Open platform docking system and method based on large language model

Abstract

The invention provides an open platform docking system and method based on a large language model, and the system comprises a demand management module which is used for receiving and recording a demand document, sending an analysis request to a bottom-layer large model, feeding back and recording an interface document, sending a task request to a task management module, and receiving a query request from the bottom-layer large model; the task management module is used for receiving and recording a task request, feeding back and recording task distribution, receiving and recording the latest state of the task and receiving a query request from a bottom-layer large model; the interaction management module is used for receiving and recording interaction content, sending an analysis request to a bottom-layer large model, and feeding back and recording reply from the large model; and the bottom layer large model module is used for feeding back query requests from other modules and sending out the query requests. Through the open platform based on the large language model, the demand side is helped to perform efficient docking, the docking progress is followed up in real time, and it is ensured that the demand side can smoothly complete the docking task with the development platform.

Description

Technical Field

[0001] The present invention relates to the fields of artificial intelligence and information technology, and more specifically, to an open platform docking system and method based on a large language model. Background Art

[0002] In the process of connecting with open platforms, the demand side usually needs to coordinate multiple links with the development platform, including interface document generation, interface development, debugging, integration, etc. However, traditional connection methods often require a lot of manual operations and communication, which is inefficient and prone to errors and delays. It is difficult for the demand side to grasp the connection progress in real time, and when encountering technical problems, the feedback and solution cycle is long, which increases the overall development cycle and cost.

[0003] Therefore, there is an urgent need for an intelligent docking system that can help the demand side to follow up on the docking progress in real time, answer technical questions immediately, and provide matching feedback on development resources, thereby accelerating the docking process and improving development efficiency. Summary of the invention

[0004] In view of the technical problems existing in the prior art, the present invention provides an open platform docking system and method based on a large language model. The system is mainly oriented to the demanders of the development platform, provides intelligent support, helps the demanders to successfully complete the docking task, follows up the docking progress in real time, evaluates whether the development resources meet the demand, and ensures the efficiency and accuracy of the docking process.

[0005] According to a first aspect of the present invention, there is provided an open platform docking system based on a large language model, comprising:

[0006] The requirement management module is responsible for receiving and recording requirement documents, issuing analysis requests to the underlying large model, feeding back and recording interface documents, issuing task requests to the task management module, and receiving query requests from the underlying large model;

[0007] The task management module is used to receive and record task requests, feedback and record task assignments, receive and record the latest task status, and receive query requests from the underlying large model;

[0008] The interaction management module is used to receive and record the interaction content, send analysis requests to the underlying large model, and feedback and record the responses from the underlying large model;

[0009] The bottom-level large model module is used to feedback query requests from other modules and send query requests to other modules.

[0010] Based on the above technical solution, the present invention can also make the following improvements.

[0011] Optionally, the system also includes: system roles; the system roles are divided into demand personnel and developers; the demand personnel have the functional permissions of demand submission, interface document viewing, task progress viewing, and intelligent interaction; the developers have the functional permissions of task assignment viewing, interface document viewing, task progress feedback, and intelligent interaction.

[0012] Optionally, the demand management module helps the demander to simplify the docking process by automatically generating interface documents, task assignments and docking suggestions.

[0013] Optionally, the task management module can provide instant answers based on natural language questions raised by the demander, and provide the demander with relevant documents or technical support.

[0014] Optionally, the interaction management module helps the demander understand the development progress and evaluate the matching of development resources through a real-time monitoring and feedback mechanism to ensure that the demander can complete the docking task on time.

[0015] Optionally, the underlying large model module uses a large language model to help the system understand the feedback and questions of the demand side. When the demand side feedbacks that the development progress of the interface is lagging behind, the system analyzes and converts it into tasks or queries through the large language model, and timely feedbacks the development progress information;

[0016] The underlying large model module tracks the docking progress in real time through intelligent analysis and evaluates whether the development resources can meet the expectations of the demander. By analyzing historical data and progress, the system can identify potential risks in a timely manner and provide feedback to the demander.

[0017] According to a second aspect of the present invention, there is provided an open platform docking method based on a large language model, comprising the following steps:

[0018] The system receives the docking requirements submitted by the demander and stores the content in the requirements table. The system uses a large language model to perform natural language processing on the requirements description, extract key information, and generate a task list.

