202 results about "Natural language interaction" patented technology

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 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 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 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.

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 discloses an intelligent timed task configuration and multi-mode feedback system based on natural language interaction, and relates to the field of industrial automation control, enterprise-level task scheduling management and instant messaging. The system comprises a natural language interaction module, a user natural language instruction is converted into structured semantic information through a BERT + BiLSTM + CRF mixed architecture, and lightweight processing is achieved in combination with TinyBERT distillation; the dynamic task arrangement module completes task creation, modification and the like based on structured information, completes missing parameters through historical task mode matching, and supports multi-session real-time synchronization; the rich media report generation module generates a multi-mode report containing charts, tables and the like, and the RAG technology and large model interpretation are combined; and the multi-channel pushing adaptation module adapts multiple platforms by adopting a strategy and factory mode. According to the system, the task configuration efficiency and flexibility are improved, the feedback form is enriched, the information pushing accuracy is guaranteed, and the exception handling capacity is enhanced.

The invention relates to the technical field of informatization system development, in particular to a natural language-based complex data model generation method, which comprises the following steps of receiving business requirement input in a natural language form, preprocessing and normalizing input contents, processing ambiguity and incompleteness of requirements through a multi-round dialogue complementation mechanism, and generating a complex data model. Obtaining a complete and clear business demand description; and carrying out deep semantic analysis on the normalized business requirements by adopting a large language model. Aiming at the pain points that an existing data modeling technology is high in threshold, low in efficiency, difficult in quality guarantee, weak in integration adaptation and the like, the method has the remarkable advantage of multiple dimensions, non-technical background personnel can directly input service requirements through natural language interaction and deep semantic analysis on the premise of reducing the technical threshold, database knowledge and SQL specifications do not need to be elaborated, and the method is suitable for large-scale popularization and application. The cognitive gap of business and technology is spanned, the dependence on professional design talents is reduced, and the learning cycle of green hands is shortened.

Methods and systems for improved natural language processing and generation of insights are described herein. Aspects of machine learning and natural language processing may be employed to interpret user queries and provide insights, recommendations, and visualizations. The system employs large language models and machine learning services to enhance the natural language interaction between users and their analytics.

The invention provides a multi-modal data joint query analysis method and system supporting natural language interaction, and relates to the technical field of data query analysis. Historical operation data, audio data and text data of a user on an intelligent search platform supporting natural language interaction are collected; modeling the historical operation data to obtain a user preference vector, performing voice recognition and text standardization processing on the audio data to obtain standard query data, and integrating the standard query data and the text data into context information; an attention mechanism-based algorithm is used for semantic understanding and intention recognition to obtain an intention feature vector, and the intention feature vector is fused with a user preference vector to obtain a classification result; and finally, based on the result, querying in a preset multi-modal database through a collaborative filtering algorithm to obtain a joint query result, so that personalized accurate query of the multi-modal data under natural language interaction can be realized, and the result fits the intention and long-term preference of the user.

The invention relates to an LLM-based comprehensive modular avionics system resource allocation method and system, and the method comprises the steps: removing a universal semantic module through model pruning, taking an IMA configuration description material as a training corpus, forming a lightweight IMA exclusive large language model which is finely adjusted by the training corpus, collecting a user natural language, analyzing a configuration interaction instruction of the user, and carrying out the resource allocation of the user. And then the configuration interpretation model / configuration generation model is called to execute interpretation and change tasks, so that natural language interaction of resource configuration of the integrated modular avionics system is realized on the basis of ensuring functional integrity, and the change cost of resource configuration is obviously reduced. Compared with the prior art, the method has the advantages that the lightweight LLM is trained and deployed in combination with avionics field knowledge, efficient deployment of the model is realized, and the accuracy of semantic understanding of the model in professional scenes in the avionics field is enhanced.

