176 results about "Natural language requirements" patented technology

The invention provides an enterprise product intelligent quotation collaborative decision-making platform. A platform architecture comprises a user interaction layer, a data processing layer and a decision-making layer, and platform functions are realized by relying on an intelligent agent. When a user releases a product quotation task, an authority management agent verifies the identity and access authority of the user, and a user interaction and demand analysis agent in a user interaction layer converts the natural language demand of the user into a structured instruction; the data processing layer receives an instruction, basic data and a special cost agent in the layer pull data from an external system through a cross-system calling agent, and a price calculation agent integrates the data through multi-objective optimization to generate candidate quoted prices; and finally, a man-machine cooperation and intelligent decision-making auxiliary agent in a decision-making layer displays all candidate schemes, and an expert performs fine adjustment to select an optimal quotation. According to the invention, rapid and accurate quotation is realized for enterprise products, the quotation model is continuously optimized through continuous feedback and self-learning, and finally a highly autonomous intelligent quotation system is formed.

The invention discloses a spacecraft intelligent design method and system capable of autonomously learning, and belongs to the technical field of spacecraft design and simulation. The problems that in spacecraft design, natural language requirements are fuzzy and difficult to process, instruction generation is prone to ambiguity, linkage between simulation and design is not smooth, a design scheme lacks an effective iterative optimization mechanism, and traditional design is low in efficiency are solved. The method comprises the following steps: firstly, receiving a natural language demand, extracting key parameters to generate prompt words, inputting the prompt words into Design.AI, and generating a simulation instruction sequence; simulation is started through MCP Server conversion, if the result does not reach the standard, feedback correction is carried out, and iterative optimization is carried out. And repeating the steps until a design target is met, and generating a design report containing multiple items of content. Through natural language processing, real-time simulation, iterative optimization, knowledge accumulation and other technologies, the spacecraft design efficiency is greatly improved, the accuracy is enhanced, the threshold is lowered, and the flexibility is improved.

The invention discloses a big data platform asset intelligent sensing method based on LLM and a customizable MCP, and the method comprises the steps: defining a standard resource library of a model context protocol, constructing a model context protocol server, integrating a plurality of scanning tools, and carrying out the automatic adaption; receiving a natural language demand input by a user, and calling a pre-trained LLM model; the scene analysis module converts a natural language demand into a standardized scene description object; the intelligent scanning module is used for describing an object according to a standardized scene, generating an optimal tool execution chain, executing a complete scanning task, obtaining a task scanning result and unifying formats of heterogeneous data in the task scanning result, and the conflict resolution module is used for removing conflicts; the multi-source data association module fuses network topology resources and flow data captured by a probe to construct a dynamic asset atlas including asset attributes, service dependence and vulnerability information, and the report generation module generates a structured report. The method can provide a global risk perspective and decision support.

The invention provides a method for generating a front-end prototype based on an artificial intelligence technology. The method comprises the steps of data acquisition, knowledge base construction, model training and optimization, demand analysis and prototype generation. The method comprises the following steps: constructing a knowledge base supporting multi-dimensional query by collecting and verifying historical data; in combination with the content of the knowledge base, a large language model is used for performing field specialized fine tuning, and low-rank matrix optimization and comparative learning are adopted to improve the task similarity detection precision; semantic analysis and structured conversion are carried out on the natural language requirements of the user; a front-end prototype is automatically generated based on structured data, and user interaction feedback and dynamic optimization are supported. According to the method, efficient data processing, intelligent model optimization and flexible prototype design are realized, the intelligent level and development efficiency of prototype generation are improved, and powerful support is provided for front-end design under rapid iteration and complex requirements.

