122 results about "Functional requirement" patented technology

The invention discloses a test case recommendation method and device, electronic equipment, a medium and a product. The method comprises the following steps: when it is monitored that a function demand is changed, obtaining change content; constructing a dynamic knowledge graph according to the change content; wherein the dynamic knowledge graph stores an association relationship among a function requirement, a test scene and a test case; collecting test environment state data; and outputting a to-be-recommended test case set according to the test environment state data and the dynamic knowledge graph. According to the method, the problems of low test case generation efficiency, incomplete coverage and inconsistency with requirements caused by frequent change of function requirements can be solved.

The invention provides a multi-agent-based code conversion system and method. The method comprises the following steps: preprocessing an input source code; performing block processing on the source code to obtain source code blocks, obtaining variable information and array size information of the blocks corresponding to the source code, and integrating the variable information and the array size information into complete variable information; fusing the code description file, the code blocks of the source codes and the variable information of the blocks corresponding to the source codes to generate target code blocks; debugging and checking the target code blocks in combination with the source code blocks; the code description file is used for guiding the intelligent agent to establish a jumping type relation in the code conversion process, and it is ensured that the generated target code better meets the actual function requirement of hardware; a block identifier is inserted into a long code to divide the long code into a plurality of blocks, so that the limitation on long code conversion is relieved; codes of different styles can be output by simply adjusting the cue word of the intelligent agent; through multiple times of debugging verification, the conversion quality of the target code is ensured.

A human-machine interface (HMI) development system leverages a generative AI model to assist in development of HMI projects in accordance with specified functional requirements, which can be provided to the development system as intuitive natural language spoken or written text. The system can formulate and implement HMI project edits during design time based on analysis of this natural language design input. After the HMI project is deployed as a runtime application, the system can also receive and process natural language requests to modify the runtime HMI in accordance with described modification criteria.

A human-machine interface (HMI) development system leverages a generative AI model to assist in development of HMI projects in accordance with specified functional requirements, which can be provided to the development system as intuitive natural language spoken or written text. The system can formulate and implement HMI project edits during design time based on analysis of this natural language design input. After the HMI project is deployed as a runtime application, the system can also receive and process natural language requests to modify the runtime HMI in accordance with described modification criteria.

An integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system uses a generative artificial intelligence (AI) model and associated neural networks to generate portions of an industrial automation project in accordance with functional requirements provided to the industrial IDE system in intuitive formats, such as spoken or written plain language text. The system uses generative AI to translate plain language requests or functional specifications into industrial control code, human-machine interface (HMI) applications, device configuration settings, or other aspects of an industrial control project.

The invention provides a business workflow dynamic arrangement and optimization method based on a large model agent, and the method comprises the steps: (1) analyzing the features and operation logic of a multi-element business workflow, and defining a business workflow description language; and (2) decomposing a complex business process construction process, and providing a layered process modeling system to guide a large model agent to construct a workflow model. And (3) constructing a task execution queue based on the task dependency relationship, and dynamically executing and monitoring the workflow. And (4) according to the execution log of the business workflow, discovering the bottleneck, redundancy and abnormity of the workflow model by adopting a process mining algorithm, and proposing a process improvement and optimization strategy. And (5) constructing a collaboration framework of a plurality of large model agents, and realizing dynamic arrangement, monitoring and optimization of the business workflow. According to the method, the defects that the current business workflow construction depends on experience of designers, and effectiveness and function requirements are difficult to guarantee are overcome; the defect that the business workflow is difficult to adapt to dynamic demand changes is overcome.

A data processing method, apparatus and device are provided. The data processing method includes: obtaining application requirement information according to function requirement information and preset template information; performing resource exploration according to the application requirement information; when a result of the resource exploration indicates that a target application is allowed to operate, according to the application requirement information, allocating, from target operation platform resource, application resource to the target application, and creating an operation environment for the target application; deploying and releasing the target application according to the application resource and the operation environment; wherein the target application is an application program corresponding to the application requirement information.