[0019] Based on the parsed requirements, the system automatically generates standardized interface documents and updates them to the interface document table. It also generates a task list based on the interface document content and assigns tasks to development resources.

[0020] The system monitors the status of tasks in real time, records the progress of tasks at each stage, and evaluates whether the development progress is in line with expectations: if the task progress is normal, the system continues to monitor; if the progress is lagging or resources are insufficient, it generates risk warnings and adjustment suggestions, stores them in the feedback record table, and notifies the demander at the same time;

[0021] During the docking process, we provide technical problem analysis, docking progress feedback and task status query services to the demand side.

[0022] Optionally, the key information includes: access type, interface requirements and technical details; the system receives the connection requirements submitted by the demander, including:

[0023] Requirement description: A detailed description of the requirements and functions of the demander;

[0024] Submission time: The timestamp of requirement submission, used for time sequence management of task records;

[0025] Demander ID: An ID that uniquely identifies the demander, used to associate tasks and feedback.

[0026] Optionally, the provision of technical problem analysis, docking progress feedback and task status query services to the demander during the docking process includes:

[0027] When the demander encounters technical problems such as interface call failure and unclear documents during the docking process, they can submit the problem through the system;

[0028] The demander can query the docking progress in real time through the intelligent dialogue function, store the feedback content in the progress feedback table, and push it to the demander;

[0029] Support dynamic generation or updating of interface documents during user interaction and adjust task allocation based on user feedback.

[0030] Technical effects and advantages of the present invention:

[0031] The present invention provides an open platform docking system and method based on a large language model, which helps the demander to efficiently dock with the open platform based on the large language model, and follows up the docking progress in real time to ensure that the demander can successfully complete the docking task with the development platform. At the same time, the system can provide the demander with real-time development progress feedback, evaluate whether the development resources can meet its needs, avoid delays in the docking process, and ensure that the resources of the development platform match the docking progress of the demander.

[0032] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 A schematic diagram of the structure of an open platform docking system based on a large language model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not 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.

[0035] It should be noted that the open platform docking system based on the large language model described in the present invention is applied to the optimization and automatic management of the open platform docking process. The system is mainly for the demand side of the development platform, providing intelligent support to help the demand side successfully complete the docking task, follow up the docking progress in real time, evaluate whether the development resources meet the demand, and ensure the efficiency and accuracy of the docking process.

[0036] It is understandable that, based on the defects in the background technology, the embodiment of the present invention proposes an open platform docking system based on a large language model, including:

[0037] The requirement management module is responsible for receiving and recording requirement documents, sending analysis requests to the underlying large model, feeding back and recording interface documents, sending task requests to the task management module, and receiving query requests from the underlying large model;

[0038] The task management module is responsible for receiving and recording task requests, feedback and recording task assignments, receiving and recording the latest task status, and receiving query requests from the underlying large model;

[0039] The interaction management module is responsible for receiving and recording interaction content, sending analysis requests to the underlying large model, and feeding back and recording responses from the underlying large model;

[0040] The underlying large model module is responsible for responding to query requests from other modules and issuing query requests to other modules.

[0041] It should be noted that the intelligent docking system provided in the embodiment of the present invention, as a docking assistant system, mainly serves the demand side of the development platform. The core responsibility of the system is to help the demand side to dock and follow up the docking progress through the open platform to ensure that the demand side can successfully complete the docking task.

[0042] In addition, specific Figure 1 As shown, this system also includes: system roles, which are divided into: demand personnel and developers. Demand personnel: have four functional permissions: demand submission, interface document viewing, task progress viewing, and intelligent interaction; developers: have four functional permissions: task allocation viewing, interface document viewing, task progress feedback, and intelligent interaction. Among them,

[0043] Demand submission function: You can submit demand documents and download demand templates here;

[0044] Interface document function: you can view and modify the interface document here;

[0045] Task progress function: You can view task assignment, development progress, and modify task status here;

[0046] Intelligent interaction function: You can ask questions about requirement documents, interface documents, and task progress here.

[0047] The functions of this system specifically include: Providing a quick docking method: The demand management module helps the demander simplify the docking process and save time and manpower by automatically generating interface documents, task allocation and docking suggestions.

