained based on the user key information and the pre-trained iterative optimization model.

[0012] The target code is obtained by generating prompt words based on the code and using a pre-trained code generation model;

[0013] The target code is converted into a specified format code, and when the specified format code meets preset conditions, the specified format code is exported to a low-code platform to generate an integration service.

[0014] In one possible implementation, it includes:

[0015] When the user interface type keyword exists in the pre-stored keyword list, the user requirement description corresponding to the user interface type keyword is used as the first target requirement description.

[0016] In one possible implementation, it also includes:

[0017] If the user interface type keyword does not exist in the pre-stored keyword list, the user requirement description is re-entered until the user interface type keyword corresponding to the re-entered user requirement description exists in the pre-stored keyword list.

[0018] The re-entered user requirement description is taken as the first target requirement description.

[0019] In one possible implementation, it includes:

[0020] The initial code is obtained based on the code-generating prompt words and the pre-trained code generation model;

[0021] The initial code is compiled and run to obtain the target code.

[0022] In one possible implementation, it includes:

[0023] When the similarity indicated by the association matching result exceeds a preset threshold, the first target requirement description corresponding to the association matching result is used as the second target requirement description.

[0024] In one possible implementation, it also includes:

[0025] Determine whether the description of the second target requirement corresponding to the user's key information is sufficient.

[0026] Secondly, embodiments of this application provide a low-code platform integration service generation apparatus, comprising:

[0027] The first processing module is used to obtain the user's requirement description and process the user's requirement description according to the pre-trained natural language processing model to obtain the user interface type keywords corresponding to the user's requirement description.

[0028] The first target requirement description acquisition module is used to obtain the first target requirement description based on the user interface type keywords and the pre-stored keyword list;

[0029] The second processing module is used to process the first target requirement description according to the pre-trained semantic analysis model to obtain the association matching result between the first target requirement description and the corresponding user interface type keywords.

[0030] The second target requirement description acquisition module is used to obtain the second target requirement description based on the association matching result;

[0031] The third processing module is used to extract the model and the second target requirement description based on the pre-trained information to obtain the user key information corresponding to the second target requirement description.

[0032] The code generation prompt word acquisition module is used to obtain code generation prompt words based on the user key information and the pre-trained iterative optimization model when the user key information includes a preset number of pre-stored interface attribute fields.

[0033] The target code acquisition module is used to obtain the target code based on the code-generating prompt words and the pre-trained code generation model;

[0034] The fourth processing module is used to convert the target code into code of a specified format, and when the code of the specified format meets preset conditions, to export the code of the specified format to a low-code platform to generate an integration service.

[0035] Thirdly, embodiments of this application provide an electronic device, including: a processor, and a memory communicatively connected to the processor;

[0036] The memory stores computer-executed instructions;

[0037] The processor executes computer execution instructions stored in the memory to implement the low-code platform integration service generation method provided in the first aspect of this application.

[0038] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, are used to implement the low-code platform integration service generation method provided in the first aspect of this application.

[0039] Fifthly, embodiments of this application provide a computer program product, including a computer program, which, when executed by a processor, is used to implement the low-code platform integration service generation method provided in the first aspect of this application.

[0040] This application provides a method, apparatus, device, medium, and product for generating low-code platform integration services, comprising: obtaining a user requirement description; processing the user requirement description according to a pre-trained natural language processing model to obtain user interface type keywords corresponding to the user requirement description; obtaining a first target requirement description according to the user interface type keywords and a pre-stored keyword list; processing the first target requirement description according to a pre-trained semantic analysis model to obtain an association matching result between the first target requirement description and the corresponding user interface type keywords; obtaining a second target requirement description according to the association matching result; obtaining user key information corresponding to the second target requirement description according to a pre-trained information extraction model and the second target requirement description; when the user key information includes a preset number of pre-stored interface attribute fields, obtaining code generation prompts according to the user key information and a pre-trained iterative optimization model; obtaining target code according to the code generation prompts and a pre-trained code generation model; converting the target code into code of a specified format, and when the specified format code meets preset conditions, exporting the specified format code to the low-code platform to generate an integration service. Through the above method, the following technical effects are achieved: obtaining corresponding specified format code from the user requirement description and generating adapted integration services for unified management. Pre-trained models can be pre-trained to target the underlying capabilities of a system and repeatedly learn the underlying logic. Therefore, users only need to make requests using natural language without needing to understand the underlying logic. The pre-trained model will complete the functional implementation according to the requirements, thereby ensuring that the generated integrated service conforms to the system architecture to the greatest extent and ensuring the stability of the system. This reduces the learning threshold for users, and non-technical personnel can also quickly get started. Attached Figure Description

