126 results about "Requirements analysis" patented technology

The invention discloses a structured text code generation and review method, which comprises the following steps of: receiving a control task demand input by a user, supporting natural language, structured form or graphical process input, performing deep semantic analysis by utilizing a demand analysis module, identifying control conditions, control actions and constraint factors, and generating and reviewing a structured text code. The task is converted into a plurality of control units through the SCoT chain; the SCoT chain reader generates a semantic vector according to a task demand, the AST reader generates AST representation through a grammar rule structure and a hierarchical relationship, and the decoder predicts a next grammar rule and generates an ST code in combination with the SCoT chain and AST features; and verifying grammar and logic of the ST code through an interactive automatic review and repair mechanism, if compiling errors occur, analyzing errors by the teacher model and providing repair suggestions, and performing code repair by the student model according to guidance until the code conforms to an expected function and passes compiling.

The invention relates to the technical field of artificial intelligence, in particular to a visual large screen development method and system based on a multi-modal large model, and the method comprises the following steps: a demand analysis step, an intelligent generation step, a visual arrangement step, a configuration management step and an operation support step. The method has the beneficial effects that the system can synchronously analyze natural language instructions, business data parameters and visual reference examples through a multi-modal semantic alignment engine, and a design scheme with both function completeness and aesthetic consistency is generated. In scenes of smart city situation awareness, industrial equipment real-time monitoring and the like, a user only needs to input demand descriptions of'displaying energy consumption trends and abnormal point locations of all regions in recent half a year 'and the like, a complete large screen scheme including a thermodynamic map, a time sequence broken line graph and a 3D alarm mark can be automatically generated, and the development period is shortened by more than 70% compared with a traditional mode.

The invention provides an end-to-end intelligent system development method and system based on an AI large model. The method comprises the following steps: S1, receiving a system demand described by a user through a natural language; s2, analyzing the requirements through an AI large model and converting the requirements into formalized specifications; s3, automatically generating a system architecture and dividing components; s4, generating each component code in a recursive manner; s5, dynamically detecting and repairing the generated code problem; s6, automatically integrating all components to form a complete system; and S7, continuously optimizing the system based on system feedback or user feedback. According to the method, work of multiple stages such as demand analysis, architecture design, module division, code writing and testing is automatically completed, distributed integration and smart contract are performed, distributed integration verification is performed through a block chain smart contract and a graph database, data consistency, interface matching and security among components are ensured, the integration process is optimized, and the integration efficiency is improved. Cross-domain cooperation is achieved, and the development efficiency is high.

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 circuit board module deformation testing system and method for SMT automatic patch forming, and belongs to the technical field of circuit board deformation testing, and the system comprises a warping judgment execution demand analysis module, a first execution strategy determination module, a second execution strategy determination module and a support sinking adjustment module. The circuit board warping and supporting structure deformation risk can be accurately quantified, the Z-axis compensation value, the mounting speed, the pressure and other parameters are dynamically adjusted, mounting deviation caused by circuit board deformation is compensated in real time, the element mounting precision is controlled within the preset range, meanwhile, through hot spot area tracking and supporting sinking graded adjustment, the element mounting precision is improved, and the element mounting precision is improved. The stress concentration risk is reduced, the problems of poor welding, element breakage and the like caused by aggravation of deformation are avoided, the stability of the SMT mounting process is improved, the deformation-related reject ratio is reduced, and the consistency and reliability of the mounting quality of the circuit board are improved.

The invention relates to the technical field of computer data processing, in particular to an intelligent model system for automatically generating a standard protocol interface, which comprises the following steps: acquiring demand information input by a user through a demand acquisition module and converting the demand information into standardized demand parameters; the protocol knowledge base module outputs a compliance rule set according to the target protocol type, and the interface generation module automatically generates an interface source code, a configuration file and an interface document according to the standardized demand parameters and the compliance rule set. The verification module verifies and optimizes the generated interface, and the self-learning module updates system analysis logic and rule content according to a verification result. The system adopts a natural language processing and structured analysis double-path fusion technology to realize accurate analysis of requirements, realizes intelligent matching of protocol rules through a protocol knowledge graph, completes automatic generation of codes based on a symbol reasoning and neural code generation technology, forms a complete closed loop of requirement analysis, protocol adaptation, code generation and verification optimization, and realizes automatic analysis of the requirements. And the interface development efficiency and quality are effectively improved.