The invention discloses an intelligent teaching assisting method and system integrated with the whole process and total elements of education and teaching, and relates to the field of education and teaching. According to the method, misunderstanding concept classification models are integrated, and a dynamic knowledge graph containing target subject knowledge is established; clustering knowledge concepts in the dynamic knowledge graph by adopting a Mapper algorithm of topological data analysis to obtain a course map; when the user completes interaction of the selected theme cluster, a cognitive state matrix is obtained by adopting a graph knowledge tracking model, an emotion category probability distribution vector is obtained by adopting an emotion classification model, and a learner state vector is obtained; a large language model optimized through process supervision and reinforcement learning is adopted as an inference engine; and outputting a targeted teaching strategy and teaching content by using an inference engine according to the learner state vector. According to the method, a personalized teaching environment which can perform smooth and dynamic natural language interaction and can accurately diagnose and effectively correct the specific deep-level cognition mistake of students in a target subject can be created.

The invention discloses a method, device and equipment for intelligently controlling a BIM model and a storage medium, and the method comprises the steps: receiving a natural language interaction instruction for a target BIM model, and converting the natural language interaction instruction into a structured instruction comprising a target building object and an interaction intention through a preset building domain knowledge base; if the interaction intention is a query intention, obtaining related model data about the target building object from the target BIM model through a data query tool, and forming query result information meeting the query intention based on the related model data; if the interaction intention is a control intention, firstly determining identity recognition information of the target building object from the target BIM model through a data query tool, and then generating and executing a control code corresponding to the control intention and the identity recognition information through a model control tool so as to implement specified operation on the target building object; according to the invention, the interaction process between the user and the BIM model is simplified.

The invention provides a natural language interaction AI agent system for an intelligent tower crane and a method thereof. Wherein the agent system comprises a natural language understanding module, a task planning module, a tool scheduling engine, a large language model interface, a data adaptation layer, a result generation module and a learning optimization module; the proxy method comprises the following steps: S1, natural language input processing; s2, intention understanding and task planning; s3, data acquisition and preprocessing; s4, intelligent analysis and reasoning; s5, verifying and optimizing a result; s6, generating a natural language answer; and S7, feedback learning and optimization are carried out. According to the method, natural language interaction between the user and the intelligent tower crane system is achieved by constructing a special AI agent architecture, the large language model is automatically called to intelligently analyze the running state of the tower crane, and analysis results and decision suggestions which are easy to understand are provided for the user.

The invention relates to the technical field of electric power design, and discloses an electric power design agent intelligent processing system based on an AI large model, which comprises an interface interaction module, a professional model module, a dialogue management module and a professional algorithm module. The interface interaction module orderly calls idle modules according to the instruction sequence; the professional model module periodically retrieves multi-source data, constructs a professional data set through dynamic denoising, secondary classification processing and verification, generates a fine tuning data set through various knowledge distillation modes, and optimizes an industry large model by adopting a low-rank adapter; the dialogue management module is connected with input and output and instruction generation; and the professional algorithm module dynamically calls five types of professional models and adapts to computing power resources. According to the system, a traditional tool data island is broken through, the problems of general LLM professional shortages and'illusion 'are solved, automatic closed loop from natural language interaction to professional design tasks is achieved, manual dependence and error risks are reduced, and power design efficiency, quality and system stability are improved.

The invention discloses a GIS map interaction method and device for emergency management and a medium, and the method comprises the steps: obtaining a query request of a user through a preset interaction interface, and carrying out the preprocessing of the query request, so as to obtain a language text; performing semantic analysis on the language text to determine a query intention of the user, and determining an interaction instruction according to the query intention; querying in a database according to the interaction instruction to obtain query results, and integrating the query results; determining address information according to the integrated query result, converting the address information to determine latitude and longitude coordinates, integrating the latitude and longitude coordinates to determine map display data, and visually displaying the map display data through a GIS map engine. The natural language interaction use threshold is low; the intention is accurately grasped through semantic analysis; data integration and coordinate conversion ensure information accuracy; and finally, visual display is performed through a GIS engine, so that rapid and accurate decision-making of emergency management is facilitated.

