149 results about "Dialogue management" patented technology

The invention relates to the technical field of information retrieval, in particular to an intelligent RAG knowledge base system fused with a dynamic knowledge graph. Comprising a data processing module for preprocessing original data to obtain a standardized data set; the graph construction and updating module is used for carrying out knowledge extraction on the standardized data set by utilizing a large model, constructing a knowledge graph and carrying out dynamic updating; the dialogue management and understanding module is used for recording complete information construction of a current dialogue, updating a dialogue state and calculating a retrieval weight adjustment amount; the mixed retrieval module is used for performing mixed retrieval in combination with DPR and PPR retrieval methods to obtain a first retrieval result and a second retrieval result, setting an initial retrieval weight according to the question type, and obtaining a comprehensive retrieval result in combination with the retrieval weight adjustment amount; and the response optimization module is used for outputting an optimal response according to the comprehensive retrieval result and user feedback by utilizing reinforcement learning based on strategy gradient. According to the method, the retrieval and response generation effect can be optimized, and the user satisfaction is improved.

The invention discloses a customer service robot system integrating knowledge base question and answer retrieval and work order processing, which comprises a natural language understanding and generating module, a knowledge retrieval module, a business system integration module, a dialogue management and decision module and a cue word management module, and relates to the technical field of artificial intelligence. Through cooperation of a large language model and a dialogue management module, the system realizes multiple rounds of deep understanding of a natural language, and can actively guide a user to clarify a demand, such as gradually inquiring an equipment model and fault details during repair, memorizing a dialogue context and avoiding information omission. Compared with traditional regular customer service, the method has the advantages that the accuracy of understanding complex problems is remarkably improved, open dialogue scenes such as technical consultation and fault diagnosis are supported, user interaction experience is closer to manual service seats, and the problems that multi-round dialogue understanding is limited, progress data cannot be obtained in real time, multiple knowledge bases are fused, and the automation degree of the business process is low are solved.

The invention relates to the technical field of artificial intelligence, and discloses a professional knowledge question and answer intelligent customer service system based on a self-optimization mechanism. The system comprises a user intention analysis module, a knowledge processing module, a self-optimization learning module and an interactive presentation module. A semantic understanding unit of the user intention analysis module generates a user intention signal; after the knowledge processing module receives the signal, a knowledge retrieval unit outputs related knowledge fragments and confidence, an answer generation unit forms candidate answers, and a quality evaluation unit determines an optimal answer according to the confidence; in the self-optimization learning module, a feedback analysis unit adjusts answer generation parameters according to user interaction data, a strategy adjustment unit optimizes a retrieval strategy in combination with an optimal answer and a historical dialogue, and a knowledge updating unit updates a knowledge base depending on an external knowledge source; and a multi-round dialogue management unit of the interactive presentation module adjusts a dialogue process, and a visual presentation unit outputs a natural language text and collects user feedback to a feedback analysis unit.

The invention discloses an AI (artificial intelligence) digital human interaction system, which comprises a dialogue management module for receiving emotional state information transmitted by an emotional recognition module and other information input by a user feedback processing module; tracking a dialogue state, selecting a proper response mode according to a dialogue strategy, and generating a corresponding system behavior; the system behavior instruction is transmitted to an action and expression generation module; the user feedback processing module is used for collecting and analyzing feedback data of the user; and the feedback module analyzes and processes the feedback data, extracts valuable information, and transmits an analysis result to the dialogue management module and the reinforcement learning module for guiding the improvement and optimization of the system. Comprehensive information acquisition is realized through the multi-modal information acquisition module, continuity and individuation can be enhanced through the dialogue management module, an interaction strategy is optimized through the reinforcement learning module, and continuous improvement and real-time feedback are ensured through the user feedback processing module; and the interaction effect of the whole system is more natural and vivid.

The invention discloses an intelligent form filling method based on voice interaction. A form filling system is composed of an intention recognition module, an entity recognition module, a data mapping module, an intelligent error correction module and a dialogue management module. According to the system, the traditional manual form filling operation is replaced by a voice input mode, the complexity and time cost of user input are greatly reduced, and the recognition precision of terminologies is remarkably improved by utilizing a voice recognition and natural language processing model optimized for the financial field, so that the user experience is improved. Specific fields of the form can be accurately recognized according to intention recognition and data mapping functions customized for a specific scene of financial reimbursement, the system is designed based on a multi-round dialogue management module, a user is actively guided to complete complex form filling step by step, the learning cost and the operation difficulty are remarkably reduced, and the user experience is improved. Full-process automation of voice recognition, data mapping and intelligent verification is realized, and manual intervention is reduced.

