95 results about "Context management" patented technology

The invention relates to the field of intelligent interaction, and discloses an intelligent hardware dynamic interaction system based on voice semantic fusion and multi-modal perception, which comprises the following steps of: constructing a context model of continuous operation by collecting continuous voice instructions, gesture actions and expression information of a user; semantic analysis and feature fusion are carried out on currently collected voice, gesture and expression features, meanwhile, credibility indexes of all modes are calculated through a weighting or deep learning model, weighting correction is carried out on a fusion result, a real-time feedback algorithm is adopted for weight adjustment for continuous optimization, the next operation intention of a user is predicted through deep learning, and the user experience is improved. And in combination with historical interaction data, online feedback and prediction errors, context management, modal weight and intention prediction strategies are adaptively optimized, and the updated strategies are used for next-round context acquisition and multi-modal fusion. The method has the advantage of improving the recognition accuracy in the continuous interaction scene.

The invention discloses a self-adaptive NL2SQL method and system based on a large language model and retrieval enhancement. The method comprises four key steps of mode linking, example enhancement and SQL generation, multi-path thinking chain fusion reasoning and multi-round self-correction. The system comprises the following modules: a knowledge base management module, an input analysis and preprocessing module, a mode link module, an example enhancement module, an SQL (Structured Query Language) generation module, an SQL fusion and optimization module, an SQL execution and feedback module, a multi-round self-correction module, a Prompt construction and context management module, a system interface module and a content generation module. The method has the advantages that multiple large models are supported, a data source is quickly accessed, a user-defined prompt structure is configured, good engineering maintainability and scene adaptability are achieved, and SQL generation accuracy, performability and universality are improved.

The invention provides a context compression method based on multi-round dialogue intention graph construction, which comprises the following steps: S1, dialogue data acquisition and preprocessing: carrying out natural language processing on each dialogue unit; s2, intention atlas construction is achieved through node design and edge design, and each node comprises original text content and structured semantic information; s3, carrying out context compression and graph structure cutting, and only retaining sub-graphs forming a core semantic link; s4, dynamic context management and topic jump processing: in a multi-topic dialogue, when a user jumps or switches to a new topic, a system records a sub-graph of a current active topic, and contextual nodes of an inactive topic are frozen; s5, context sequence generation and model input: linearizing node contents in the cut sub-graph according to a dependent link sequence to generate a compressed context sequence, and transmitting the context sequence and current user input to a large language model for reasoning; and S6, continuous updating and feedback optimization are carried out.

The embodiment of the invention provides a question and answer management method and device based on a large model, a storage medium and a program product. In the scheme, a'short-term first and long-term 'progressive recall strategy is introduced, that is, in a storage stage, session abstracts are generated in a segmented manner under the condition that whether the collection frequency of a preset round and the topic category are changed or not, the session abstracts are sequentially written into a short-term memory storage area and sink to a long-term memory storage area after the preset storage duration is reached, and a subsequent recall path is pre-buried; in the acquisition stage, the latest abstract closest to the current round is recalled from the short-term region, and if the abstract is missing, the cross-session or cross-round historical session abstract continues to be complemented from the long-term region. According to the mechanism, session context management of'instant light and thin 'and'long-term consistent' is considered under the condition that the computing power and the bandwidth cost are not remarkably increased, accurate contexts are provided for complex session scenes with long-period, multi-topic and multi-file cooperation, and a large question and answer model can make more accurate and more consistent questions and answers based on complete and related context information.