[0041] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0042] Figure 1 A flowchart illustrating the low-code platform integration service generation method provided in this application embodiment;

[0043] Figure 2 A schematic diagram of the structure of the low-code platform integration service generation device provided in the embodiments of this application;

[0044] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0045] Explanation of reference numerals in the attached figures:

[0046] 801 - Processor; 802 - Memory; 803 - Communication components; 804 - Bus.

[0047] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0048] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0049] The collection, storage, use, processing, transmission, provision, and disclosure of financial data or user data involved in the technical solution of this application all comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0050] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0051] First, let's explain the terms used in this technical solution.

[0052] Large-scale agent engineering: also known as agent or intelligent agent, its core function is to have the ability to autonomously achieve goals, to perceive the external environment, and to be an intelligent agent with autonomy, feedback, initiative and emotional social attributes.

[0053] Integration service data: Low-code platform underlying storage of integration service logic-specific formatted data.

[0054] In order to clearly understand the technical solution of this application, the solutions of the prior art will be described in detail.

[0055] Currently, low-code platform integration services are implemented through manual coding. Users need to write their own code to implement the interface logic and embed it into the platform. This method of manual configuration or coding relies on an understanding of the system's underlying logic, requiring users to have low-level technical knowledge, resulting in a high learning curve and making it difficult for non-technical users to operate.

[0056] In summary, the urgent problem to be solved in this application is how to design a technology that can solve the problem of high learning threshold and difficulty for non-technical personnel to operate due to the requirement of users having underlying technical knowledge in existing technologies.

[0057] Therefore, in view of the above-mentioned technical problems existing in the prior art, the embodiments of this application provide a low-code platform integration service generation method, apparatus, device, medium and product, which aims to effectively reduce the learning threshold for users.

[0058] The following describes the application scenarios of the low-code platform integration service generation method, apparatus, device, medium, and product provided in the embodiments of this application. The following application scenarios are merely examples, intended to help those skilled in the art understand the technical content of this application, but do not imply that the embodiments of this application cannot be used in other devices, systems, environments, or scenarios.

[0059] System Development: When building applications quickly and efficiently using a low-code platform, the low-code platform integration service generation method, apparatus, device, medium, and product provided in the embodiments of this application can quickly generate adapted integration services based on user requirements descriptions, reducing the learning threshold for users.

[0060] The technical solution of this application and how it solves the above-mentioned technical problems will be described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments. The embodiments of this application will be described below with reference to the accompanying drawings.

[0061] Figure 1 This is a flowchart illustrating the low-code platform integration service generation method provided in this embodiment. The low-code platform integration service generation method provided in this embodiment includes the following steps:

[0062] S101. Obtain the user requirement description and process the user requirement description according to the pre-trained natural language processing model to obtain the user interface type keywords corresponding to the user requirement description.

[0063] In this embodiment, the first step is to determine the user interface type. The user requirement description is the description entered by the user. A pre-trained natural language processing model is used to process the user requirement description using natural language to identify and extract user interface type keywords from the description. The pre-trained natural language processing model is a fully trained model capable of performing natural language processing, such as a Large Language Model (LLM).

[0064] S102. Based on the user interface type keywords and the pre-stored keyword list, obtain the first target requirement description.

[0065] In this embodiment, user interface type keywords include, but are not limited to, obtaining, querying, adding, creating, modifying, and deleting. The pre-stored keyword list contains various interface type keywords. The purpose of user interface type determination is to identify user interface type keywords from the user requirement description, ensuring that the interface type described in the user requirement description matches the expected interface type. The first target requirement description is the user requirement description after confirming the user interface type.

[0066] S103. Process the first target requirement description according to the pre-trained semantic analysis model to obtain the association matching result between the first target requirement description and the corresponding user interface type keywords.

