783results about "Intelligent editors" patented technology

The invention discloses a low-code development automatic generation method based on a large language model, which comprises the following steps: collecting natural language description information input by a user, and preprocessing; inputting the standardized demand corpus set into a large language model, and executing semantic understanding and context modeling; matching the low-code component library based on the structured semantic representation to generate component assembly description information; generating and verifying an engineering skeleton according to the assembly description information, and outputting an executable low-code application initial version; running the executable low-code application initial version, and monitoring and analyzing execution difference to generate an increment adjustment instruction; and inputting the increment adjustment instruction into the large language model, performing reconstruction and adaptive optimization, and outputting an executable low-code application final version. According to the method, large language model semantic understanding and adaptive optimization technologies are fused, automatic generation and continuous optimization of low-code applications are realized, and the method has the advantages of intelligence, high precision and engineering reliability.

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 computer aided design, in particular to a text-driven CAD modeling method and system based on a diffusion and visual language model.The method comprises the steps that natural language text description is obtained, and CAD semantic features of the natural language text description are extracted; carrying out geometric standardization on the CAD semantic features by adopting a fine-tuning diffusion model, and generating a CAD view image conforming to engineering specifications; carrying out fusion by adopting a fine-tuned visual language model to generate a parameterized CAD construction sequence; a three-mode alignment mechanism is adopted, and the semantic consistency of the CAD semantic features, the CAD view images and the CAD construction sequences is checked; performing verification and post-processing on the CAD construction sequence, and outputting an executable Python code or STEP file; the CAD modeling method disclosed by the invention performs explicit modeling based on flexible modal description, and has the characteristics of high geometric constraint and high usability.

Methods, systems, products, services, and apparatuses for generative AI systems configured to produce computer program code using a knowledge base that includes a client-specific code base, including: receiving one or more input tokens associated with a computer program; accessing information describing a domain-specific codebase; and generating, based on the one or more input tokens associated with the computer program and information describing a domain-specific codebase, suggested code to insert into the computer program.

The invention provides an intelligent instruction execution method, system and device and a medium. The intelligent instruction execution method comprises the steps that current environment state information of a robot and a natural language task instruction input by a user are obtained; generating cue words at least based on the environment state information, the task instruction, a predefined standardized function interface document and a thinking chain example, and inputting the generated cue words into a large language model; the method comprises the following steps: decomposing a task instruction into sub-task sequences by utilizing a large language model, and organizing the decomposed sub-task sequences into complete executable code blocks; performing static logic verification on the code blocks before code execution; if verification is not passed, error description is generated and fed back to the large language model for code correction; and in a code execution stage, if abnormal information is returned after the code is executed, the abnormal information is returned to the large language model, so that the large language model regenerates a correction code according to an abnormal reason. The autonomous decision-making level and the task execution success rate of the robot are improved.

The invention discloses a page generation method, system and device and a medium, and the method comprises the steps: obtaining page demand content and target page configuration through responding to a page generation instruction of a target field; obtaining a component knowledge base of the target field and a layout rule base configured by the target page; extracting a target component demand set, a target layout demand and a target priority demand from the page demand content; matching in a component knowledge base according to the target component demand set to obtain a corresponding candidate component set; matching in a layout rule base according to the target layout requirement to obtain a corresponding layout template; generating a target layout scheme according to the candidate component set and the layout template based on the target priority demand; the target page is generated according to the target layout scheme, automatic configuration and generation of the page can be achieved through natural language analysis and intelligent matching logic, and then page configuration and deployment are rapidly completed.

The invention belongs to the technical field of artificial intelligence task planning, and provides a task planning PDDL file automatic generation method based on natural language input, and the method comprises the steps: (10) constructing task environment description words: analyzing environment elements and participants, and designing a diversified scene framework; (20) building a knowledge enhancement fine tuning model: fusing a vector RAG knowledge base retrieval result and pre-training model parameters by the model, and inhibiting logic illusion; (30) PDDL file generation and knowledge constraint: driving the fine tuning model to output a correct PDDL file in combination with scene semantics and knowledge base rules; (40) multi-stage dynamic evaluation: executing a feasibility index verification instruction through a knowledge base rule matching degree; and (50) model closed-loop iterative optimization: updating RAG knowledge base content and model parameters based on execution feedback, and constructing a closed-loop iterative optimization model. The method has the beneficial effects that the instruction logic deviation rate is reduced through RAG knowledge base constraint, the complex scene instruction generation accuracy is improved, and the dynamic decision response time is shortened.

