1778results about "Requirement analysis" patented technology

The invention discloses an intelligent programming auxiliary method and system based on a multi-modal AI language model, and belongs to the technical field of programming auxiliary tools. The method comprises the following steps: a multi-modal input processing stage; a dynamic context modeling stage; a hierarchical semantic analysis stage; in the code generation stage, codes are generated in two stages by adopting a Codex-Plus large model; the reinforcement learning driven code optimization stage is used for carrying out multi-objective optimization and reward function design on the codes generated in the code generation stage; a multi-dimensional feedback stage; an interaction and visualization stage; code semantic deep analysis, dynamic context sensing, multi-target optimization generation and real-time interactive feedback are realized by fusing code texts, natural language description, developer behavior data and a domain knowledge graph, and programming efficiency and code quality can be remarkably improved.

The disclosure presents a multi-agent AI system utilizing specialized Large Language Models (LLMs) to automate and enhance software development workflows. This system integrates a memory-augmented generative pre-trained transformer (MemGPT) agent for dynamic context management, a Critic Agent for semi-adversarial quality feedback, and other specialized agents for task delegation and execution. The MemGPT agent interacts with an embedding storage to manage extended contextual information, enabling the system to handle complex software projects with enhanced accuracy and efficiency. This innovative approach significantly reduces manual intervention, streamlines the development process, and improves software quality, offering a robust solution to the challenges of modern software development environments.

The invention relates to a code generation method and system based on heterogeneous multi-agent collaboration and electronic equipment. According to the method, a special commentator agent instantiated by a heterogeneous large language model is added into a multi-agent collaboration process; according to the method, consistency and feasibility verification is carried out on a demand document given by an analyst agent by adopting a commentator agent, a modification suggestion is proposed in an early stage, deviation and problems are found in time in the early stage of a task, the cost and complexity of later correction are reduced, the robustness and reliability of codes are improved, and the method is suitable for large-scale popularization and application. And the generated result can still maintain high-level correctness under extreme conditions or specific boundary conditions. And the method has practical significance in improving software development efficiency, reducing project risks and improving the quality of an intelligent automatic solution.

The invention discloses an ST code generation method based on graph alignment coding large model and multi-agent collaboration, and the method comprises the steps: receiving an ST code programming demand inputted by a user through a demand analysis module, refining and analyzing the demand based on multiple rounds of interactive conversations of the user and insight agents, and generating a standardized ST code programming demand; the retrieval module receives a standardized ST code programming requirement, and retrieves and obtains related knowledge through a retrieval agent in combination with an ST code knowledge base; and the double-agent collaborative self-correction code generation module receives standardized ST code programming requirements and retrieved related knowledge, a graph alignment coding large model constructed based on a graph neural network and a cross-modal alignment technology serves as a coding agent to cooperatively work with a review agent, ST code structure information is injected into the large model, and a final ST code is generated. According to the method, high-accuracy and high-reliability ST code automatic generation can be realized, and the development efficiency of a PLC program in the industrial control field is improved.

The invention discloses a code generation method and device based on LLM multi-agent cooperation and computer equipment. The method comprises the following steps: acquiring a user programming demand; analyzing the user programming demand based on LLM and decomposing a programming task to generate a task tree; selecting a to-be-allocated sub-task from the task tree, and evaluating the matching degree of an intelligent agent in an intelligent agent pool and the to-be-allocated sub-task based on LLM to generate a task allocation instruction; sending the task allocation instruction to a corresponding target agent, so that the target agent executes the to-be-allocated sub-task, and feeding back an execution result; and outputting the execution result. By implementing the method provided by the invention, the programming efficiency and quality can be improved, the resource allocation can be optimized, and the efficient development of complex requirements and large projects can be supported.