[0067] In this embodiment, after confirming the user interface type, the next step is to determine the legality of the interface description. This determination primarily assesses whether the first target requirement description is relevant to the topic generated by the interface, i.e., whether the first target requirement description contains irrelevant questions. The legality determination first utilizes a pre-trained semantic analysis model to perform semantic analysis on the first target requirement description, determining its relevance to the interface type and obtaining the association matching results between the first target requirement description and the corresponding user interface type keywords. The pre-trained semantic analysis model is a fully trained model capable of semantic analysis, such as BERT.

[0068] S104. Obtain the second target requirement description based on the association matching results.

[0069] In this embodiment, the association matching results include those with no association and those with high association. The first target requirement description, which has a high association with the corresponding user interface type keyword, is used as the second target requirement description.

[0070] S105. Extract the model and the second target requirement description based on the pre-trained information to obtain the user key information corresponding to the second target requirement description.

[0071] In this embodiment, after obtaining the second target requirement description that meets the interface description validity requirements, the next step is to perform an interface description integrity check. The interface description integrity check is mainly used to check whether the second target requirement description contains all the necessary key information and whether the information is sufficient. First, a pre-trained information extraction model is used to extract key user information from the second target requirement description. The pre-trained information extraction model is a trained model capable of information extraction, such as an LLM (Local Level Model).

[0072] S106. When the user's key information includes a preset number of pre-stored interface attribute fields, code generation prompts are obtained based on the user's key information and the pre-trained iterative optimization model.

[0073] In this embodiment, for example, when querying an order, the preset number of pre-stored interface attribute fields consists of two pre-stored interface attribute fields: path parameters and query parameters. The path parameters are the user identification code (ID), used to indicate which customer's order is being queried, and the query parameters are the order status, used to filter orders. If the user's key information corresponding to the second target requirement description includes the preset number of pre-stored interface attribute fields, the next step is performed; if the user's key information corresponding to the second target requirement description does not include the preset number of pre-stored interface attribute fields, a message indicating that key information is missing is displayed, prompting the user to re-enter the information until the user's key information includes the preset number of pre-stored interface attribute fields.

[0074] When the user's key information includes a preset number of pre-stored interface attribute fields, code generation prompts are generated. The description of the second objective requirement corresponding to the user's key information is input into a pre-trained iterative optimization model. The user engages in multiple rounds of dialogue with the model, which understands the context and performs historical backtracking to complete the description of the second objective requirement. The model then organizes this description into a coherent statement and generates standardized code generation prompts to guide subsequent code generation.

[0075] It supports users engaging in multi-turn dialogues with a pre-trained, iteratively optimized model to complete the description of code generation prompts. The model can not only understand information from single-turn dialogues but also capture the logical relationships between dialogues across multiple turns. Based on the current dialogue state, it provides subsequent suggestions to guide users step-by-step in completing the description of code generation prompts.

[0076] By parsing and breaking down user requirements, complex business needs can be transformed into clear and executable code generation prompts. Through the collaborative work of multiple sub-functions, it can ensure that the user's input is accurately understood and transformed into effective prompt information.

[0077] S107. Obtain the target code based on the code-generated prompts and the pre-trained code generation model.

[0078] In this embodiment, the target code is obtained by inputting code generation prompts into a pre-trained code generation model. The pre-trained code generation model is a model that generates initial code based on the code generation prompts, such as the Qwen3 model.

[0079] S108. Convert the target code into code of a specified format, and when the code of the specified format meets the preset conditions, export the code of the specified format to the low-code platform to generate an integration service.

[0080] In this embodiment, the target code is converted into a specified format code that can be used for secondary development using a code converter. The generated specified format code is then directly compiled, and the system verifies whether there are any errors. If no errors are found after compilation, the code is generated and a success message is returned, at which point the preset conditions are met. If errors are found during compilation, a detailed error log is returned to locate the problem, and the code conversion is re-executed. When the converted specified format code meets the preset conditions, it is stored in the platform library of the low-code platform, generating an integration service, which is then used by the low-code platform's front-end for page display and designer use.

[0081] Integration services are generated dynamically. This means that the platform does not provide general integration service functions, but instead provides a set of integration service application programming interface (API) rules. These rules define the API specifications for the platform to call external services, such as the input parameters, format requirements, and return information requirements for calling external interfaces.