The invention discloses an intelligent completion code optimization method based on texture pre-rendering and multi-level cache, which comprises the following steps of: pre-establishing a grammar rule set and a GPU cache characteristic database, storing a grammar style sheet when a code editor is started, generating a font image set and loading current GPU cache parameters; after a candidate code segment list pops up through intelligent completion, dividing candidate code segments into data blocks, analyzing syntax types, determining character styles, and generating candidate code segment textures through instantiation rendering and a fragment shader; according to the current GPU cache parameter, the selected priority of the candidate code segment and the compressed volume, the texture is distributed to a first-level buffer, a second-level buffer or a global video memory according to a rule, and metadata is recorded and added into a global texture cache table; and after the user selects the candidate code segment, reading texture data from the corresponding GPU buffer, calculating a display coordinate by combining a cursor row and column index, an editor basic style parameter and a DPI scaling, completing texture rendering and drawing, and realizing optimized display of the intelligent completion code.

The invention discloses a graphical file editing system and method. The system is deployed in the form of an IDE plug-in, and comprises an editor extension module of a JBCefBrowser embedded browser, an editor extension module of a JBCefBrowser embedded browser, and an editor extension module of a JBCefBrowser embedded browser, the file identification module is used for scheduling a corresponding editor based on a file suffix; the graphical engine is used for performing classified rendering on the JSON / XML data based on the component metadata registry; the event processing module supports three-stage dragging and eight-direction zooming; an event driving communication module based on window.post Message, and an event driving communication module based on window.post Message; the operation module is used for synchronizing files and codes through a PSI interface; and a file template management module based on a. Ft template. The method comprises the steps of editor registration and embedding, file identification and loading, content analysis and rendering, interaction event processing, data persistence synchronization and template file creation. According to the method, graphical dragging and editing of the structured file are realized in the IDE, and the development efficiency and the normalization are remarkably improved.

The invention discloses a code generation method and system based on multi-objective reinforcement learning, and belongs to the technical field of digital integrated circuit design, and the method comprises the following steps: S1, generating an SFT training data set; s2, generating an MORL data set; s3, performing supervision and fine tuning on the model by taking the SFT training data set as input, so that the model masters the basic RTL code generation capability; s4, taking the MORL data set as input to train the model after supervision and fine tuning in the S3, and realizing multi-objective optimization; and S5, according to a user demand, describing a multi-target constraint through vector rewards, screening non-dominated candidate codes by using Pareto optimization to obtain an optimal RTL code, and outputting the optimal RTL code, vector reward details and a PPA equilibrium curve. According to the method and the system, basic constraints are met, and meanwhile generation of RTL codes with PPA indexes capable of being customized according to requirements is achieved.

The invention belongs to the technical field of code-free development, and particularly relates to a Web3D zero-code interactive design platform and method based on a unified model, and the platform comprises a unified business logic model UBLM and a platform-independent, self-described and structured data container which is used for completely defining all behavior logics in 3D interactive application, including events, conditions, actions, parameters, variables and flow relations; the dual-mode editor comprises a form-type editor and a blueprint-type editor, and the form-type editor and the blueprint-type editor can be used for completely creating, editing and visualizing the same UBLM and realizing real-time state synchronization; and the interpreted rule execution engine IREE is a lightweight engine running in a JavaScript environment of a browser and is responsible for analyzing the UBLM during running and converting the declarative logic into an executable instruction in a 3D scene through a rule matching and function registration mechanism. According to the invention, the development and cooperation efficiency can be improved, and the iterative maintenance process is simplified.

Techniques for natural language based chat are described. In some examples, a chat service receive a request to answer a natural language query (NLQ), generate one or more embedding vectors for the NLQ, performs a search of a vector database having information regarding one or more of enterprise context, natural language summaries of enterprise context, embedding vectors for the enterprise context, and embedding vectors for natural language summaries of the enterprise context according to the request to generate a search result, augmenting the NLQ using the search result, predicts a response for the NLQ using the search result augmented NLQ, and provides the predicted response in the chat.