[0048] Timely answers to docking questions: The task management module can provide instant answers based on natural language questions raised by the demander, and provide the demander with relevant documents or technical support.

[0049] Real-time docking progress feedback: The interactive management module helps the demand side understand the development progress and evaluate the matching of development resources through real-time monitoring and feedback mechanism, so as to ensure that the demand side can complete the docking task on time.

[0050] The underlying large model module uses a large language model and intelligent analysis technology to help the demand side track the docking progress in real time and provide feedback.

[0051] Big language model: Natural language processing technology is used to help the system understand feedback and questions from demanders. For example, if a demander reports that the interface development progress is lagging behind, the system will analyze it through a big language model and convert it into tasks or queries, providing timely feedback on the development progress.

[0052] Intelligent analysis: The system uses intelligent analysis to track the progress of docking in real time and evaluate whether the development resources can meet the expectations of the demander. By analyzing historical data and progress, the system can identify potential risks in a timely manner and provide feedback to the demander.

[0053] The following are the working steps of each link in the process of the system serving the development platform demanders. Combined with the data model and data flow, it shows how to provide development progress feedback, answer docking questions and track progress.

[0054] According to a second aspect of the present invention, there is provided an open platform docking method based on a large language model, comprising the following steps:

[0055] S1. The system receives the docking requirements submitted by the demander and stores the content in the requirements table. The system uses a large language model to perform natural language processing on the requirements description, extract key information, and generate a task list;

[0056] S2. The system automatically generates a standardized interface document based on the parsed requirements and updates it to the interface document table. It also generates a task list based on the interface document content and assigns the tasks to development resources.

[0057] S3. The system monitors the status of tasks in real time, records the progress of tasks at each stage, and evaluates whether the development progress is in line with expectations: if the task progress is normal, the system continues to monitor; if the progress is lagging or resources are insufficient, risk warnings and adjustment suggestions are generated, stored in the feedback record table, and the demander is notified at the same time;

[0058] S4. Provide technical problem analysis, docking progress feedback and task status query services to the demand side during the docking process.

[0059] In step S1, the system receives the docking requirements submitted by the demander, including:

[0060] Demand description (demand_description): A detailed description of the docking objectives and functions of the demand side;

[0061] Submission time (demand_time): The timestamp of demand submission, used for timing management of task records;

[0062] Demand party ID (demand_id): an ID that uniquely identifies the demand party, used to associate tasks and feedback.

[0063] The content is stored in the demand table (Demand), and the fields include demand description, submission time, demander identification, etc., to ensure that the demand records are clear, complete and traceable.

[0064] The system uses a large language model to perform natural language processing on the demand description (demand_description), extract key information (such as access type, interface requirements and technical details), and generate a task list. The analysis results are used in the following links:

[0065] Update the task assignment table (Task_Assignment); provide feedback on resource matching and task list to the demander.

[0066] In step S2, the system automatically generates a standardized interface document based on the parsed requirements and updates it to the interface document table (API_Document). The fields include:

[0067] Interface type (document_type): describes the functional category of the document, such as data transmission interface or user management interface;

[0068] Document content (content): the body of the generated interface document, including interface field descriptions, call examples, etc.

[0069] Demand ID (demand_id): used to associate records in the demand table.

[0070] Generate a task list based on the content of the interface document and assign tasks to development resources. Task information is stored in the task assignment table (Task_Assignment). The fields include:

[0071] Task ID (task_id): uniquely identifies each task;

[0072] Task name (task_name): a detailed description of the task;

[0073] Task status (task_status): identifies the current progress of the task, such as "not started", "in progress" or "completed";

[0074] Related documents (document_id): link to related interface document records.

[0075] In step S3, the task status is monitored in real time to record the progress of the task at each stage. The monitoring information is stored in the monitoring record table (Monitoring_Record), and the fields include:

[0076] Current stage (current_stage): the current development stage of the task;

[0077] Progress status (status): task completion progress and risk assessment results;

[0078] Task ID (task_id): Link to related records in the task allocation table.