Disclosed herein are systems and methods for enabling decentralized, schema-driven interoperability across distributed computing environments through the use of a Standard Knowledge Language (SKL). An Enterprise Mesh Platform (EMP) interprets and executes SKL specifications—such as capabilities, objects, mappings, policies, and workflows—as composable, machine-interpretable contracts that define data structures, logic, and governance protocols. The system supports dynamic versioning, validation, semantic linking, and recursive execution of SKL-defined components. A mesh-wide analytics server coordinates execution, issue detection, and resolution propagation. Capabilities can be orchestrated, remediated, and adapted in real-time based on SKL-defined relationships, while preserving compliance and traceability. The disclosed architecture facilitates federated development, adaptive system integration, and fine-grained policy enforcement across complex digital ecosystems.

The invention relates to the technical field of software development management, in particular to an internet software development management system and method. The method comprises the following steps: collecting an original function demand set and carrying out function clustering analysis; extracting and classifying demand function features and generating independent demand units; carrying out dependency analysis on the independent demand units; constructing a dependency relationship set and a demand logic structure; the method comprises the following steps: generating a demand framework matrix, carrying out organization network simulation to obtain a demand connection network, carrying out code reverse evolution according to the demand connection network, carrying out basic parameter matching to obtain evolution basic parameters, carrying out a pressure simulation test on the evolution basic parameters to generate a pressure test result, and capturing resource pressure defects based on the pressure test result. And optimizing and adjusting the evolution basic parameters to generate optimized development parameters. According to the invention, a more efficient Internet software development management method is realized.

A platform for dynamically generating application experiences. The platform comprises a design management system, an agent orchestration system, an analytics system, a model management system, a user management system, and databases for storing design elements and templates. The design management system provides a portal for application owners / designers to create UX / UI designs, allowing them to select design elements from a set of categories or templates. The platform gathers existing websites / applications to identify common design patterns, stored in a design catalogue database, and suggests historical interfaces for design exploration. It enables the generation of templated applications that integrate with legacy systems. The agent orchestration system parses user specifications, selects generative AI systems, and generates UX / UI content based on the specifications. The analytics system collects and analyzes data to provide insights for improving UX / UI design and optimizing website performance. The model management system trains and maintains generative AI models used for content generation.

The invention provides a multi-agent-based low-code component development method and system and electronic equipment, and the method comprises the steps: obtaining a user demand, and converting the user demand into an initial business model through a first agent; obtaining a prefabricated component library of the low-code platform, and automatically generating a demand component by using a second agent according to the initial business model and the prefabricated component library; automatically adapting and integrating a cross-platform data source by using a preset cross-domain data integration mechanism, and performing intelligent scheduling on the automatically generated demand component through a third agent; and establishing an automatic service arrangement mechanism based on the micro-service architecture, automatically identifying each service dependency relationship, and dynamically adjusting the execution sequence and resource allocation of each demand component according to the service dependency relationship. Through modular component design, the low-code platform has high flexibility and expandability, efficient collaboration of the components in the complex industry is guaranteed, and resource allocation and use efficiency is optimized.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a page generation method and device based on a natural language, equipment and a medium, and the method comprises the steps: obtaining a demand description in a natural language form, analyzing the demand description based on a preset intention recognition material, and generating a user intention; retrieving in the page feature vector library according to user intention to obtain a page template, performing graphical user interface rendering on the page template to generate a visual component set, injecting associated data into a target component in the visual component set to generate a target page, publishing the target page or publishing and collecting use effect data after modification, and displaying the use effect data. And updating the page feature vector library by using the use effect data. By combining natural language analysis, intention recognition, vector retrieval, graphical rendering and effect data updating, automation from demand input to page generation is realized, manual intervention and development cycle are greatly reduced, and page generation efficiency and quality are improved.

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

The invention discloses a structured text code generation and review method, which comprises the following steps of: receiving a control task demand input by a user, supporting natural language, structured form or graphical process input, performing deep semantic analysis by utilizing a demand analysis module, identifying control conditions, control actions and constraint factors, and generating and reviewing a structured text code. The task is converted into a plurality of control units through the SCoT chain; the SCoT chain reader generates a semantic vector according to a task demand, the AST reader generates AST representation through a grammar rule structure and a hierarchical relationship, and the decoder predicts a next grammar rule and generates an ST code in combination with the SCoT chain and AST features; and verifying grammar and logic of the ST code through an interactive automatic review and repair mechanism, if compiling errors occur, analyzing errors by the teacher model and providing repair suggestions, and performing code repair by the student model according to guidance until the code conforms to an expected function and passes compiling.