The invention relates to the technical field of artificial intelligence and natural language processing, in particular to a law and regulation question answering system and method based on an industry large model, and the system comprises a data layer, a model layer, a service layer and an application layer. The data layer is responsible for collecting, storing and managing law and regulation data; the model layer comprises an industry large model and an RAG module; the service layer provides question and answer generation, multi-round dialogue management and security control core services; the application layer provides diversified application scenes for different user groups; the method has the beneficial effects that the general large model is finely adjusted in a targeted manner by using massive professional data in the legal field, so that the model can deeply learn logic association between connotation and extension of legal concepts and provisions and judgment goals of cases. Through the fine adjustment, the model can accurately grasp the core of the question when facing a complex legal question, provides an answer meeting a legal professional standard, and ensures the accuracy and preciseness of the answer, thereby effectively solving the problem of understanding deviation of professional knowledge.

The invention discloses an intelligent customer service dialogue management method and system fused with multi-layer memory, and belongs to the technical field of computers, and the method obtains a three-dimensional context through multi-mode context awareness, achieves the dynamic scheduling of a memory system through a memory coordinator, and combines a dynamic construction mechanism of a service situation portrait, thereby achieving the intelligent customer service dialogue management. The problem that the response of the intelligent customer service system is not matched with the complex and dynamic service demand of the user due to the single situation awareness dimension and the rigid memory scheduling mechanism is effectively solved, and the adaptive personalized service response can be generated according to the real-time change of the dialogue situation.

The invention relates to the field of data analysis, in particular to a customer service interaction system and method based on natural language processing. The system comprises a multi-channel access module used for supporting interaction of at least one channel and providing a uniform interface; the semantic understanding module is used for carrying out semantic analysis on the input problem through a natural language processing technology so as to determine a problem type and a corresponding intention; the knowledge base retrieval module is used for maintaining a knowledge base containing common questions and answers and quickly retrieving related answers through a semantic similarity matching algorithm; the dialogue management module is used for tracking the dialogue state, understanding the context and managing multiple rounds of dialogue processes; and the natural language generation module is used for converting the answers retrieved by the knowledge base retrieval module into natural languages and replying. And intelligent customer service interaction is realized by utilizing a natural language processing technology, so that efficient and accurate solutions are provided, and the customer service efficiency and satisfaction are improved.

An end-side-cloud three-in-one active chat robot system for high-emotional merchants comprises an end side, a local edge server and a cloud end, and the end side is used for collecting multi-modal data of a user and performing local lightweight real-time processing and response execution; the local edge server is used for receiving and fusing the multi-modal features and the context information from the end side, and carrying out sentiment calculation, dialogue management and active trigger decision making with medium complexity; the cloud end is used for operating a super-large-scale model and providing global knowledge management, long-term user portrait storage and model training optimization; and the end side, the local edge server and the cloud end carry out cooperative communication through an encrypted channel to form a distributed intelligent processing architecture. According to the method, global optimization is realized by integrating end-side lightweight sensing, edge multi-modal fusion and cloud long-term memory. A composite finite state machine (FSM) active questioning mechanism is combined with sentiment calculation, and is different from traditional rule type triggering. Off-line and on-line fusion scheduling and multi-agent role playing are combined to be applied to a high-emotional-quotient interaction scene, and the simulation is improved. Multi-modal emotion perception circulation is introduced, and the problem of single text emotion misjudgment is solved.

The invention provides a service data voice processing method and system based on artificial intelligence, and relates to the field. The service data voice processing method based on artificial intelligence comprises multi-mode voice preprocessing, domain enhancement voice recognition, knowledge graph driven semantic analysis, a dynamic dialogue management mechanism and a closed-loop feedback optimization system. According to the method, high-precision recognition, semantic understanding and structured processing of the service voice data are realized by fusing the domain knowledge graph, the dynamic model optimization and the context sensing technology, and the service data processing efficiency and accuracy are improved; secondly, the system comprises a voice acquisition and preprocessing module, an AI voice processing module, a business data processing module, a model optimization module and a data interface module, the efficiency and accuracy of business data voice processing can be remarkably improved, and the system is suitable for various professional scenes such as finance, customer service and medical treatment.

