56 results about "Requirements model" patented technology

The invention relates to the technical field of software development tools, and discloses a micro-application generation system and method based on natural language interaction, and the system comprises four core components: a natural language understanding module, a demand modeling module, a code generation engine and a deployment and integration platform. The natural language understanding module generates structured semantic representation through intention recognition, entity extraction and context analysis; the demand modeling module constructs a data model, a process logic and an interface prototype based on the semantic representation, and optimizes model parameters by utilizing reinforcement learning; the code generation engine generates a cross-platform code of the micro-service architecture through template matching and modularization assembly; and the deployment and integration platform realizes seamless integration of the external system and the Internet of Things equipment through a standardized interface. According to the method, the problems that a traditional low-code platform depends on visual dragging operation, demand analysis is rough and cross-domain adaptation is insufficient can be solved.

According to the multi-mode man-machine interaction view and component expression method and system, the design state, the development state and the operation state are integrated, and the problems of scene linkage integration, multi-model association collaborative scheduling and the like of an aerospace equipment MOM system are solved by means of graphical interaction and the like. The method comprises construction of a knowledge graph and demand modeling in a design state, visual design and code generation in a development state, and fault diagnosis in an operation state. Compared with the prior art, knowledge can be deeply mined, codes can be automatically generated, system stability is guaranteed, development efficiency and reliability are improved, and the manufacturing and development requirements of aerospace equipment are met.

A system and method for an AI-based Automated Software Application Development Platform which intelligently gathers requirements from a business analyst and generates a runnable software application from those requirements. The AI-based platform enables the business analyst with no technical knowledge to visually model their requirements rapidly without requiring any programming Using the abstract requirements architecture and the platform, the analyst creates requirements models for the application's business processes and business tasks for each of the processes, including defining the supporting information needs. The system intelligently assists the user in defining the information needs for each of the business processes and their tasks. Once the requirements models are created by the analyst with the help of the platform, the platform automatically designs and creates an appropriate persistent canonical data model for the application from the defined information needs. The platform then creates a runnable software application. The system also provides a template for the user to input their requirements in descriptive text, and accepts requirements document for applications as written free form text or text documents written in English as input, and in a non-English language that the system automatically translates them into English. The system provides a method for reverse engineering an existing application, including generating a requirements document for the existing application such as a legacy modernization or digital transformation an old applications for redesigning, updating or revising them to accommodate a new requirements set for such applications.

The invention provides an engine software FADEC demand-oriented modeling analysis and test case automatic generation method, and belongs to the field of aero-engine control. According to the method, DeepSeek is used for assisting preprocessing at the system demand level, and modeling work based on a formalized table is completed; through multi-level demand model analysis, vulnerabilities existing in the demand are automatically detected; and finally, automatically generating a test case set meeting DO-178C airworthiness requirements by establishing a grammar logic architecture. The method focuses on formalized verification work of an engine software FADEC demand level, by using an executable model, defects appearing in the demand or design process can be effectively found, errors can be eliminated in time, test cases are automatically generated, and therefore rework and iteration cost in the later period of development is greatly reduced.

The invention discloses a personalized design knowledge recommendation method based on knowledge graph and dynamic demand modeling, which comprises the following steps: firstly, constructing a design knowledge presentation layer which comprises a knowledge ontology and a knowledge graph, and then constructing a knowledge recommendation layer which comprises a knowledge ontology and a knowledge graph; the knowledge recommendation layer comprises a task stage knowledge demand model and a packet receiving party knowledge demand model, a dynamic knowledge demand vector and a task stage knowledge demand vector of a packet receiving party are matched with design knowledge nodes in the knowledge graph, and knowledge nodes most relevant to the demand of the packet receiving party are screened out; and according to a knowledge graph matching result, screening out a knowledge node most related to the demand of the packet receiving party, and generating a final recommendation result through an optimization algorithm. The method has the advantages that dynamic changes of knowledge requirements of a crowdsourcing party can be effectively solved, more personalized and accurate design knowledge recommendation can be provided, and the execution efficiency and design quality of crowdsourcing design tasks can be improved.

The invention discloses a system demand intelligent analysis and model construction method based on natural language processing, and relates to the technical field of system engineering and model-driven engineering, and the method comprises the following steps: S1, analyzing field metadata; s2, constructing a domain knowledge heterogeneous graph; s3, introducing a theme-guided attention mechanism and a graph-guided dynamic sparse attention mechanism to improve a Longform model; s4, calling an LLM model to generate an entry demand model; s5, the LLM and GNN double-engine analysis is executed; s6, introducing a PPO model as a reinforcement learning agent, and optimizing the semantic mapping rule base; and S7, inputting the design description text into the improved Longform model to generate a behavior model, a structure model and a parameter model. According to the method, the limitations of information extraction generalization, key element omission and weak semantic association of a traditional natural language processing technology in professional field text understanding are overcome, and an efficient and accurate solution is provided for system demand intelligent analysis and model automatic construction.