The invention discloses a document element extraction method based on an AI large model technology. The method comprises the following steps that various receipts input by a user are received; carrying out analysis and layout analysis on the document through a visual language model or a text analysis technology to generate a unified intermediate representation; a dynamic grading module is adopted to evaluate document complexity from three dimensions of format, structure and variation, and the document complexity is divided into a simple regular type, a structure complex type or a height variation type; adaptively selecting a processing flow according to a rating result, wherein the processing flow comprises high-speed template matching, multi-modal cognitive fusion or intelligent agent driving processing; the processing result is converted into an EasyEX format which is easy to process; the method comprises the following steps of: converting a natural language demand into an execution rule by extracting an Agent and utilizing a Prompt dynamic compiling technology, and positioning and extracting a target field by LLM (Logical Language Model); and finally, outputting structured data after type verification, knowledge graph verification and compliance review. According to the invention, the universality, the accuracy and the automation degree of receipt processing are improved, and the labor cost is greatly reduced.

The invention discloses an AADL model automatic generation method based on a large language model. The method comprises the steps that a safety key field AADL knowledge base is constructed; injecting the constructed safety key field AADL knowledge base into a local large language model through a fine tuning technology to obtain a fine-tuned large language model; plug-in the safety-critical field AADL knowledge base to the fine-tuned large language model to obtain a special large language model for the safety-critical software; inputting cue words into the special large language model of the safety-critical software to obtain an AADL model generation result; meanwhile, the invention provides a prompt framework based on a natural language demand limiting template, and the performance of a large language model in an AADL model automatic generation task is remarkably improved. The measures not only optimize the understanding of the model on AADL specifications and safety requirements, but also enhance the automatic generation capability of the model in complex system architecture description, thereby improving the generation accuracy and practicability of the model while ensuring the system safety and reliability.

The invention provides a low-code application logic generation method and system based on a large language model, and the method comprises the steps: obtaining a natural language demand and an operation environment snapshot of a user, and constructing a prompt project context of the large language model based on the natural language demand and the operation environment snapshot; based on the context of the prompt project, performing retrieval by utilizing a large language model to obtain a logic component; processing the logic component based on a pre-constructed agent to generate a logic DSL text; analyzing and rendering the logic DSL text to obtain a visual logic flow graph, and updating the logic DSL text in response to a modification operation of a user on the visual logic flow graph; based on the logic DSL text, verifying application logic corresponding to the logic DSL text in the sandbox environment, and deploying the application logic to the current application after verification is passed. According to the invention, complex business requirements can be met, and logic development thresholds are reduced.

The invention discloses an automatic page generation method of a software construction platform based on a large language model, which comprises the following steps of: extracting a scene, a page and constraint description from a natural language demand, and generating JSON (JavaScript Object Notation) content containing a data model, a query condition, a table column and static data through the large language model; according to component type constraints and verification rules, the JSON is mapped to components and combinations of buttons, labels, texts, pictures, pull-down selection, multi-choice, single choice, tables, dates and the like of the code-free construction platform; and automatically filling a component set based on the upper and lower partition templates, and injecting static data and verification logic. Through industry knowledge adaptation, component semantic binding and dynamic constraint, it is ensured that the page conforms to field specifications and interaction logic, the learning cost is reduced, manual intervention is reduced, and the method is suitable for the fields of smart cities, natural resources, urban and rural residence construction, emergency management, enterprise digital transformation and the like.

The invention provides a retrieval enhancement code generation method and system based on retrieval query statements. The method comprises the following steps: acquiring a plurality of retrieval query statements; screening out context information matched with the natural language requirement and the retrieval query statement from the code library; generating a first code segment according to the natural language requirement and the context information; through a plurality of first code segments in one-to-one correspondence with the plurality of retrieval query statements and a corresponding label of each first code segment, training and optimizing a retrieval query statement generator to obtain a target retrieval query statement generator; based on a target natural language requirement, utilizing a trained and optimized target retrieval query statement generator to generate a query statement; and using the query statement to retrieve the related code context, and inputting the target natural language demand and the retrieved code context into the code generation model to complete the code generation of retrieval enhancement. According to the embodiment, the interference of redundancy and irrelevant content can be effectively reduced, so that the code generation quality is improved.

