1935results about "Software design" patented technology

The invention discloses a method and system for dynamically calling a Java statistical analysis interface based on LLM and MCP protocols, belongs to the technical field of data processing and analysis technology, artificial intelligence technology and application program interface (API) integration, and aims to solve the technical problem of how to improve the response speed and flexibility of statistical analysis requirements. In order to reduce the development and maintenance cost of a statistical analysis function, the adopted technical scheme is as follows: a Java annotation mechanism is utilized to mark and describe an existing Java statistical analysis system, a Java method of which the annotation is defined is obtained, and the Java method of which the annotation is defined can be automatically identified by an MCP server and registered as an MCP tool; the MCP server follows an MCP protocol and communicates with the LLM serving as an MCP client, and the LLM dynamically discovers available MCP tools through the MCP protocol; after natural language query of a user is understood through LLM, one or more appropriate MCP tools are intelligently selected, calling parameters are generated, and an MCP server is requested to execute through an MCP protocol.

The invention provides a low-code dynamic configuration method based on a natural language, and relates to the technical field of natural language processing, and the method comprises the steps: carrying out semantic analysis on a natural language description input by a user through an intelligent agent, extracting a page type, a field attribute, a verification rule and interaction logic, and generating structured configuration data; dynamically generating JSON metadata for describing page layout, field components, verification rules and interaction logic according to a preset template on the basis of the structured configuration data; according to page function requirements, interface metadata matched with the JSON metadata is generated, and the interface metadata comprises a request address, a parameter list and corresponding SQL (Structured Query Language) operation logic; and analyzing the JSON metadata by utilizing a low-code configuration rendering engine, dynamically generating an interactive management function page, and modifying the JSON metadata in real time and re-rendering the page through natural language interaction between a user and an intelligent agent.

The invention discloses a CAD automatic generation system and method based on intelligent model selection and application, and aims at achieving automatic modeling under the multi-modal design requirement. The system comprises a user interaction module for receiving multi-modal input such as natural language, sketch and voice; the intelligent demand analysis module is used for combining an industrial large language model and a product knowledge graph, combining semantic analysis and generating a structured demand; the intelligent model selection calculation module is used for matching the optimal parameter combination and the component list based on a multi-objective optimization algorithm; the CAD automatic generation module calls a parametric modeling engine to generate an editable three-dimensional model; the constraint solving module is used for processing hard constraints and soft constraints in real time and dynamically adjusting model parameters; and the model output and interaction module feeds back a design state and supports user iteration. The system realizes full-process automation from the design intention to the CAD model, improves the design efficiency and accuracy, and is suitable for the fields of mechanical design, intelligent manufacturing and the like.

Techniques for leveraging a large language model (LLM) in software development are described. A description of a software development task is received from a user. Data associated with the software system is obtained from a data source. An LLM is prompted to identify at least one aspect of the task which requires clarification from the user, at least partly by providing the obtained data to the LLM and asking the LLM to identify a question for the user which remains unanswered by the obtained data. The question is presented to the user. An answer to the question is received from the user. The LLM is prompted to respond to propose an implementation of the task at least partly based on the data associated with the software system and the answer received from the user. The proposed implementation is received from the LLM and caused to be displayed to the user.

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

The embodiment of the invention provides a dynamic page rendering method and system based on a front-end framework, electronic equipment and a storage medium, and belongs to the technical field of page rendering. According to the scheme, rule expansion processing is conducted on JSON structure specifications, a metadata structure model is obtained, and the metadata structure model is used for defining page rendering rules; obtaining JSON configuration data, and performing data preprocessing on the JSON configuration data based on the metadata structure model to obtain structure data; dynamic structure analysis is conducted on the structure data through an analysis engine, a virtual document object model tree structure is obtained, and the analysis engine adopts a layered architecture; and performing difference rendering according to the virtual document object model tree structure to obtain a target page. According to the method, seamless connection between a low-code platform and professional development can be realized, the development efficiency is improved, and the page rendering dynamic capability is enhanced.

The present disclosure provides systems and methods performed in a central system. An initial entity model comprising a plurality of artefacts is formed. A runtime engine determines a plurality of available agent engagements based on the initial entity model. Responsive to an input selecting one of the available agent engagements, a notification is sent to an external autonomous agent. An indication of an action to be performed is received from the external autonomous agent. Responsive thereto, a message is sent to an external service indicating the action, thereby causing the action to be performed. The entity model is updated with a new artefact corresponding to the performed action, and a further plurality of agent actions is determined based on the updated entity model.

