314 results about "Natural language interaction" patented technology

The invention discloses a sign language translation method and device based on vision and word feature pre-training alignment, and belongs to the technical field of sign language translation.The sign language translation method comprises the steps that vision features of sign language videos are extracted through a vision encoder, and word text features are extracted through a text encoder; performing comparative learning based on vision and word text features to obtain a pre-trained vision encoder; performing word text mask prediction pre-training on the text decoder; constructing the pre-trained visual encoder and the pre-trained text decoder into a sign language recognition model so as to recognize a word text sequence from the sign language video; and accessing the pre-trained large language model in the domain into a sign language recognition model to construct a sign language translation model, and performing combined fine tuning so as to translate the word text sequence into a natural language text. The sign language recognition and translation method can realize more efficient, accurate and reliable sign language recognition and translation, is applied to the fields of intelligent sign language translation, barrier-free communication, sign language education and the like, and provides more accurate and natural language interaction experience for hearing-impaired groups.

The invention provides a dynamic order making method and device based on multiple rounds of dialogues and electronic equipment, and belongs to the technical field of intelligent interaction. The method comprises the steps of guiding a user to gradually fill in a form through a multi-round dialogue mechanism, generating a question based on a business logic dependency relationship, and mapping a natural language answer of the user into a structured field value in combination with semantic analysis and field dependency management; integrating a business rule engine in a multi-round dialogue mechanism process, executing field-level, logic-level and process-level multi-dimensional verification in real time in a user input process, and dynamically adjusting a verification strategy based on context; constructing and adaptively adjusting a form structure in real time based on a dynamic form generation engine, multi-round dialogue contents and business rules; based on a low-code configuration development platform, in combination with a preset intelligent interaction component and a visual tool, an intelligent filling system supporting natural language interaction is constructed. And the intelligence, flexibility and accuracy of form filling are obviously improved.

An intelligent multi-modal component search and procurement system is provided. The system enables users to search for industrial components through multiple input modalities, including keyword queries, BOM (Bill of Materials) file uploads, and natural language interactions. The system dynamically refines search results using a combination of structured filtering, semantic similarity analysis, and machine learning. Key features include a chat interface for natural language processing (NLP), a selection panel for real-time filtering, and a product listing that adapts to user inputs. The invention improves efficiency in industrial procurement by integrating contextual understanding, schema validation, and embedding vector conversions.

The invention relates to the technical field of system engineering modeling, and particularly discloses an MBSE optimization method based on a large language model, which realizes MBSE whole process automation and intelligentization by constructing a'demand-knowledge-model 'dynamic closed-loop framework and fusing RAG and LLM. The method specifically comprises the following steps: constructing a domain knowledge enhancement library, and integrating LLM to construct a demand analysis engine and a dynamic modeling optimization system; a domain expert inputs a demand through a natural language interaction interface, and the demand is analyzed into structured data through LLM; generating a parameterized model conforming to the MBSE specification by combining the RAG technology with the knowledge in the library; after the model runs through a simulation tool chain, the LLM adjusts parameters according to a simulation result to generate an iteration scheme; and after the modeler passes verification, storing the model and data into a database to form a knowledge source. According to the method, domain knowledge dual-drive modeling and cross-role collaboration are achieved, the knowledge base self-evolution capacity is achieved, the problems that traditional MBSE is high in manual dependence and insufficient in semantic fault and knowledge fusion are effectively solved, and the modeling efficiency and reliability are remarkably improved.

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 provides an intelligent data query method based on a natural language, and relates to the technical field of intelligent data processing and natural language interaction.The intelligent data query method comprises the steps that firstly, enterprise original data is subjected to standard treatment, and a standardized theme database and a data directory and index definition document are constructed; key semantics are extracted based on unstructured knowledge, and a domain knowledge vector library is fused and constructed by combining document text fragments and vector representation of a mapping relation between historical questions of a user and an SQL (Structured Query Language). And after receiving a natural language question of a user, calling a large language model to identify a task type, and distinguishing knowledge questions and answers, data query and complex analysis. Executing corresponding operations according to different types: directly retrieving a vector library by knowledge questions and answers to generate answers; extracting keywords in data query and generating a query request in combination with context; and in the complex analysis, predefined workflow is judged and executed or intelligent agent processing is called, and query or analysis requirements are output.