The invention provides a software system intelligent development method and system based on an AI technology, and relates to the technical field of software development. Through natural language demand analysis, intelligent component recommendation, code-free generation and continuous optimization closed loop, the problems that a traditional development mode is low in efficiency, insufficient in intelligence and rigid in system are solved, and the method has the advantages that the development threshold is lowered, the development efficiency is improved, continuous optimization of the system is achieved, and data security is guaranteed.

The invention discloses a life omics knowledge graph construction method and system based on a large language model, and relates to the technical field of computer data processing. The method comprises the following steps: acquiring and preprocessing multivariate life omics data, wherein the multivariate life omics data at least comprises an unstructured biomedical text; performing information extraction on the text data based on a large language model to obtain entity mention and relation description; standardizing and normalizing the entity mention and the relation description on the basis of a large language model in combination with an external knowledge base to obtain a standard knowledge triple; and storing the triple into a graph data storage system, and constructing the knowledge graph. According to the method, the powerful natural language understanding ability of the large language model is utilized, efficient information extraction is achieved through structured prompt or field fine tuning, the model is innovatively utilized for entity standardization of relation perception, and the accuracy of knowledge fusion is remarkably improved.

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 relates to the technical field of industrial control, and provides an industrial control logic intelligent generation system and method. The multi-modal input module is used for acquiring multi-modal data for a to-be-controlled process; the domain knowledge graph module is used for storing a pre-constructed knowledge graph; the preprocessing module is used for preprocessing the multi-modal data based on a pre-constructed knowledge graph; the controlled logic generation module is used for generating a logic control intermediate representation for a to-be-controlled process through a large language model according to the preprocessed multi-modal data; the formalized verification and interaction module is used for carrying out formalized verification on the logic control intermediate representation; and the target file generation and output module is used for compiling the logic control intermediate representation which is qualified through formalization verification into a control logic file aiming at the target platform. Under the constraint of the domain knowledge graph, the large language model can generate flexible and high-adaptability industrial control logic according to background knowledge to accurately adapt to different process requirements.

The invention relates to the technical field of CAM intelligent programming, and discloses a CAM programming algorithm and system based on a generative artificial intelligence large model. The algorithm comprises the steps of analyzing a CAD document, fusing geometric features, tolerances and material information through a generative artificial intelligence large model, and generating a structured semantic skeleton. Then, the semantic skeleton is matched with a process knowledge base, a preliminary processing instruction cluster is generated, and a dynamic processing graph with a node dependency relationship is constructed; secondly, receiving an editing intention of a user for the graph, analyzing the intention by using a generative artificial intelligence model, and automatically reconstructing node logic and connection of the graph to form a corrected processing logic network; and finally, before execution, simulating a network execution process by using a generative artificial intelligence model, and identifying potential logic and resource conflict nodes. According to the invention, intelligent conversion from design to processing instructions, and intent-driven process chain dynamic optimization and conflict pre-verification are realized.

The invention provides an automatic vulnerability detection rule generation method based on LLM and static code analysis, and relates to the technical field of network security and software engineering. The method comprises the following steps of: constructing unified structured representation of project code semantics and vulnerability features, classifying taint'source 'and'sink' based on a heuristic rule and LLM (Logical Language Model), and constructing a taint propagation path assisted by a large language model and performing risk assessment; automatically generating a rule based on a'generation-verification-repair 'closed loop mechanism; according to the method, uniform structured representation of code side information and vulnerability side information is constructed, accurate identification of specific project taint sources and sinks and effective paths in multiple environments is achieved, a path risk sorting mechanism is introduced to achieve priority detection of high-risk paths, an automatic rule of error feedback circulation aiming at correction is established, and the accuracy of detection is improved. And the correctness and the performability of the rule grammar are greatly improved, and logic vulnerabilities are reduced, so that the manual debugging and maintenance cost is reduced.

