Method for realizing agent demand module development through computing power of intelligent computing center

Abstract

The invention provides a method for realizing agent demand module development through computing power of an intelligent computing center, which is executed by a demand module development agent and comprises the following steps of: S1, receiving task information for constructing a to-be-developed agent demand module through an interactive interface; s2, determining a development demand of a demand module according to the task information in combination with a large language model method and a user; s3, determining a design scheme of a demand module according to the development demand in combination with a large language model method; s4, generating a code of a demand module according to the development demand and the design scheme in combination with a large language model method; s5, deploying a running environment of the demand module in combination with a large language model method, and debugging a code of the demand module; when the debugging result does not meet the development requirement, the design scheme and the code of the requirement module are checked in combination with the large language model method, the design scheme needs to be modified, the step S3 is executed, and the code needs to be modified, the step S4 is executed until the requirement is met; and S6, feeding back result information through the interactive interface.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent computing centers, intelligent computing centers, and computing power infrastructure, and particularly relates to a method for developing an intelligent agent demand module through the computing power of an intelligent computing center. Background Technique

[0002] With the rapid development of artificial intelligence technology, "intelligent computing centers" and "intelligent computing centers" have emerged as the times require.

[0003] An "intelligent computing center" refers to a facility that uses large-scale heterogeneous computing power resources, including general computing power and intelligent computing power, and mainly provides the required computing power, data, and algorithms for artificial intelligence applications (such as scenarios like artificial intelligence deep learning model development, model training, and model inference). An intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from underlying computing power to top-level application enabling.

[0004] The "intelligent computing center" includes, but is not limited to, the "intelligent computing center".

[0005] An "intelligent computing center", that is, an artificial intelligence computing center, is a type of computing power infrastructure that is based on artificial intelligence theory, adopts an artificial intelligence computing architecture, and provides computing power services, data services, and algorithm services required for artificial intelligence applications.

[0006] "Computing power" is the core of "intelligent computing centers" and "intelligent computing centers". It is the ability of computer devices or computing / data centers to process information, the ability of computer hardware and software to cooperate to jointly execute a certain computing requirement, the computing ability to achieve the output of the target result by processing information data, and a new type of productive force that integrates information computing power, network carrying capacity, and data storage capacity, and mainly provides services to society through computing power infrastructure.

[0007] An "intelligent agent" is an agent that can perceive the environment and take actions to achieve specific goals. It can be software, hardware, or a system, and has autonomy, adaptability, and interaction capabilities. An intelligent agent perceives changes in the environment (such as through sensors or data input), makes judgments and decisions based on the knowledge and algorithms learned by itself, and then executes actions to affect the environment or achieve a predetermined goal. Intelligent agents are widely used in the field of artificial intelligence, commonly found in automation systems, robots, virtual assistants, and game characters, and their core lies in the ability to learn independently and evolve continuously to better complete tasks and adapt to complex environments.

[0008] Currently, the vast majority of agents lack a functional module (which can be called a requirements module) for interacting with users to improve the task requirements put forward by users. Agents usually directly execute tasks according to the task requirements given by users. When the task requirements given by users are imperfect, it will cause agents to be forgetful in the process of completing tasks. Moreover, if a requirements module needs to be developed for an agent, the requirements confirmation requirements for different agents are highly customized, and the development of the requirements module depends on manual design and debugging, with high development costs and low development efficiency. Summary of the Invention

[0009] The present invention provides a method for developing an agent requirements module through the computing power of an intelligent computing center, which is used to solve the problem that existing agents lack a requirements module for interacting with users to improve the task requirements put forward by users, and the cost of manually developing an agent requirements module is high and the efficiency is low.

[0010] To solve the above technical problems, the present invention is implemented as follows:

[0011] In a first aspect, the present invention provides a method for developing an agent requirements module through the computing power of an intelligent computing center, which is executed by a requirements module development agent and includes:

[0012] Step S1: Receive task information provided by the user for constructing the requirements module of the to-be-developed agent through an interaction interface;

[0013] Step S2: According to the task information, determine the development requirements of the requirements module in combination with the large language model method and the user;

[0014] Step S3: According to the development requirements, determine the design scheme of the requirements module in combination with the large language model method;

[0015] Step S4: According to the development requirements and design scheme, generate the code of the requirements module in combination with the large language model method;

[0016] Step S5: Deploy the operating environment of the requirements module and debug the code of the requirements module in combination with the large language model method; when the debugging result indicates that the requirements module does not meet the development requirements, check the design scheme and code of the requirements module in combination with the large language model method. When the design scheme needs to be modified, return to step S3. When the code needs to be modified, return to step S4 until the debugging result indicates that the requirements module meets the development requirements;

[0017] Step S6: Feedback result information to the user through the interaction interface.

[0018] Optionally, the development requirements of the requirements module include at least one of the following: interactive function requirements, location requirements in the workflow of the to-be-developed intelligent agent, requirements confirmation point requirements, test cases, technical requirements, performance requirements, security and privacy, application field, output requirements, acceptance criteria.

[0019] Optionally, step S2 includes:

[0020] Step S21: According to the task information, communicate with the user through the interaction interface for at least one round in combination with the large language model method;

[0021] Step S22: Determine the development requirements of the requirements module according to the task information and the communication content with the user in combination with the large language model method.

[0022] Optionally, step S3 includes:

[0023] Step S31: According to the development requirements of the requirements module, obtain learning results by using at least one of the following learning methods in combination with the large language model method: search relevant materials through a search engine, view and learn open source code, learn the existing code and documents of the to-be-developed intelligent agent;

[0024] Step S32: Determine the design scheme of the requirements module according to the learning results.