The invention provides a truss structure autonomous design system and method based on multiple agents. A layered multi-agent architecture is adopted, a general control agent is taken as a core, and a high-level design intention is analyzed and a plurality of downstream special agents are arranged for cooperative work; the whole design process realizes automatic closed loop through a graph-based workflow engine; the core innovation of the method is a hybrid intelligent optimization algorithm of LLM heuristic exploration and genetic algorithm accurate fine tuning, and the algorithm is driven by an original and quantifiable design quality scoring system to efficiently carry out analysis-diagnosis-optimization iteration so as to cooperatively improve the safety and economy of the design. The design time can be shortened from several hours to several minutes, about 7.0% of material consumption is saved on average, it is ensured that the scheme completely conforms to industry specifications, the design efficiency, economy and reliability are greatly improved, and meanwhile the use threshold of professional design software is remarkably reduced through natural language interaction.

The invention relates to the technical field of rehabilitation training, and discloses a three-dimensional reconstruction rehabilitation training auxiliary method based on AgenticAI and MCP protocols, which is used for providing personalized rehabilitation training support for a user and improving the body function state. The method comprises the steps that current state description information of a user is obtained through a natural language interaction interface, and a decision scheme containing multiple candidate rehabilitation actions is generated through fusion analysis of an AgenticAI decision module in combination with historical rehabilitation training records and physiological index data, obtained by a context engineering engine, of the user. And a corresponding three-dimensional reconstruction training tool is matched by means of an MCP protocol server, personalized calling parameters are generated according to a user capability evaluation result and preference setting, the tool is executed, real-time operation data are collected, the data are stored, and a user capability evaluation portrait is updated. According to training effect feedback data, weight parameters of the decision module are adjusted through a federated learning optimization algorithm, a cross-cycle personalized rehabilitation training plan is finally generated, and the personalization and systematicness of rehabilitation training are improved.

The invention provides a smart farm brain and natural language interaction system based on big data. The system comprises a land parcel selection module, a data perception module, an intelligent decision module, a job execution module and an AI question and answer module. The land parcel selection module is responsible for determining a target land parcel and crops, the data sensing module displays sensor data such as soil and weather of the land parcel according to the selected land parcel, then the intelligent decision-making module generates farming decision-making content according to the sensor data of the selected land parcel, and the operation execution module coordinates agricultural machinery group collaborative operation based on a decision-making result. The AI questions and answers provide natural language interactions with agricultural knowledge. The system can be widely applied to large and medium-sized digital farms and scientific research test farmlands, the farm management cost can be remarkably reduced, the farm operation efficiency is improved, and the agricultural intelligent upgrading and the village revitalizing development process are promoted.

A multi-modal signal recognition and dialogue method based on a large language model comprises the steps that a signal coding module based on time sequence modeling is constructed, preprocessing and feature extraction are conducted on input I / Q signal data, and semantic alignment pre-training of signal features and modulation type text description is achieved through a contrast learning mechanism; designing a multi-modal fusion architecture, mapping signal features to a hidden space of a large language model by adopting a signal projector, and realizing deep fusion of the signal features and text features through special markers; constructing a dialogue generation module based on a pre-trained large language model, receiving the fused multi-modal input, and generating a natural language answer about signal analysis; performing feature alignment by training a double-layer MLP projector to realize end-to-end multi-modal signal understanding and dialogue ability; an intelligent question-answering system in a reasoning stage is constructed, natural language interaction between a user and the system is realized through a predefined professional prompt word template and a signal feature fusion mechanism, and multi-dimensional signal analysis query requirements are supported. According to the invention, the organic combination of signal understanding and natural language generation is realized, and the accuracy of signal identification and the user interaction experience are improved.