The embodiment of the invention provides a context information management method and device based on hierarchical memory, equipment and a medium, and the method comprises the steps: analyzing a task request to generate a sub-task node sequence containing a target description and acceptance function, and arranging a plurality of agents to cooperatively execute a task, and after the audit is passed, a complete execution context is stored in a long-term memory, and a structured abstract is generated and stored in a short-term memory, so that the problem that the context management mechanism of the existing multi-agent system is single is effectively solved, stable maintenance of cross-session cognitive continuity is realized, and the efficiency of the multi-agent system is improved. The method improves the execution reliability of a complex long-process task, optimizes the information storage and retrieval efficiency through hierarchical memory, reduces the risk of semantic deviation accumulation and amplification, clarifies the task target and acceptance standard of each stage, enhances the multi-agent cooperation consistency, and improves the efficiency of task execution. And the execution efficiency and the task completion quality of the intelligent agent system in a complex application scene are comprehensively improved.

The embodiment of the invention relates to the technical field of natural language processing, and discloses a multi-task agent execution method and device based on a large language model, electronic equipment and a storage medium, and the method comprises the following steps: receiving natural language target task description and constraint conditions input by a user; generating a task tree with a hierarchical structure and a dependency relationship; calculating an initial priority for each task node based on a preset priority scoring model, and dynamically adjusting the real-time priority of the task node according to the execution feedback of the task node to form a scheduling queue; asynchronously executing each task node in the task tree according to the scheduling queue, and calling a large language model to complete the generation of subtask contents corresponding to each node; and integrating the execution results of the task nodes, and generating a final output result oriented to the target task. According to the method, the intelligent agent system with self-adaptive dynamic scheduling, hierarchical task decomposition and stable long-range context management capabilities is constructed, so that efficient, reliable and user-controllable multi-task automation is realized.

The invention relates to the technical field of process orchestration, and discloses an automatic orchestration system and implementation method based on event driving and context management, and the system comprises a model orchestration module which receives an external request through an OpenAPI, carries out the model packaging based on namespace attributes, and abstracts the workflow execution capability into a configurable model entity; the workflow engine module is used for providing a dynamic arrangement and execution mechanism based on a directed graph structure and carrying out unified context management; the parameter configuration module is used for constructing a global data constraint and management mechanism of a parameter-result double-layer model, and establishing a data mapping relation between an execution layer and a display layer of the process; and the data source management module uniformly manages and configures the external service capability units which can be called by the nodes in the workflow. According to the method, asynchronous, self-driven and distributed parallel execution of the workflow nodes can be realized, the operation efficiency in a high-concurrency scene is improved, and organic unification of performance, stability and expansibility is realized.

The embodiment of the invention provides a memory information processing method of a large language model and a computer program product, and relates to the technical field of large language models. The method comprises the steps of obtaining interaction request information of a target object, extracting at least one piece of memory information needing to be memorized from the interaction request information based on a large language model, performing classification and importance evaluation on each piece of memory information to obtain category information and an importance score of each piece of memory information, and according to each piece of category information, determining the importance score of each piece of memory information. And determining the expiration duration of each piece of memory information, and storing each piece of memory information, the corresponding category information, the corresponding expiration duration and the corresponding importance score into an active memory pool of the target object. Context management can be carried out by using the memory information, and the context management accuracy of a large language model in a cross-session scene is remarkably improved.

The invention discloses a context dynamic cutting method for a multi-agent dialogue. The method comprises the following steps: S1, input acquisition and upper and lower text field object standardization: standardizing multi-source heterogeneous data into upper and lower text field objects with state marks and storing the upper and lower text field objects; s2, context storage and clipping trigger judgment: monitoring the state of a storage area to trigger dynamic clipping; s3, context compression: compressing the completed historical context into a structured abstract; s4, context evaluation and dynamic clipping: when clipping is triggered, obtaining active and abstract contexts from a storage area to form a candidate set, and executing dynamic clipping processing of a core; s5, task routing, and then routing the task to the sub-agents based on the capability matrix and the weight; and S6, executing result aggregation by the sub-agents, and generating a final result after aggregating the output of each agent. According to the invention, the problem of unbalanced efficiency and precision in long context management is solved, and intelligent retention and precise routing of key information are realized.