[0025] Optionally, the design scheme includes at least one of the following: design of the interactive function of the requirements module, design of the location of the requirements module in the workflow of the to-be-developed intelligent agent, design of requirements confirmation points, design of adding requirements confirmation points to the prompt words of the subsequent work steps after requirements confirmation, detailed design documents, interface design documents, database design documents.

[0026] Optionally, step S3 includes:

[0027] Step S33: If the design of the requirements module does not meet the development requirements, adjust the design scheme of the requirements module by using at least one of the following adjustment methods in combination with the large language model method:

[0028] The first adjustment method is to optimize the design scheme of the requirements module;

[0029] The second adjustment method is to adopt other design schemes.

[0030] Optionally, step S5 includes at least one of the following sub-steps:

[0031] Step S52: When the number of debugging attempts reaches the first preset threshold, or the number of modifications to the design solution reaches the second preset threshold, or the number of modifications to the code of the requirements module reaches the third preset threshold, and the code of the requirements module still fails to meet the development requirements, terminate the development task of the requirements module, and feedback the termination reason and related situation to the user through the interaction interface;

[0032] Step S53: During the debugging process, record the running logs, intermediate data, and debugging results in combination with the large language model method, and archive and save them.

[0033] In a second aspect, the present invention provides a device for realizing the development of an intelligent agent requirements module through the computing power of an intelligent computing center, including:

[0034] A receiving module, configured to receive, through an interaction interface, task information provided by a user for constructing a requirements module of a to-be-developed intelligent agent;

[0035] A requirements confirmation module, configured to determine the development requirements of the requirements module in combination with the large language model method and the user according to the task information;

[0036] A design module, configured to determine the design solution of the requirements module in combination with the large language model method according to the development requirements;

[0037] An encoding module, configured to generate the code of the requirements module in combination with the large language model method according to the development requirements and the design solution;

[0038] A debugging module, configured to deploy the running environment of the requirements module and debug the code of the requirements module in combination with the large language model method; when the debugging result indicates that the requirements module does not meet the development requirements, check the design solution and code of the requirements module in combination with the large language model method, trigger the design module to continue working when the design solution needs to be modified, and trigger the encoding module to continue working when the code needs to be modified, until the debugging result indicates that the requirements module meets the development requirements;

[0039] A feedback module, configured to feedback result information to the user through the interaction interface.

[0040] In a third aspect, the present invention provides an electronic device, including: a processor, a memory, and a program stored on the memory and executable on the processor, and when the program is executed by the processor, the steps of the method for realizing the development of an intelligent agent requirements module through the computing power of an intelligent computing center as described in the first aspect above are implemented.

[0041] Fourthly, the present invention provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for realizing the development of the intelligent agent requirement module through the computing power of the intelligent computing center as described in the first aspect above are realized.

[0042] Fifthly, the present invention provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, the steps of the method for realizing the development of the intelligent agent requirement module through the computing power of the intelligent computing center as described in the first aspect above are realized.

[0043] In the present invention, by running the requirement module development intelligent agent in the intelligent computing center, it can communicate with the user and confirm clear and detailed development requirements, and automatically complete the development of the requirement module of the intelligent agent to be developed. It can automatically develop the requirement module for different types of intelligent agents, which has universality, and replaces the process of manually designing and developing the code of the requirement module, thus effectively saving the development cost. Moreover, the intelligent computing center can provide sufficient computing power resources, greatly improving the development efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0045] Figure 1 is a schematic flowchart of the method for realizing the development of the intelligent agent requirement module through the computing power of the intelligent computing center of the present invention;

[0046] Figure 2 is a schematic structural diagram of the device for realizing the development of the intelligent agent requirement module through the computing power of the intelligent computing center of the present invention;

[0047] Figure 3 is a schematic structural diagram of the electronic device of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0048] The technical solutions in the present invention will be clearly and completely described below with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art without making creative efforts based on the embodiments in the present invention belong to the scope of protection of the present invention.

[0049] First, the technical terms related to the present invention will be briefly described below.

[0050] The "computing power" described in the present invention refers to: the ability of computer devices or computing / data centers to process information, the ability of computer hardware and software to cooperate to jointly execute a certain computing requirement, the computing ability to achieve the output of the target result through processing information data, and a new type of productive force integrating information computing power, network carrying capacity, and data storage capacity, which mainly provides services to society through computing power infrastructure.

[0051] The "computational power" (Computational Power, CP) described in the present invention refers to: the ability of the data center server to process data and achieve the result output, which is a comprehensive index to measure the computing ability of the data center and includes general computing ability, supercomputing ability, and intelligent computing ability. The commonly used measurement unit is the number of floating-point operations per second (FLOPS, 1EFLOPS = 10^18 FLOPS), and the larger the value, the stronger the comprehensive computing ability. It is estimated that 1EFLOPS is approximately the computing power output of 5 Tianhe-2A or 500,000 mainstream server CPUs or 2 million mainstream laptops. The calculation formula is: CP = CP_general + CP_intelligent + CP_super

[0052] The "network power" (Network Power, NP) described in the present invention refers to: the performance of the data transmission ability of the computing power facility, which is a comprehensive ability including network architecture, network bandwidth, transmission delay, intelligent management and scheduling, etc., and involves network transmission inside and between data centers, and is a comprehensive index to measure the network transmission scheduling ability.

[0053] The "storage power" (Storage Power, SP) described in the present invention refers to: the comprehensive ability of the data center in four aspects: data storage capacity, performance, security and reliability, and green and low-carbon, which is a comprehensive index to measure the data storage ability of the data center and includes external storage devices such as storage arrays and server internal storage devices. The commonly used measurement unit for storage capacity is exabyte (EB, 1EB = 2^60 bytes), the commonly used measurement unit for performance is the number of read / write operations per second per unit capacity (IOPS / TB, Input / Output Operations Per Second / TB), and the disaster recovery ratio is an important manifestation of security and reliability.