The invention discloses a complex transaction task execution and risk control method and system based on an artificial intelligence large model (LLM), electronic equipment and a storage medium, and relates to the field of financial science and technology. According to the method, a transaction demand input by a natural language (preferably voice) of a user is obtained, a structured task instruction sequence containing varieties, directions, number and trigger conditions is generated through large model semantic analysis, risk control verification is carried out according to account funds, position limitation and supervision rules, an executable transaction instruction is formed after the risk control verification is passed, and the transaction requirement of the user is met. And a manual confirmation link can be selected and sent to the transaction terminal for execution, and an execution result is returned for updating the session and risk state. A voice mode is preferably selected for feedback, and automatic transaction execution under complex and continuous conditions is supported. According to the method, natural language interaction is kept, meanwhile, automatic disassembly and rearrangement of complex transaction tasks are achieved, manual one-by-one operation is converted into automatic processing, and the execution efficiency and the risk control capability are improved.

The invention discloses a multi-satellite platform intelligent query and retrieval method based on natural language interaction. The method comprises the following steps: receiving a natural language query of a user and preprocessing the query; different satellite platform naming specifications are processed in a unified manner through a multi-format satellite identification intelligent identification technology; a mixed decision mechanism combining deep learning and a rule engine is adopted to deeply analyze query semantics; accurately positioning a target knowledge base based on a self-adaptive routing strategy; and obtaining an optimal retrieval result by using a parallel mixed retrieval technology. According to the method, through a mixed understanding mechanism combining deep learning and a rule engine, the accuracy and speciality of understanding the natural language query instruction by the user are remarkably improved, and then the accuracy of retrieval query is improved. In addition, through a parallel mixed retrieval strategy, two methods of semantic vector retrieval and keyword retrieval are comprehensively utilized to retrieve the query instruction, and the retrieval results of the two methods are fused, so that the retrieval effect is further optimized.

The invention relates to the field of natural language semantic analysis, in particular to a natural language-driven CIM scene interaction method and system, and the method comprises the steps: receiving a natural language interaction instruction inputted by a user side in real time; analyzing the natural language interaction instruction, and extracting scene data in the natural language interaction instruction; calling a semantic mapping library based on the extracted scene data, and mapping the scene data into corresponding CIM platform functions and space elements through the semantic mapping library; according to the CIM platform function and the space element, generating an interface calling instruction of the CIM platform, sending the interface calling instruction to the CIM platform, and executing a corresponding space element operation through the CIM platform; an operation result returned by the CIM platform is received, multi-modal feedback information is generated based on the operation result, and the multi-modal feedback information comprises visual feedback carried out in the CIM scene; visually feeding back synchronous natural language explanation feedback; and outputting the multi-mode feedback information to the user side.

The invention relates to the technical field of data analysis and natural language processing, and discloses a multi-source data analysis method and device, equipment and a storage medium, and the method comprises the following steps: carrying out metadata integration on a pre-accessed heterogeneous data source, and constructing a global logic data model; identifying semantic elements of a pre-acquired natural language query statement, mapping the semantic elements to corresponding logic entities in the global logic data model, and generating a structured intermediate representation; generating a unified SQL query scheme containing a plurality of SQL sub-query statements based on the intermediate representation; the SQL sub-query statements are distributed to corresponding data sources in the heterogeneous data sources to be executed, and execution results of all the data sources are obtained; and performing unified calculation processing on the execution results of the data sources to generate a unified result set, and outputting the unified result set according to a preset format. By means of natural language interaction and SQL generation, simplification of multi-source heterogeneous data analysis is achieved.