A human-machine interface (HMI) development and runtime system leverages generative artificial intelligence (AI) to create HMI dashboards or graphical interfaces without the need for manual coding based on natural language prompts submitted by the user, which specify the information the user wishes to see. In one or more embodiments, the system can support industry-specific prompt engineering services that assist a user in generating a customized HMI dashboard that satisfies specified criteria using natural language prompts that describe functional and visual requirements of the dashboard. The system can make use of a generative AI model and associated neural networks to generate suitable dashboards in accordance with functional requirements provided to the system as intuitive natural language inputs (e.g., spoken or written natural language text).

The invention discloses a manned space mission scene design and orbit analysis interaction method, which comprises the steps of S1, defining a space mission scene, and defining a mission target, a mission range and a mission constraint; s2, an MBSE model is constructed according to the scene, and modeling of a system architecture, function requirements and performance indexes is achieved; s3, generating an activity graph and an executable script based on the MBSE model, and establishing script execution logic; s4, starting simulation according to the activity graph and the script; s5, realizing linkage of MBSE simulation and track analysis software, and constructing a joint simulation environment; and S6, generating an orbit analysis scene in joint simulation, simulating a spacecraft trajectory, and generating a visual result and a parameter report. According to the method, a data link between the MBSE model and an orbit analysis tool is broken through, the problems of low manual construction efficiency, asynchronous parameters and insufficient verification are solved, the space mission scene design efficiency, model consistency and simulation reliability are remarkably improved, and effective support is provided for rapid design and optimization of complex manned space missions.

An integrated method for supporting complex system design and optimization selects first graphical-textual model frameworks in a problem domain; implements functional requirement verification in a validation domain and guides a physical architecture model; selects second graphical-textual model frameworks in a solution domain to establish a physical architecture model; generates an integrated simulation model oriented towards functional and non-functional requirements in the validation domain; selects third graphical-textual model frameworks in an optimization domain to establish a multidisciplinary optimization model, and establishes binding with preset subsystem parameters in the physical architecture model of the solution domain and output values of variables related to an objective function; automatically feeds an optimal optimization scheme obtained through iterative solving based on intelligent optimization algorithms to a system design model for automatic verification. The integrated method breaks down barriers between models of various stages, improves the development efficiency, and avoids the difficulty of seamless integration.

This invention discloses a method and system for constructing an AI-native operating system based on ecosystem collaboration, belonging to the field of operating system technology. The invention first acquires and decomposes target requirements to form a set of functional and non-functional requirements, constraints, and priority lists. Based on this, it generates an operating system architecture description including kernel configuration and subsystem division. Then, it generates and completes multi-dimensional checks on module code and configuration files, and compiles, links, and packages them through a toolchain to generate an operating system image. This invention employs a three-layer AI model to realize requirement reasoning, system construction, and test optimization, forming a complete automated closed loop from requirement input to runnable image output. Simultaneously, it establishes a unified ecosystem collaboration mechanism to support multi-vendor collaborative development. This invention effectively reduces errors and repetitive work caused by manual intervention, shortens the development cycle, improves system stability and adaptability, and promotes the collaborative evolution and improvement of the AI-native operating system ecosystem.

The invention belongs to the technical field of vehicle-road cloud integrated systems, and discloses a method for analyzing functional requirements of a vehicle-road cloud integrated system based on LLM, and the method comprises the following steps: S1, collecting and preprocessing basic data; s2, structured extraction of VRCIS requirements: semantic analysis is performed on the design requirement document based on LLM, a data structure is converted, and functional requirements and non-functional requirements are extracted; s3, constructing a VRCIS demand and benefit-critical person matrix: based on the output of the S2, mapping the function demand of each VRCIS benefit-critical person and the system design key dimension, and generating a standardized quantization matrix which can be identified by a machine; s4, quantitative analysis of demand priorities: identifying the demand priorities through quantitative analysis by utilizing the benefit-critical person matrix constructed in the S3 and fusing cost-benefit analysis; and S5, outputting a demand analysis result. The method is high in analysis efficiency, and the problems that in the analysis process, demand decomposition is not in place, the demand priority is not clear, and a general demand template cannot fully express VRCIS complexity are solved.