The invention provides a cell culture medium aided design system and method based on a large oracle model, and belongs to the technical field of artificial intelligence. A plurality of candidate culture medium formula schemes meeting user requirements are generated by obtaining culture medium design parameters and requirements and designing structured cues and inputting the designed structured cues into a large language model; a substance matched with the structured cue word is retrieved by utilizing a retrieval enhancement technology, a replacement substance is recommended for a user to refer, the user selects and purchases a required experimental material according to own requirements, a candidate culture medium formula scheme is fed back and evaluated in an actual experiment, and the system performs iterative optimization based on the feedback and evaluation, so that the accuracy of the experiment is improved. And a more appropriate candidate culture medium formula scheme is provided. According to the cell culture medium aided design system and method, intelligence, scientization and individuation of culture medium formula scheme design are achieved, experiment cost is reduced, repeatability and stability of cell culture experiments are improved at the same time, and therefore wide application of culture medium design in the field of biological manufacturing is promoted.

The embodiment of the invention provides an RPA process generation method and device based on a large model, a medium and a product. The method comprises the following steps: receiving business demand information in a natural language form input by a user; based on a preset large model and a business capability knowledge base, analyzing the business demand information, and determining a business operation sequence matched with the business scene; based on a robot process automation (RPA) component knowledge base and a preset multi-modal visual understanding model, analyzing the service operation sequence into a single-step executable atomic operation sequence; and according to the atomic operation sequence and the large model, generating an executable code of the target RPA component. According to the scheme, a user is supported to input business process requirements in a natural language form, the requirements are automatically converted into the business process operation sequence based on a large model, and the corresponding RPA code is generated and executed, so that the understanding cost of the user is greatly reduced, the execution efficiency of the business process is improved, and the labor cost is reduced.

The invention provides an industrial PLC code generation system and method based on artificial intelligence, and the system comprises an input module, a model optimization module, a code generation module and a deployment module, the input module is used for receiving and analyzing a natural language, a flow chart and an I / O configuration table, and generating a structured logic constraint; the model optimization module is used for supporting low-precision reasoning and dynamic expert network load balancing by adopting an artificial intelligence model of industrial control field data fine tuning; the code generation module is used for outputting PLC codes meeting industrial standards and supporting simulation verification and error correction; and the deployment module is used for downloading the generated code to target PLC equipment or integrating the generated code to engineering software. According to the industrial PLC code generation system and method based on the artificial intelligence, a tool for generating the PLC code based on the natural language of the artificial intelligence is provided, the code development efficiency is improved, and the error rate is reduced.

The disclosed technology provides for an improved approach to AI code generation. In various embodiments, the disclosed technology provides for an AI agent architecture platform for generating, revising, testing, and debugging code using a customizable team of agents with specific tasks.

A method and an apparatus for generating an interaction component of a client UI, a terminal, and a medium, relating to the technical field of IT and software. A UI effect picture and requirement description information are acquired, feature extraction and feature linguistic conversion are performed on the UI effect picture by means of a preset multi-modal model, attribute text information of the UI effect picture is obtained and same is converted into an attribute vectorized feature, and then, by using a pre-constructed UI component knowledge base, attribute vectorized data obtained from the UI effect picture is used as retrieved keyword information for performing similarity matching with UI component objects in the UI component knowledge base, thereby finding a similar target UI component object. The attribute information automatically extracted on the basis of the inputted UI effect picture is used as a keyword, solving the problem of inaccurate searching due to existing reuse code keyword summaries being ununified, and the accuracy of reuse code querying and the stability of a querying effect are improved.