The invention provides a collection strategy generation and execution system and method based on debtor portrait driving, and belongs to the technical field of data processing, and the system specifically comprises a portrait access module which is used for obtaining a user portrait of a debtor, a strategy generation engine which is used for automatically generating a collection strategy of the debtor based on the user portrait, and an execution module which is used for executing the collection strategy. The execution control module adopts an event-driven architecture to be responsible for automatic execution and real-time control of a collection strategy, the communication management module adopts a natural language processing and dialogue management technology to be responsible for intelligent communication and interaction management in the collection process, and the strategy optimization module is responsible for intelligent management. And online learning and reinforcement learning technologies are adopted to continuously optimize the collection strategy based on the execution result and effect feedback, so that the individuation and pertinence of collection processing are improved.

An intelligent, predictive customer loyalty system for social media marketing, consisting of: a data acquisition unit configured to collect user interaction data, demographic attributes, and sentiment analysis metrics from multiple social media platforms via platform-specific application programming interfaces (APIs), web scraping techniques, and direct data ingestion pipelines; a real-time engagement prediction engine comprising a machine learning model trained on historical engagement patterns, sentiment fluctuations, and content performance metrics, with the model dynamically predicting user responses, determining the optimal engagement time, and prioritizing interaction opportunities based on a probabilistic ranking technique; a real-time engagement prediction processing unit configured to generate and optimize social media posts, ads, and interactive media using a neural network-based natural language generation (NLG) model, where the model processes contextual user behavior and trending topics to dynamically adapt marketing content; an AI-powered automation unit for interactive engagement, comprising a deep learning-based chatbot engine integrated into a dialogue management system, with the chatbot engine analyzing the user's intentions, sentiment development, and engagement history to enable predictive, context-aware conversations with users; a cloud-connected processing unit that includes an AI inference accelerator configured to perform deep learning calculations for real-time engagement prediction units, personalized content recommendations, and chatbot response generation, with the processing unit integrated into a distributed computing framework to optimize inference latency.

The invention discloses an accompanying dialogue system and method based on real-time environment perception and knowledge graph enhancement. The system comprises a multi-mode real-time environment perception module, a knowledge graph construction and enhancement module, a natural language understanding and dialogue management module, a personalized recommendation and narration generation module and an immersive multi-mode interaction module. The method comprises the steps of multi-modal real-time environment perception and situation data generation; performing dynamic association, query and enhanced reasoning on the knowledge graph; natural language understanding and dialogue management, personalized recommendation and narrative generation, and immersive multi-modal interaction presentation and feedback reception. According to the method and the system, the concern point of the user, namely scenery or details, can be accurately positioned, and context information required by subsequent service intelligence is provided, so that the problems of poor environment perception ability, weak interaction immersion, dull knowledge service, lack of individuation, insufficient intelligent accompanying experience and the like in the existing tourism auxiliary technology are solved.

The invention provides an enterprise-level intelligent assistant-oriented multi-modal dialogue management and task execution system establishment method, which is characterized by comprising the following steps of Step 1, multi-modal data acquisition and preprocessing; step 2, carrying out cross-modal information alignment and fusion; 3, multi-mode dialogue management is carried out, and after data fusion is completed, the system enters a dialogue management stage; step 4, multi-modal task execution: in the dialogue management process, when a user initiates a specific task request, the system enters a task execution stage; automatically distributing and executing a plurality of tasks in the enterprise by using the fused multi-modal information; step 5, designing and optimizing a system architecture; and an integrated multi-mode intelligent assistant architecture is designed. By integrating multi-modal data processing and natural language understanding (NLU) technologies, the accuracy and efficiency of the enterprise intelligent assistant in task execution are remarkably improved. The system can process various modal information such as texts, voices and images at the same time, and seamless connection of cross-modal dialogue management and task execution is achieved.