The invention belongs to the field of Web application technology, XML document processing technology and online editor technology, and discloses an XML semantic editing system based on an aviation knowledge graph. The system comprises a front-end application module, an XML processing engine module, a DTD rule verification module, a visual editing module, a plug-in extension module, a collaborative editing module and an aviation knowledge graph integration module. According to the method, professional and efficient editing of XML documents in the aviation field is achieved through the full-process design of front-end interaction, XML processing, DTD verification, visual editing, semantic assistance and collaborative management, and the Web technology, the XML processing technology, the DTD verification technology, the knowledge graph technology and the collaborative editing technology are integrated, so that the XML documents in the aviation field are edited in a professional and efficient mode. The problems that an existing XML editing tool is difficult to deploy, poor in compliance, low in efficiency and weak in cooperation in the aviation maintenance field are solved, and a specialized and efficient XML semantic editing scheme is provided for the aviation maintenance industry.

An assert statement generator employs a neural transformer model with attention to generate candidate assert statements for a unit test method that tests a focal method. The neural transformer model is pre-trained with source code programs and natural language text and fine-tuned with test-assert triplets. A test-assert triplet includes a source code snippet that includes: (1) a unit test method with an assert placeholder; (2) the focal method; and (3) a corresponding assert statement. In this manner, the neural transformer model is trained to learn the semantics and statistical properties of a natural language, the syntax of a programming language, and the relationships between the code elements of the programming language and the syntax of an assert statement.

The invention discloses a C + + Mock class automatic generation method based on an inverse memory agent and a contrast knowledge graph, and aims to solve the problems that a virtual function coverage gap in a large C / C + + code library is difficult to find, the Mock code maintenance cost is high, and the automatic repair efficiency is low. According to the method, a difference context matrix is constructed by analyzing and merging request patch metadata, and an incremental abstract syntax tree is generated; generating a backtracking queue based on the event intensity and the semantic overlapping degree, and extracting a virtual function change list; the multi-scale difference extraction is completed in combination with an external expansion operator according to the rule that the path is not disassembled and the node is movable; a self-adaptive strategy table and a Mock specification are generated, through code synthesis and lightweight compilation verification, automatic self-healing is achieved through root cause positioning and minimum cost repair, finally, a result is written into a knowledge graph, and parameters are dynamically adjusted. According to the invention, Mock generation accuracy and version adaptability are improved, manual intervention is reduced, and test maintenance efficiency is improved.

The invention provides a quality inspection rule generation and deployment method which can be applied to the technical field of artificial intelligence, and relates to application of a large model in the fields of information security and financial science and technology. The quality inspection rule generation and deployment method comprises the steps of obtaining a to-be-analyzed file; inputting a preset prompt word, the target quality inspection rule and the constraint set into the first large model for semantic structured analysis to obtain structured data corresponding to the target quality inspection rule; comparing the structured data with historical rule data in a quality inspection rule production library, screening out target structured data, and constructing an object relation attribute graph based on the target structured data; performing rule logic analysis and code generation on the object relation attribute graph by utilizing a second large model to obtain intermediate rule language data; and generating a quality inspection rule code based on the intermediate rule language data, and deploying the quality inspection rule code to a quality inspection rule production library. The invention further provides a quality inspection rule generation and deployment device, equipment, a storage medium and a program product.

Embodiments of the present disclosure relate to applications, platforms, architecture, etc. for automating software requirement verification. In particular, one or more generative language model (GLM) prompts may be generated based at least on program information that describes a software program and based on requirement information that corresponds to a requirement of the software program. Based on such prompts, the GLM may be able to automatically identify segments of the software program information that relate to the requirement. Further, based on the identified segments and the GLM prompts, the GLM may be able to automatically create (e.g., based on one or more additional prompts) testing architecture that may be used to verify whether the software program satisfies the requirement.

The invention relates to the technical field of computers, in particular to a code sample optimization method and system based on a language large model, and the method comprises the steps: firstly, obtaining a source code sample, determining a code sample boundary, and generating a mark sequence; then, based on a compiling result, a static analysis result and a unit test result, a quality label is generated, and a training sample set is formed; then, taking a Transform neural network model of bidirectional context coding as an encoder, and carrying out training under the supervision of quality annotation to obtain a quality evaluation model; and finally, screening according to a preset quality score threshold to obtain a high-quality code sample set. According to the technical scheme, by combining the deep grammar analysis and context understanding of the code samples, the accuracy and efficiency of automatic screening are improved, manual intervention is reduced, and the method can adapt to automatic evaluation of a large-scale code library.