The invention discloses an automatic construction system for a biological information analysis process, and the system comprises an intention understanding and semantic analysis module which is used for analyzing a natural language text inputted by a user into a structured task description meeting the requirements of a biological information analysis task; the knowledge graph and retrieval module is responsible for constructing a knowledge graph special for the bioinformatics field so as to provide knowledge retrieval and recommendation services; the process generation core module is used for receiving the structured task description, actively associating the knowledge graph with the retrieval module so as to supplement the field large model, and generating a biological information analysis target process language code; and the execution and monitoring module is used for guaranteeing workflow execution, full-life-cycle state monitoring, real-time fault diagnosis and intelligent self-healing decision making of target process language codes. According to the system, the executable analysis process can be directly generated according to the natural language requirement of the user, the tool compatibility and parameter validity are verified through the borrowed knowledge graph before execution, the dependency on the programming ability of the user is greatly reduced, and the process operation reliability is improved.

The invention provides a UI interface automatic generation method and system based on natural language description, and the method comprises the steps: receiving a natural language demand description text, carrying out the guided completion of fuzzy description in the demand description text, and outputting a standardized demand text; semantic analysis is carried out on the standardized demand text, functional association nodes of UI components and data fields in the text are positioned, analysis deviation is corrected through a domain knowledge graph, and key information including component parameters, field verification rules and component priorities is obtained; calling a design specification library comprising a sub-scene visual standard and a dynamic layout algorithm, and a component library comprising a parameterized configuration function; performing component matching and parameter adjustment based on the key information, a design specification library and a component library to obtain a target component, and generating a front-end code or a low-code blueprint of the target component; and rendering to obtain a UI interface. According to the method and the device, the problems of low demand transmission efficiency, high development threshold and difficulty in multi-terminal adaptation in the prior art are solved.

The invention provides an undercarriage system modeling method based on a natural language demand text, and relates to the technical field of model-based computer aided design, and the method comprises the steps: S1, constructing a SysML meta-model knowledge base and an undercarriage system model knowledge base; s2, analyzing the natural language demand text, and generating a structured demand text of the undercarriage system; s3, mapping a structured demand text entity of the undercarriage system into a node type of a semantic graph; and S4, in combination with a historical model template and real-time retrieval, generating an undercarriage system SysML model according to the semantic map obtained in the step S3, and performing undercarriage system engineering analysis through the undercarriage system SysML model, thereby improving the efficiency and quality of aviation equipment development. On the basis of a retrieval enhancement generation technology and an intention routing mechanism, the modeling efficiency and accuracy of the undercarriage system are remarkably improved; a graph neural network and a semantic vector space model are adopted to realize deep fusion and conflict resolution of cross-domain knowledge; and through an incremental learning mechanism, model continuous evolution and design experience closed-loop iteration are supported.

The invention belongs to the field of electronic design automation, and discloses a multi-agent radio frequency circuit netlist generation method and system based on a large language model, and the method comprises the steps: preparing a data set, and constructing a knowledge base; the method comprises the following steps: receiving and analyzing a natural language demand of a user, combining a cue word project and an RAG mechanism, carrying out chained reasoning, introducing a radio frequency design rule, and converting the information into structured parameter information; combining RAG knowledge base retrieval and new topology generation, and fusing PDK process constraints to obtain candidate topologies and a simulation netlist; a simulation tool is called to execute parameter analysis, iterative optimization is conducted on parameters in combination with a closed-loop feedback mechanism, and a final netlist meeting user requirements is obtained. Automatic decomposition, parallel cooperation and high-speed iteration of design tasks are achieved through multi-agent cooperation and in combination with the radio frequency knowledge graph and the RAG; introducing physical constraints to enable the generated topological structure and parameters to meet the physical constraints; and a closed-loop simulation feedback mechanism is fused, so that the dependence on manpower is reduced.

The invention relates to the technical field of test case generation, and discloses a test case batch generation method, which comprises the following steps: acquiring a to-be-tested business scene, and querying a natural language demand and a historical test report of the to-be-tested business scene to analyze the dynamic change of the to-be-tested business scene to obtain test evolution information; constructing a code change influence probability matrix of the to-be-tested business scene, and predicting a defect probability of a code unit corresponding to the to-be-tested business scene; constructing a generation sequence and a test generation weight of test cases of the to-be-tested business scene, and constructing an initial test case of the to-be-tested business scene; and performing parameter optimization on the initial test case to obtain an optimized parameter, and performing test case batch generation on the to-be-tested service scene based on the optimized parameter. According to the invention, the accuracy of batch generation of the test cases can be improved.