The invention discloses an enterprise data analysis system based on natural language interaction, and relates to the field of enterprise data analysis, the system comprises seven core function modules: a dialogue management module supports a user to create a new dialogue and automatically jumps to an initial page; the report interaction module realizes query support and switching functions of a real-time report and a T + 1 report through a floating button; the file import module allows uploading of Word, PDF and JPG format files to supplement question and answer contexts; the model configuration module supports dynamic switching of different AI service interfaces; the interaction auxiliary module provides a quick question button and a dialogue interruption function; the report preview module integrates Excel and PDF plug-ins to realize detail data preview; the session sharing module generates a sharing link and sets an access permission rule; dynamic model configuration optimizes response speed and accuracy; the operation process is simplified by a suspension button and a quick questioning function; the session sharing mechanism supports cross-team cooperation; and the real-time collaborative design with the T + 1 report meets the multi-dimensional data analysis requirement.

The invention discloses a multi-round interactive AIAgent agent based on multi-module collaboration and an implementation method thereof, and relates to the field of artificial intelligence. The intelligent agent comprises an information acquisition module, a dialogue state judgment module, an intention recognition module, a parameter extraction module, a rule engine module and a dialogue strategy module which work cooperatively. The information acquisition module is used for maintaining tool structured information and a historical dialogue library; the conversation state judgment module distinguishes task continuation from new tasks; the intention recognition module recognizes a user intention; the parameter extraction module extracts structured parameters; the rule engine module verifies parameter integrity and legality; the dialogue strategy module guides the user to supplement input. According to the implementation method, multiple rounds of interaction are completed through multiple steps. Modular design is adopted, model illusion is avoided, multi-round dialogue management is achieved, system expansibility is enhanced, and tool calling accuracy and user experience are improved.

The invention relates to an intelligent agent method and device based on an intelligent language model, equipment and a medium, and the intelligent agent method based on the intelligent language model comprises the steps: setting corresponding interaction parameters according to the type of a communication protocol, so as to establish a data interaction channel with a target customer service system; on the basis of the intelligent language model, multiple rounds of dialogue management processes are executed on the user input text, corresponding user consultation content is generated, and the user consultation content comprises holiday and festival consultation verbal skills generated for a target area in the user input text; receiving response information returned by the target customer service system based on the user consultation content, and extracting structured field information used for optimizing the multi-round dialogue management process; generating and pushing a corresponding question-seeking verbal skill; according to the invention, the docking efficiency in a multi-platform and multi-protocol environment is effectively improved, the dependence on manual configuration is reduced, and the processing efficiency and accuracy of an intelligent customer service system in a high-concurrency scene are improved.

The invention discloses an artificial intelligence-based intelligent question and answer method in the human society field, and relates to the technical field of artificial intelligence and government affair services. Comprising the following steps: collecting and cleaning laws and regulations, files, common questions and case library contents related to human society through a knowledge graph construction module; constructing a structured human social knowledge graph, and performing intention recognition and slot extraction on the content input by the user by using a pre-training language model through a natural language understanding module; context memory and state tracking are carried out through a dialogue management module, and multi-round dialogue logic is controlled for an interaction mode of inquiry, clarification and guidance; providing a voice and character dual-channel input and output interface; an answer generation module generates a natural language answer based on a human society knowledge graph query result, the natural language answer is used for visualized display of structured data, and an API interface can be called to obtain real-time data; user interaction behaviors and identity information are recorded through a user portrait and personalized recommendation module, personalized policy interpretation and service recommendation are provided in combination with a user portrait, and historical question and answer record retrieval and reuse are provided; and obtaining question and answer effect feedback through a feedback learning and adaptive optimization module, carrying out question and answer strategy optimization by utilizing a reinforcement learning algorithm, automatically discovering uncovered questions and triggering a knowledge base updating process.

The invention relates to the technical field of artificial intelligence, in particular to a large model dialogue management method and device.The method comprises the steps that dialogue information of a user input dialogue initiated by a user to a large model is intercepted; obtaining user information corresponding to a user; filling the first cue word template based on dialogue information of a dialogue input by a user and user information to obtain a first cue word; sending the first cue word as first-round user input dialogue information to the large model, so that the large model generates large model output dialogue information for the first-round user input dialogue based on the first cue word; and in response to establishment of the update trigger condition, updating the user contour information based on the historical round dialogue of the current session and / or the dialogue content of the current round dialogue. According to the method, the structured user background information is provided for the large model at the beginning of the dialogue, so that the reply accuracy and pertinence of the large model are remarkably improved, and cross-session personalized interaction experience is realized.