[0054] The "computing power infrastructure" described in the present invention refers to: a new type of information infrastructure integrating information computing power, network carrying capacity, and data storage capacity, which can realize the centralized computing, storage, transmission, and application of information.

[0055] The "new information infrastructure" described in the present invention refers to: mainly including network infrastructures such as 5G networks, fiber broadband networks, backbone networks, international communication networks, and satellite Internet, computing power infrastructures such as data centers, general computing power centers, intelligent computing centers, and supercomputing centers, and new technology facilities such as artificial intelligence, blockchain, and quantum computing.

[0056] The "computing power" described in the present invention includes: general computing power, intelligent computing power, and super computing power.

[0057] The "general computing power" described in the present invention refers to: the computing power provided by servers based on CPU (Central Processing Unit) chips, which is used to support basic general computing such as cloud computing and edge computing.

[0058] The "intelligent computing power" described in the present invention refers to: for various artificial intelligence innovation applications, a computing platform is deployed on a large scale based on dedicated chips such as GPU (Graphics Processing Unit), FPGA (Field Programmable Gate Array), and ASIC (Application Specific Integrated Circuit), such as natural language processing, machine vision, and so on.

[0059] The "super computing power" described in the present invention refers to: mainly the computing power provided by high-performance computing clusters such as supercomputers. It utilizes the centralized computing resources of multiple computer systems working in parallel and processes extremely complex or data-intensive problems through a dedicated operating system. It is mainly used for computing in cutting-edge scientific fields, such as planetary simulation, drug molecule design, gene analysis, etc.

[0060] The "intelligent computing center" described in the present invention refers to: a facility that provides the required computing power, data, and algorithms mainly for artificial intelligence applications (such as scenarios like artificial intelligence deep learning model development, model training, and model inference) by using large-scale heterogeneous computing power resources, including general computing power (CPU) and intelligent computing power (GPU, FPGA, ASIC, etc.). The intelligent computing center covers facilities, hardware, and software, and can provide full-stack capabilities from underlying computing power to top-level application enabling.

[0061] The "intelligent computing center" described in the present invention includes but is not limited to the "intelligent computing center".

[0062] The "intelligent computing center" described in the present invention, namely the artificial intelligence computing center, is a type of computing power infrastructure that provides computing power services, data services, and algorithm services required for artificial intelligence applications based on artificial intelligence theory and adopting an artificial intelligence computing architecture.

[0063] The "computing power center" described in the present invention refers to: a facility mainly composed of infrastructure such as wind, fire, water, and electricity and IT software and hardware devices, with computing power, transportation power, and storage power, including general data centers, intelligent computing centers, supercomputing centers, etc.

[0064] The "supercomputing center" described in the present invention refers to: that is, a supercomputing data center, which is a data center based on supercomputers or large-scale computing clusters, capable of providing functions such as large-scale computing, storage, and network services, and is widely used in application scenarios such as aerospace, national defense, oil exploration, climate modeling, and genome sequencing.

[0065] The "computing power resources" described in the present invention refers to: technologies and facilities required for the development of the digital society with information computing, transmission, storage, and application capabilities, including but not limited to computing resources such as CPUs and GPUs, network resources such as switches and routers, storage resources such as storage arrays and distributed storage, security resources such as firewalls and intrusion detection systems, and support and guarantee resources such as wind, fire, water, and electricity.

[0066] The "large language model" described in the present invention refers to a large language model (LLM), which is a language model with a relatively large number of parameters, aiming to understand and generate human language, trained through a large amount of text data, and can perform a wide range of tasks including text summarization, translation, sentiment analysis, etc.

[0067] The "requirement module" described in the present invention can also be referred to as a requirement confirmation module or a requirement confirmation function. The intelligent agent needs to interact with the user through the requirement module to determine the user's task requirements, and then execute the task based on the determined task requirements.

[0068] The "method of combining large language models" described in the present invention refers to organizing input information and designing precise prompt words to call large language models to generate various content forms, including thinking, replies, decisions, tool selection, code, and documents, etc. This method supports the intelligent agent to perform tasks such as reasoning and thinking, design, dialogue with users, generation of solution designs, and code generation, and combines with code frameworks to implement the execution of instructions such as tool method calls and code program executions, achieving the completion of specific tasks such as consulting documents, searching for information, file processing, and script running.

[0069] To solve the problem of high cost and low efficiency in manually developing the requirement module of the intelligent agent, please refer to Figure 1 , the present invention provides a method for developing the requirement module of the intelligent agent through the computing power of the intelligent computing center. This method can also be referred to as a method for developing the intelligent agent by realizing the requirement module development of the intelligent agent to be developed through the computing power of the intelligent computing center, which is executed by the requirement module development intelligent agent. This method includes:

[0070] Step S1: Receive, through an interaction interface, task information provided by a user for constructing a requirements module of an agent to be developed.

[0071] The "requirements module development agent" described in the present invention has the ability to develop requirements modules for other agents by combining large language model methods.

[0072] In the present invention, the requirements module development agent may be an agent specifically for developing the requirements module of the agent to be developed, or a development agent for developing the entire agent to be developed.

[0073] The requirements module development agent may be one agent or multiple agents. The multiple agents form a multi-agent cooperation system. Each agent has a specific ability, and the multiple agents can cooperate to complete the task of developing the requirements module for the agent to be developed.

[0074] In the present invention, the agent to be developed may be an agent for completing a specified task, such as a mathematical research agent, a data analysis agent, etc.