[0082] It dynamically creates the required integrated service functions using natural language, supports continuous adjustments and modifications, and can also provide adjustment suggestions.

[0083] As an optional implementation method, existing integrated services can be reused to the greatest extent possible. When producing integrated services, user requirements can be used to find out if there are any integrated services that meet the requirements, and then provide them to the user for selection.

[0084] A large-scale proxy project is built, comprising a pre-trained natural language processing model, a pre-trained semantic analysis model, a pre-trained information extraction model, a pre-trained iterative optimization model, and a pre-trained code generation model. The parameters specified above are used as data samples to pre-train the large-scale proxy project. Simultaneously, information from third-party interfaces is compiled into input data according to requirements, enabling the trained large-scale proxy project to generate code in a specified format conforming to the system architecture to call the necessary third-party interfaces.

[0085] The large model proxy project, based on the user's description, calls the large model interface, combines code to generate prompts to optimize input information and parse and process the returned results, thereby obtaining the specified format code that meets the requirements. Finally, it calls the platform's interface to embed the specified format code into the platform for user use.

[0086] The system generates corresponding code in a specified format based on user requirements, and then produces adapted integrated services for unified management. Pre-trained models are pre-trained to target the system's underlying capabilities, repeatedly learning the underlying logic. Therefore, users only need to provide requirements in natural language, without needing to understand the underlying logic. The pre-trained model will implement the functionality based on the requirements, ensuring that the generated integrated services conform to the system architecture to the greatest extent possible, guaranteeing system stability. This lowers the learning curve for users, allowing even non-technical personnel to quickly get started.

[0087] This application provides a method for generating integrated services on a low-code platform, comprising: obtaining a user requirement description, processing the user requirement description according to a pre-trained natural language processing model to obtain user interface type keywords corresponding to the user requirement description; obtaining a first target requirement description according to the user interface type keywords and a pre-stored keyword list; processing the first target requirement description according to a pre-trained semantic analysis model to obtain an association matching result between the first target requirement description and the corresponding user interface type keywords; obtaining a second target requirement description according to the association matching result; obtaining user key information corresponding to the second target requirement description according to a pre-trained information extraction model and the second target requirement description; when the user key information includes a preset number of pre-stored interface attribute fields, obtaining code generation prompts according to the user key information and a pre-trained iterative optimization model; obtaining target code according to the code generation prompts and a pre-trained code generation model; converting the target code into code of a specified format, and when the specified format code meets preset conditions, exporting the specified format code to the low-code platform to generate an integrated service. Through the above method, the following technical effects are achieved: obtaining corresponding specified format code from the user requirement description and generating an adapted integrated service for unified management. Pre-trained models can be pre-trained to target the underlying capabilities of a system and repeatedly learn the underlying logic. Therefore, users only need to make requests using natural language without needing to understand the underlying logic. The pre-trained model will complete the functional implementation according to the requirements, thereby ensuring that the generated integrated service conforms to the system architecture to the greatest extent and ensuring the stability of the system. This reduces the learning threshold for users, and non-technical personnel can also quickly get started.

[0088] Based on the above embodiments, this application provides a method for generating low-code platform integration services. In the low-code platform integration service generation method provided in this embodiment, step S102 includes:

[0089] S201. When the user interface type keyword exists in the pre-stored keyword list, the user requirement description corresponding to the user interface type keyword shall be used as the first target requirement description.

[0090] In this embodiment, if the user interface type keyword extracted from the user requirement description exists in the pre-stored keyword list, then the user requirement description corresponding to the user interface type keyword is taken as the first target requirement description, and the interface type of the user requirement description is confirmed. The interface type includes, but is not limited to, request protocol and request type. For example, if the user requirement description is: "We need a function that allows users to query their order list," and the user interface type keyword extracted from the user requirement description is "query," and if "query" exists in the pre-stored keyword list, then the user requirement description is taken as the first target requirement description, and the request protocol of the user requirement description is confirmed to be HTTP protocol, and the request type is GET. Ensuring that the user interface type keyword exists in the pre-stored keyword list and only processing predefined and verified types can guarantee the stability and security of the system, avoid errors or abnormal behavior caused by the inability to identify or process unknown keywords, and improve processing efficiency while reducing maintenance costs.

