2427results 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.

An example operation may include one or more of storing software architecture documents which include text-based descriptions of architecture components, training a generative artificial intelligence (GenAI) model to understand the architecture components based on the software architecture documents in the memory, receiving an input comprising a description of a software architecture implemented by a computing platform, executing the GenAI model based on the description of the software architecture to generate a description of a change to be made to the software architecture, and outputting the description of the change on a user interface.

An example operation may include one or more of receiving a plurality of architecture documents of a plurality of different domains of a software architecture, identifying a missing component that is missing from within the software architecture between a first domain and a second domain among the plurality of domains based on execution of a generative artificial intelligence (GenAI) model based on the plurality of architecture documents of the software architecture, generating a recommended modification to the software architecture based on the identified missing component, and displaying the recommended modification via a user interface.

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.

An example operation may include one or more of receiving and recording electronic communications that occur between users of an organization within a data store, receiving an input via a user interface, retrieving the electronic communications of the organization and software architecture documents from the data store in response to receipt of the input, generating a diagram of a software architecture of the organization based on execution of a generative artificial intelligence (GenAI) model on the electronic communications of the organization and the software architecture documents, and displaying the diagram of the software architecture via a user interface.

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.

A system allows generation of information used by an integrated development environment using a machine learning-based language model, for example, a large language model. The integrated development environment is for developing applications, e.g., database applications. The system receives a natural language request for information related to development of the database application. The system determines contextual information describing a development task associated with the database application and generates a prompt for a machine learning based language model based on the natural language request and the contextual information and receives a response. The system extracts the information related to development of the database application from the response. In response to a natural request, the system may generate a database query, provide a resultset by executing the database query, automatically determine a type of chart and generate one or more charts for visually displaying the result.

The invention discloses a PLC code generation and verification method based on a large language model, and the method comprises the steps: generating multi-stage multi-mode input data which is used for representing user demands and formulating a system design scheme according to a demand analysis result; the multiple stages comprise a demand analysis stage and a design stage of a complete engineering cycle; the multi-mode input comprises a Pamp (Passive Amplitude Point); an ID graph and a natural language; then, according to the multi-stage multi-mode input data, a PLC code is generated; then, verification testing is carried out on the generated PLC code, and the verification testing comprises code verification and compiler verification; and finally, after the verification test on the PLC code is completed, deploying the PLC code. The PLC code deployment method is high in accuracy and automation degree, an efficient and accurate PLC code deployment method can be provided for network security researchers, and the PLC code deployment method can be widely applied to the technical field of computers.

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.

The invention provides a test case generation method and system based on a large model and retrieval enhancement generation, and the method comprises the steps: obtaining a software description document, carrying out text fragment segmentation and vectorization, and constructing a software knowledge base; obtaining and vectorizing a demand document, and recalling a content optimization demand document in the reference software knowledge base based on the retrieval enhancement generation framework; constructing a first Prompt instruction, and splitting the optimized demand document into demand fragments of different test points by using a large language model; and finally, constructing a second Prompt instruction for the demand fragment of each test point, generating a test case by using a large language model, and establishing a mapping relationship between each test point and the test case for linkage output. According to the method, the understanding ability of the large model on the demand document is effectively enhanced; meanwhile, the requirements are decomposed into test points, mapping is carried out on the test points and related test cases, and completeness and accuracy of the requirements and the test cases can be conveniently sorted.

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 provides an intelligent product service system concept design method based on cue word engineering. The method comprises the following steps: constructing a knowledge base, including data collection, integration and knowledge graph construction; then, a cue word project is constructed, a reasoning mode is determined, and an optimization cue template is created; and then, gradually designing from a demand layer to a physical layer, enabling LLMs in each layer to play different roles for cooperation, realizing demand collection, arrangement and conversion, function determination and decomposition, behavior and logic architecture construction, physical architecture determination and related detection analysis and the like, integrating various technologies to improve the design quality and efficiency, and meeting the market demand.