The invention discloses a development management system and method for an API interface, and relates to the technical field of software engineering. The system comprises: an intention understanding and design engine, which analyzes a multi-modal service demand into a structured API development task; the agents collaboratively develop a network, and the multiple AI agents are dispatched to generate API specifications, codes, test cases and deployment configurations in parallel; the API ecological dynamic map construction module is used for automatically constructing and updating a dynamic map of dependency and data flow direction between APIs based on a generated product; a continuous learning and evolution engine analyzes the atlas and operation data to identify optimization points and generate or execute evolution policies. All the modules form a closed loop, and output of the evolution engine is fed back to the intention understanding engine to optimize subsequent design. The corresponding method comprises the steps of demand analysis, intelligent collaboration, atlas construction and closed-loop optimization. According to the method, automatic development, intelligent collaboration and continuous architecture optimization of the API are realized, and the development efficiency and the system maintainability are improved.

The invention relates to the technical field of automatic programming, in particular to a front-end and back-end code generation and deployment system based on a text AI model, and the system comprises a demand analysis module, a code generation module, an environment adaptation module, a task scheduling module and a process optimization module. According to the method, through the intelligent task scheduling and environment adaptation strategy, the efficiency and accuracy of front and rear end code deployment are remarkably improved, the task execution path and time interval are optimized, the resource utilization rate of the system is increased, the priority and execution area of task deployment are accurately matched, redundant calculation and resource waste are reduced, and the task deployment efficiency is improved. The execution efficiency of distributed tasks is accelerated, an intelligent adaptive script identifies and adjusts environment differences in real time, the problems caused by environment configuration differences are solved, the code deployment automation and precision are improved, resource allocation and task scheduling path selection are optimized, the system can quickly meet the complex deployment requirement, and the deployment efficiency is improved. And efficient completion and quick response of tasks are ensured.

The invention relates to the technical field of software testing, in particular to an AI-based low-code intelligent test generation system which comprises a demand analysis module, a test path generation module, a test script conversion module, an execution environment management module and a result analysis module. A test path of the test path generation module is supplied to the test script conversion module and the result analysis module, a test script generated by the test script conversion module is sent to the execution environment management module, and an execution result of the execution environment management module is fed back to the result analysis module; and an optimization suggestion generated by the result analysis module is sent to the test path generation module and the test script conversion module. Through cooperation of multiple modules, full-process automation of low-code testing is achieved, testing paths and scripts are dynamically optimized, testing efficiency and coverage pertinence are improved, manual intervention and the error rate are reduced, and dynamic adaptation and execution stability of a testing scheme are ensured.

The invention provides a unified computing power management and control platform and method.The computing power management and control platform is integrated with a plurality of intelligent computing centers in different position areas through a unified northbound interface, a resource management module abstracts physical resources of the intelligent computing centers into virtual resource data, and a unified resource pool is constructed; the data acquisition module receives operation monitoring data of each intelligent calculation center and preprocesses the operation monitoring data; the resource sensing module is used for constructing a dynamic resource portrait for the uniform resource pool based on the preprocessed operation monitoring data; the task sensing module performs resource constraint and service quality demand analysis on a calculation task submitted by a user, and constructs a task portrait; the resource scheduling module forms a resource scheduling decision result according to the dynamic resource portrait and the task portrait; and the resource arrangement module performs resource scheduling and calculation task allocation according to the resource scheduling decision result. The overall utilization rate of the heterogeneous computing power cluster is remarkably improved, and the island effect of a traditional resource pool is broken through.

The invention relates to the technical field of computers, and discloses a test case generation method and device, equipment and a medium, and the method comprises the steps: receiving a software test demand through a demand analysis agent, extracting a test point of the software test demand, and constructing a target knowledge graph according to the test point; generating an initial test case based on the test target knowledge graph through the case generation agent; executing the initial test case through the execution verification agent to obtain a test result; the test result comprises validity judgment and judgment basis of the initial test case; and in response to the test result, judging that the initial test case is an invalid test case, optimizing strategy parameters of each agent in the agent system based on the judgment basis through the evolutionary learning agent, and generating a new test case. The application can respond to the change of the test demand in real time, automatically adjust the test strategy, and improve the test quality and efficiency.