The invention provides a firmware non-inductive updating method and system for storage equipment, and belongs to the technical field of the storage equipment, and the system for the firmware non-inductive updating of the storage equipment comprises the following steps: reserving an RAM (Random Access Memory) area; a context management module; a firmware update processing module; a reset control module; and a context recovery module. According to the method, device reset and service interruption caused by firmware updating are thoroughly eliminated, and strict requirements of high-availability scenes such as a cloud server and an industrial control system are met; state decoupling is carried out between front-end and back-end / firmware transactions, and a key front-end operation state is solidified in an RAM (Random Access Memory), so that new firmware can'thermally take over all unbiased transactions, and seamless switching in a real sense is realized.

The invention relates to a high-concurrency real-time dialogue state tracking and context management method, and belongs to the technical field of natural language processing and human-computer interaction. The method comprises the following steps: firstly, unifying multi-modal input into standardized semantic representation; then fusing the historical context and external knowledge to perform semantic analysis; the state change triggered by analysis is packaged into an event and the event is published to a distributed log; an independent service asynchronous consumption event is used for atomic updating of a global state through version control and an optimistic lock mechanism; the read-write performance is improved through multi-level cache and incremental compression; meanwhile, streaming maintenance is based on a long context of a graph, and resources are optimized through life cycle management. Through unified semantic representation, event-driven state management, long context modeling and optimistic concurrency control, a closed-loop collaborative system is constructed, and the concurrent processing capacity, context coherence and response robustness of a dialogue system are remarkably improved.

The invention relates to a generative artificial intelligence context management technology, and discloses a knowledge tree-based structured memory system and method, a computer readable storage medium and electronic equipment. According to the system, structured memory with a father-son relationship is stored through a knowledge tree, a retrieval strategy is determined according to current input, related nodes or branches are recalled, and a memory context is generated for a model to call after deduplication, truncation, pruning, hierarchical summarization and priority retention processing are carried out under context budget constraint; and meanwhile, node addition, deletion, modification and combination, conflict marking, version management and cutting control are supported, so that the accuracy, consistency and long-term task stability of generated contents are improved. The knowledge tree construction module is used for acquiring external information and executing node generation, updating, deletion and branch maintenance, and can write back a knowledge tree after output; when conflicts exist, version management is executed, and cutting is carried out according to correlation, credibility, redundancy and the like.

The invention discloses a context-aware dialogue system and method based on AI assistant intelligent question answering, and belongs to the technical field of artificial intelligence and man-machine interaction, and the system comprises a user interaction interface module which is used for receiving a query request input by a user and returning a response result to the user; the dialogue context management module is used for capturing, storing and structurally processing historical information in the dialogue session in real time so as to construct and dynamically update a dialogue context knowledge base containing original dialogue records, refined dialogue states, user intentions and entity information; and the context perception understanding module is used for carrying out deep semantic analysis on the current query of the user and carrying out joint understanding in combination with the dialogue context knowledge base so as to analyze anaphora, omission and implicit intentions in the context and generate a complete semantic representation rich in context information. According to the method, the AI assistant can have strong context memorizing, understanding and reasoning capabilities, and human-like and coherent multi-round intelligent dialogues are realized.

Methods, systems, and computer storage media for providing dense context management using a dense context engine in an artificial intelligence (AI) system are described. Dense context management is a systematic approach that combines specific industry knowledge with contextual understanding to generate accurate, relevant, and specific industry-tailored responses to user queries. Dense context management includes dense context generation and contextual response generation using a programmatically-generated dense context. Dense context generation serves as a framework for extracting key findings and producing comprehensive dense context outputs across various documents. Contextual response generation serves as a framework for integrating the dense context into queries to reframe queries in a manner that makes it easier to generate more accurate and contextually relevant response. By leveraging the dense context, the dense context engine ensures that the responses are not only informed by general information but also tailored to the user's specific needs and the organizational environment.