The present disclosure describes a system that includes a resource management system that is coupled to a plurality of processing systems and a plurality of memory systems. The resource management system determines resource capabilities provided by each of the plurality of processing systems, each of the plurality of memory systems, and connectivity between the plurality of processing systems and the plurality of memory systems. When the resource management system receives a workload request to perform a first workload, it identifies a first Directed Acyclic Graph (DAG) that includes a plurality of functions for performing the first workload and, for each of the plurality of functions, determines a processing system / memory system / connectivity combination based on the resource capabilities that provides a function performance capability that satisfies a function requirement for that function, and configures that respective processing system / memory system / connectivity combination to perform that function.

This invention discloses an AI workflow intelligent orchestration method and system based on a large model, comprising: receiving a user-input task description, input data, and target constraint information; performing semantic parsing on the task description using a large model to obtain a task intent model; decomposing the overall task into multiple atomic task nodes according to the task intent model, and generating a candidate workflow topology including atomic task nodes and logic control nodes; matching target tool components for each atomic task node in a pre-built tool library and initializing node parameters based on the functional requirements of each atomic task node; performing a pre-evaluation of the candidate workflow topology, and when the evaluation result is lower than a preset threshold, correcting the nodes, connections, parameters, or control logic to obtain the target workflow; and executing the target workflow to obtain the target result. This improves the orchestration flexibility, result quality, and execution reliability in complex task scenarios.

An integrated development environment (IDE) for designing, programming, and configuring aspects of an industrial automation system uses a generative artificial intelligence (AI) model and associated neural networks to generate portions of an industrial automation project in accordance with functional requirements provided to the industrial IDE system in intuitive formats, such as spoken or written plain language text. The system uses generative AI to translate plain language requests or functional specifications into industrial control code, human-machine interface (HMI) applications, device configuration settings, or other aspects of an industrial control project.

The application discloses a kind of deviation degree evaluation method and system between software requirement and code development, it is related to software engineering analysis technical field, including: the chapter title in software requirement specification, table and list item content are matched with functional requirement, according to the matching result, to the corresponding text content, extract the subject and action of function and the object and condition of action, and get requirement function point sentence according to set sentence pattern rule assembly;After extracting syntax unit and function call relationship from code generation, it is matched with the word in the preset code feature library, and the code function point sentence is obtained;According to requirement function point sentence and code function point sentence, calculate deviation degree, combined with preset deviation threshold, obtain evaluation result.Through the difference between code and expected requirement target, objectively evaluate code development deviation degree, solve the problems, such as inefficient collaboration, quality out of control and requirement disconnection, existing in large-scale code development.

The invention provides a method for testing a centralized self-healing function of a master station based on an RTDS (Real Time Digital System), which tests the centralized self-healing function of the master station based on a test system constructed on the basis of the RTDS, and fully considers various test conditions and implementation modes of the various test conditions which may influence the centralized self-healing function of the master station by a multi-energy scene by utilizing the flexible modeling capability of the RTDS. The test software judges whether the centralized self-healing function of the master station meets the function requirements under the multi-energy scene or not by comparing the preset column of the information comparison table with the actual condition column, and the method has the characteristics of intuition and convenience in implementation.

This invention relates to a customizable SaaS configuration method for multiple backend applications, specifically in the field of custom configuration for multiple backend applications. It defines basic database configuration, basic server configuration, basic security configuration, basic network configuration, basic storage configuration, and basic log configuration for different application systems. Based on single responsibility and application system components, the application system is divided into multiple independent modules. Dependencies between modules are identified by recording the call order and time relationships between modules through time-series analysis. Parameters and configuration items are categorized and defined according to their module and function, applicable environment, importance and scope of influence, security level, and data type. Resource templates are freely combined to form an overall architecture based on the functional requirements of different applications. The behavior and style of modules are controlled by parameters and configurations corresponding to the functional requirements of the applications within the architecture, establishing an efficient and reliable application hosting system.