The invention relates to the technical field of software assembly, and discloses a contract framework model-based generative software automatic assembly method and system, and the method comprises the steps: obtaining user demand data, and decomposing the user demand data into demand protocols based on a language model; constructing a software database based on historical software data, and generating a contract framework model; retrieving a software database through a language model based on the demand specification, and generating a software similarity candidate list; performing suitability screening on the software similarity candidate list based on an assembly engine, and generating a software similarity recommendation list; assembling the software based on the software similarity recommendation list to generate an assembly PBC; inputting the assembled PBC into a language model to determine a demand matching degree of the assembled PBC; and determining an assembly result based on the demand matching degree. According to the invention, the understanding degree of demands is improved, and the workload of manual coding is reduced.

Disclosed herein is a system and method for generating computer code for a plurality of components of a software development project. An artificial intelligence code generator can generate computer code in response to a natural language text input describing a component of the software development project. A first database can store natural language text describing components of the software development project. A second database can store computer code generated at least partially by the artificial intelligence code generator and defining components of the software development project and corresponding to the natural language text stored in the first database. Using a pre-trained language model, the system can generate a natural language summary text based on the code intent of the first component, the identified characteristics of the objects, and the natural language text retrieved from the first database.

The invention discloses an industrial design automation method based on an artificial intelligence large model and an intelligent agent, relates to the technical field of industrial design, and solves the technical problem that industrial design automation is difficult to realize at present. The method comprises the following steps: automatically matching a large model according to input industrial product related information; extracting a design optimization problem through a problem analysis agent based on a large model, and decomposing the design optimization problem into optimization tasks; a target function or a simulation code is generated through the target quantification agent, and an interface is provided; mapping from intelligent body fitting simulation input to output is built through the agent model, and an interface is provided; packaging the interface through an optimization packaging agent and executing an optimization task; according to the embodiment of the design automation, a hierarchical agent architecture for industrial design automation by utilizing an artificial intelligence large model is obtained through generalization. According to the invention, high automation of industrial design can be realized.

The invention relates to the technical field of front ends, can be applied to the field of science and technology finance / digital medical treatment, and discloses an activity landing page intelligent building method and device, electronic equipment and a storage medium. The method comprises the following steps: analyzing activity demand data through a large language model, and matching the activity demand data with features of components in a component library according to an analysis result to obtain candidate components; determining a target component and component attributes according to the user operation instruction to generate an initial page structure; configuring a business association rule between the target components based on the rule engine, and converting the natural language description of the business rule into a form which can be recognized by the rule engine through a large language model; generating a preview page of the activity landing page according to the initial page structure and the business association rule, and updating the page structure and the business association rule according to the adjustment instruction; and compiling and packaging the updated page structure and the business association rule. According to the method, intelligent construction of the landing page from demand to deployment is realized, and the efficiency and normalization are improved.

The invention provides a software demand defect detection method based on a large language model and a satisfiability problem solver, and the method comprises the steps: constructing a domain knowledge base, guiding the large language model to extract key elements in a demand text through combining with a cue word project, and recognizing imperfect and ambiguous defects; and meanwhile, the demand is converted into a logic expression, and logic inconsistency is detected by using an SAT solver. According to the method, incremental updating of the knowledge base is supported, efficient retrieval and comparison are achieved through vectorization and Hash coding technologies, experiments show that the method is remarkably superior to a traditional technology in demand detection in the fields of finance and the like, defects can be accurately positioned, correction suggestions can be provided, and the efficiency and accuracy of demand analysis are effectively improved.

The invention discloses a code generation method, which comprises the following steps that: a code development platform receives input information of a user in a first code file in a project, acquires a context in the file and a first cross-file context according to a static structure of the project, and acquires a second cross-file context according to behavior characteristics of the user in a development project, obtaining a third cross-file context according to the evolution coupling degree of at least one second code file in a code warehouse of the project and the first code file, and generating prompt information according to the input information, the context in the file, the first cross-file context, the second cross-file context and the third cross-file context, and inputting the prompt information into the code generation model for reasoning to obtain at least one group of generation codes, and displaying the at least one group of generation codes to the user. According to the method, the multi-dimensional item-level context and the user input serve as the input of the code generation model, the capability of the code generation model for utilizing the cross-file context in the code generation process is improved, and the code generation effect is improved.