[0075] In the present invention, the task information may include at least one of the following information: the development requirements of the requirements module, the framework code of the agent to be developed or the storage path of the code, the storage path of the relevant documents of the framework of the agent to be developed, etc.

[0076] Step S2: The requirements module development agent determines the development requirements of the requirements module with the user according to the task information by combining large language model methods.

[0077] Optionally, the development requirements may be to develop a new agent and develop a requirements module for the new agent, or to develop a requirements module for an existing agent or upgrade the requirements module of an existing agent.

[0078] Optionally, the development requirements of the requirements module include at least one of the following: interactive function requirements, location requirements in the workflow of the agent to be developed, requirements confirmation point requirements, test cases, technical requirements, performance requirements, security and privacy, application fields, output requirements, acceptance criteria.

[0079] The above-mentioned development requirements are respectively explained below.

[0080] The interactive function requirements refer to whether the agent to be developed supports the multi-round dialogue function when receiving a user question for specific requirement confirmation of the question.

[0081] The location requirement in the workflow of the to-be-developed agent refers to the position where the requirement confirmation is located in the entire workflow of the to-be-developed agent. For example, it is located between "receiving data analysis instructions" and "generating data analysis code".

[0082] The requirement confirmation point requirement refers to which requirement confirmation points the requirement module of the to-be-developed agent needs to confirm with the user. For example, the requirement confirmation points may include: core goals, data confirmation, analysis dimensions, chart types, delivery forms, etc.

[0083] Test cases refer to examples used to test whether the requirement module meets the development requirements.

[0084] Technical requirements refer to the specific requirements for the technical implementation of the requirement module, such as algorithm support, computing power requirements, compatible development environments, etc. For example, whether the requirement module needs to support multi-modal data processing (such as text, images, audio, etc.), whether it needs to be integrated with existing systems, etc. The clarification of technical requirements helps to ensure the feasibility and applicability of the requirement module.

[0085] Performance requirements refer to the specific requirements put forward for the performance performance of the requirement module, including inference speed, accuracy, resource consumption, scalability, etc. Security and privacy refer to the data security and privacy protection capabilities of the requirement module during use. For example, whether the requirement module can prevent the leakage of sensitive data, whether it supports data encryption, and whether it complies with relevant laws and regulations (such as GDPR, CCPA, etc.). Security and privacy protection are important requirements that cannot be ignored in the actual application of the framework, especially in scenarios involving user data or sensitive information.

[0086] The application field refers to the specific scenarios and task types applicable to the requirement module, such as logical reasoning, problem-solving, knowledge generation, decision support, etc.

[0087] Output requirements refer to the requirements for the content or format of the output of the requirement module, which are convenient for understanding and analysis.

[0088] Acceptance criteria are the basis for evaluating whether the requirement module meets the development requirements, and may include at least one of the following: whether the function implementation meets the expectations (such as whether it supports multi-round dialogue interaction, whether it has confirmed all required requirement confirmation points, etc.), whether the performance indicators (such as speed, accuracy, resource consumption) meet the standards, whether the technical implementation meets the compatibility and environmental requirements, whether the security and privacy protection are compliant, whether the output is clear and intuitive, whether the user experience is friendly, whether the application effect meets the scenario requirements, and whether necessary documents and technical support are provided.

[0089] In the present invention, the development requirements of the requirements module determined by combining the large language model method with the user may be: the requirements module development agent constructs prompt words and invokes the large language model to generate replies or questions, etc., completes the task of the requirements module development agent to have a conversation with the user, and obtains the development requirements according to the conversation content.

[0090] Step S3: The requirements module development agent determines the design scheme of the requirements module according to the development requirements, in combination with the large language model method;

[0091] The design scheme in the present invention may include at least one of the following: the design of the interaction function of the requirements module, the design of the position of the requirements module in the workflow of the to-be-developed agent, the design of the requirement confirmation points, the design of adding requirement confirmation points to the prompt words of the work steps after requirement confirmation, the detailed design document, the interface design document, and the database design document.

[0092] The detailed design document refers to a document that describes in detail the function implementation of each module in the requirements module, including the logical process of the module, algorithm design, data processing method, exception handling mechanism, and other contents.

[0093] The interface design document refers to a document that describes the interfaces between each module in the requirements module or the interfaces for interacting with external systems, including the function definition of the interface, input and output parameters, call method, data format, communication protocol, and error code description.

[0094] The database design document refers to a document that designs the data storage structure involved in the requirements module, including the table structure of the database, field definition, data type, primary key and foreign key relationships, index design, stored procedures, and triggers.

[0095] In the present invention, the requirements module development agent determines the design scheme of the requirements module according to the development requirements, in combination with the large language model method. Specifically, it may be: the requirements module development agent constructs prompt words and invokes the large language model according to the development requirements to complete the task of generating the design scheme of the requirements module.

[0096] Step S4: Generate the code of the requirements module according to the development requirements and the design scheme, in combination with the large language model method;

[0097] In the present invention, the requirements module development agent generates the code of the requirements module according to the development requirements and the design scheme, in combination with the large language model method. Specifically, it may be: the requirements module development agent constructs prompt words and invokes the large language model according to the development requirements and the design scheme to complete the task of generating the code of the requirements module.

[0098] Step S5: Deploy the operating environment of the requirement module and debug the code of the requirement module in combination with the large language model method; when the debugging result indicates that the requirement module does not meet the development requirements, check the design scheme and code of the requirement module in combination with the large language model method. When the design scheme needs to be modified, return to step S3; when the code needs to be modified, return to step S4 until the debugging result indicates that the requirement module meets the development requirements;

[0099] Specifically, the deployment of the operating environment of the requirement module and the debugging of the code of the requirement module in combination with the large language model method can be as follows: The requirement module development agent constructs prompt words and invokes the large language model according to the development requirements, design scheme and the code to complete the tasks of operating environment deployment and code debugging.