A computer that includes a processor and a memory, the memory including instructions executable by the processor to determine when a natural language requirement statement includes an atomic statement using a large language model (LLM) neural network based on a first prompt statement. The first prompt statement can include a first request to label the natural language requirement statement, a description of a requirements language, and the natural language requirement statement. The first output from the LLM includes an explanation statement which indicates reasons the natural language requirement statement is atomic or not atomic. When the LLM determines that the natural language requirement statement is atomic, the LLM can translate the natural language requirement statement into the requirements language based on a second prompt statement that includes a second request to translate the natural language requirement into the requirements language and the natural language requirement statement.

The invention discloses a method for generating a text system, and aims to solve the problems of long development cycle, disjunction of requirements and technology implementation and the like of traditional software and realize automatic generation from a natural language requirement to a deployable service system based on a large language model. The method is divided into two stages: in the first stage, a system is disassembled into a logic subsystem through man-machine cooperation, core business logic is extracted, a large language model is called by adopting a divide-and-conquer strategy, and a detailed specification containing a functional process and an interface form is generated; in the second stage, a large language model is called to generate a database table structure and a DDL script, an engineering framework of a basic technology stack is initialized and integrated through a predefined scaffold, CRUD codes are generated, the codes are analyzed to generate a transformation plan for structured data, and target logic codes are incrementally integrated. According to the method, control is achieved through Python, the problem of large model input limitation is solved, and generated codes are standard and extensible. The development cycle is shortened, the requirement-design-code consistency is guaranteed, the threshold is lowered, platform locking is avoided, and enterprise digital transformation is assisted.

The invention relates to a method for generating a back-end interface by a large language model based on a dynamic embedding mechanism, and aims to improve the efficiency and the intelligent level of interface development. The method comprises the steps that historical data are collected, cleaned and concluded, and a knowledge base supporting real-time updating is constructed; performing data preprocessing, training and optimization on the large language model, constructing a dynamic embedding layer, and injecting the latest content of the knowledge base into the context of the model; analyzing a natural language demand by using a large language model, generating interface metadata, and ensuring data consistency through grammar verification and optimization; and finally, importing the interface metadata into the low-code platform and completing verification. According to the method, a dynamic embedding mechanism is combined with a multi-task learning mode, the instantaneity, the accuracy and the standardization degree of interface metadata generation are improved, manual participation is remarkably reduced, the interface development efficiency is improved, and the method is suitable for large-scale interface management and rapid iteration scenes.

The invention discloses an automatic game testing method and system, and the method achieves the automatic generation and execution of testing logic through the building of a knowledge base and an event base, and the cooperative management of the knowledge base and the event base, and reduces the manual intervention. Converting a natural language demand input by a user side into a semantic feature, and matching the semantic feature with an operation instruction in an event library to obtain an executable test logic so as to reduce a test threshold; triggering the event in the event library according to the executable test logic, pushing the execution result of the event to the user side, and updating the knowledge base and the event library by using the execution feedback data of the event, thereby forming an execution-feedback-update closed loop, and improving the self-learning ability of the test system. In this way, the logic rules in the game development kit are converted into the knowledge base, intelligent analysis and dynamic feedback optimization of natural language requirements are achieved in combination with the preset operation instruction of the event base, and the test efficiency and the adaptive capacity are remarkably improved.

The invention relates to the technical field of intelligent gateways, in particular to a method and system for automatically expanding intelligent router plug-ins through LLM. The method comprises the steps that a user describes a plug-in function requirement of the intelligent router in a natural language through voice or text input equipment; and the cloud preprocessing service carries out character conversion processing on the voice data, carries out contradiction check and invalid information filtering on natural language requirements, extracts structured requirement information and inputs the structured requirement information to the cloud big language model. According to the intelligent router plug-in development method and system, the problems that in traditional intelligent router plug-in development, the manual intervention degree is high, the process is long and compatibility is insufficient are effectively solved by constructing an automatic system with the cloud large language model and the multi-service module cooperating. Through cooperation of multiple technical modules, a full-automatic process covering demand input, code generation, engineering integration, test verification and terminal deployment is constructed, and the intelligent router plug-in development efficiency and quality are remarkably improved.