[0079] If the progress is delayed or resources are insufficient, the system generates risk warnings and adjustment suggestions, stores them in the feedback record table (Feedback_Record), and notifies the demander at the same time. Feedback content includes:

[0080] Description of the risk;

[0081] Recommendations for resource adjustments;

[0082] New task completion time estimate.

[0083] In step S4, the system provides comprehensive support to the demand side through technical problem solving and intelligent interaction functions during the docking process, including technical problem analysis, docking progress feedback, and task status query services. The following is the specific implementation process:

[0084] 4.1 Technical Questions Answered

[0085] During the docking process, the demander may encounter technical problems such as interface call failure, unclear documents, etc. and submit the problems through the system.

[0086] The question content (question description (question_content), submission time (question_time)) is stored in the conversation record table (Chat_Record).

[0087] The system uses a large language model to parse questions and retrieve answers from the QA knowledge base or general knowledge base:

[0088] If the match is successful, generate the answer;

[0089] If no answer is matched, a reference suggestion is generated.

[0090] The final answer is processed in a fixed format (answer_prompt), stored in the feedback record table (Feedback_Record) and responded to the demander in real time.

[0091] 4.2 Connection Status Query and Feedback

[0092] The demander can query the docking progress (task status, interface document update status, etc.) in real time through the intelligent dialogue function.

[0093] The query content is stored in the conversation record table (Chat_Record), and the feedback content is stored in the progress feedback table (Progress_Feedback) and pushed to the demander.

[0094] 4.3 Document generation and task support. The system supports dynamic generation or updating of interface documents during user interaction, and the updated content is stored in the interface document table (API_Document).

[0095] Adjust task assignments based on user feedback, and store updates in Task_Assignment.

[0096] The open platform docking method based on a large language model described in the embodiment of the present invention can realize intelligent management of docking tasks based on a large language model and intelligent analysis technology. The following are the working steps and specific implementations of each link in the process of serving the demand side of the development platform:

[0097] [Requirement personnel] enter the [Interconnection Assistant System], enter the [Requirement Submission Function] interface, download the requirement template, and submit the requirement document;

[0098] [Requirement Management Module] receives and records requirement documents and sends analysis requests to the underlying large model;

[0099] The [bottom-level large model module] receives the analysis request and feeds back the interface document to the [demand management module];

[0100] The [Requirement Management Module] provides feedback and records the interface document, and sends a task request to the [Task Management Module];

[0101]

Task Management Module

[0102] [Developers] enter the [Interconnection Assistant System], enter the [Task Progress Function] interface, view task assignments and receive documents, and the [Task Management Module] receives and records the latest task status;

[0103] [Developer] Enter the [Interface Documentation Function] interface and view the interface documentation for related work;

[0104] [Developer] enters the [Task Progress Function] interface to update the task status, and the [Task Management Module] receives and records the latest task status;

[0105] [Requirement personnel] enter the [Task Progress Function] interface to check the task status;

[0106] [Requirement Personnel] enter the [Interface Document Function] interface and view the interface documents for related work;

[0107] [Requirement personnel] enter [Intelligent Interaction Function] to ask questions related to the interface documentation and task progress;

[0108] [Interaction management module] receives and records interaction content, and sends analysis requests to the underlying large model;

[0109] The [bottom-level large model module] sends query requests to the [demand management module] and [task management module], and feedback is received from the [interaction management module].

[0110] To sum up, the open platform docking system and method based on a large language model provided by the embodiments of the present invention can reduce the manual burden in the docking tasks. The system automates the parsing of requirement documents, generates standardized interface documents, and performs task allocation and resource matching analysis, thereby greatly reducing the repetitive manual operations of the demander in the docking process and improving work efficiency.

[0111] It can also improve the efficiency of answering technical questions. With the help of large language models and intelligent analysis technology, the system can quickly parse and answer technical questions from the demand side, including cross-domain or complex questions, thereby significantly reducing the burden on technical personnel during the docking process. At the same time, it can optimize the docking process and progress tracking. The system monitors the status of development tasks in real time, evaluates resource matching, and provides timely adjustment suggestions to help demand sides quickly identify and avoid potential risks and ensure that docking tasks are completed on time.