A method or system for implementing a machine learning pipeline on a chipset comprising a plurality of hardware compute elements. The system accesses a hardware-agnostic functional description of the machine learning pipeline, wherein the description specifies a plurality of functional modules, including at least one machine learning model. Hardware specifications of the chipset are accessed to identify the available hardware compute elements. Based on the hardware specifications, the functional modules are synthesized into a plurality of interconnected executable components configured to execute on at least two different hardware compute elements. An implementation package is generated, comprising the executable components and metadata describing interconnections between them. The implementation package is then deployed to the chipset, where the executable components are executed by the identified hardware compute elements.

A quantum computing service provides a quantum algorithm development kit that enables a customer to define a quantum task, a quantum algorithm, or a quantum circuit using an intermediate representation. The quantum computing service is then configured to automatically translate the quantum task, quantum algorithm, or quantum circuit into a specific representation specific to a particular quantum computing technology selected by the customer to be used to execute the customer's quantum task, quantum algorithm, or quantum circuit.

The invention discloses an agent development arrangement scheduling method and system based on a natural language, and belongs to the technical field of agent development creation, and the agent development arrangement scheduling method based on the natural language comprises the following steps: S100, obtaining a user demand and a demand constraint condition according to a natural language instruction input by a user; according to the user demand and the demand constraint condition, decomposing the user demand to form a plurality of sub-targets; according to each sub-target, generating a sub-target cue word template corresponding to each sub-target, and further obtaining a plurality of sub-target cue word templates; and S200, obtaining a plurality of tools corresponding to the plurality of sub-targets, analyzing the tool capability, evaluating the matching degree of the tools and the plurality of sub-targets, and obtaining an optimal tool list. According to the method, user demand analysis, task decomposition and tool scheduling are completed through a natural language processing technology, manual intervention is greatly reduced, and the efficiency of agent development is improved.

The invention discloses a research and development test bidirectional synchronization method based on artificial intelligence, and particularly relates to the technical field of artificial intelligence and software testing. Deep semantic modeling of research and development task behaviors is realized by constructing a multi-modal causal intention map, a test coverage blind area is inferred and recognized based on a graph neural network, a completion task is reversely generated, a complete state set is formed in combination with an original task set, and a condition control vector is adopted to drive generation of a structured test case. Collecting behavior and operation logs after the test is executed, detecting a mismatch area through modal alignment, generating a repair suggestion by utilizing a deviation correction model, and automatically writing back a task system; meanwhile, a state graph dynamic updating mechanism and a reverse task scheduling engine are introduced, and linkage feedback of test results on research and development tasks is achieved; the method can improve test coverage precision and research and development cooperation efficiency, and is suitable for agile development and continuous integration scenes.

The invention provides an automatic workflow code generation method driven by a large model, which comprises the following steps: sequentially converting an original demand into a subtask set, a node relation graph and structured workflow description data through multi-stage agent cooperation, and forcibly checking a data closed loop between nodes by the last-stage agent; tool nodes and content generation nodes are defined in the workflow description data, and decoupling of content generation and tool calling is achieved; analyzing the structured workflow description data, dynamically generating a strong type state class mapped with the description data, and automatically filling a node calling logic and a parallel routing rule based on a preset code template to generate a workflow code; when the code runs wrongly and the number of retry times exceeds a preset threshold value, an execution track is generated through the error positioning agent, candidate schemes are generated in parallel through the multiple repairing agents, and after self-adaptive scoring is conducted through the scoring agent based on the error type, the repairing schemes are verified and executed in sequence.

The invention relates to a method, a device, equipment and a medium for dynamically configuring a window control and layout, and the method comprises the following steps: firstly, obtaining and analyzing an XML configuration file to generate a data model tree of a structured window interface in a memory; then, objects are dynamically created based on a Qt reflection mechanism, attributes and signal slots are set, Qt reflection characteristics are fully utilized, object creation and attribute processing are flexible and efficient, hard coding interface elements in the compiling stage are not needed, needed controls and layouts can be dynamically generated according to a data model tree, and the dynamic nature and expandability of interface configuration are greatly enhanced; establishing a set membership tree between the controls and the layouts in the recursive traversal process to obtain a complete interface tree; and finally, performing layout calculation and rendering to generate a display interface, so that automation of interface layout is realized, interface element layout can be automatically adjusted according to a data model tree structure, and efficient dynamic configuration of window controls and layout is realized.