Methods and apparatuses for handover key management are provided according to one or more embodiments. A wireless transmit / receive unit (WTRU) may be handed over from a source base station to a target base station. The source base station may request a handover to a WTRU context management function (UCF). The UCF may generate a freshness parameter associated with the target base station. The UCF may utilize the freshness parameter to generate one or more access stratum (AS) keys. The freshness parameter may be provided to the WTRU by the UCF via the source base station. The WTRU may also utilize the freshness parameter to generate the one or more AS keys. The one or more AS keys may be securely shared with the target base station. The one or more AS keys may be utilized for securing an AS communication between the WTRU and the target base station.

This invention relates to a multi-agent orchestration and execution method supporting complex cyclic graph structures, comprising: S1: receiving workflow configuration and constructing a directed graph data structure; S2: performing a depth-first search traversal on the directed graph data structure, detecting and outputting strongly connected components in the directed graph data structure; S3: abstracting strongly connected components containing multiple nodes into super nodes, and keeping single-node strongly connected components as ordinary nodes, thereby constructing a directed acyclic graph containing super nodes; S4: performing topological sorting on the directed acyclic graph to generate a hierarchical execution plan containing multiple execution layers; S5: scheduling execution items according to the hierarchical execution plan, and when an execution item is the super node, starting a loop executor for iterative execution. This method, by constructing a multi-layered technical system, integrates graph algorithm innovation, unified context management, abstraction layer design, metadata-driven techniques, etc., to achieve automated orchestration and execution of multi-agent workflows supporting cyclic graph structures.

The invention provides a large model context protocol implementation system and method based on an enterprise bus, belongs to the technical field of enterprise information system integration, and aims to solve the problems of non-uniform protocols, lack of context management, insufficient service governance and the like in large model integration. The system comprises an MCP protocol adaptation layer used for converting internal and external protocols; the unified service management module is used for service discovery and intelligent routing; the context management module is used for recovering and persisting the context before and after calling the service to realize stateful interaction; and the enterprise resource access module is used for packaging the knowledge base and the data center into standard services. The method comprises the steps of protocol conversion, context recovery, service routing, target service calling, context persistence, response conversion and the like. Uniform integration and stateful management of large models are realized, and a channel between the models and enterprise private knowledge data is opened.

The invention relates to an electronic medical record quality evaluation and verification method, which comprises the following steps of: S1, cleaning, analyzing and standardizing a complete electronic medical record to obtain a standardized patient panoramic context and a text block set capable of being retrieved by a vector; s2, constructing a mixed quality control knowledge base according to the external medical knowledge base and the quality control rule set; and S3, inputting the patient panoramic context, the mixed quality control knowledge base and a to-be-executed quality control point into a dynamic context manager, checking the document sequence set by adopting a quality evaluation function, and outputting a structured defect report. According to the method, an electronic medical record quality evaluation and verification framework integrating dynamic context management, multi-source knowledge fusion and large-scale language model reasoning ability is constructed, and the core contribution of the method is that the context window limitation of LLM is effectively avoided by designing an exquisite context manager mathcal {M}, and the reliability of the system is improved. And therefore, deep and explainable logic review can be executed on high-dimensional and time-sequence EMR (Empirical Mode Register) data.

The invention discloses a data processing method and device, equipment and a storage medium, and relates to the technical field of computers, and the data processing method comprises the steps that in response to first information input by a user, when it is judged that the first information indicates to modify result data output by a historical session, the result data output by the historical session is modified; determining an identifier corresponding to a target session round where the modified result data indicated by the first information is located; based on the identifier corresponding to the target session round, obtaining target result data output in the target session round; and generating second information output in response to the first information based on the target result data. According to the method, the target session round needing to be modified by the user can be efficiently and accurately positioned based on the identifier, and the result data correspondingly output by the session round can be accurately extracted for further rewriting, so that the capability of a multi-round session model in the aspects of long context management and an error correction mechanism is effectively improved; and thus, the interaction accuracy and flexibility are improved.