[0112] Finally, an embodiment of the present invention further provides an electronic device, which includes: a processor, a communications interface, a memory, and a communication bus, wherein the processor, the communications interface, and the memory communicate with each other through the communication bus. The processor can call the logic instructions in the memory to execute the implementation steps of the above-mentioned open platform docking method based on a large language model.

[0113] In addition, the logic instructions in the above-mentioned memory can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0114] Through the description of the above implementation methods, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solution is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0115] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0116] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An open platform docking system based on a large language model, characterized in that: include: The requirement management module is used to receive and record requirement documents, send analysis requests to the underlying large model, feedback and record interface documents, send task requests to the task management module, and receive query requests from the underlying large model; The task management module is used to receive and record task requests, feedback and record task assignments, receive and record the latest task status, and receive query requests from the underlying large model; The interaction management module is used to receive and record the interaction content, send analysis requests to the underlying large model, and feedback and record the responses from the underlying large model; The bottom-level large model module is used to feedback query requests from other modules and send query requests to other modules.

2. The open platform docking system based on a large language model according to claim 1, characterized in that: The system also includes: system roles; the system roles are divided into demand personnel and developers; the demand personnel have the functional permissions of demand submission, interface document viewing, task progress viewing, and intelligent interaction; the developers have the functional permissions of task assignment viewing, interface document viewing, task progress feedback, and intelligent interaction.

3. The open platform docking system based on a large language model according to claim 1, characterized in that: The demand management module helps the demand side to simplify the docking process by automatically generating interface documents, task allocation and docking suggestions.

4. The open platform docking system based on a large language model according to claim 1, characterized in that: The task management module can provide instant answers based on natural language questions raised by the demander, and provide the demander with relevant documents or technical support.

5. The open platform docking system based on a large language model according to claim 1, characterized in that: The interactive management module helps the demand side understand the development progress and evaluate the matching of development resources through real-time monitoring and feedback mechanism, so as to ensure that the demand side can complete the docking task on time.

6. The open platform docking system based on a large language model according to claim 1, characterized in that: The underlying large model module uses a large language model to help the system understand the feedback and questions from the demand side. When the demand side reports that the interface development progress is lagging behind, the system analyzes and converts it into tasks or queries through the large language model, and promptly feeds back development progress information.

7. The open platform docking system based on a large language model according to claim 6, characterized in that: The underlying large model module tracks the docking progress in real time through intelligent analysis and evaluates whether the development resources can meet the expectations of the demander. By analyzing historical data and progress, the system can identify potential risks in a timely manner and provide feedback to the demander.

8. An open platform docking method based on a large language model, used in an open platform docking system based on a large language model as claimed in any one of claims 1 to 7, characterized in that: The method comprises the following steps: The system receives the docking requirements submitted by the demander and stores the content in the requirements table. The system uses a large language model to perform natural language processing on the requirements description, extract key information, and generate a task list. Based on the parsed requirements, the system automatically generates standardized interface documents and updates them to the interface document table. It also generates a task list based on the interface document content and assigns tasks to development resources. The system monitors the status of tasks in real time, records the progress of tasks at each stage, and evaluates whether the development progress is in line with expectations: if the task progress is normal, the system continues to monitor; if the progress is lagging or resources are insufficient, it generates risk warnings and adjustment suggestions, stores them in the feedback record table, and notifies the demander at the same time; During the docking process, we provide technical problem analysis, docking progress feedback and task status query services to the demand side.

9. The open platform docking method based on a large language model according to claim 8, characterized in that: The key information includes: access type, interface requirements and technical details; the system receives the connection requirements submitted by the demander, including: Requirement description: A detailed description of the requirements and functions of the demander; Submission time: The timestamp of requirement submission, used for time sequence management of task records; Demander ID: An ID that uniquely identifies the demander, used to associate tasks and feedback.

10. The open platform docking method based on a large language model according to claim 8, characterized in that: The services provided to the demander during the docking process for technical problem analysis, docking progress feedback and task status query include: When the demander encounters technical problems such as interface call failure and unclear documents during the docking process, they can submit the problem through the system; The demander can query the docking progress in real time through the intelligent dialogue function, store the feedback content in the progress feedback table, and push it to the demander; Support dynamic generation or updating of interface documents during user interaction and adjust task allocation based on user feedback.

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