This application discloses a method and system for audit data analysis requirements parsing based on metadata relationship graphs. By acquiring audit text, metadata relationship graphs, audit rule bases, business-level templates, and business benchmark data, the audit text undergoes semantic flow parsing and is mapped to the metadata relationship graph. A field strength correlation graph is generated through propagation calculation. Activated nodes are selected based on field strength thresholds and their relationships are expanded using the audit rule base to construct activated subgraph data. Based on the business-level templates, the activated subgraphs are reconstructed into a tree-like parsing structure and pruned according to field strength to generate a pruned parsing tree. Finally, node perturbation coefficients are calculated based on the business benchmark data and hierarchically aggregated according to the tree structure to generate an audit requirement perturbation graph. This transforms the traditional audit data analysis process, which relies on manual experience, into an automated and intelligent graph calculation process, significantly improving the accuracy, comprehensiveness, and efficiency of audit data analysis.

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.

This invention relates to the technical fields of data quality, data management, and data governance. Specifically, it relates to a method for simultaneously conducting data governance within a newly built business information system. This method establishes a general data management standard covering data specifications, database specifications, and process specifications; builds a supervisory standard that runs through the entire process of project requirements analysis, design, development, deployment, and operation and maintenance; and integrates these two types of standards into the technical requirements of the newly built business system project. Data governance is implemented step-by-step at each stage of the project, thus avoiding data quality problems from the source of business system construction. This avoids the additional costs and schedule risks of traditional post-event governance, ensures that the system data format is uniform, secure, and compliant, and can seamlessly integrate with the enterprise's existing data governance system, solving the problems of data silos, redundancy, and inconsistency in multi-system parallel scenarios.

This invention relates to a multi-objective trade-off optimization and decision-making method and system for electric drive controllers, including: constructing a requirements analysis model; constructing a top-level performance target analysis model; constructing a top-level system architecture design model, constructing test models for subsystems, sub-functions, and sub-modules, and designing test cases; constructing design modules, implementation path models, and corresponding simulation models for subsystems, sub-functions, and sub-modules; multi-objective trade-off optimization; prototyping and verification; building a digital twin system; and knowledge base construction and iteration. This invention enables early quantitative evaluation and automatic optimization of design schemes.

A requirements analysis system outputs an existing requirement, to which a topic is assigned, as training data. The number of first requirements, to which at least one is assigned by a topic and a sub-topic, is greater than or equal to a threshold. This training data is used to train a classification model that assigns topic candidates to at least a second requirement, described in a second document as the analysis target, and in which the number of topics is adjusted. The system receives the training data to create the classification model, feeds the second requirement into the classification model, assigns topic candidates to the second requirement, and assigns sub-topics to the second requirement for each topic candidate, in relation to the topic candidates obtained from the hierarchical dictionary.

This invention discloses a software testing requirements analysis method based on knowledge graphs, comprising the following steps: Step 1: Collecting requirements-side and verification-side data to form a test requirements analysis corpus; Step 2: Extracting semantic objects of requirements, behaviors, and assertions; Step 3: Constructing a three-domain test knowledge graph; Step 4: Generating a test assertion tension field; Step 5: Inputting the test assertion tension field into an improved Hypergraph Transformer model to obtain a test requirement tension propagation feature map; Step 6: Generating a test requirement gap map; Step 7: Obtaining the test requirement decomposition results; Step 8: Updating the three-domain test knowledge graph and the test assertion tension field based on test execution feedback. This invention employs an assertion gap tension resonance mechanism and an improved Hypergraph Transformer model to improve the accuracy of software testing requirement gap identification and decomposition.