The invention relates to a code generation method and device, equipment, a storage medium and a program product. The method comprises the following steps: acquiring a demand model; obtaining an initial class diagram of a design model based on the demand model; optimizing the initial class diagram of the design model by using a multi-objective genetic algorithm and a large model to obtain an optimized design model; and generating a single system code matched with the demand model based on the optimized design model. The code generation efficiency and the code generation quality can be improved.

The invention discloses a helicopter transmission system digital engineering overall technology analysis method, and relates to the field of helicopter transmission system digital engineering, and the method comprises the steps: building a helicopter transmission system overall design digital environment; demand capturing is carried out around all benefit critical people and facing scenes; obtaining preliminary design requirements, node parameters, interface requirements and overall requirements of the helicopter transmission system; carrying out helicopter transmission system-component-part demand analysis, and establishing a demand model, a function model, a logic model and a physical model; establishing a design model, a manufacturing model, an assembly model and a test model; and establishing a comprehensive database, and storing the model established in each stage into the comprehensive database to realize numerical model multiplexing and complete digital design closed loop. The problems that a traditional analysis method is low in efficiency, low in integration level, short in simulation evaluation and long in closed loop iteration period are solved.

The invention discloses a dynamic post demand model construction method based on fuzzy clustering. The method comprises the following steps: S1, collecting a comprehensive post demand data set related to post demands; s2, generating a feature vector set of the comprehensive post demand data set; s3, performing clustering analysis by applying an improved fuzzy C-means clustering algorithm to generate a category membership matrix; s4, designing and training a first-layer generative adversarial network by taking the key feature vector and the category membership matrix as input; s5, designing and training a second-layer generative adversarial network by taking the potential feature vector and post demand historical feedback data as input; and S6, dynamically optimizing the post demand model. According to the method, the reasonability and the accuracy of post demand characteristics generated by the model are remarkably improved.

The invention discloses an MBSE-based material allocation and transportation system forward design method, which comprises the following steps of: dividing an overall design process into four levels, namely an overall task layer, a system layer, a subsystem layer and a design verification layer, respectively carrying out material allocation and transportation system design from four dimensions, namely requirements, behaviors, structures and parameters, constructing a requirement model, a function model and a performance model, and designing a material allocation and transportation system according to the requirement model, the function model and the performance model. Gradually forming a system scheme, and performing rapid simulation verification on the system scheme; wherein the overall task layer pays attention to a top-layer functional architecture and a system context environment of the material allocation and transportation system, and a preliminary scheme of the system is formed; the system layer deeply studies the functional architecture, the logic architecture and the performance indexes of the material dispatching system to form a detailed scheme of the system; the subsystem layer carries out the design of the demands, behaviors, structures and parameters of subsystems based on the functions, composition and performance indexes of the material allocation and transportation system, and forms subsystem schemes. And the design verification layer verifies the rationality of the design scheme. The design quality and the design efficiency are improved.

The present invention relates to a method for converting a Simulink-StateFlow model into a NuSMV model, which includes the following steps: conducting a requirements analysis on the system to be evaluated and constructing a Simulink-StateFlow model; extracting program statements, custom function statements, and logical sequence relationships in the model, and respectively constructing a program statement data table, a custom function data table, and a logical sequence data table; integrating the model program and sequence to generate a model information table; analyzing the Matlab programming language and constructing Matlab syntax rules; processing the program statements in the model information table according to the syntax rules and parsing them into an abstract syntax tree; processing the abstract syntax tree to determine an algorithm for converting it into NuSMV statements; and processing the model information table according to the conversion algorithm to generate a NuSMV model. The present invention greatly improves the efficiency and accuracy of the NuSMV tool for model checking of the Simulink-StateFlow model, and realizes the safety verification of the Simulink requirements model of the control system.

The application provides a civil aircraft strength evaluation requirement model construction and verification method based on system engineering, which comprises the following steps: S1, establishing a stakeholder requirement database; S2, constructing a top-level requirement model; S3, establishing an application scenario database based on the top-level requirement model, and establishing a corresponding relationship between the top-level requirement and the scenario; S4, constructing a civil aircraft strength evaluation requirement model based on the corresponding relationship between the top-level requirement and the scenario; S5, verifying the requirement traceability chain in the civil aircraft strength evaluation requirement model, and rectifying the requirement traceability chain that does not meet the requirements. The method can establish a stakeholder requirement-top-level requirement-scenario-architecture-verification requirement traceability chain, so that the source of a verification requirement from a stakeholder requirement can be quickly understood, when a stakeholder requirement is changed, the strength evaluation method that needs to be modified related to the stakeholder requirement can be quickly located, and the related model can be modified, and various scenario requirements can be met.

