207 results about "Functional requirement" patented technology

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 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.

The invention discloses a low-code automatic generation method based on AI agent collaboration, and relates to the technical field of computer processing, and the method comprises the following steps: S1, receiving a function demand description text input by a user through a natural language; s2, an input analysis agent reads and analyzes the function requirement description text, key information is extracted, and the key information comprises a key intention, an entity object and an operation constraint condition; s3, performing semantic similarity matching in a predefined model library by a model retrieval agent according to the key information, and retrieving and defining a basic generation model with closest semantics; and S4, receiving the basic generation model and the key information by an adaptive modification agent, and performing context semantic understanding enhancement on the basic generation model. According to the method, end-to-end automatic conversion from business demand description to executable codes is realized through natural language understanding and an automatic code generation technology.

The invention provides a personalized function page generation method and system based on a large model, and belongs to the technical field of large models.The personalized function page generation method comprises the steps that firstly, function page requirement description including function expectation and style preference input by a user through a natural language is obtained, and then a requirement analysis module of the large model is called for semantic analysis; the method comprises the steps of obtaining function demand features and style preference features, then retrieving a preset component library matching page component set based on the function demand features, determining component display attributes according to the style preference features, then generating an initial function page layout according to the component set, the display attributes and a preset layout rule, and finally monitoring user interaction behavior data in real time. And a large model dynamic adjustment module is input to adjust the display attribute and the layout structure of the page component to obtain a personalized function page, so that the function page meeting the personalized requirements of the user can be efficiently and accurately generated.

The invention discloses a cloud native security configuration system and method based on container analysis and LLM, and the system comprises a mirror image static analysis module which is used for compiling a Linux kernel and a standard library source code to extract intermediate representation, and tracking and constructing a double-layer mapping relation library from the system call to the container capability; the dynamic binary extraction module is used for executing the container mirror image and extracting a binary file of a core function; the mirror image static and dynamic association module is used for executing two-stage static binary file analysis and extracting function requirements required by system calling and mirror image for loading during container running; and the cue word construction and model fine adjustment module is used for constructing cue words, performing fine adjustment on the LLM model and generating a minimum privilege function limitation configuration file of the container. According to the method, static analysis and runtime analysis are combined, the container mirror image is thoroughly checked, the function, which is specified by a user and exceeds the original definition of the mirror image, of the LLM model is finely adjusted through the cue word, and privileged function security configuration is generated according to specific requirements.

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 invention provides a communication module function extension method and system applying dynamic loading, and relates to the technical field of communication, and the method comprises the steps: firstly, determining a dynamic loading trigger condition space containing function requirements, module resources and external environment trigger conditions, and collecting data to generate a trigger condition matching data set; then screening adaptive function modules from a preset function module library, and matching corresponding information based on different triggering conditions to generate an adaptive function module set; then carrying out cooperative adaptation processing on the adaptation function module and the communication module to generate a cooperative adaptation result; dynamically injecting a module based on a collaborative adaptation result and constructing a function extension calling architecture; and finally, collecting operation interaction data to generate a communication module function extension running chart, thereby realizing flexible, efficient and stable extension of the communication module function, and improving the adaptive capacity and competitiveness of the communication module.

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.

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 discloses an adaptation and cutting method and system based on an electric power terminal equipment swan operating system, and the method comprises the steps: firstly carrying out the adaptation between the function demands and performance requirements of the electric power terminal equipment and the type of the swan operating system and the type of a chip; then, a service management center, an equipment event monitor and an application incubator process are started in sequence through an initialization script, system service adaptation is achieved, hardware nodes needing to be clipped are removed based on a layered clipping strategy, customized function clipping can be conducted on the swan mongolian operating system according to different electric power application scenes, then drive adaptation is conducted, and the operation efficiency of the swan mongolian operating system is improved. According to the method, the power terminal equipment is ensured to stably and efficiently operate under the selected swan-gap operating system, and finally old application transplantation and new application deployment on the power terminal equipment are realized, so that efficient operation and function customization of the power terminal equipment under the swan-gap operating system can be realized; and the overall performance and reliability of the power Internet of Things system are greatly improved.

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.

The invention relates to the technical field of electronic design automation and artificial intelligence, in particular to an AI-assisted multi-target PCB automatic design system and method, and the system comprises a user interface layer which is used for a user to input a request through a Web-based interface; the natural language analysis module is used for processing the request and generating a JSON (JavaScript Object Notation) containing function requirements; the device selection module queries an internal device database and suggests an ESP32-C3 MCU (Microprogrammed Control Unit), a compatible relay and a support circuit; the circuit generation module is used for creating a netlist and a schematic diagram file and comprises an MCU (Microprogrammed Control Unit), a relay driver and related passive elements; the layout optimization module runs a multi-target algorithm and is integrated with PCB design software; experimental verification shows that compared with a traditional one-way process, by means of the method, the requirement interpretation accuracy can be improved to 94.2% from 76.3%, the design intention preservation rate is improved to 89.5% from 42.7%, the requirement change adaptation time is shortened to 2.1 h from 8.4 h on average, and the requirement interpretation efficiency and accuracy are improved.

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.

The embodiment of the invention provides a code checking method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence application, and the method comprises the steps: obtaining a code file and a project document, and carrying out the content extraction of the project document through a literary and physical agent, the method comprises the steps of obtaining function requirement information and programming specification requirements for a code file, conducting code analysis on the code file through a programming agent according to the function requirement information and the programming specification requirements, extracting a first code block needing to be modified from the code file, and outputting a code suggestion for the first code block, the code suggestions are evaluated through the review intelligent agent and the literary and sports intelligent agent, the evaluation result for the code suggestions is obtained, and the review report file corresponding to the evaluation result is output, so that the comprehensiveness and accuracy of inspection are improved, the inspection efficiency is effectively improved, developers can visually and effectively learn based on the review report file, and the development efficiency is improved. And the programming capability is improved.