[0091] Based on the above embodiments, this application provides a method for generating low-code platform integration services. In the low-code platform integration service generation method provided in this embodiment, step S102 further includes:

[0092] S301. If the user interface type keyword does not exist in the pre-stored keyword list, re-enter the user requirement description until the user interface type keyword corresponding to the re-entered user requirement description exists in the pre-stored keyword list.

[0093] In this embodiment, for example, if the user's requirement is described as "Put these two things together for me," the user interface type keyword extracted from the user requirement description is "put them together." If this user interface type keyword does not exist in the pre-stored keyword list, a message will be displayed indicating that no matching interface was found, and the user will be prompted to re-enter the information. If the re-entered user requirement description is "Merge the data of these two orders," the corresponding user interface type keyword "Merge" will exist in the pre-stored keyword list.

[0094] S302. The re-entered user requirement description shall be used as the first target requirement description.

[0095] In this embodiment, the user requirement description "merge the data of these two orders" is re-entered as the first target requirement description. This enforces the standardization of user requirement descriptions, ensuring that only identifiable and supported types are processed, thereby avoiding the execution of invalid or erroneous instructions, guaranteeing the accuracy and reliability of system processing, reducing anomalies, and maintaining system security.

[0096] Based on the above embodiments, this application provides a method for generating low-code platform integration services. In the low-code platform integration service generation method provided in this embodiment, step S107 includes:

[0097] S401. Obtain the initial code based on the code generation prompts and the pre-trained code generation model.

[0098] In this embodiment, the code generation prompts contain rich and detailed descriptions that can describe the development task. The execution task capability of the proxy project sends the code generation prompts to the pre-trained code generation model, which then generates initial code that meets the user's needs and requirements based on the code generation prompts.

[0099] S402. Compile and run the initial code to obtain the target code.

[0100] In this embodiment, the generated initial code is then compiled and run online by a code debugging service. The code debugging service primarily checks the initial code for syntax errors and nesting timeouts. This service uses a configured proxy plugin, for example, by calling other compilation software or directly using a script written in Python. If the online compilation and execution are error-free, the generated initial code is considered error-free and is automatically saved; otherwise, the error information and the erroneous code are returned to the pre-trained code generation model to regenerate a completely new set of initial code.

[0101] Users can flexibly select and configure different compiler tool plugins according to business needs or personal preferences. Furthermore, by automatically triggering the compilation process and providing error reports, problems can be identified promptly, allowing for quick problem location and correction.

[0102] The initial code is generated based on code generation prompts and a pre-trained code generation model. The correctness and executability of the initial code are verified through compilation and execution. Finally, high-quality target code that can be directly deployed or further optimized is obtained, which can significantly improve development efficiency and reduce human coding errors.

[0103] Based on the above embodiments, this application provides a method for generating low-code platform integration services. In the low-code platform integration service generation method provided in this embodiment, step S104 includes:

[0104] S501. When the similarity indicated by the association matching result exceeds the preset threshold, the first target requirement description corresponding to the association matching result is used as the second target requirement description.

[0105] In this embodiment, the similarity between the first target requirement description and the corresponding user interface type keyword can be calculated using a pre-trained text embedding model, such as BERT. If the similarity exceeds a preset threshold, it is considered that the first target requirement description and the corresponding user interface type keyword are highly correlated; otherwise, it is considered that the first target requirement description and the corresponding user interface type keyword are not correlated. If there is no correlation, that is, the first target requirement description and the corresponding user interface type keyword are unrelated, then "Your requirement was not identified; please describe the interface requirement again," and the first target requirement description is retrieved again. For example, the preset threshold can be 90%. No specific limit is placed on the preset threshold here.

[0106] By using an automated matching mechanism to quickly and accurately identify the first target requirement description with high similarity and using it as the second target requirement description, the efficiency and accuracy of requirement processing can be significantly improved, the subjective error and cost of manual screening can be reduced, and subsequent processes can be carried out based on high-quality and highly relevant target requirements.

[0107] Based on the above embodiments, this application provides a method for generating low-code platform integration services. In the low-code platform integration service generation method provided in this embodiment, when the user's key information includes a preset number of pre-stored interface attribute fields, step S106 further includes:

[0108] S601. Determine whether the description of the second target requirement corresponding to the user's key information is sufficient.