The invention relates to a complex embedded system software requirement modeling method based on multi-domain knowledge, and belongs to the field of embedded system modeling. The method comprises the following steps that domain analysis is conducted on the complex embedded system from the four aspects of system architecture, function requirements, real-time requirements and safety requirements, entities, attributes and the incidence relation of the entities and the attributes in the system are extracted, and a meta-model of the complex embedded system is established; establishing a demand template according to the class of the meta-model and the attribute of the class; and according to the modeling sequence, calling the corresponding demand template to model the complex embedded system. According to the method, the meta-model and the demand template are established by extracting entities, attributes and association relationships of the complex embedded system from multiple angles, so that modeling is performed, and the problem that a method for constructing the demand model based on comprehensive demand analysis for the complex embedded system does not exist at present is solved.

The invention discloses a resource scheduling method and device, terminal equipment and a storage medium, and relates to the technical field of power systems, and the method comprises the steps: converting collected resource data into a resource sensing state value; identifying a feature parameter data set of each task, analyzing a matching relationship between the feature parameter data set and a task demand, screening out a parameter combination matched with the current resource sensing state value from the feature data set according to the matching relationship, and generating a demand modeling parameter set according to the parameter combination; collecting sample data related to the demand modeling parameter set, performing time sequence analysis on the sample data to obtain sample analysis data, comparing difference conditions of task completion rates under different sample analysis data, and adjusting parameters of the modeling parameter set according to the difference conditions to obtain a parameter docking result; and screening out a parameter combination matched with the current sample condition from a parameter docking result, and converting the parameter combination into a resource scheduling strategy. According to the invention, the resource scheduling effect can be effectively improved.

The invention provides a micro-service code automatic generation method, system and equipment based on a multi-level model and a medium, and relates to the technical field of software development, the method comprises the following steps: according to system development stage characteristics and information dimensions, the system is subdivided into a multi-level model description framework comprising a demand model, an architecture model, a design model and a deployment model; and based on a mapping rule between a predefined model and a code, converting the multi-level model into an executable template code based on a Spring micro-service framework through a hierarchical code generation mechanism, so as to realize an automatic multi-level code generation process from demand to a final system. According to the method, the problems that in existing model driving code generation, input is not standard, generated codes are not complete, a complete information system is difficult to generate during large language model code automatic generation, and the codes are possibly inconsistent are solved, and a more efficient, standard and complete code automatic generation scheme is provided.

The application relates to the technical field of software development tools, and discloses a micro application generation system and method based on natural language interaction, which comprises four core components of a natural language understanding module, a requirement modeling module, a code generation engine and a deployment and integration platform; the natural language understanding module generates a structured semantic representation through intent recognition, entity extraction and context analysis; the requirement modeling module constructs a data model, a process logic and an interface prototype based on the semantic representation, and optimizes model parameters by using reinforcement learning; the code generation engine generates cross-platform code of a micro service architecture through template matching and component assembly; and the deployment and integration platform realizes seamless integration of external systems and Internet of Things devices through standardized interfaces. The application can solve the problems of traditional low-code platforms, such as dependence on visual dragging operation, rough requirement analysis and insufficient cross-field adaptation.

The application discloses an intelligent splitting and assembling method of a database model based on a business scene, belongs to the technical field of database management and application, and relates to the technical field of database management and application. The splitting method comprises the following steps: converting an abstract or unstructured business model into a calculable or structured graph representation form, mapping a business function unit into a node in the graph, mapping the relationship between businesses into an edge, and constructing a business topology structure. The assembling method comprises the following steps: acquiring the business topology structure, automatically analyzing a natural language business requirement into a structured requirement model, performing feature extraction on the analyzed requirement, generating a multi-dimensional feature representation, and assembling business model slices in a progressive matching mode. The application can improve the database operation efficiency, reduce the database construction and management cost, and enhance the adaptability and flexibility of the database to business changes.