[0100] Specifically, the requirement module development agent checks the design scheme and code of the requirement module in combination with the large language model method. It can be that the requirement module development agent constructs prompt words and invokes the large language model according to the design scheme, code and debugging result of the requirement module to complete the task of checking the design scheme and code. That is, the requirement module development agent inputs the prompt words into the large language model and obtains the inspection result output by the large language model. The inspection result can indicate errors or defects in the design scheme or code, thus supporting the optimization and improvement of the requirement module.

[0101] It should be noted that when it is necessary to modify the design scheme and return to step S3, in the new step S3, the requirement module development agent needs to re-determine the design scheme of the requirement module in combination with the large language model method according to the development requirements, the design scheme and the inspection result.

[0102] It should be noted that when it is necessary to modify the code and return to step S4, in the new step S4, the requirement module development agent regenerates the code of the requirement module in combination with the large language model method according to the development requirements, the code and the inspection result.

[0103] In the embodiments of the present invention, the test cases, acceptance criteria, performance requirements, etc. in the above development requirements can be used to determine whether the requirement module meets the development requirements.

[0104] Step S6: Feedback result information to the user through the interaction interface.

[0105] Among them, the feedback result information may include at least one of the following: information about the code of the requirement module, debugging information of the requirement module, analysis of the applicable scenarios added by the requirement module, optimization suggestions for the requirement module, and the conclusion of successful or failed development of the requirement module;

[0106] Among them, the conclusion of development failure can be a conclusion after multiple debugging sessions and the number of debugging sessions exceeds the failure threshold number.

[0107] The information of the code of the requirement module can be the code itself or the storage location of the code, etc.

[0108] The debugging information may include at least one of the following: running logs, intermediate data, debugging results, etc.

[0109] In the present invention, by running a requirement module in an intelligent computing center to develop an intelligent agent, it can communicate with the user and confirm clear and detailed development requirements, automatically complete the development of the requirement module of the intelligent agent to be developed, can automatically develop the requirement module for different types of intelligent agents, has universality, and replaces the process of manually designing and developing the code of the requirement module, thereby effectively saving development costs, and the intelligent computing center can provide sufficient computing power resources to greatly improve development efficiency.

[0110] In some embodiments, optionally, the step S2 includes:

[0111] Step S21: Based on the task information, communicate with the user through the interaction interface for at least one round based on the large language model;

[0112] After the requirement module development intelligent agent receives the task information sent by the user through the interaction interface, it can construct a prompt word according to the task information. The prompt word is used to prompt the large language model to analyze whether the task information contains development requirements, whether the development requirements are complete, and whether it is necessary to confirm more detailed development requirements with the user; the requirement module development intelligent agent inputs the prompt word into the large language model and obtains the analysis result output by the large language model. The analysis result includes the content of the development requirements to be determined; the requirement module development intelligent agent displays the content of the development requirements to be determined to the user based on the interaction interface.

[0113] When the user provides new development requirements through the interaction interface, the requirement module development intelligent agent can construct a prompt word according to the new development requirements. The prompt word is used to prompt the large language model to analyze whether the development requirements are complete and whether it is necessary to confirm more detailed development requirements with the user. The requirement module development intelligent agent inputs the prompt word into the large language model and obtains the analysis result output by the large language model. The analysis result includes the content of the development requirements to be determined; the requirement module development intelligent agent displays the content of the development requirements to be determined to the user based on the interaction interface.

[0114] The process of determining development requirements by combining the large language model method above can be carried out in multiple rounds to obtain more detailed development requirements.

[0115] Step S22: According to the task information and the communication content with the user, combine the large language model method to determine the development requirements of the requirement module.

[0116] In the present invention, the development agent communicates with the user for at least one round to confirm the detailed development requirements of the requirement module, ensuring a comprehensive and accurate understanding of the functions, performance, compatibility, etc. of the requirement module expected by the user, and laying a solid foundation for subsequent design and development work.

[0117] In the present invention, optionally, the step S3 includes:

[0118] Step S31: According to the development requirements of the requirement module, combine the large language model method to obtain learning results by using at least one of the following learning methods: searching for relevant materials through a search engine, viewing and learning open source code, learning the existing code and documents of the agent to be developed, and consulting data;

[0119] Step S32: Determine the design scheme of the requirement module according to the learning results.

[0120] During the process of sorting out and understanding the development requirements by the requirement module development agent, unclear or unknown concepts and knowledge points are sorted out, and targeted searches are carried out for these contents. Relevant information is obtained through a search engine, and the search results are screened and classified to extract key contents, ensuring a comprehensive understanding of the requirements and providing necessary knowledge support for the development of the requirement module of the agent.

[0121] The learning method is indicated by the large language model to the requirement module development agent according to the development requirements, and the requirement module development agent obtains the learning results.

[0122] Specifically, the requirement module development agent can generate prompt words according to the development requirements and the learning results. The prompt words are used to prompt the large language model to determine the design scheme of the requirement module according to the development requirements and the learning results. The requirement module development agent inputs the prompt words into the large language model and obtains the design scheme output by the large language model.

[0123] Through the above learning methods, the requirement module can be deeply understood, and the learning results will be used as important references to provide diverse ideas and methods for the design of the requirement module, and then a requirement module that better meets the requirements and is more complete can be designed.

[0124] If the intelligent agent for developing the requirements module finds during the debugging process that the design of the current requirements module cannot meet the development requirements, and after analysis, it belongs to a design defect, the existing design can be overturned or optimized.