An integrated development environment (IDE) leverages a generative AI model to generate industrial control code in accordance with specified functional requirements, which can be provided to the industrial IDE system as intuitive natural language spoken or written text. The industrial IDE can also analyze written code in response to natural language prompts submitted against the code, generate answers to user-submitted questions about the code, and offer recommendations for improving the code in response to specific questions or requests submitted by the user.

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention provides a script preservation method which can be applied to the technical field of artificial intelligence. The script preservation method comprises the following steps: carrying out semantic understanding on demand related files by utilizing a large model, extracting core elements, and generating a structured demand change list; analyzing to obtain an abstract syntax tree of an existing script code, and constructing a mapping relationship between the software automation test element and a script code block; dynamically comparing the structured demand change list with an existing script code structure tree based on a mapping relation between software automation test elements and script code blocks, identifying a script range influenced by demand change, and outputting a change influence matrix; and based on the change influence matrix, generating an automatic script code snippet needing to be updated, and realizing preservation of the automatic script. The invention further provides a script preservation device and equipment, a storage medium and a program product.

Implementations relate to generating a plurality of training instances to train a generative model in performing tasks using external tool(s) / service(s) accessible via corresponding API(s). The plurality of training instances each includes a synthetic natural language user instruction and execution step(s) to perform a task (e.g., a single-API task or a multi-API task) specified in the synthetic natural language user instruction. The synthetic natural language user instructions selected for the synthetic training data can be generated based on processing textual prompt(s) using a first LLM, where the textual prompt(s) can each include a list of APIs and associated API documents, one or more seed examples, and a request to synthesize a natural language user instruction. The one or more execution steps for a corresponding synthetic natural language user instruction can be generated based on parsing the corresponding synthetic natural language user instruction using the first LLM or a second LLM.

The invention provides a multi-agent construction method and device and related equipment, and relates to the technical field of artificial intelligence, and the method comprises the steps: according to a first target instruction input by a first user, obtaining a plurality of sub-tasks corresponding to the first target instruction and a logic relation between the sub-tasks; obtaining at least one target agent from an agent capability library based on the first target instruction, wherein the target agent is an agent matched with the subtask in the agent capability library; determining a behavior tree structure according to the plurality of subtasks and the logical relationship, wherein the behavior tree structure is used for mapping a connection relationship between the at least one target agent; and adjusting a connection relationship between the at least one target agent based on the behavior tree structure to obtain a target multi-agent. The ability to understand complex business contexts and user intentions is enhanced, and accurate construction of a multi-agent system is realized.

The present disclosure provides systems and methods performed in a central system. An initial entity model comprising a plurality of artefacts is formed. A runtime engine determines a plurality of available agent engagements based on the initial entity model. Responsive to an input selecting one of the available agent engagements, a notification is sent to an external autonomous agent. An indication of an action to be performed is received from the external autonomous agent. Responsive thereto, a message is sent to an external service indicating the action, thereby causing the action to be performed. The entity model is updated with a new artefact corresponding to the performed action, and a further plurality of agent actions is determined based on the updated entity model.

The invention discloses a tool path programming method and device, a readable storage medium and a computer program product, and relates to the technical field of automation, and the method comprises the steps: obtaining an electrode three-dimensional model of a to-be-generated electrode; feature extraction is carried out on the electrode three-dimensional model to obtain electrode features, and the electrode features comprise one or more of a three-dimensional structure feature, a curved surface feature, a recess feature and a boundary feature; according to the electrode features, a corresponding target machining strategy template is matched in a preset template library, tool path programming is carried out according to the target machining strategy template to obtain a tool path machining program, and one or more electrode machining strategy templates are stored in the template library. According to the invention, the tool path programming efficiency and accuracy are improved.