The invention relates to a code generation method and system based on a large model and a medium, and the method comprises the steps that in response to a received code generation request, a corresponding cue word template is obtained from a template database according to a technology stack type in the code generation request, and the cue word template comprises universal cue words; and performing semantic analysis and optimization on personalized requirements in the code generation request through the large model to obtain personalized cue words. And combining the personalized cue word and the universal cue word to obtain a target cue word. And taking the target cue word as the input of the large model, and outputting front and rear end codes. Automatic closed loop from demand description to code generation is achieved, the overall development efficiency is remarkably improved, and the problem of how to improve the code generation quality in the related technology is solved.

The invention provides a rich text editor component, a structured exporting method and a content editing system, and relates to the technical field of computers. According to the method, a componentization packaging module is used for packaging a rich text editor core library into a single file component based on a front-end componentization framework and completing initialization and destruction of an editor instance in the life cycle of the single file component; the attribute parameter module is used for defining and receiving configuration parameters from the upper calling component, and the configuration parameters at least comprise an editor mode type, a content export format and a remote content address; the core logic module is used for realizing key operation logic of the editor; and the business editor module is used for calling the rich text editor component in a declarative mode in a business application page, so that rapid integration without compiling bottom layer codes is realized. According to the embodiment of the invention, the development efficiency and function flexibility of the rich text editor can be improved.

The invention provides an AI auxiliary code generation method and system applied to a low-code platform, and relates to the technical field of low-code platforms.The AI auxiliary code generation method comprises the steps that firstly, component demand scenarized information is obtained and analyzed to obtain a scenario feature set, and a dynamic evolution rule set is generated in combination with platform real-time metadata; calling an AI code generation module, inputting the scene feature set and the rule set, and generating an initial component code; then deploying the initial code to a test environment, collecting and analyzing operation data, generating an iterative optimization instruction adjustment code and a rule set, and obtaining a target component code; and finally, embedding the target code into a platform specified function module to complete code generation. The method can adapt to diversified scene requirements, has a self-optimization capability in combination with the real-time state of the platform, and can improve the development efficiency and code quality of the low-code platform.

Disclosed in the embodiments of the present application is a cloud service-based design code generation method, the method being applied to a cloud management platform. The method can not only acquire from a configuration interface an explicit design intention input by a user, but also query a knowledge graph for target design knowledge associated with the current explicit design intention of the user so as to capture an implicit design intention of a target design object. Thus, the explicit design intention and the target design knowledge from the knowledge graph can be combined to guide a large language model in efficiently and accurately generating target design code that meets a design requirement of the user, thereby intelligently generating the target design code so as to accurately and efficiently implement a design task on the basis of the target design code to obtain the target design object.

Various embodiments of the present disclosure include systems, methods, and / or apparatuses for executing an agentic-AI-based software modernization system. An example system may be configured to receive one or more portions of monolithic legacy code of a legacy software program product, the monolithic legacy code comprising a plurality of code segments, group the monolithic legacy code by functionally related code segments of the plurality of code segments to define a plurality of groups, generate an API for each identified group, generate modern microservice code for each identified group to define a modernized software program product, and validate the modernized software program product.

The embodiment of the invention provides a verification code file generation method and device, equipment, a medium and a program product, and the verification code file generation method comprises the steps that an editing interface is displayed, in response to a configuration operation for the editing interface, domain knowledge of a target domain is obtained, the editing interface comprises a configuration area and guide information, and the domain knowledge is stored in the target domain; the guide information is used for guiding the front end to input domain knowledge in the configuration area, and the domain knowledge is used for representing a logic rule of a verification task in the target domain; based on the domain knowledge, verification guiding prompt information is constructed, and the verification guiding prompt information is used for guiding generation of a verification code file according to a logic rule; and calling a code generation model, generating a verification code file containing verification logic based on the prompt information, and deploying the verification code file to the target verification platform. Structured domain knowledge is collected through a guide type interface, accurate prompt information is automatically constructed, a verification code file conforming to a domain rule is generated, and the generation accuracy and efficiency are improved.