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 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 an intelligent demand structuring method and an intelligent demand structuring system, which are used for generating a structured demand document which can be used for subsequent software development based on business demand description of a natural language. For the simple requirement of a single module, one sentence input is adopted; for complex demands of multiple modules, a business modeling agent is called to carry out modeling understanding, natural language demands are converted into a business concept set, and the business concept set is further decomposed into specific structured demands by a demand structured agent. In the whole process, the user can adjust and refine the business software demand in real time through interaction with the business modeling agent and the demand structuring agent. According to the method, the structured demand document capable of being used for subsequent software development can be completed through natural language description, an efficient, flexible and intelligent new mode is provided for the structured demand of multi-field personalized service software and even software development, and the efficiency and quality of software development can be greatly improved.

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 relates to the technical field of garment processes, in particular to a garment process modular design method and system based on adjustable parameters. The method comprises the following steps: acquiring a basic process parameter set; defining a process association relationship according to the basic process parameter set to obtain a process association diagram; performing meta-model construction according to the process association diagram to obtain a process meta-model; obtaining a target style parameter set; mapping parameters in the target style parameter set into the process meta-model, and performing process parameter adjustment to obtain an adjusted process parameter set; performing process flow optimization according to the adjusted process parameter set to obtain a parameterized process model; performing process flow analysis on the parameterized process model to obtain a process flow diagram; and performing module boundary division on the process flow diagram to obtain a module boundary. According to the method, the defects of the existing method in the aspects of efficiency and flexibility are overcome, and a more intelligent, efficient and flexible process design system is realized.

Currently, there is no means for a user to build an integration process using natural language. Accordingly, in an embodiment, a user submits a user request via natural language (e.g., via interaction with a screen-based or voice-based chatbot). Embodiments wrap the user request with a contextual wrapper to generate a prompt, submit the prompt to a generative AI model (e.g., large language model) to produce an actionable set of objectives, and then programmatically and recursively generate software instances of each necessary component, including the final integration process, based on the set of objectives and using the generative AI model.

The invention provides a business demand confirmation method and system based on a cloud platform, and relates to the technical field of cloud platforms, and the method comprises the steps: building a business process engine on the cloud platform according to a business scene and a business rule, carrying out the visual modeling of a business demand through the business process engine, and generating a standardized business demand model; deploying a demand verification service on the cloud platform, and through a formalized verification method of control flow analysis or data flow analysis and in combination with the business domain ontology model, performing automatic verification on the business demand logic represented in a formalized manner, and generating a demand verification report; and according to the demand verification report, judging whether the business demand logic has semantic contradictions or logic vulnerabilities, and when the semantic contradictions or logic vulnerabilities are detected, correcting the business demand logic which does not pass the verification in combination with the business domain ontology model. And deploying a demand collaboration service on the cloud platform, and publishing the corrected business demand logic in the form of a standardized interface for the collaboration party to subscribe.

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.

Systems, methods, and other embodiments associated with adaptive code scanning are described. In one example method, a valid configuration that specifies approved parameters of use for a software component is defined. Software code is scanned to detect that the software component exists in the software code. Where the component is detected, the software code is scanned to identify implemented parameters of use for the software component. The implemented parameters are compared to the approved parameters. Based on the comparison, the software component is determined to be not implemented according to the valid configuration. Where the software component is implemented according to the valid configuration, the method automatically determines to proceed with the automated action based on the implemented parameters. The automated action is performed to adapt to the implemented parameters.

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 a software demand analysis method which can be applied to the field of artificial intelligence. The method comprises the steps that demand information of target software is collected in a multi-modal mode, and initial data of m modals are obtained; wherein m is a positive integer greater than 1; based on the initial data of the m modalities, performing multi-modal demand analysis by using a first large language model to obtain information data of the m modalities; performing information fusion on the information data of the m modes to obtain software demand data; and performing demand optimization processing on the software demand data to obtain a software demand document corresponding to the target software. According to the software demand analysis method and device, intelligent demand analysis and automatic and intelligent processing of the target software are achieved, manual intervention is reduced, the processing efficiency is improved, and the invention further provides a software demand analysis device and device, a storage medium and a program product.

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.