The invention provides a multi-agent system and a multi-agent dialogue management method. The system comprises a main agent, a dialogue management module and a plurality of sub-agents, and the sub-agents correspond to processing of tasks in different business fields respectively; the main agent is used for calling the dialogue management module under the condition that a user request is received; the dialogue management module is used for acquiring historical information corresponding to the user request, analyzing the historical information and returning an analysis result to the main agent; the main agent is further used for determining corresponding request task data and a target sub-agent in the multiple sub-agents according to the analysis result and the user request, and sending the request task data to the target sub-agent; the sub-agent is used for processing based on the request task data, obtaining a task processing result and returning the task processing result to the main agent; and the main agent is also used for returning a corresponding natural language reply to the user according to the task processing result of the sub-agent.

The invention discloses a multi-module cooperative intelligent role playing system, which belongs to the crossing field of artificial intelligence and human-computer interaction technology, and mainly comprises a dynamic role definition module, a dynamic constraint module, a long-term memory module, an RAG module and a React module. Through combination of long-term memory, emotion recognition, dynamic role definition, dynamic constraint, real-time response and feedback and retrieval enhancement generation (RAG) technologies, role simulation and dialogue generation in a complex interaction scene are realized. The method is especially suitable for situational dialogue management and real-time decision support, and an error prevention mechanism is integrated to improve the robustness and adaptability of the system. According to the method, an innovative solution is provided for scenes such as enterprise-level virtual assistants, game intellectualization and educational training, and the use threshold of non-technical personnel in a complex interaction system is remarkably reduced.

The invention belongs to the technical field of medical information processing and intelligent question answering, and particularly relates to a layered medical map and dynamic retrieval fused verifiable question answering system, which comprises a multi-layer knowledge map construction module used for sequentially carrying out hierarchical map association and multi-channel data updating after semantic unit partitioning; the progressive retrieval and verifiable answer module is used for sequentially carrying out underlying reasoning supplement and traceability and quality inspection after high-level target locking; and the interactive question and answer and privacy protection module is used for performing security compliance operation on the patient information after multiple rounds of dialogue management. According to the system, by arranging the multi-layer knowledge graph construction module, the progressive retrieval and verifiable answer module and the interactive question answering and privacy protection module, cross-level association and dynamic retrieval of multi-source medical data can be realized, and the security of sensitive data of a patient can be strictly guaranteed; and targeted question and answer and decision support is provided for different crowds.

The invention belongs to the field of digital consumption, and discloses an omni-channel consumer digital intelligent operation method and system, and the method comprises the steps: updating an industry knowledge graph based on a language model, an industry knowledge base and an enterprise own knowledge base; performing intelligent question and answer in an e-commerce scene by utilizing an industry knowledge graph and a language model and combining a multi-round dialogue management mechanism; on the basis of the language model and the input information, generating a marketing short message copywriting by using a version test framework; carrying out prediction analysis and trend mining by utilizing a machine learning framework based on multi-source heterogeneous data; analyzing the input demand text of the user by using the sentiment analysis model and the industry knowledge graph, and feeding back an analysis result to the user; and constructing a user portrait by using the user behavior data, and generating a recommended item list for the user in combination with the recommendation model. According to the method, the efficiency of customer service, marketing and data analysis is remarkably improved, and the safety and privacy compliance of user data are ensured.

The invention discloses an interaction system and method based on artificial intelligence, and relates to the technical field of man-machine interaction. The system comprises a data acquisition module configured to acquire voice signals and facial expression data of a user in real time through a microphone array and a depth camera, the microphone array focuses the voice of a target user by adopting a beam forming technology, and the depth camera supports 120 fps high-speed face capture; and the personalized analysis module is used for establishing and maintaining an exclusive emotion feature model of the user, and the model comprises dynamically adjusted expression influence factors and voice feature weights. According to the method, the expression and voice weight are dynamically adjusted by constructing the user exclusive emotion feature model, so that the emotion recognition accuracy and personalized experience are remarkably improved. And multi-modal emotion analysis is fused with voice visual information, so that the stability and fault-tolerant capability of the system are enhanced. The dialogue management engine is combined with the graph neural network and context memory to ensure continuity and logicality of multiple rounds of dialogues and provide natural and smooth interaction experience.