[0109] In this embodiment, when the user's key information includes a preset number of pre-stored interface attribute fields, it is also determined whether the second target requirement description corresponding to the user's key information has sufficient information. If the information is insufficient, a prompt message "To ensure generation quality, please supplement more requirement information" is returned, and this prompt message is sent back to the user to guide the user to supplement more interface requirements.

[0110] As an optional implementation, firstly, the total number of characters, words, or tokens after word segmentation in the second target requirement description is calculated; secondly, based on the user key information extracted from the second target requirement description, the proportion of the user key information to the total number of characters, words, or tokens after word segmentation in the second target requirement description is calculated, i.e., the frequency of its appearance in the second target requirement description. If the frequency of its appearance in the second target requirement description is lower than a first preset threshold, it is determined that the amount of information is too small. For example, the first preset threshold is 70%, and no specific limit is imposed on the first preset threshold here.

[0111] As an optional implementation, the key user information extracted from the second target requirement description is populated into a predefined attribute field template. If the vacancy rate of a field in the attribute field template is higher than a second set threshold, it is determined that the information content is insufficient. Similarly, even if a field is populated, if the value itself is ambiguous, i.e., the quality of the extracted value is not high, it is also determined that the information content is insufficient. For example, the second set threshold is 60%, and no specific limitation is made to the second set threshold here.

[0112] Ensuring sufficient information in the description of the second objective requirements can prevent subsequent development deviations or rework caused by missing information, and ensure that the requirements can be directly used in subsequent processes, thereby improving development efficiency, reducing communication costs, and ensuring that the final delivery quality meets expectations.

[0113] Figure 2 This is a schematic diagram of the structure of the low-code platform integration service generation device provided in an embodiment of this application. Figure 2 As shown, in this embodiment, the low-code platform integration service generation device includes:

[0114] The first processing module 701 is used to obtain the user requirement description and process the user requirement description according to the pre-trained natural language processing model to obtain the user interface type keywords corresponding to the user requirement description.

[0115] The first target requirement description acquisition module 702 is used to obtain the first target requirement description based on the user interface type keywords and the pre-stored keyword list.

[0116] The second processing module 703 is used to process the first target requirement description according to the pre-trained semantic analysis model to obtain the association matching result between the first target requirement description and the corresponding user interface type keywords.

[0117] The second target requirement description acquisition module 704 is used to obtain the second target requirement description based on the association matching results.

[0118] The third processing module 705 is used to extract the model and the second target requirement description based on the pre-trained information to obtain the user key information corresponding to the second target requirement description.

[0119] The code generation prompt word acquisition module 706 is used to obtain code generation prompt words based on the user's key information and the pre-trained iterative optimization model when the user's key information includes a preset number of pre-stored interface attribute fields.

[0120] The target code acquisition module 707 is used to obtain the target code based on the code generation prompts and the pre-trained code generation model.

[0121] The fourth processing module 708 is used to convert the target code into code of a specified format, and when the code of the specified format meets the preset conditions, to export the code of the specified format to the low-code platform to generate integration services.

[0122] The low-code platform integration service generation device provided in this embodiment can execute... Figure 1 The technical solution of the low-code platform integration service generation method embodiment shown herein, its implementation principle and technical effects are similar to Figure 1 The implementation method for generating low-code platform integration services shown is similar and will not be described in detail here.

[0123] Meanwhile, the low-code platform integration service generation device provided by the present invention further refines the low-code platform integration service generation device based on the low-code platform integration service generation device provided in the previous embodiment.

[0124] Optionally, in this embodiment, the first target requirement description acquisition module 702 is used for:

[0125] When the user interface type keyword exists in the pre-stored keyword list, the user requirement description corresponding to the user interface type keyword is taken as the first target requirement description.

[0126] Optionally, in this embodiment, the first target requirement description acquisition module 702 is further configured to:

[0127] If the user interface type keyword does not exist in the pre-stored keyword list, re-enter the user requirement description until the user interface type keyword corresponding to the re-entered user requirement description exists in the pre-stored keyword list; the re-entered user requirement description is then used as the first target requirement description.