The invention discloses an intelligent engineering integrated construction and operation and maintenance management method suitable for multiple types of buildings, and relates to the technical field of building intellectualization, and the method is realized through the following core steps: firstly, carrying out building type adaptation and demand modeling, and constructing an intelligent engineering demand model adaptive to a target building type; generating a modular construction scheme based on the demand model and implementing dynamic management and control; after construction is completed, cross-system integrated debugging is carried out, and heterogeneous system data interaction adaptation is achieved; an integrated operation and maintenance management platform is built and intelligent monitoring is carried out; and finally, operation and maintenance decision and full-life-cycle management are realized based on the monitoring data. The method can be adapted to various building types, complete-process collaborative management and control of construction and operation and maintenance are achieved, the construction quality and operation and maintenance efficiency of intelligent engineering are improved, and data support is provided for full-life-cycle optimization of a building intelligent system.

The present application relates to a method, system, device, and storage medium for generating a product manufacturing process specification, wherein the method comprises: constructing a process specification requirement model for product manufacturing that includes product quality, production cost, and production efficiency requirements; obtaining process-related test data from a pre-conducted product development process to obtain a process data sample; and generating a process specification that meets the target requirements based on the process specification requirement model and the process data sample. The present application can positively form a process specification that meets product quality requirements, production cost, and production efficiency requirements, and can get rid of dependence on manual experience. By means of knowledge acquisition and coupling, it can guide the precise writing of process technology documents.

The present invention relates to the field of drone design technology, and in particular to a method, system, medium and program product for modeling the reliability of drone propellers. The method first constructs a requirement model, system context, use case and activity diagram of drone propellers based on SysML, and defines the mathematical function relationship of core performance parameters such as thrust, torque and speed; then statistical fitting is performed in combination with experimental data to construct a joint performance margin equation, and the reliability theory is introduced to calculate the reliability value of the propeller through the standard normal cumulative distribution function; finally, the above constraint equation is constrained and bound to the system structure model to realize dynamic response and model linkage under parameter changes. The present invention realizes the integrated integration of reliability analysis in the MBSE model, significantly improving the automation level, evaluation accuracy and engineering application efficiency of propeller system reliability modeling.

The invention discloses an LLM-based system-level demand modeling and analysis method and platform, and belongs to the field of demand engineering and system modeling. The invention provides an LLM (Logical Language Model)-driven automatic demand behavior model construction and conflict detection method aiming at the problem that accurate modeling and consistency verification of natural language system-level software demands are difficult. According to the method, control flow logic in a demand text is recursively analyzed by simulating an expert stratified analysis process, and a behavior model is automatically generated. The modeling process comprises the steps of recognizing atomic activities, extracting a hierarchical relation and constructing a behavior model. On the basis, a system-level behavior tree is constructed based on preposition and postposition conditions of behavior nodes, a plurality of demand models are comprehensively analyzed, and behavior consistency analysis and conflict detection on the system level are achieved. According to the method, automatic expansion from the demand-level behavior model to the system-level behavior model can be realized, and an effective technical scheme is provided for automatic modeling and analysis of system-level demands.

The invention discloses an inter-model consistency analysis method applied to engine control system software, and the method comprises the following steps: firstly, carrying out the formalized modeling of a demand document and the formalized extraction of a design model through a preset formalized modeling language, and obtaining a formalized demand document and a formalized design model; and generating a corresponding demand model and a design model. Generating one or more demand-based demand model simulation cases based on the demand model, and generating corresponding design model simulation cases by referring to the demand model simulation cases; then, dynamic analog simulation is conducted on the demand model and the simulation model through the demand model simulation case and the design model simulation case respectively, and a demand model simulation result and a design model simulation result are obtained. And finally, according to a demand model simulation result and a design model simulation result, carrying out model consistency analysis on the demand model and the design model so as to determine whether a consistency problem exists between the demand model and the design model.

The application relates to a model verification method suitable for a distributed system of SysML, Simulink and AADL, and belongs to the technical field of system integration verification. The method comprises the following steps: according to model description files of SysML, Simulink and AADL, extracting model element sets and related attribute information respectively to obtain SysML, Simulink and AADL model information mapping tables; according to selected verification rules, obtaining required model elements and related attribute information from the SysML, Simulink and AADL model information mapping tables; according to the selected verification rules, verifying the obtained required model elements and related attribute information, if the verification is passed, no prompt information is generated; otherwise, it is prompted that the selected verification rules are not met, and a verification result is output. The application solves the problem that the consistency, real-time performance and safety of the design integration stage and the corresponding demand models and design models of each subsystem cannot be verified at present.

The invention relates to the technical field of digital twinning, and particularly discloses an MBSE-based production line digital twinning system design method, which comprises the following steps of: constructing a physical operation scene model and a digital operation scene model of a production line; constructing a physical system demand model and a digital operation scene model of the production line; constructing a physical operation activity model and a digital operation activity model of the production line; constructing a physical validity measurement model and a digital validity measurement model of the production line; constructing a physical system module model and a digital system module model of the production line; constructing a physical system function model and a digital system function model of the production line; constructing a physical module component model and a digital module component model of the production line; and constructing a physical system parameter model and a digital system parameter model of the production line.