The invention discloses a demand development method, device and equipment. The method comprises the following steps: calling a demand analysis agent to analyze a natural language demand into a structured function demand unit comprising an input specification, an output specification and an execution constraint; the calling code generation agent generates source codes corresponding to the development requirements according to data type definitions and function calling specifications in the structured function requirement unit and the local project code library; calling a code test agent to generate and execute a test case based on the source code and the structured function demand unit; and after determining that a software deployment condition is met based on a test result, calling a code deployment agent to generate a deployment script and configuration information, and submitting the deployment script and the configuration information to a deployment execution engine to complete automatic deployment. According to the application, end-to-end automatic development from natural language requirements to application deployment can be realized.

The invention relates to the technical field of artificial intelligence, and discloses an artificial intelligence-based front-end automatic code generation and optimization system and method.A demand analysis and code generation module analyzes a natural language demand through a natural language processing technology and generates a front-end code; the design draft conversion module analyzes the UI design draft through an image recognition technology and generates a response type code; the compatibility detection and repair module automatically repairs the cross-browser compatibility problem; the code optimization module is used for compressing codes, combining styles and proposing reconstruction suggestions; and the self-learning module dynamically adjusts a generation strategy according to developer behaviors. According to the method, development requirements can be automatically analyzed, a design draft can be converted, front-end codes can be generated and optimized, and intelligent interaction, compatibility detection and performance optimization functions can be provided. Through the tool, the development efficiency, the code quality and the team cooperation ability are greatly improved, multiple challenges in the traditional development process are solved, and the tool has wide application prospects.

The invention relates to a visual process automatic arrangement method and system, and belongs to the technical field of computers.The method comprises the steps that a natural language requirement input by a user is obtained; modeling knowledge is determined, logic typesetting and structured processing are conducted on the natural language requirement input by the user and the modeling knowledge through a large language model LLM, and XML process definition data meeting the BPMN standard are generated; sending the XML process definition data conforming to the BPMN standard to a process engine so as to create a visual process model; and rendering the visual process model to a front-end interface for a user to review, and determining a review result. According to the method, business personnel can directly participate in process design without learning professional modeling languages such as BPMN, and a business-driven development mode is really realized.

The invention provides a business process intelligent configuration and security execution method, system and device, and the method comprises the steps: converting a natural language demand into a BPMN 2.0 process model based on a large language model and an enterprise knowledge base; the service rule description is converted into a Grouping script through multi-level security compiling; the process and the script are executed in the Flowable process engine integrated with the security sandbox, and execution data are collected; and calculating a process health score based on the data, generating and implementing an optimization suggestion, and feeding back a result to the knowledge base. The problems that a traditional process system depends on manual coding, the script safety is poor, and continuous optimization is lacked can be solved. The full-link automation and intelligentization of the business process from design, execution to optimization can be realized, the development efficiency and the system security are greatly improved, and the process has the self-evolution capability through closed-loop feedback.

The invention provides a method for generating a front-end prototype based on a generative adversarial network. The method comprises the steps of data acquisition and processing, knowledge base construction, natural language demand analysis and structured conversion, GAN model optimization, and front-end prototype generation and dynamic optimization. A data management system supporting multi-dimensional retrieval is constructed by collecting and verifying front-end design historical data, and efficient matching of task input and output pairs is achieved by combining GAN model optimization task similarity detection. The method comprises the following steps: performing semantic analysis on natural language requirements of a user, extracting function points, data fields and logical relationships, converting the function points, the data fields and the logical relationships into structured JSON metadata, automatically mapping the structured JSON metadata to a front-end component template, and generating a dynamic interface. According to the method, the prototype design efficiency and the intelligent level are remarkably improved, the labor cost is reduced, and reliable technical support is provided for rapid development and deployment of complex applications.