The invention discloses an intelligent agent life cycle management method and system based on an eight-stage closed loop. The method comprises the following steps: determining eight life cycles, which are connected in sequence, of an intelligent agent; implementing a Prompt stage, and receiving and analyzing a target function demand; in the implementation Code stage, automatic programming is carried out, and implementation codes meeting the target function requirements are created; implementing the Build stage, and integrating the codes generated in the Code stage into a target agent capable of running; implementing a Test stage, and performing automatic simulation and evaluation of non-determinacy behaviors on the target agent; in the Release implementation stage, a target agent is released, and check before deployment is executed; implementing a Deploy stage, and deploying the target agent to a target operation environment; implementing an Operate stage, and continuously detecting the running state of the target intelligent agent; according to the method, a Retrain stage is implemented, a large language model on which a target agent depends is optimized on the basis of operation data collected in an Opate stage, the improved large language model acts on a Prompt stage, and a new round of development and optimization circulation is driven.

The invention discloses a control system deployment method and device, equipment and a storage medium, and the method comprises the steps: determining at least one to-be-deployed physical equipment in a target control system according to a subsystem of the target control system, and determining a data communication protocol between different physical equipment according to the equipment type of the physical equipment, generating an equipment topological structure; determining at least one system function block corresponding to a subsystem based on system function requirements of different subsystems in the target control system, and generating a function block topological structure based on a function logic relationship between the system function blocks; and determining a system deployment model of the target control system based on the equipment topological structure and the functional block topological structure. According to the technical scheme, the system logic structure of the target control system is optimized, and the association degree between different subsystems is improved.

This invention relates to a cockpit simulation instrument system based on modular design and a configuration and testing method based on this system. The system includes: a host computer control system for generating configuration calibration commands and verification test commands; and a slave computer control system including a main control unit, common function components, and pointer drive components, wherein the common function components are selected according to the instrument's functional requirements, and the pointer drive components are selected according to the motor type and instrument type. The slave computer control system communicates with the host computer control system via a CAN bus and is used to: respond to configuration calibration commands, drive the instrument pointer to complete calibration and feed back a first operating status frame to the host computer control system; and respond to verification test commands, drive the instrument pointer to perform verification test movements and feed back a second operating status frame to the host computer control system. This invention can effectively realize the configuration and testing functions of the slave instrument, improve design efficiency, and reduce functional testing time.

The application discloses an AI auxiliary-based plug-in automatic generation method and device, which comprises the following steps: receiving a natural language plug-in function requirement input by a user; performing deep semantic understanding and intention recognition on the requirement by using a large language model, and analyzing the requirement into a structured plug-in specification, including a function point list, a data structure definition, an interface calling plan and a plug-in function type; selecting an adaptive template from a preset template library according to the plug-in function type, and automatically constructing a plug-in basic engineering skeleton; calling an AI model to generate a business logic code containing an API calling placeholder on the skeleton; referring to a main system API definition document, automatically generating adaptive code and replacing the placeholder, and forming a complete code engineering; automatically packaging and deploying the engineering to a sandbox test environment, generating simulation data based on the data structure to perform automatic testing and capture errors; and after the testing, automatically registering the plug-in to a main system plug-in market or a plug-in library, so that the automatic generation and release of the plug-in are realized.

A human-machine interface (HMI) development system leverages a generative AI model to assist in development of HMI projects in accordance with specified functional requirements, which can be provided to the development system as intuitive natural language spoken or written text. The system can formulate and implement HMI project edits during design time based on analysis of this natural language design input. After the HMI project is deployed as a runtime application, the system can also receive and process natural language requests to modify the runtime HMI in accordance with described modification criteria.

The application discloses a control system design method for space gravitational wave detection. The method first performs modeling on a problem domain of the control system, constructs a first mapping matrix from a functional requirement to a behavior element, performs decoupling verification on the first mapping matrix until the first mapping matrix is in a lower triangular structure. Then, modeling is performed on a solution domain of the control system, a second mapping matrix from the behavior element to a design parameter is constructed, and independence verification is performed on the second mapping matrix until the second mapping matrix is in a lower triangular structure. Finally, a composite equivalent mapping matrix of the two-level matrix is calculated, and is used for change management of the control system. The application can realize formalized design and quantitative evaluation of a strong coupling control system architecture, identify coupling problems in an early design stage, improve design iteration efficiency and change management accuracy, and is mainly applied to control system design for space gravitational wave detection, and can also be applied to design scenes of various high-coupling high-precision space control systems.