The invention relates to the technical field of deep learning, in particular to a PyTorch framework-based compatibility expansion system, which comprises a cross-framework model converter, a heterogeneous hardware abstraction layer, a hybrid computational graph execution engine, an intelligent distributed trainer and a self-adaptive optimizer, the cross-frame model converter converts input model formats of different frames into PyTorch executable formats, the heterogeneous hardware abstraction layer supports rear ends of various hardware and automatically selects an optimal calculation path, and the hybrid calculation graph execution engine fuses dynamic graph flexibility and static subgraph optimization and supports dynamic control function execution flow. An intelligent distributed trainer automatically selects a parallel scheme and is compatible with multi-protocol communication, a self-adaptive optimizer performs dynamic optimization based on hardware characteristics and a PyTorch tensor, a cross-frame model converter supports model conversion of multiple deep learning frames, the cost of migration among different frames by a user is reduced, and the universality of the PyTorch frame is improved.

The invention belongs to the technical field of industrial system integration, and provides a design method for rapid adaptive integration of a cross-domain system. Through the method, developers extract business logic rules of the industry, standardize characteristic mapping and select, develop and integrate functional modules, logic processes and scene components of the cross-domain system, so that the cross-domain system which conforms to industry characteristics and meets business circulation is formed, and meanwhile, the development efficiency of the cross-domain system is improved. The newly added industry logic component can be classified into each industry characteristic model based on the knowledge base, and a multi-dimensional characteristic vector is generated in the platform, so that cross-industry integrated component multiplexing is realized, and the cross-field applicability and flexibility of the platform are further improved. Modularized construction and configuration type development are supported, mapping and matching functions are carried out on various industrial manufacturing industry characteristics such as machining, electronic and electrical appliances and equipment sets, the requirements for subdivision production in the industrial field and rapid adaptation of business processes are met, the system integration cost is reduced, and the application development efficiency is greatly improved.

The invention relates to the technical field of LINUX operating systems, and discloses a software customization method and system in an ISO mirror image of the LINUX operating system, and the method comprises the steps: recursively analyzing a construction dependency tree and an operation dependency tree of a target component through a pre-analysis module, recognizing a missing component and / or a conflicting component, creating an isolation task for the conflicting component, and generating a customized work task set; through a construction module, deploying a dependency environment of a to-be-constructed component, executing an isolation task, constructing an independent directory for a conflict component, and generating a temporary finished product warehouse; executing a test on the target component in the temporary finished product warehouse through a test module; and through a distribution module, constructing a customized ISO and distributing the customized ISO to a product warehouse. According to the embodiment of the invention, manual demand analysis and dependency relationship processing are replaced by an automatic mode in a software customization system, the dependence on high-skill research and development personnel is reduced, a complex customization process is standardized, and the research and development and implementation cost is reduced.

The invention relates to the technical field of artificial intelligence learning, and discloses an AI-based scene decision interface dynamic generation and continuous optimization method. The method comprises the following steps: acquiring a user real-time interaction behavior sequence, task context information and an interface component library; fusing the codes to generate a real-time task state vector; predicting the probability distribution of the interface component required by the next decision step through the sequence prediction model; rendering a target interface according to the probability distribution and layout constraint dynamic combination; and calculating a reward value based on user feedback, and continuously optimizing the prediction model by utilizing reinforcement learning. According to the method, dynamic evolution and self-adaptive optimization of the interface along with user intention are realized, and the interaction efficiency and the user experience are improved.

The invention relates to the technical field of software collaborative research and development assistance, and discloses a software collaborative research and development assistance method and device, electronic equipment and a storage medium. The method comprises the steps that instruction information input by a user is analyzed to obtain intention information, and then a corresponding demand sub-agent, a corresponding development sub-agent and a corresponding test sub-agent are determined; performing retrieval analysis on the intention information to compare the intention information with conflict detection of a preset historical demand library to obtain a demand document, executing code generation based on the demand document to obtain an intermediate software code, inputting the intermediate software code into the test sub-agent for testing to obtain a test result, and adjusting the intermediate software code to obtain the test result. And obtaining the target software code. The method has the beneficial effects that the software research and development process is converted from manual cooperation to intelligent execution, and the research and development efficiency is greatly improved while the quality is guaranteed.