The invention relates to the technical field of artificial intelligence, in particular to a ChatBI dynamic data query and visualization system and method.The ChatBI dynamic data query and visualization system comprises a natural language interaction and intention analysis module, a metadata knowledge graph module, a dynamic SQL generation and optimization module, an intelligent visualization recommendation module and a multi-source data connection and real-time calculation module; the method has the beneficial effects that by fusing natural language interaction, intelligent query generation and self-adaptive visualization technologies, the core pain points of high technical threshold, large response delay, complex visualization configuration and the like of a traditional BI tool are thoroughly solved.

The invention discloses an intelligent voice recognition and natural language interaction method based on a quadruped robot, and the method comprises the following steps: S1, collecting a user voice instruction, generating a standardized voice text, and extracting a semantic keyword set; s2, collecting multi-source sensing data of the quadruped robot and generating a structured state data tensor; s3, constructing a multi-modal collaborative modeling mechanism, and generating a multi-modal joint semantic embedding vector; s4, constructing a semantic map based on semantic embedding and generating an action chain plan structure; s5, executing each sub-action in the action chain, and performing path analysis and execution monitoring; s6, storing the interaction task as a multi-modal semantic behavior memory unit; and S7, performing similarity retrieval based on current semantic input and historical memory to realize behavior migration and action chain multiplexing. The method has the advantages of accurate semantic understanding, intelligent interaction response, high behavior migration capability and the like.

The embodiment of the invention discloses a life omics research method and device based on artificial intelligence, and the method comprises the steps: carrying out the preprocessing of a project according to the intelligent interaction between a user and a system, and generating standardized data; and meanwhile, carrying out transfer learning on the large language model to construct a life science large language model. And according to the standardized data, decomposing a research target by adopting a life science big language model in cooperation with an intelligent agent, and generating an analysis plan. And based on the analysis plan, the intelligent agent is scheduled through the coordinator, and a hierarchical task is generated. And calling a multi-omics analysis tool by the intelligent agent to calculate and analyze the grading task, and outputting an analysis result. And based on data features, integrating analysis results through an integrated advanced model, and outputting a final report to complete the research of life omics. According to the embodiment of the invention, full-process automation and natural language interaction are realized, the standardization of data analysis and the repeatability of results are ensured, the research efficiency is remarkably improved, and the technical threshold is reduced.

The invention discloses a large-model-driven intelligent calculation method and system for a water conservancy mechanism model. According to the method, natural language input, structured conversion and intelligent optimization calculation of a scheduling target are realized by integrating a field-enhanced large language model and a water conservancy professional mechanism model. The method comprises the steps of receiving a calculation target expressed by a user in a natural language, and analyzing and converting the calculation target into a constraint condition and a target function which can be recognized by a water conservancy mechanism model; a hydrological model, a hydraulic model, a hydrodynamic model and other models are called based on a workflow engine, and reverse calculation is carried out by adopting a hybrid optimization strategy of'coarse adjustment-fine adjustment-verification '; synchronously and visually displaying the parameter change and the result convergence state in the calculation process; and outputting a calculation result including parameter adjustment logic, standard conformity analysis and multi-scheme comparison. The system comprises a natural language interaction module, a target conversion module, an intelligent calculation engine module, a visualization module and a result generation module, and supports multiple application scenes such as multi-target scheduling, emergency decision making and ecological guarantee. Compared with a traditional scheme, the method has the advantages that the model use threshold is lowered, the dispatching efficiency and calculation transparency are improved, and the method is suitable for complex hydraulic engineering calculation tasks such as reservoir dispatching, cross-basin water transfer and flood control emergency.