Described herein is a method for managing generating of developmental requirements for a software application for achieving a business objective comprising receiving user input comprising input user requirements for meeting the business objective, and processing the user input by a first language model using a first reference template for generating first artifacts therefrom, the first reference template comprising reference input, a plurality of reference requirements and reference descriptors and the first artifacts comprising output requirements and output descriptors substantially correlated with the reference requirements and reference descriptors. At least a portion of the first artifacts forms a portion of the functional requirements. The first artifacts are further processable by at least one additional language model using at least one additional reference template for generating additional artifacts.

A calculator system holds an element database managing an element as design information for implementing a non-functional requirement, and a history database storing an implementation history of a past non-functional requirement. The calculator system acquires information on the non-functional requirement implemented on an IT system achieved by an architecture as a base, refers to the element database to generate a plurality of combinations of the elements, and for each of the plurality of combinations of the elements, gives a priority representing the recommendation rank of each of the plurality of combinations of the elements. When the combination of the elements satisfying a constraint condition is not present, a substitute plan is generated by using the implementation history. The calculator system outputs at least one of the combination of the elements or the substitute plan sorted on the basis of the priority.

The invention discloses a modeling and verification method and system for a multi-level system functional architecture of complex equipment. The method comprises the following steps: by establishing a traceability relation matrix of use cases and demands, ensuring the integrity of demand coverage; carrying out platform-level function logic flow modeling based on the activity graph and an OODA ring; generating a plurality of alternative architecture schemes; performing quantitative optimization by using a comprehensive evaluation function fusing the function coupling degree, the interface complexity and the information flow efficiency; function requirements and interface requirements are automatically mapped to a first-level system module; constructing a unified time state interaction graph model and compiling to generate an executable code; closed-loop verification is carried out through human-in-loop testing in a digital twin environment; and the verified accurate demand is output in a structured manner. According to the method, the problems that in complex equipment development, demand tracing is difficult, architecture design depends on experience, and verification and design are disjointed are solved, automation, modeling and precision of the whole process from demand to verification are achieved, and design efficiency and system reliability are remarkably improved.

Various implementations relate to methods, computer-readable media, and to automatically insert a script in a virtual environment. In some implementations, a method includes receiving a natural language prompt from a user, the natural language prompt specifying one or more functional requirements for the script. The method further includes providing the natural language prompt, a default prompt, and context information to a large language model (LLM). The method further includes obtaining, as output of the LLM and in response to the providing, the script and an attachment location for the script, wherein the attachment location identifies a particular entity associated with the virtual environment. The method further includes inserting the script in the virtual environment at the attachment location, wherein the script is executable in the virtual environment after the inserting.

The invention discloses a plug-in processing method and device, a storage medium and electronic equipment. The method relates to the technical field of computers, and comprises the following steps: determining a to-be-loaded first plug-in according to a function requirement of a target object; obtaining the first plug-in from a plug-in warehouse through a target plug-in platform; checking the first plug-in to obtain a checking result; and loading the first plug-in to a target plug-in platform according to the plug-in dependency relationship of the first plug-in under the condition that the test result represents that the test is passed, so as to provide service for the target object through the first plug-in. Through the method and the device, the problem of relatively low plug-in application efficiency caused by lack of a plug-in management system supporting plug-in in related technologies is solved.

Generative AI cue cycling and refinement for industrial automation visualization are provided. A human-machine interface (HMI) development system utilizes a generative AI model to assist development of HMI projects according to specified functional requirements, and the functional requirements can be provided for the development system as visual natural language spoken language or written text. The system may formulate and implement HMI item edits during design time based on analysis of the natural language design input. After the HMI item is deployed as a runtime application, the system may also receive and process natural language requests to modify the runtime HMI according to the described modification criteria.