The invention discloses a software product multi-version parallel development method based on a large language model, and the method comprises the steps: receiving a software multi-version development request, and generating a multi-version demand model through natural language processing and analysis; constructing a training data set containing version difference information based on the multi-version demand model, and performing fine tuning on the large language model in combination with the instruction template to obtain a target large language model adapted to multi-version development; double storage spaces are configured for each software design stage, and initial design achievements and final design achievement storage of all versions are processed in parallel; generating, complementing and optimizing intelligent codes of all versions by utilizing a target large language model; design and code conflicts among multiple versions are detected in real time and intelligently coordinated and solved; and integration verification of all versions is completed through combination of automatic testing and manual testing. According to the method, parallel efficient development of multi-version software is realized, the code generation accuracy is improved, and the development cost caused by demand change and version conflict is reduced.

The application discloses a software requirement document analysis method and device and medium, and belongs to the technical field of document analysis, and is used for solving the technical problems that the information analysis in the existing requirement document has content deviation, manual analysis and extraction are complex and tedious, and the requirement document cannot be efficiently and accurately analyzed. The method comprises the following steps: performing layout analysis on text information and image information of the requirement document to obtain layout area information; classifying and positioning a plurality of elements in the page requirement information to obtain first key information based on the page requirement information; performing text analysis on the word sequence of the function requirement information to obtain second key information based on the function requirement information; performing layout recognition training on the requirement document based on the first key information and the second key information to obtain a comprehensive requirement model; and defining the requirement document to be analyzed to generate corresponding software development parameter information.

The invention provides a client heterogeneous federated learning method for a mobile computing environment, and relates to the technical field of federated learning methods, and the method comprises the following steps: screening a client most suitable for participating in federated learning from federated learning candidates through a prediction-based heterogeneous federated learning active configuration strategy; in combination with an interactive knowledge propagation strategy based on channel distillation and decoupling knowledge distillation, knowledge migration is realized through channel distillation and decoupling knowledge distillation technologies; a client-side in-turn chairman strategy is introduced, that is, each client side is allowed to serve as a chairman teacher in turn, and transmission of knowledge in the client sides is promoted. According to the method, on the premise of ensuring data privacy and security, core challenges such as strong dynamic property of client equipment, data isomerism and client personalized requirements (model isomerism) in a mobile computing environment can be effectively handled, so that the model performance in federated learning is remarkably improved, and an excellent comprehensive effect is realized.

A method for analyzing system safety requirements. The method includes parsing a plurality system and / or software specific requirements, and filtering the plurality system and / or software specific requirements using a generic system safety requirements (GSSR) model to generate a matched generic safety requirement in pre-set tables.

The application provides a software test case generation method and system based on domain knowledge constraints. A domain knowledge base covering domain basic knowledge and industry testing criteria is constructed; the original requirement text is normalized, the semantic understanding ability of the knowledge base and the large language model is combined, the requirement type is identified and the structured decomposition rule is dynamically matched, and a structured requirement model is constructed; the test focus features in the requirement are extracted, and the test item decomposition requirements are formulated; the large language model is driven to expand the scene and systematically disassemble the requirement, and the systematic test items are generated according to the test item decomposition requirements; the test cases are generated in multiple dimensions with the test items as the input, and the traceable relationship among the requirements, the test items and the test cases is established; the integrity check and consistency check are performed, and the test design result is output. The application enables the large language model to perform the test design task in a controlled and professional knowledge environment, and realizes the systematic generation from the natural language requirement to the executable test case.

The invention belongs to the technical field of computers, and particularly relates to a scheduling management method for server-free integrated learning reasoning service. Comprising the following steps: S11, acquiring historical request data, a base learner model and a system resource state in a cloud application system; s12, performing off-line model preference analysis on the historical request data, and performing model static weight distribution; s13, according to the static weight, the system resource state and the request requirement, the online requests are classified, the weight is finely adjusted, and the requests are scheduled to integrate reasoning results; s14, carrying out dynamic optimization on the scheduling model; and the like; according to the invention, by considering multiple factors such as demands, models, resources and the like, dynamic scheduling of requests is realized, and the balance of service precision, throughput and resource utilization rate is improved; besides, according to the method, the service performance and the resource occupation condition are improved by combining the characteristics of integrated learning reasoning service without a server and adopting a multi-model instance scaling algorithm based on reinforcement learning.