[0125] In some embodiments, optionally, step S3 includes:

[0126] Step S33: If the design of the requirements module does not meet the development requirements, combine the large language model method and adopt at least one of the following adjustment methods to adjust the design scheme of the requirements module:

[0127] The first adjustment method is to optimize the design scheme of the requirements module to better meet the development requirements;

[0128] The second adjustment method is to adopt other design schemes, that is, explore and try new designs of the requirements module to cope with special requirements or complex scenarios.

[0129] In some embodiments, optionally, step S5 includes at least one of the following sub-steps:

[0130] Step S52: When the number of debugging times reaches the first preset threshold, or the number of modifications to the design scheme reaches the second preset threshold, or the number of modifications to the code of the requirements module reaches the third preset threshold, and the code of the requirements module still cannot meet the development requirements, terminate the development task of the requirements module, and feedback the termination reason and related situations to the user through the interaction interface to help the user re-evaluate the requirements and adjust the development strategy to more efficiently promote the subsequent work.

[0131] This step can avoid excessive consumption of computing power resources in the intelligent computing center.

[0132] Step S53: During the debugging process, combine the large language model method to record the running log, intermediate data, and debugging results, and archive and save them.

[0133] During the development and debugging of the requirements module, key information such as running logs, intermediate data, and test results can be recorded and properly archived and saved. For error information and related codes, key records and sorting will be carried out to facilitate subsequent in-depth analysis of the root cause of the problem, summarize improvement experience, and provide important reference basis for future optimization and development of the requirements module.

[0134] As mentioned above, the intelligent agent for developing the requirements module may include multiple intelligent agents. In some embodiments, optionally, the multiple intelligent agents include at least one of the following intelligent agents: requirements confirmation intelligent agent, framework design intelligent agent, search intelligent agent, learning intelligent agent, coding intelligent agent, debugging intelligent agent, archiving intelligent agent, and deployment intelligent agent, etc.

[0135] The requirement confirmation agent is responsible for interacting with the user, accurately understanding and clarifying the development requirements, and transforming them into an executable design document;

[0136] The framework design agent is responsible for designing the overall architecture and functional modules of the requirement module of the agent to be developed;

[0137] The search agent is responsible for finding relevant technical materials and best practices to provide references for the design;

[0138] The learning agent summarizes the materials found and extracts key technologies and implementation methods;

[0139] The coding agent transforms the design scheme into code implementation and develops the core functional modules;

[0140] The debugging agent is responsible for testing and fixing problems and recording error information during the debugging process;

[0141] The archiving agent archives and manages the documents, code, and logs during the development process to ensure the traceability of the materials;

[0142] The deployment agent is responsible for deploying the developed agent to the target operating environment and monitoring the running status.

[0143] These agents work together to ensure the completion of the design, implementation, and optimization of the requirement module of the requirement module development agent to meet the requirements for developing the requirement module of the agent to be developed as required by the user.

[0144] The following is an example of "determining the development requirements of the requirement module in combination with the large language model method according to the task information" in step S2 above in combination with a specific application scenario.

[0145] In this embodiment, the user needs the requirement module development agent to develop a requirement module for the data analysis agent (i.e., the agent to be developed).

[0146] On the interaction interface, the conversation between the user and the requirement module development agent can be as follows:

[0147] User: I want to add the ability to confirm requirements to the data analysis agent so that some confirmations can be made in advance during data analysis before conducting requirement analysis.

[0148] Requirement module development agent: Wait a moment while I check the project documents and code.

[0149]

In this step, the requirement module development agent views the existing documents and code of the data analysis agent through the large language model method

[0150] Requirement Module Development Agent: The current data analysis agent is an AI agent based on large language models. Its workflow is: receive data analysis instructions → generate data analysis code → execute the code → output results. It is necessary to add a requirement confirmation module between "receiving data analysis instructions" and "generating data analysis code". May I ask at which confirmation points does the data analysis agent need to confirm with the user? For example:

[0151] 1. Analysis objective (What is the main objective of the user's analysis? What is the usage scenario after analysis?)

[0152] 2. Data scope (Does the user need to confirm the dataset fields / time range again?)

[0153] 3. Analysis dimension (Does the variable combination or comparison method need to be refined?)

[0154] 4. Chart preference (Does the default chart type need to be adjusted?)

[0155] 5. Delivery format (Do you need to generate PPT / Excel / plain text reports simultaneously?)

[0156] User: 1. The core objective needs to be clarified before starting the analysis. For example, when the user says "analyze the reasons for the decline in sales", the agent should actively propose which aspects of factors to focus on and list the reference candidate factors, such as external factors (competitors / market comparison) or internal factors (price / inventory price changes).

[0157] 2. Check the data in advance to identify which fields are unclear and need to be confirmed in advance.

[0158] 3. Let me choose the chart type to avoid the situation where a heat map is automatically generated but the user needs a line chart.

[0159] Requirement Module Development Agent: Received. The requirement confirmation function will be implemented according to the following ideas:

[0160] Interaction function extension: When receiving user instructions, support multi-round dialogue functions and support the confirmation of requirements for specific questions.

[0161] Add a requirement confirmation module:

[0162] - Add a requirement confirmation module between "receiving data analysis instructions" and "generating data analysis code".

[0163] - The requirement confirmation module traverses the requirement confirmation points according to the user's questions, communicates with the user, and confirms.

[0164] Requirement confirmation points:

[0165] - Core objective confirmation, asking the user about the core concerns of data analysis;

[0166] - Conduct data checks and confirmations in advance, explore the data and confirm it with the user.

[0167] - Confirm in advance which type of chart analysis to use for confirmation, think about suitable chart types, and provide candidate options for the user to choose from.