The invention discloses a pair of blind person intelligent glasses based on binocular vision. The glasses comprise a glasses terminal and a cloud end. A data acquisition and transmission module, a voice interaction and feedback module and a power supply system are arranged in the glasses terminal, and the glasses terminal is used for acquiring and processing data and receiving and feeding back voice instructions; the cloud integrates a sensing module, a task planning module, an action module and a path planning and navigation module, the sensing module analyzes instructions and environment information, the task planning module extracts natural language intentions and generates task structures, the action module dynamically generates task templates to execute tasks, and the path planning and navigation module achieves fine-grained obstacle avoidance navigation. The environmental perception precision is improved through binocular vision and GPS fusion, the use threshold is reduced through natural language interaction, the complex task processing capacity of the system is improved through dynamic task decomposition, and the obstacle avoidance accuracy is improved through hierarchical path planning.

The invention discloses an intelligent interaction system based on a large language model and a knowledge graph, and relates to the technical field of artificial intelligence and intelligent interaction. According to the system, data processed by a data acquisition and preprocessing module is stored in a local knowledge warehouse in a three-layer nested structure; the multi-modal semantic understanding module constructs a knowledge system in which a triple knowledge graph and a vector database are complementary by optimizing a BERT model and performing dual-channel analysis; the intelligent interaction module realizes natural language interaction and business task automatic triggering based on an RAG technology and a dialogue memory mechanism; the information change identification and analysis module generates a personnel change risk report and performs early warning; and the summary report automatic generation module outputs a structured report. According to the method, the problems of data splitting, complex interaction and slow response in traditional enterprise management are solved, the functions of system intelligent question answering, personnel change risk analysis, structured report automatic generation and the like are realized, and the management efficiency and the information consistency are improved.

The invention relates to a business travel data processing system and method based on a large model and an intelligent agent and a medium, and the system comprises a natural language interaction and structured data conversion module which is used for receiving unstructured business travel text information inputted by a user, and carrying out semantic analysis, entity extraction and context association through a multi-modal intention recognition engine, converting the data into structured travel data; the agent-driven dynamic process adaptation module is used for dynamically adapting the approval process and the system operation logic according to the request input by the user and the business process state; the multi-dimensional intelligent compliance verification module is used for verifying the structured travel data; the cross-system collaboration and data communication module is used for realizing automatic synchronization and data consistency guarantee of the travel data and the heterogeneous system; and the system architecture and key technology integration module is used for providing layered architecture design, large-model lightweight deployment and security and privacy protection of the system. The method has the advantages of being high in efficiency, high in adaptability, low in compliance risk, good in collaboration and the like.

The invention discloses a zero-sample-driven dialogue type industrial defect detection system and method, and aims to solve the problems of sample scarcity, weak model generalization ability and the like in industrial defect detection. The system comprises a query image coding module, an object-independent text prompt module, a prompt learner module and a defect identification dialogue module. The industrial defect detection without target scene training data is realized by utilizing the prior knowledge of the pre-training model and combining the prompt design irrelevant to the object and the dynamic feature fusion mechanism. By utilizing the method, the industrial defect detection efficiency and accuracy are improved, natural language interaction is supported, and the method is particularly suitable for multi-type defect detection in a complex industrial environment.

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 invention discloses an air-ground cooperation system and an air-ground cooperation method based on intelligence, and relates to the field of intelligent unmanned system control. The system comprises an intelligent unit, a multi-element sensing unit, a communication unit and a task execution unit. Based on a Vision-Language-Action (VLA) framework, an intelligent unit with a body realizes end-to-end analysis of a natural language instruction and a visual instruction, fuses sensing data of an unmanned aerial vehicle and an unmanned vehicle, generates a joint feature code with time-space alignment, and dynamically allocates a cooperative task. The communication unit supports low-delay end-to-end communication and real-time data synchronization between clusters, and ensures efficient interaction between agents. The multi-element sensing unit is integrated with a visual camera, a laser radar and high-precision positioning equipment, and air-ground environment multi-mode sensing is achieved. And the task execution unit supports the unmanned aerial vehicle to complete coordination actions in a complex scene. According to the scheme, the problems of insufficient multi-mode perception, limited natural language interaction and low dynamic cooperation efficiency of the existing system are solved, and the cooperation capability and system robustness of the unmanned aerial vehicle and the unmanned aerial vehicle in a complex environment are remarkably improved.