[0128] Optionally, in this embodiment, the target code acquisition module 707 is used for:

[0129] The initial code is obtained based on code generation prompts and a pre-trained code generation model; the initial code is then compiled and run to obtain the target code.

[0130] Optionally, in this embodiment, the second target requirement description acquisition module 704 is used for:

[0131] When the similarity indicated by the association matching result exceeds a preset threshold, the first target requirement description corresponding to the association matching result is used as the second target requirement description.

[0132] Optionally, in this embodiment, the code generation prompt word acquisition module 706 is used for:

[0133] Determine whether the description of the second objective requirement corresponding to the user's key information is sufficiently informative.

[0134] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The electronic device is intended for use by various electronic devices capable of executing low-code platform integration service generation methods, such as microcomputers, microcontrollers, and other suitable computers. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0135] like Figure 3 As shown, the electronic device includes at least one processor 801 and a memory 802. The electronic device also includes a communication component 803. The processor 801, memory 802, and communication component 803 are connected via a bus 804.

[0136] In the specific implementation process, at least one processor 801 executes computer execution instructions stored in memory 802, causing at least one processor 801 to execute the low-code platform integration service generation method executed on the electronic device side as described above.

[0137] The specific implementation process of processor 801 can be found in the above-described embodiment of the low-code platform integration service generation method. Its implementation principle and technical effect are similar, and will not be repeated here.

[0138] In the above embodiments, it should be understood that the processor 801 can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), etc. The general-purpose processor 801 can be a microprocessor or any conventional processor. The steps of the method disclosed in this invention can be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules within the processor.

[0139] The memory 802 may include high-speed RAM memory, and may also include non-volatile memory (NVM), such as at least one disk storage.

[0140] Bus 804 can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Bus 804 can be divided into an address bus, a data bus, and a control bus. For ease of illustration, the bus 804 in the accompanying drawings of this application is not limited to only one bus or one type of bus.

[0141] The above description addresses the functions implemented by electronic devices and main control devices, and introduces the solutions provided in the embodiments of this application. It is understood that, in order to achieve the above functions, the electronic device or main control device includes hardware structures and / or software modules corresponding to the execution of each function. By combining the units and algorithm steps of the various examples described in the embodiments disclosed in this application, the embodiments of this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the technical solutions of the embodiments of this application.

[0142] This application also provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the low-code platform integration service generation method described above.

[0143] The aforementioned computer-readable storage media can be implemented by any type of volatile, non-volatile storage device or combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0144] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. The readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in application-specific integrated circuits (ASICs). Alternatively, the processor and the readable storage medium can exist as discrete components in an electronic device or a host device.

[0145] The memory 802 is the non-transitory computer-readable storage medium provided by this invention. The non-transitory computer-readable storage medium of this invention stores computer information, enabling the computer to execute the low-code platform integration service generation method provided by this invention.

[0146] The memory 802, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs, and modules. The processor 801 executes various functional applications and data processing by running the non-transitory software programs, instructions, and modules stored in the memory 802, thereby implementing the low-code platform integration service generation method in the above method embodiments.

[0147] In addition, this embodiment also provides a computer program product, including a computer program, which, when executed by a processor, is used to implement the low-code platform integration service generation method of the above embodiment.

[0148] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, data stored, data displayed, etc.) involved in one or more embodiments of this specification are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, use and processing of related data must comply with relevant laws, regulations and standards, and corresponding operation entry points are provided for users to choose to authorize or refuse.

[0149] It should be noted that, for the sake of simplicity, the foregoing method embodiments are all described as a series of actions. However, those skilled in the art should understand that this application is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to this application. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are all optional embodiments, and the actions and modules involved are not necessarily essential to this application.

[0150] It should be further noted that although the steps in the flowchart are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0151] It should be understood that the above-described device embodiments are merely illustrative, and the device of this application can also be implemented in other ways. For example, the division of units / modules in the above embodiments is only a logical functional division, and there may be other division methods in actual implementation. For example, multiple units, modules, or components may be combined, or integrated into another system, or some features may be ignored or not executed.

[0152] Furthermore, unless otherwise specified, the functional units / modules in the various embodiments of this application can be integrated into one unit / module, or each unit / module can exist physically separately, or two or more units / modules can be integrated together. The integrated units / modules described above can be implemented in hardware or as software program modules.