The invention provides an ECU safety design method and device, a medium and a vehicle. The method comprises the steps of obtaining a knowledge graph; the knowledge graph comprises a plurality of entities and entity relationships among the plurality of entities; the plurality of entities include vehicle functions of the ECU of the vehicle, security levels of the vehicle functions, and security mechanisms; determining a target security mechanism matched with the function link from the knowledge graph according to the function link of the ECU; the function link is used for realizing the vehicle function of the ECU; the function link is determined according to an original system architecture diagram of the ECU; the knowledge graph comprises a target security mechanism; and adding the target security mechanism in the original system architecture diagram to obtain a target system architecture diagram. According to the embodiment of the invention, the accuracy of ECU safety design can be improved.

The invention discloses a front-end multi-module development method and system based on module federation. The method comprises the following steps: extracting necessary examples, public components and public methods in a front-end project to a public basic module, and constructing a module federated product; decoupling other functional modules, and configuring a module code and a module federation construction entry in the construction configuration file; remote module lists and configuration information are managed in a centralized mode through the project configuration module; executing a construction command to call the project construction module to independently package each module and update the project configuration module; and when the application is started, the project loading module loads the public basic module and the remote function module according to the project configuration module. Compared with an existing Module Federation scheme with application as granularity, independent construction, configuration management and flexible loading during operation are achieved on the module level, dependence on repeated and redundant logic is avoided, module plug and play and version switching are supported, and therefore development efficiency, construction performance and expansibility are improved.

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 software development system and method and a storage medium. The software development system comprises a process management module and a plurality of software development robots. The process management module is configured to obtain a development design document of to-be-developed target software according to a user instruction; and constructing a file tree according to a logical relationship among a plurality of development tasks recorded in the development design document, and creating a code file and a corresponding prompt word for a function node corresponding to each development task in the file tree. The cue word indicates the function content of the corresponding function node and the logical relationship between the cue word and other associated function nodes; and according to the function content of the at least one function node, determining a matched software development robot so as to enable the matched software development robot to execute the development process of the corresponding code file. The method can be used for autonomously arranging and managing the workflow of software development, so that the multiple robots are used for efficiently and collaboratively developing the software.

The invention discloses a complex system comprehensive multi-view consistency detection method based on a large model, and relates to the technical field of information system architecture design. The method comprises the steps that N view models to be detected are determined, and a rule base is constructed and formed; forming a triple set; performing rule retrieval on the triple set to obtain a corresponding rule subset; and constructing a complete prompt statement according to the prompt template and the rule subset, and reasoning the prompt statement by the large language model to obtain a consistency detection result. Based on the MBSE method, the ability of understanding, reasoning and applying knowledge in the field of system architecture and system engineering is remarkably improved, the automation degree, accuracy and robustness of multi-view consistency detection are improved, and the correctness, completeness and consistency of complex information system architecture design are guaranteed.

To provide a template creation support system capable of supporting creation of a template of a flow close to a system configuration desired to be realized by a user.SOLUTION: A template creation support system includes: a first conversion unit that converts each template of a template group into a graph including a node indicating a service and an edge indicating a relation between services based on dependencies between set resources; a second conversion unit that converts an input including information indicating a system configuration desired by a user and operation of each component into the graph composed of a node indicating service and an edge indicating a relation between services; a retrieval unit that retrieves a template of the graph close to the graph converted by the second conversion unit from graphs converted by the first conversion unit from the template group; and an output unit that outputs a setting item set in the template retrieved by the retrieval unit.SELECTED DRAWING: Figure 1

A system for a visual design system (VDS) includes storing at least one layout and an associated layout signature where the associated layout signature represents a hierarchical composition of the semantic types of the components; a unit to analyze components of an existing layout provided by a user of the VDS, to determine a component set signature, to compare it with at least one stored associated layout signature and to find a set of candidate layouts which are visually diverse and semantically similar to the existing layout; where the unit presents the set of candidate layouts to the user, updates the existing layout according to a user selected layout. It also includes an experiment system to create, run and analyze the results of at least one experiment using at least one of: A / B and multivariate testing on user selected layouts to provide information on user preferred layouts for the VDS.