A building system can operate to receive a requirement to implement an artificial intelligence (AI) service. The requirement can include an indication of a type of an entity, wherein the AI service is configured to generate an analytic for the entity of the type of the entity or a control setting for the entity of the type of the entity. The requirement can include a data element that the AI service is configured to operate on to generate the analytic or the control setting. The building system can operate to determine that the building system meets the requirement to implement the AI service responsive to a determination that a digital twin of the building system includes the entity of the entity type and the data element and implement the AI service responsive to a determination that the building system meets the requirement.

The invention discloses an intelligent automatic test system and a test method, and belongs to the technical field of software testing, the system comprises a hardware layer and six software modules, namely a demand analysis module, a large model processing module, a test execution module, a self-healing optimization module, a result analysis module and a knowledge base management module, which are deployed on the hardware layer. The method comprises the following steps: extracting structured data through a demand analysis module, and intelligently generating test assets by a large model core processing module; the test execution module adopts a fusion positioning technology to carry out multi-terminal automatic test; the self-healing optimization module monitors the interface change in real time and automatically corrects the script; the result analysis module identifies defects and generates a report; and the knowledge base module continuously iterates to provide support. According to the invention, full-process automatic closed loop from demand to report is realized, the problems of low efficiency, poor compatibility and high maintenance cost of the traditional test technology are solved, and the test intelligence level and the adaptive capability are obviously improved.

The invention discloses a normalized programming method and device for an information system and electronic equipment. The method comprises the steps of S1, demand analysis; step S2, specific specification conversion; s3, generating a subfield code; step S4, testing sub-field codes; s5, field codes are generated; and step S6, field code testing. Through meta-specification driving, closed-loop verification and intelligent repair during operation, full-link normalization and automatic development from demand to operation and maintenance are realized, and the technical problems of demand-implementation fault, specification deficiency, high manual dependency degree, abnormal response lag and the like existing in traditional information system development are effectively solved; and the development efficiency, the system reliability and the maintainability are obviously improved.

The invention discloses a function test method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a to-be-optimized test case set from a test case library according to optimization content; according to the optimization content, determining an equivalence class updating type and an associated equivalence class of input and output items related to updating in the to-be-optimized test case set; determining a corresponding test case optimization scene according to the equivalence class update type and the associated equivalence class, and updating a to-be-optimized test case set according to the test case optimization scene to obtain an updated test case set; and controlling the updated test case set to execute a function test task. By adopting the technical scheme of the embodiment of the invention, specific operation steps are given in links of demand analysis, test case set selection, test case set maintenance, test case set execution and the like, the integrity is realized, and the scientificity, sufficiency and effectiveness of function test design and execution are improved.

The application discloses a PLC code generation and verification method based on a large language model, which comprises the following steps: generating multi-stage multi-modal input data, wherein the multi-stage multi-modal input data is used for representing user requirements and formulating a system design scheme according to a requirement analysis result; the multi-stage comprises a requirement analysis stage and a design stage of a complete engineering cycle; the multi-modal input comprises a P&I D diagram and natural language; then, PLC code is generated according to the multi-stage multi-modal input data; then, the generated PLC code is verified and tested, wherein the verification and testing comprises code verification and compiler verification; finally, the PLC code is deployed after the verification and testing of the PLC code are completed. The embodiment of the application has high accuracy and high automation degree, can provide a network security researcher with an efficient and accurate PLC code deployment method, and can be widely applied to the technical field of computers.

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 belongs to the technical field of software analysis, and provides a function point extraction processing method and system. The method obtains data sets by collecting requirement analysis texts, software design texts and system design texts available through public channels, and performs function point labeling to establish a knowledge base. According to the established knowledge base, the KMP algorithm is used to perform string matching between the function points in the knowledge base and the texts to be processed, so as to extract the function points contained in the texts to be processed and determine the category labels of the function points. The texts to be processed are input into a pre-constructed automatic extraction model, and the function points contained in the texts to be processed and the categories to which the function points belong are output. The function points extracted based on the knowledge base and the function points extracted using the automatic extraction model are sorted, evaluated and calculated to screen the function points. The application realizes a faster and more effective automatic extraction process of function points, and effectively avoids the problem of missing function points.