[0153] In the above embodiments, the descriptions of each embodiment have their own emphasis. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments. The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as the combination of these technical features does not contradict each other, it should be considered within the scope of this specification.

[0154] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this application are indicated by the following claims.

[0155] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this application is limited only by the appended claims.

Claims

1. A method for generating low-code platform integration services, characterized in that, include: Obtain a user requirement description and process the user requirement description according to a pre-trained natural language processing model to obtain user interface type keywords corresponding to the user requirement description; Based on the user interface type keywords and the pre-stored keyword list, the first target requirement description is obtained; The first target requirement description is processed according to the pre-trained semantic analysis model to obtain the association matching result between the first target requirement description and the corresponding user interface type keywords; The second target requirement description is obtained based on the association matching results; Based on the pre-trained information extraction model and the second target requirement description, the user key information corresponding to the second target requirement description is obtained; When the user key information includes a preset number of pre-stored interface attribute fields, code generation prompts are obtained based on the user key information and the pre-trained iterative optimization model. The target code is obtained by generating prompt words based on the code and using a pre-trained code generation model; The target code is converted into a specified format code, and when the specified format code meets preset conditions, the specified format code is exported to a low-code platform to generate an integration service.

2. The method according to claim 1, characterized in that, The step of obtaining the first target requirement description based on the user interface type keywords and the pre-stored keyword list includes: When the user interface type keyword exists in the pre-stored keyword list, the user requirement description corresponding to the user interface type keyword is used as the first target requirement description.

3. The method according to claim 2, characterized in that, The step of obtaining the first target requirement description based on the user interface type keywords and the pre-stored keyword list also includes: If the user interface type keyword does not exist in the pre-stored keyword list, the user requirement description is re-entered until the user interface type keyword corresponding to the re-entered user requirement description exists in the pre-stored keyword list. The re-entered user requirement description is taken as the first target requirement description.

4. The method according to claim 1, characterized in that, The step of generating the target code based on the code-generated prompt words and the pre-trained code generation model includes: The initial code is obtained based on the code-generating prompt words and the pre-trained code generation model; The initial code is compiled and run to obtain the target code.

5. The method according to claim 1, characterized in that, The step of obtaining the second target requirement description based on the association matching result includes: When the similarity indicated by the association matching result exceeds a preset threshold, the first target requirement description corresponding to the association matching result is used as the second target requirement description.

6. The method according to claim 1, characterized in that, When the user's key information includes a preset number of pre-stored interface attribute fields, it also includes: Determine whether the description of the second target requirement corresponding to the user's key information is sufficient.

7. A low-code platform integration service generation device, characterized in that, include: The first processing module is used to obtain the user's requirement description and process the user's requirement description according to the pre-trained natural language processing model to obtain the user interface type keywords corresponding to the user's requirement description. The first target requirement description acquisition module is used to obtain the first target requirement description based on the user interface type keywords and the pre-stored keyword list; The second processing module is used to process the first target requirement description according to the pre-trained semantic analysis model to obtain the association matching result between the first target requirement description and the corresponding user interface type keywords. The second target requirement description acquisition module is used to obtain the second target requirement description based on the association matching result; The third processing module is used to extract the model and the second target requirement description based on the pre-trained information to obtain the user key information corresponding to the second target requirement description. The code generation prompt word acquisition module is used to obtain code generation prompt words based on the user key information and the pre-trained iterative optimization model when the user key information includes a preset number of pre-stored interface attribute fields. The target code acquisition module is used to obtain the target code based on the code-generating prompt words and the pre-trained code generation model; The fourth processing module is used to convert the target code into code of a specified format, and when the code of the specified format meets preset conditions, to export the code of the specified format to a low-code platform to generate an integration service.

8. An electronic device, characterized in that, include: A processor, and a memory communicatively connected to the processor; The memory stores computer-executed instructions; The processor executes computer execution instructions stored in the memory to implement the low-code platform integration service generation method as described in any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by a processor, are used to implement the low-code platform integration service generation method as described in any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes a computer program, which, when executed by a processor, is used to implement the low-code platform integration service generation method as described in any one of claims 1 to 6.

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