The invention relates to a visual transformer substation intelligent inspection system and method based on a multi-mode large language model, and belongs to the technical field of power system intelligence. The system obtains image, temperature, vibration and noise data of substation equipment in real time through a multi-modal data acquisition module, and performs preprocessing and fusion. A high-precision three-dimensional semantic model is constructed by using a three-dimensional dynamic modeling module, and the device attributes are automatically labeled by fusing LLM semantic understanding capability. A multi-modal large language model (LLM) engine is combined with cross-modal feature extraction, a dynamic knowledge base and a self-adaptive reasoning unit to realize accurate diagnosis of equipment faults. An augmented reality (AR) interaction module displays the real-time state of equipment through AR glasses and supports natural language interaction. The self-interpretation decision support module generates interpretable fault reports and maintenance suggestions, and the communication and feedback module is responsible for data uploading and remote alarm. According to the method, multi-dimensional perception, dynamic knowledge reasoning and self-adaptive decision support of the equipment state are realized, the intelligent level of substation inspection is remarkably improved, the inspection efficiency is improved by more than 40%, the omission ratio is reduced to less than 1%, and rapid diagnosis of more than 95% of novel faults is supported.

The invention relates to an RPA system based on natural language interaction and an implementation method thereof. The system comprises a user input module, an intention recognition module, a process planning module, an execution module, an inspection module and an output module. A user inputs a natural language instruction through a textbox or a microphone, the intention recognition module recognizes the intention of the user through an artificial intelligence large language model, and the process planning module plans an operation target and operation steps according to the intention of the user and a screen interface. The execution module controls the mouse and the keyboard to execute planned operation on the operation target through the technology related to automatic testing, the checking module checks whether the execution result meets the intention of the user or not after the operation is completed, and finally the output module feeds back the execution result to the user. According to the method, a common user can easily create a complex automatic process through a natural language, and the use threshold of an RPA system is greatly reduced.

The invention discloses a data governance agent system and an implementation method, and relates to the field of data processing, and the system comprises a data analysis module, a data storage module, a user response module, a quality monitoring and feedback module and a field large language model module. The data analysis module performs unified analysis modeling on structured and unstructured data; the data storage module adopts a graph database and a vector database to store knowledge data; the user response module supports natural language interaction and intelligent question and answer; the quality monitoring and feedback module optimizes data quality through closed-loop feedback; and the field large language model module constructs an optimization model adaptive to the vertical field. Through multi-modal analysis, knowledge graph construction, double-library collaborative storage and model dynamic optimization, a data barrier is broken through, manual dependence is reduced, the data treatment efficiency, quality and intelligent level are improved, and the method is suitable for specialized data treatment requirements of industries such as manufacturing and finance.

The invention discloses a multi-type central air conditioner integrated optimization automatic control system, which relates to the technical field of air conditioner control, and comprises an equipment dynamic modeling module, an MPC optimization control module, a dynamic reference and diagnosis module, an RL driving MPC setting module, a dynamic sliding window processing module and a large model management module, the dynamic sliding window processing module gathers and performs continuous cutting and context association processing on information streams from the modules to generate serialized information fragments adaptive to the input length of the large model management module, and the large model management module serves as a core cognition and decision center; and performing deep fusion analysis on the received serialized information fragments by using an internal attention mechanism to realize advanced operation strategy generation and deep fault reason tracing, and providing decision support and system state interpretation through a natural language interaction interface. And finally, comprehensive cognition, predictive insight and self-adaptive evolutionary management of the global state of the central air conditioning system are achieved.