A system and methods for latent contextual threading for personalized dialogue through geometric manifold-based conversation management. The system maintains a personalized cognitive manifold as a geometric manifold in latent space that encodes user-specific dialogue patterns as navigable geometric structures. Multiple dialogue contexts are maintained as geometric trajectories within the manifold, with dialogue responses generated through manifold traversal rather than discrete context retrieval. A bidirectional adaptation system modifies the manifold's geometric structure based on user interactions. The system preserves dialogue continuity across session boundaries by serializing manifold geometry during session termination and restoring geometric positioning during session resumption. Dialogue coherence is evaluated through geometric analysis including curvature calculations and geodesic deviation measurements. The system maintains conversations through real-time manifold geometry modifications, providing dialogue experiences across session boundaries while maintaining contextual threading and personalized interaction patterns through geometric principles.

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 an express customer service voice robot multi-round task dialogue system based on a large model, which relates to the technical field of artificial intelligence and natural language processing and comprises a dialogue domain classifier, a long and short term memory module, a sub-agent module, an information source module, a dialogue management module and a large model fine tuning and data support module. The dialogue domain classifier is used for routing user query to the corresponding sub-agent module; and the long-short-term memory module is used for constructing a layered persistent memory mechanism. According to the express customer service voice robot multi-round task dialogue system based on the large model, by introducing the large language model, accurate understanding of fuzzy and spoken expression of a user is achieved, the generalization ability of intention recognition is enhanced, a multi-level memory mechanism is adopted, continuity and stability of a dialogue state are ensured, and the dialogue efficiency is improved. Even if the user jumps or asks a question, the smoothness of the conversation can be kept, and the maintenance cost is greatly reduced.

The invention discloses an intelligent questioning and answering method and system for health and epidemic prevention knowledge, and relates to the field of artificial intelligence. Wherein the multi-source data integration module is used for carrying out normalization processing on structured, semi-structured and unstructured data and constructing a knowledge graph containing entities, relationships and attributes so as to realize automatic association of cross-domain data; the deep semantic processing module is used for performing word segmentation and semantic understanding on a natural language input by a user and constructing an ontology model supporting cross-layer reasoning; the intelligent consultation service module is used for analyzing user intentions through multiple rounds of dialogue management and performing risk assessment in combination with real-time data to push early warning information; the multi-mode retrieval module is used for generating unified semantic vectors for texts, pictures and video resources and providing a mixed retrieval mode based on keywords and semantic vectors. According to the method, efficient integration, accurate semantic analysis, dynamic knowledge pushing and natural interaction of multi-dimensional data can be realized, and the intelligent level of public health information service is improved.

The invention discloses a robot control system and method based on AI voice, and belongs to the technical field of robot intelligent control. The system comprises a local voice processing module, a dialogue management and context understanding module, an instruction mapping and generating module and a robot communication and control module. The local voice processing module is integrated with a 16 kHz sampling ASR engine, voice-to-text conversion is realized through a 6-layer LSTM network and beam search decoding, and the TTS engine adopts a WaveNet architecture to synthesize voice. A 7 billion parameter large language model is arranged in the dialogue management module, a 32-layer Transform decoder architecture is adopted, 10 rounds of dialogue history is maintained, and intention recognition and entity extraction are achieved through a multi-head self-attention mechanism. And the instruction mapping module performs trajectory planning by adopting quintic polynomial interpolation and quaternion spherical linear interpolation. According to the invention, full-link localization processing is realized, the speech recognition delay is 180 milliseconds, the position error is less than 0.5 mm, and the problems of high delay, high privacy risk and weak complex instruction understanding ability in the prior art are effectively solved.

The invention relates to the technical field of intelligent dialogue management, and discloses an intelligent dialogue management method and system for adaptive reinforcement learning, and the method comprises the steps: obtaining a dialogue sequence in multiple rounds of interaction of a user, extracting context correlation features and user feedback real-time data, and generating an initial dialogue sequence propagation model, constructing a dynamic propagation path and adjusting the weight of the path; adjusting the priority sequence of the dialogue content in combination with real-time feedback; generating a final dialogue sequence propagation scheme according to the optimized propagation path and the priority sequence; through combination of a graph neural network and a context reasoning mechanism, context information can be effectively maintained in multiple rounds of dialogues, the efficiency and accuracy of information transmission are improved, and intelligent response can be performed according to real-time requirements of a user; the technical method can be widely applied to intelligent customer service, virtual assistant and other dialogue systems, and has high intelligence, flexibility and user experience.