This invention provides a method and apparatus for data verification and backup in a power control center, comprising: acquiring hardware resource information corresponding to the operating system and constructing a thread resource pool; connecting to the power control center's data source, classifying and rating the data source, performing real-time requirement analysis using a parameterized hybrid time algorithm, and obtaining a data task queue with priority marking; monitoring the operating system load status and task queue length, applying an online distributed queue length estimation algorithm for resource evaluation, dynamically creating worker threads and allocating resources; performing parallel data verification using a sub-exponential algorithm on independent sets based on the thread resource allocation scheme; evaluating backup requirements based on the data verification result set, determining the backup strategy, and performing parallel data backup. This invention fully utilizes the characteristics of domestically produced operating systems, achieving efficient verification and backup of power control center data, improving system resource utilization and data security.

The application discloses a method for automatically recommending explainability for machine learning models, including the following steps: step 1, constructing a target model. Step 2, modeling the explainability requirement analysis framework. Analyze the target to indicate what goals need to be achieved to solve the problem, and the quality target indicates which quality attributes may be involved when achieving this goal. Then model the solution. Step 3, design a prototype tool to implement the framework, design a prototype tool to implement the modeling results in step 2. Finally, the participants are experimented to verify and evaluate the feasibility of the method.

The invention relates to an intelligent synthesis-oriented spacecraft component equipment description method, and belongs to the technical field of architecture design. Abstract modeling and unified description are carried out on management operation of common component equipment in a spacecraft control subsystem, an application example is given, and the development efficiency and quality of spaceflight embedded software intelligent composite codes can be improved. According to the method, firstly, component equipment composition of an existing spacecraft control subsystem is combed, then general and difference demand analysis is carried out on component equipment management functions of the spacecraft control subsystem, then a software unified equipment model oriented to control system components is established, and finally a specific example is given. The method can quickly adapt to software codes formed by different parts of the control system, the reuse capability of the control software is improved, and then intelligent synthesis of the software is supported.

This invention discloses an edge-side intelligent scheduling method and system for satellite and ad hoc network collaboration, relating to the field of wireless communication technology. The method includes: constructing a terminal map based on terminal device information; activating a pre-analysis platform based on task release signals; performing requirement analysis on the intelligent tasks of a first terminal to obtain multi-dimensional resource constraints; combining the terminal map and resource constraints, obtaining the independent completion probability through a preset capability evaluation model built into the first terminal; if the probability does not reach a preset threshold, initiating a collaborative scheduling process; a second terminal performs feasibility verification and provides feedback; the first terminal then determines the optimal collaborating terminal and completes intelligent task scheduling accordingly. This invention solves the technical problem that single-terminal devices cannot independently complete intelligent tasks in multi-terminal cross-domain collaborative communication scenarios due to resource and capability limitations, achieving accurate task allocation and efficient execution in edge-side communication scenarios, and improving the reliability of task completion and resource utilization efficiency in complex communication environments.

The invention relates to the technical field of airborne system design, and discloses an airborne system digital forward design method, device, equipment and medium, a design process from demand analysis, architecture design, architecture tradeoff to simulation verification is established, integration and optimization of the airborne system design process are effectively realized, machine learning and multidisciplinary design optimization are combined, and the design efficiency is improved. According to the method, knowledge in multiple fields such as machinery, electronics and software is fused, design rules are mined from historical design cases through deep learning, a multi-target tradeoff analysis model is established, automatic generation and optimization of an architecture scheme are achieved, dependence on artificial experience is remarkably reduced, airborne system design efficiency is improved, design change cost is reduced, and the method is suitable for large-scale popularization and application. And the reliability of the airborne system is improved.

The application discloses a kind of information system standardization programming method, device and electronic equipment.The method comprises the following steps: step S1, requirement analysis;Step S2, specific specification conversion;Step S3, subfield code generation;Step S4, subfield code test;Step S5, field code generation;Step S6, field code test.The application realizes the full-link standardization and automatic development from requirement to operation by meta-specification driving, closed-loop verification and runtime intelligent repair, effectively solves the technical problems, such as requirement-implementation fault, specification loss, high artificial dependence and abnormal response lag, in traditional information system development, and significantly improves development efficiency, system reliability and maintainability.