The invention relates to the technical field of semantic cognition modeling and natural language interaction, in particular to an electric power digital twin question-answer interaction generation method based on semantic cognition modeling, which comprises the following steps of: accessing a power grid topological data flow in real time, analyzing equipment hierarchy, power supply attribution and physical connection relationship, and constructing a dynamic topological knowledge graph; the method comprises the following steps of: establishing a topological change monitoring channel, mapping the topological change monitoring channel to a semantic cognition model, embedding a topological hierarchy field and an association constraint rule, generating an enhanced semantic cognition model with topological association constraints, and establishing a topological change monitoring channel to update an association relationship in real time; receiving user natural language query, identifying keywords, extracting power supply attribution constraint rules, filtering invalid entities, and converting implicit topological semantics into combined query statements; and executing query to obtain topological associated data, verifying the power supply attribution consistency of a result set, filtering homonymy matching errors, triggering model updating and secondary verification during topology adjustment, and outputting question and answer responses conforming to topology constraints, so that the interaction accuracy and timeliness are improved.

The invention discloses a resume generation and optimization method and system based on multi-round natural language interaction, and belongs to the technical field of natural language processing, the resume generation and optimization method and system based on multi-round natural language interaction comprises the following specific steps: 1, receiving initial information input by a user through a natural language mode; the natural language mode comprises voice input, text input and multi-mode input containing images, and if the input is voice input, the input is converted into initial text information through an automatic voice recognition engine. Through multiple rounds of natural language interaction, the resume input and updating threshold is remarkably reduced, and a user can generate a first version of resume and match posts in real time only by short voices. The system actively excavates the potential advantages of the user, guides and complements key information, supports multi-modal input and dynamic updating, and effectively improves the resume quality, the matching efficiency and the user experience.

The invention discloses an urban traffic cooperative scheduling method and system based on a large model and multiple agents. The method comprises the steps that natural language task description is converted into a task semantic graph and a structured prompt; generating a plurality of strategy candidates by using a large language model, and performing language scoring and reasoning arbitration; based on the scoring result, selecting an optimal strategy for multi-agent execution; behavior execution data are collected and evaluated and fed back, and strategy closed-loop updating is achieved through parameter optimization and Prompt fine tuning; and a distributed task embedding mechanism and a lightweight migration module are combined, so that the multi-task adaptability and the training efficiency of the system are improved. The cooperative training system constructed by the invention has the capabilities of natural language interaction, strategy interpretability, behavior controllability and task migration, and is suitable for various complex urban tasks such as traffic jam dispersion, emergency response, signal lamp linkage and the like.

The invention discloses an intelligent number asking system and method based on SQL (Structured Query Language) layer data authority control, and aims to realize safe and efficient query and visualization of multi-heterogeneous application data by a user through natural language interaction, analyze a data access rule in real time through an intelligent strategy engine, dynamically generate and modify SQL query, and improve the query efficiency. The system comprises a query processing module used for receiving a natural language query; the question classifier module is used for identifying a target application; the strategy engine is used for analyzing the data access rule; and the SQL query modification module is used for dynamically generating a safe SQL query according to the analyzed rule and the identified application, and the system is combined with the visualization module to present a query result as a statistical report with a chart, and dynamically filters and displays according to the data permission, so that the data permission control is thoroughly realized, the user experience is improved, and the user experience is improved. And complexity and security risks of traditional data access are effectively avoided.

The invention discloses an intelligent mining method for dispatching knowledge of a water-wind-light complementary system based on a large language model, which belongs to the technical field of dispatching of the water-wind-light complementary system and comprises the following steps: S1, constructing an ontology model in the field of water-wind-light dispatching according to'demand traction-reuse verification-concept extraction-semantic modeling-formalized implementation '; s2, collecting and processing data; s3, the RoBERTa-BiLSTM-CRF architecture is trained, and entity mining is achieved; s4, realizing relation mining by combining a mixed relation mining method with a three-layer semantic constraint mechanism; s5, performing multi-dimensional fusion on entity semantics; s6, relying on Neo4j graph database storage, combining a man-machine natural language interaction interface of the generative LLM and realizing knowledge base iteration updating. According to the intelligent mining method for the scheduling knowledge of the water-wind-light complementary system based on the large language model, multi-source data collaborative fusion and mining are achieved, and core knowledge support is provided for multi-energy collaborative scheduling and optimization decision making.