[0168] Function optimization for generating data analysis code:

[0169] When the data analysis agent combines with the large language model method to generate data code, add the requirement confirmation points confirmed with the user to the prompt.

[0170] Is the implementation correct according to the above ideas? Additionally, can you provide test cases?

[0171] User: The test case can be "Analyze the user churn rate in Q3".

[0172] Requirement module development agent: Okay, are there any other requirements?

[0173] User: None.

[0174] Requirement module development agent: Received. Subsequently, the requirement confirmation function will be added and developed for the data analysis agent according to the above requirements.

[0175] The above example is an example of the requirement module development agent communicating with the user in multiple rounds to determine the development requirements of the requirement module. It can be seen from the above example that:

[0176] "I want to add the requirement confirmation ability to the data analysis agent so that some confirmations can be made in advance during data analysis and then requirement analysis can be carried out" in the above example is the "task information" mentioned in the above embodiment.

[0177] "The requirement module development agent views the existing documents and code of the data analysis agent through the large language model method" in the above example is the learning result obtained by learning the existing code and documents of the agent to be developed in the above embodiment.

[0178] "The current data analysis agent is an AI agent based on the large language model, and its workflow is: receive data analysis instructions → generate data analysis code → execute the code → output the result" and "a requirement confirmation module needs to be added between'receive data analysis instructions' and 'generate data analysis code'" in the above example are the learning results.

[0179] In the above example, the implementation ideas (expansion of interaction functions, addition of a requirement confirmation module, requirement confirmation points, and optimization of the function of generating data analysis code) fed back by the demand module development agent are the confirmation of the "design solution" ideas of the demand module. When designing the demand module in step S3, the implementation ideas will be used as one of the prompt inputs for the large language model method.

[0180] The "analysis of the user churn rate in Q3" in the above example is the "test case" in the above embodiment.

[0181] Please refer to Figure 2 , The present invention also provides a device 10 for developing an intelligent agent demand module through the computing power of an intelligent computing center, including:

[0182] A receiving module 11, configured to receive task information provided by a user for constructing a to-be-developed intelligent agent demand module through an interaction interface;

[0183] A requirement confirmation module 12, configured to determine the development requirements of the demand module in combination with the large language model method and the user according to the task information;

[0184] A design module 13, configured to determine the design solution of the demand module in combination with the large language model method according to the development requirements;

[0185] A coding module 14, configured to generate the code of the demand module in combination with the large language model method according to the development requirements and the design solution;

[0186] A debugging module 15, configured to deploy the operating environment of the demand module and debug the code of the demand module in combination with the large language model method; when the debugging result indicates that the demand module does not meet the development requirements, check the design solution and code of the demand module in combination with the large language model method. When it is necessary to modify the design solution, trigger the design module to continue working. When it is necessary to modify the code, trigger the coding module to continue working until the debugging result indicates that the demand module meets the development requirements;

[0187] A feedback module 16, configured to feedback result information to the user through the interaction interface.

[0188] In the present invention, through the demand module development agent running in the intelligent computing center, it is possible to communicate with the user and confirm clear and detailed development requirements, automatically complete the development of the demand module of the to-be-developed intelligent agent, and can automatically develop demand modules for different types of intelligent agents, which has universality and replaces the process of manually designing and developing the code of the demand module, thereby effectively saving development costs, and the intelligent computing center can provide sufficient computing power resources, greatly improving the development efficiency.

[0189] Optionally, the development requirements of the requirements module include at least one of the following: interaction function requirements, location requirements in the workflow of the to-be-developed intelligent agent, requirements confirmation point requirements, test cases, technical requirements, performance requirements, security and privacy, application field, output requirements, acceptance criteria.

[0190] Optionally, the requirements confirmation module 12 is used to communicate with the user through the interaction interface for at least one round according to the task information, in combination with the large language model method; and determine the development requirements of the requirements module according to the task information and the communication content with the user, in combination with the large language model method.

[0191] Optionally, the design module 13 is used to obtain learning results by using at least one of the following learning methods according to the development requirements of the requirements module, in combination with the large language model method: searching the requirements module knowledge base built in the requirements module development intelligent body, searching relevant materials through a search engine, viewing and learning open source code, learning the existing code and documents of the to-be-developed intelligent agent, and consulting data;

[0192] The design module 13 is further used to determine the design scheme of the requirements module according to the learning results.

[0193] Optionally, the design scheme includes at least one of the following: the design of the interaction function of the requirements module, the design of the location of the requirements module in the workflow of the to-be-developed intelligent agent, the design of requirements confirmation points, the design of adding requirements confirmation points to the prompt words of the work steps after requirements confirmation, detailed design documents, interface design documents, database design documents.

[0194] Optionally, the design module 13 is used to, if the design of the requirements module does not meet the development requirements, adjust the design scheme of the requirements module by using at least one of the following adjustment methods in combination with the large language model method:

[0195] The first adjustment method is to optimize the design scheme of the requirements module;

[0196] The second adjustment method is to adopt other design schemes.

[0197] Optionally, the debugging module 15 includes at least one of the following sub-modules:

[0198] The first debugging sub-module is used to terminate the development task of the requirements module and feedback the termination reason and related situations to the user through the interaction interface when the number of debugging times reaches the first preset threshold, or the number of modifications to the design scheme reaches the second preset threshold, or the number of modifications to the code of the requirements module reaches the third preset threshold, and the code of the requirements module still cannot meet the development requirements;

[0199] The second debugging sub-module is used to record the running logs, intermediate data, and debugging results in combination with the large language model method during the debugging process, and archive and save them.