The invention discloses an automatic data modeling and optimizing system and method fusing a knowledge graph and ChatBI, and relates to the technical field of data analysis. In order to solve the problems of high interaction threshold and insufficient analysis depth of a traditional BI tool, the scheme adopted by the invention comprises a data layer which has the capabilities of multi-source data access, domain knowledge graph construction, intelligent mapping recommendation, federal calculation and data mild governance, and realizes data integration and semantic unification; the analysis layer realizes accurate conversion from a natural language to an SQL and semantic reasoning of a knowledge graph through graph vectorization, multi-model cooperation, natural language understanding, intelligent SQL generation and graph dynamic updating, and supports efficient data query and analysis; and the application layer has the functions of natural language interaction, visual recommendation and generation, root cause analysis and intelligent early warning, and provides a visual interaction interface and data display service for a user. According to the invention, intelligent data analysis and visualization based on natural language interaction can be realized.

The invention provides an interaction method and device based on retrieval enhancement, equipment and a storage medium, and after question information input by a user is detected, the question information and a retrieval prompt word are input into a strategy selection model. Obtaining a target retrieval strategy output by the strategy selection model, performing information retrieval according to the target retrieval strategy, inputting a retrieval result and question information into a language model, outputting reply information through the language model, and utilizing a retrieval prompt word for indicating a matching relationship between the retrieval strategy and a question type to retrieve the question. According to the method, the strategy selection model is guided to select the appropriate retrieval strategy for the question input by the user to obtain the retrieval result, the accuracy and the retrieval efficiency of the retrieval result can be improved, the reply information is generated based on the retrieval result and the question information, and therefore natural language-based interaction is assisted by utilizing the language model, and the man-machine interaction efficiency is improved.

The invention provides a three-dimensional graphic engine natural language interaction method and system based on an MCP and an AI Agent, and relates to the technical field of intelligent control, and the method comprises the steps: autonomously judging an MCP tool set needing to be called according to the analyzed intention and scene demands, and planning the precedence logic and cooperation mode of tool calling; converting the structured semantic information and the tool calling plan into a unified semantic data packet through an MCP protocol to form a cross-tool collaborative MCP instruction; based on an MCP instruction, the adapter module performs adaptation conversion according to API characteristics of a target three-dimensional engine, a corresponding interface is called to execute object operation in a three-dimensional scene, the AI Agent can automatically correct a subsequent instruction or tool calling strategy based on feedback, and closed-loop intelligent interaction is achieved. According to the invention, the interaction barrier between the natural language and the three-dimensional graphic engine is broken, so that the user can conveniently and intelligently control the three-dimensional object, the scene view angle and the dynamic effect directly through the natural language, and the interaction efficiency and flexibility are remarkably improved.

The invention discloses a multi-agent self-adaptive cooperation method and system based on a large language model, and belongs to the technical field of artificial intelligence, and the method comprises the steps: obtaining task description and environment information, and inputting the task description and environment information into the large language model; generating a personalized initial instruction corresponding to the multiple agents; constructing a multi-agent topological state space, and establishing a relation topological structure among the agents; based on a distributed negotiation mechanism, determining an action set through natural language interaction; generating a self-adaptive cooperation strategy according to the action set and the current environment state; the multiple agents execute corresponding actions and obtain feedback, and a cooperation strategy is dynamically adjusted; according to the method, the semantic understanding capability of a large language model is innovatively combined with the technologies of topological state mapping, distributed negotiation and the like, seamless conversion from high-level semantic understanding to specific execution actions is achieved, and the method has the advantages of being high in practicability and high in practicability. And the environmental adaptability, the cooperation efficiency, the safety and the reliability of the multi-agent system are greatly improved.