The invention discloses a game function development system and method based on a natural language instruction, and relates to the technical field of game development, the system comprises a project code library analysis module, a development instruction receiving and analysis module, a function deconstruction and code generation module, an automatic integration and debugging module and a development report generation module; the project code library analysis module constructs a code dependency graph and recognizes an API interface to provide a precise basis for subsequent development, the development instruction receiving and analysis module converts natural language requirements into structured information, efficient understanding of the requirements is achieved, and the development efficiency is improved. The function deconstruction and code generation module disassembles requirements and calls a specific engine programming large model to generate standard codes, manual coding and automatic integration are reduced, the problems of automatic code insertion and test development of the debugging module according to a graph and rapid recognition and repair are solved, dependence on professional programmers is reduced, the function development period is shortened, and efficiency is improved. And compatibility errors caused by manual operation are reduced.

The invention relates to a 5G security demand extraction and assertion generation technology based on LLM and RAG, and the technology comprises the following steps: 1, a standard document preprocessing module, splitting a 3GPP standard document according to a security process hierarchy, mapping the 3GPP standard document to a corresponding label, and solving a knowledge fragmentation problem; the RAG knowledge base construction module analyzes document slices by using an RAGF low tool, divides the document slices into text blocks, stores the text blocks into a database through vectorization of an embedded model, and realizes efficient retrieval by adopting hierarchical indexing; 3, an LLM semantic understanding and assertion generation module, which is used for converting a natural language demand into a logic expression based on a DeepSeek model in combination with a cue word framework, a knowledge base and a signaling alphabet; and the assertion post-processing module is used for performing grammar check, standard conformity verification, simplification and logic conflict detection on the LTL assertion. According to the technology, automatic extraction of security requirements and assertion generation are realized, efficiency and accuracy are improved, security requirements in multiple aspects are covered, and a reliable scheme is provided for 5G terminal protocol security testing.

The invention provides an interval occupancy logic check system modeling method based on formalized verification, which comprises the following steps: classifying signal equipment according to attributes and behavior characteristics of the signal equipment in an interval occupancy logic check system; according to the classification of the signal equipment, defining object attributes of the signal equipment, and establishing an object model of the interval occupation logic checking system; and establishing a security demand verification model based on the object model according to the rule demand of the interval occupancy logic check system. According to the method, formalized modeling is carried out on the interval occupancy logic checking system, the natural language requirement is abstracted into a mathematical model, formalized verification can be conveniently carried out on the interval occupancy logic checking system, and the test comprehensiveness of the interval occupancy logic checking system is guaranteed.

The invention relates to the technical field of data analysis, in particular to a data and knowledge combined driven intelligent decision-making method and system.The method includes the steps that multi-source heterogeneous data and knowledge base concepts are unified into a structured graph structure through multi-dimensional feature extraction and multi-head attention fusion context prompt, and a structured semantic basis is provided for intelligent decision-making; by combining data-driven mining and knowledge prior constraints, the accuracy and robustness of causal reasoning are improved, and the reliability of root cause diagnosis is ensured; a root cause path is searched and enumerated in a reverse breadth-first manner by taking an abnormal entity as a starting point, so that rapid and traceable abnormality positioning is realized, and a user is assisted to focus a high-probability root cause; through modular assembly and full-link element traceability, while the analysis threshold is reduced, decision full-link transparency is realized, and the auditing requirement of a high-risk scene is met; by automatically converting natural language requirements into executable scripts, the decision-making efficiency of non-technical users is improved.

The invention discloses a computing power resource determination method, apparatus and device, a medium and a product. The method comprises the steps of obtaining real-time computing power resource data of a natural language demand and a resource library; performing intention analysis on the natural language demand based on the historical performance database, and determining a computing power task instruction; and on the basis of the real-time computing power resource data, performing dual-algorithm collaborative matching on the computing power task instruction, and determining a target resource combination meeting requirements from a resource library. According to the method, intention analysis is carried out on natural language requirements in combination with a historical performance database, fuzziness of intention analysis is eliminated from the source, cooperative matching is carried out through a bi-pass algorithm so as to reduce matching deviation of computing power resources, accurate analysis of the requirements is achieved, and then accurate matching of the computing power resources is achieved.