The invention relates to the technical field of software engineering, and discloses a software development code review system and method based on reinforcement learning, and the system comprises a historical code feature collection module, a software development code operation parameter collection module, a code feature matching optimization module, a code feature optimization analysis module, and a code review result output module. The method comprises the following steps: extracting code features in a historical code library according to a time sequence, collecting code operation parameters in a software development process in real time, completing evaluation on software development code operation, performing feature matching optimization on the code operation parameters based on an evaluation result, obtaining an evaluation result of a to-be-evaluated operation code after matching optimization, and performing evaluation on the to-be-evaluated operation code according to the evaluation result. Based on the initial evaluation result and the feature optimization analysis result of the software development code, a code review result is output, early warning information is provided for the client through initial evaluation and matching optimization evaluation, and the code quality and safety in the software development process are improved.

The invention provides an action chain dynamic arrangement and verification method based on metadata, and relates to the technical field of computer software, and the method comprises the steps: constructing an action metadata model which comprises an action identifier, a name, a type, an input parameter, a front dependency relationship and a rear dependency relationship; analyzing the action metadata model based on an action chain arrangement engine to identify and obtain business logic, dynamically constructing an action chain according to the business logic, configuring variable parameters of the action chain through an expression, and performing condition verification of the action chain by adopting a preposed verification mechanism; and the action chain executes corresponding business logic on the input parameters according to the sequence, and outputs an obtained output result to the next action.

The invention discloses an ERP (Enterprise Resource Planning) system-oriented interface service design method, which relates to the technical field of computer software, and comprises the following steps of: loading and analyzing a standardized interface description file, extracting a calling party demand, constructing a data structure according to the calling party demand, and defining a preliminary field mapping rule; based on the preliminary definition and the data structure of the field mapping rule, a mapping rule file is generated and verified, a test case is constructed, the effect of the mapping rule is verified, and a final mapping rule file is dynamically generated according to the verification result; and according to the final mapping rule file, the interface service processes the external calling request in real time, adjusts the field mapping rule, updates the interface description file, and generates an updated interface description file. Through a dynamic mapping rule generation and verification mechanism, the problem that a static rule cannot deal with a complex or changing scene is avoided, and real-time optimization of the mapping rule is realized, so that adaptability and reliability are enhanced.

The invention relates to the technical field of business management, in particular to an artificial intelligence business intelligent docking service platform, in the invention, task dependency relationships are rearranged by adopting topological sorting, and waiting time and resource waste caused by improper dependency relationships among tasks are reduced through scientific execution sequence adjustment, so that the service efficiency is improved. The method comprises the following steps of: extracting a business semantic structure chart, mining tasks which can be executed in parallel, recombining an execution path, reducing redundant nodes and unnecessary delay of a process, improving the execution efficiency of the whole process, extracting key operation rules through natural language processing, establishing the business semantic structure chart by adopting a support vector machine, accurately reflecting the hierarchy and association relationship of the operation nodes, and improving the execution efficiency of the whole process. Visualization and operability of complex business logic are achieved, flow optimization is carried out through the greedy algorithm, node priorities are defined, adaptive execution paths are matched, high efficiency and execution accuracy of flow design are ensured, and the response speed, the resource utilization rate and the execution accuracy of the business flow are improved.

The invention provides a natural language guided CAD model generation method and system, belongs to the field of computer aided design and artificial intelligence, and aims to provide a more personalized aided design scheme for designers and ensure the controllability of CAD model generation. The method comprises the steps that S1, a real engineering sketch and three-dimensional model data are collected; s2, constructing a CSFC data set according to the data acquired in the step S1; s3, according to the CSFC data set, LoRA fine tuning is carried out on the DeepSeek-R1-Distil-Llama-8B model, and a final model is obtained; and S4, designing a model evaluation index for evaluating the final model. According to the method, the Python code is generated based on deep learning, the DXF file which can be directly opened on CAD software / platform is obtained after compiling operation, the editable CAD model is finally obtained, and the method is suitable for the field of computer aided design and artificial intelligence.