The invention discloses an interactive data insight system based on a large language model. The interactive data insight system comprises an intention recognition and classification module, an entity extraction module, an entity and business term alignment module, an SQL generation module and a structured query verification module. The key technical problems that an existing natural language data analysis system is insufficient in semantic understanding, inaccurate in entity alignment, weak in multi-round dialogue support, unstable in SQL generation, lack of safety and the like can be solved, semantic understanding and natural language interaction of complex business data are achieved, the data analysis threshold is greatly reduced, and the data analysis efficiency is improved. And the query efficiency and the result controllability are improved.

Methods and systems for interacting with users via a chatbot. A natural language query is received and processed by submitting a search query to a search engine. The search engine identifies relevant information including textual information and images for formulating a response. The identified information and query are submitted to a Large Language Model which generates a response displayed via the chatbot. The response may include textual information and relevant images. The system can extract text from images of documents and convert textual information into numerical vector representations for processing. Selectable options based on clustered relevant information can be provided to users for query refinement when appropriate. The chatbot interface enables natural language interactions while leveraging search capabilities and Artificial Intelligence to provide informative and helpful responses with both text and visual elements.

The invention discloses an electric power system dispatching man-machine cooperation model construction method based on a large language model, and relates to the technical field of new energy electric power system dynamic analysis. According to the method, multi-modal data fusion and natural language interaction capability of ChatGPT are utilized, and a prioritized experience replay (PER) technology is combined, so that bidirectional collaborative decision optimization of the dispatcher and the AI can be realized, and theoretical analysis is verified through an experimental result. A research result shows that after the LLM is introduced, the performance of the model on an active unit scheduling strategy is remarkably improved.

The invention discloses an intelligent query system and method for power transmission and distribution production data based on natural language interaction, and relates to the technical field of knowledge maps. The method comprises the steps that a multi-source database in power transmission and distribution production is collected, a basic metadata unit is constructed, statistical characteristics are calculated, and a metadatabase is constructed; the method comprises the following steps: setting a hard rule and a soft rule for a metadatabase, establishing semantic association of cross-database fields by utilizing the two rules, and constructing an initial dynamic data graph; the dynamic data graph edges are endowed with weights through the relation strength; metadata increment is monitored, and an updating strategy is set to update the dynamic data atlas; an entity in the query statement is extracted, ambiguity judgment is carried out, the entity is mapped into a dynamic data graph to formulate an active guiding strategy, and a user query intention is obtained; reasoning in the dynamic data graph according to the query intention of the user to obtain a query result, and constructing a visual chart by using the query result to display the query result to the user.

Provided are system, apparatus, device, method and / or computer-program product embodiments, combinations and / or sub-combinations thereof for using an AI model to facilitate natural language interactions with databases. An example method can include receiving a natural language prompt associated with a user and identifying tables in a database based on the natural language prompt. The method can further include determining a table schema(s) of each of the tables identified, generating, using a large language model, a query to the tables in the database based on the natural language prompt and the table schema(s), and obtaining, using the query, data from at least one table of the tables in the database. The method can include generating, using the large language model or another large language model, a response to the natural language prompt based on the data obtained from the at least one table of the tables in the database.

The invention relates to a sonar fault intelligent diagnosis method and system based on a knowledge graph and a large language model. The sonar fault intelligent diagnosis method comprises the following steps: receiving an initial fault description text input by a user through a natural language interaction interface; carrying out integrity analysis on the initial fault description text, and judging whether the initial fault description text contains all preset key information dimensions or not; if the judgment result of the integrity analysis is no, starting an interactive guide mechanism until a complete fault description text is obtained; based on the complete fault description text, performing reasoning in a pre-trained large language model configured with a sonar fault knowledge graph; and based on the conclusion of the joint reasoning, generating and outputting a structured fault diagnosis report. According to the invention, by introducing a fault description integrity evaluation and interaction guide mechanism, the problem that the system diagnosis accuracy is reduced due to fuzzy or incomplete fault description input by a user in the prior art is solved, and the accuracy and stability of overall diagnosis are improved.