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.

The invention designs and implements a performance test environment construction method oriented to cross-chain transaction of block chains. The system supports a user to customize test scripts and execution parameters according to test requirements, tasks are executed and workloads are initiated at the same time through multiple actuators, a load scene of a tested cross-chain application system is created, the whole process of cross-chain transaction from an initiator to a receiver is tracked at the same time, key data are collected, and performance test indexes are calculated. And a visual result report is generated. Comprising the following steps: analyzing system requirements, and describing and analyzing functional requirements and non-functional requirements of a system; carrying out summary design on the system, and respectively designing and describing the system from a logic view angle, a process view angle, a development view angle and a physical view angle; detailed design is carried out on each module of the system, and a scheme is designed for the modules from two different perspectives of a class diagram and a time sequence diagram; and carrying out system testing and summarization on the system.

The present application relates to the technical field of computers. Provided are a frequency adjustment method and an electronic device, which make the frequency of a GPU meet GPU performance requirements or functional requirements, thereby ensuring the usage experience of a user. In the method, a load that matches a scenario corresponding to the currently rendered image can be determined from among a predicted load and a historical load, and the frequency of the GPU is then determined on the basis of the load, wherein the predicted load can be obtained on the basis of rendering information of the currently rendered image frame. A scenario of an image frame can be determined on the basis of an application state or a system state. The greater the load matching the scenario, the higher the determined GPU frequency, and thus a GPU can process more loads, thereby improving performance, reducing stuttering during image frame display, and improving the usage experience of a user; and the smaller the load matching the scenario, the lower the determined GPU frequency, and thus loads to be processed by the GPU are minimized, thereby reducing the power consumption of the GPU.

An artificial neural network (ANN)-based program testing method, which tests a target program on which functions and codes are run. The method includes constructing a test model by means of an ANN, then inputting input values of a target program into the test model, and computing test output values according to the test model; then inputting the input values into the target program so as to obtain actual output values; and selecting, according to deviations between the test output values and the actual output values that correspond to the same input values, input values corresponding to top-ranked 50 to 150 deviations, transmitting the selected input values to the target program for running, comparing running results with an actual functional requirement, and determining that the target program has a defect under the condition that one of the running results does not satisfy the actual functional requirement.

The specification relates to the technical field of computers, in particular to a transaction processor implementation method and device in a simulation acceleration platform. The method comprises: based on the functional requirements of a transaction processor to be developed, analyzing the functional logic of a verification component UVC in a simulation environment, which is related to the functional requirements; converting the time sequence related transaction processing logic in the functional logic into hardware description language code, loading the hardware description language code into the hardware resources of the simulation acceleration platform to obtain the hardware part of the transaction processor; converting the dynamic resources and control logic in the functional logic into software code and packaging it as a function that can be called through a direct programming interface DPI as the software part of the transaction processor. The method avoids the huge workload required for developing a transaction processor from scratch for a simulation acceleration platform, and realizes the migration and reuse of verification assets between different verification platforms.

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 discloses a combined drive control method and system for a functional concrete processing cycle, and relates to the technical field of intelligent control, and the method comprises the steps: carrying out the multi-dimensional feature collection of raw materials of concrete, obtaining the feature information of the raw materials, and carrying out the screening analysis of a combined drive control database through taking the feature information as a constraint, thereby obtaining a control reference database; dividing a control reference database; analyzing functional requirements of the concrete, and determining a functional evaluation plan; performing optimization traversal on the reference library without the additives to obtain a first optimal control strategy, and performing optimization traversal on the reference library with the additives to obtain a second optimal control strategy; and comparing the first optimal control strategy with the second optimal control strategy to obtain a target control strategy, and performing combined drive control on the concrete. The technical problem that the processing control strategy cannot be intelligently matched according to the characteristics of the concrete raw materials in the prior art is solved, and the technical effects of accurate matching and functional control optimization of the concrete processing strategy are achieved.

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 invention provides a convolution operator deployment method and device, equipment, a storage medium and a program product, and relates to the technical field of computation.The convolution operator deployment method comprises the steps that a weight value in a convolution kernel is split, and convolution kernel splitting information is obtained; the convolution kernel splitting information comprises N initial weight value combinations, and copying each initial weight value combination to obtain at least one copied weight value combination corresponding to each initial weight value combination; deploying each initial weight value combination and the corresponding at least one copy weight value combination on a target array in a computing array of the chip; one target array comprises N sub-arrays; one weight value combination is deployed in one column of the subarray, any two adjacent columns of weight value combinations are deployed in a dislocation mode, the row occupied space of a calculation array of the chip is reduced, the number of times of repeatedly reading input data is reduced, and the storage function requirement and operation power consumption of the chip are reduced.

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.

The invention relates to the technical field of data enhancement and system engineering, in particular to a user behavior data enhancement method of a large language model based on RFLP driving. Comprising the steps of determining a core driving problem and an expected application scene of to-be-generated user behavior data, determining a task target, a data structure requirement, a data characteristic requirement, a constraint condition and a non-functional requirement of a large language model data generation system based on RFLP driving according to the core driving problem and the expected application scene, and encoding the requirement specifications into a plurality of functional modules; and forming a data generation function set according to the modules, constructing a logic scheme based on'strategy-component-combination 'for the system based on the function set, converting the logic scheme into an executable scheme, finally controlling the large language model to execute the executable scheme, and generating to-be-generated user behavior data. Therefore, the problem that the cue word is difficult to ensure the quality and consistency of the generated data is solved, and a scientific methodological support is provided for constructing a high-quality, controllable and extensible synthetic user data generation system.

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.