[0200] Please refer to Figure 3 , the present invention also provides an electronic device 20, including a processor 21, a memory 22, and a computer program stored on the memory 22 and executable on the processor 21. When the computer program is executed by the processor 21, it implements each process of the above method embodiment for developing an agent demand module through the computing power of an intelligent computing center, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0201] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements each process of the above method embodiment for developing an agent demand module through the computing power of an intelligent computing center, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here. Among them, the computer-readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0202] The embodiment of the present application also provides a computer program product, including computer instructions. When the computer instructions are executed by a processor, they implement each process of the above Figure 1 shown method embodiment for developing an agent demand module through the computing power of an intelligent computing center, and can achieve the same technical effect. To avoid repetition, it will not be elaborated here.

[0203] It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "including a..." does not exclude the existence of additional identical elements in the process, method, article or device including that element.

[0204] Through the description of the above embodiments, those skilled in the art can clearly understand that the above-described example methods can be implemented by means of software plus a necessary general hardware platform. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions for causing a terminal (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0205] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make many forms without departing from the purpose of the present invention and the scope protected by the claims, and all of them belong to the protection scope of the present invention.

Claims

1. A method for realizing the development of an intelligent agent demand module through the computing power of an intelligent computing center, characterized in that, Executed by the demand module development agent, including: Step S1: Receive the task information for building the demand module of the to-be-developed agent provided by the user through the interaction interface; Step S2: Determine the development requirements of the demand module in combination with the large language model method and the user according to the task information; Step S3: Determine the design scheme of the demand module in combination with the large language model method according to the development requirements; Step S4: Generate the code of the demand module in combination with the large language model method according to the development requirements and the design scheme; Step S5: Deploy the running environment of the demand module and debug the code of the demand module in combination with the large language model method; when the debug result indicates that the demand module does not meet the development requirements, check the design scheme and code of the demand module in combination with the large language model method. When the design scheme needs to be modified, return to Step S3. When the code needs to be modified, return to Step S4 until the debug result indicates that the demand module meets the development requirements; Step S6: Feedback the result information to the user through the interaction interface.

2. The method according to claim 1, wherein The development requirements of the demand module include at least one of the following: interaction function requirements, location requirements in the workflow of the to-be-developed agent, requirement confirmation point requirements, test cases, technical requirements, performance requirements, security and privacy, application fields, output requirements, acceptance criteria.

3. The method according to claim 1, wherein The Step S2 includes: Step S21: Communicate with the user through the interaction interface at least once in combination with the large language model method according to the task information; Step S22: Determine the development requirements of the demand module in combination with the large language model method according to the task information and the communication content with the user.

4. The method according to claim 1, characterized in that The Step S3 includes: Step S31: Obtain learning results by using at least one of the following learning methods in combination with the large language model method according to the development requirements of the demand module: search relevant materials through a search engine, view and study open source code, learn the existing code and documents of the to-be-developed agent; Step S32: Determine the design scheme of the demand module according to the learning results.

5. The method according to claim 1 or 4, characterized in that, The design scheme includes at least one of the following: the design of the interaction function of the demand module, the design of the location of the demand module in the workflow of the to-be-developed agent, the design of the requirement confirmation point, the design of adding the requirement confirmation point to the prompt words of the subsequent work steps after requirement confirmation, the detailed design document, the interface design document, the database design document.

6. The method according to claim 1, wherein The Step S3 includes: Step S33: If the design of the demand module does not meet the development requirements, adjust the design scheme of the demand module by using at least one of the following adjustment methods in combination with the large language model method: The first adjustment method is to optimize the design scheme of the demand module; The second adjustment method is to adopt other design schemes.

7. The method according to claim 1, wherein The Step S5 includes at least one of the following sub-steps: Step S52: When the number of debugging attempts reaches the first preset threshold, or the number of modifications to the design solution reaches the second preset threshold, or the number of modifications to the code of the requirements module reaches the third preset threshold, and the code of the requirements module still fails to meet the development requirements, terminate the development task of the requirements module, and feedback the termination reason and related situation to the user through the interaction interface; Step S53: During the debugging process, record the running logs, intermediate data, and debugging results in combination with the large language model method, and archive and save them.

8. A device for realizing the development of an intelligent agent demand module through the computing power of an intelligent computing center, characterized in that, Comprising: A receiving module, configured to receive, through the interaction interface, task information provided by the user for constructing a requirements module of an agent to be developed; A requirements confirmation module, configured to determine the development requirements of the requirements module in combination with the user according to the task information and the large language model method; A design module, configured to determine the design solution of the requirements module in combination with the large language model method according to the development requirements; A coding module, configured to generate the code of the requirements module in combination with the large language model method according to the development requirements and the design solution; A debugging module, configured to deploy the running environment of the requirements module and debug the code of the requirements module in combination with the large language model method; When the debugging result indicates that the requirements module does not meet the development requirements, check the design solution and code of the requirements module in combination with the large language model method. When it is necessary to modify the design solution, trigger the design module to continue working. When it is necessary to modify the code, trigger the coding module to continue working until the debugging result indicates that the requirements module meets the development requirements; A feedback module, configured to feedback result information to the user through the interaction interface.

9. An electronic device, characterized in that, Comprising: A processor, a memory, and a program stored on the memory and executable on the processor. When the program is executed by the processor, it implements the steps of the method for developing an agent requirements module through the computing power of an intelligent computing center as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, A computer program is stored on the computer-readable storage medium. When the computer program is executed by the processor, it implements the steps of the method for developing an agent requirements module through the computing power of an intelligent computing center as described in any one of claims 1 to 7.

11. A computer program product, characterized in that, Comprising computer instructions. When the computer instructions are executed by the processor, it implements the steps of the method for developing an agent requirements module through the computing power of an intelligent computing center as described in any one of claims 1 to 7.

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