The invention discloses an intelligent dialogue system and method supporting real-time voice interruption, and relates to the technical field of man-machine interaction. The system comprises a voice activity detection module, a voice recognition module, a dialogue management and LLM interaction module, a voice synthesis module and an interruption control module. Wherein the dialogue management and LLM interaction module is used for managing dialogue states and contexts and calling LLM to generate reply texts; when the dialogue is interrupted, re-integrating the context and the new instruction text, and then calling the large language model to generate a new reply text; the interruption control module is used for sending an interruption signal to the voice synthesis module when the voice activity detection module detects effective user input, and triggering the dialogue management and LLM interaction module to re-integrate the context to generate a new reply; the voice activity detection module and the voice synthesis module operate at the same time and are not blocked. The method supports interruption at any time, and ensures semantic coherence of the interrupted dialogue through a context management mechanism, and the user experience is smooth.

In some embodiments, a method may include determining hierarchical agents including a first agent and a second agent based on a document defining a UI, the hierarchical agents having access to initial context data. The method may include delegating a task related to a UI element to the first agent based on information associated with the UI element and restricting portions of the initial context data available to the first agent to a propagated subset based on input data types mapped to the first agent and portions of the initial context data available to the second agent. The method may include generating interaction data by providing a machine learning model with the propagated subset and updating the document (e.g., by populating or interacting with the UI element based on the interaction data).

The invention relates to a multi-agent system and method for Simulink modeling in the aviation field. The existing automatic modeling method for Simulink modeling in the aviation field highly depends on hard coding, converts formalized requirements into model design, and has the defects of high development cost, dependence on expert knowledge, incapability of automatic verification, no support of a third-party library and the like. According to the multi-agent system provided by the invention, a plurality of expert agents are integrated and designed and workflows are strictly arranged, so that the design, implementation and verification processes of Simulink modeling in the aviation field are fully automated. With reference to abstract drawings, the whole system comprises a model designer, a test review device, a model builder, an execution and debugger, a report compiler and an Agent RAG subsystem, and different sub-modules depend on large model capability and a context management strategy to collaboratively complete tasks in a specific process. Compared with a traditional automatic modeling technology, the system and the method have the advantage that the modeling flexibility and efficiency are greatly improved.

The invention is applicable to the technical field of automobiles, and provides a vehicle control system, which comprises a context management module, a control module, a control module and a control module, wherein the context management module is used for acquiring environment data and constructing an environment model based on the environment data; the scheduling module is used for determining a target processing model based on the environment model and sending a model loading request to the model management module; the model loading request carries a target processing model; the model management module is used for loading a target processing model based on the model loading request; the scheduling module is further used for receiving the model processing result and generating a control instruction based on the model processing result so that the vehicle can run based on the control instruction. The model processing result is obtained by deploying the target processing model at the target computing node for processing by the scheduling module. The distributed architecture of the system improves the reliability of the system and reduces the complexity of the system. Meanwhile, the system loads the model as required and allocates the model to the corresponding computing node for processing, so that the resource utilization rate is improved, and the system performance is further improved.

The invention relates to a method and a device for optimizing a large language model interactive service for a weak network environment, which are particularly suitable for regions with limited network infrastructures. The method comprises the following steps: establishing an interactive self-adaptive hierarchical service architecture of a large language model, and dynamically adjusting LLM context management and generation control according to a network condition; establishing a semantic local cache mechanism oriented to large language model interaction, and performing local storage and offline response of LLM specific key data at a user equipment side; establishing an asynchronous fault-tolerant processing mechanism of large language model interaction, and providing an interaction continuity guarantee when the network is abnormal; and establishing a transmission optimization strategy of the large language model, and optimizing the transmission characteristics of the data. According to the method, the special problems of response delay, interaction interruption, bandwidth limitation and the like of interactive application of the large language model in a weak network environment are solved, and the service stability and the user interaction fluency of the large language model are effectively improved.

This invention relates to a method for state tracking and context management in high-concurrency real-time dialogue, belonging to the fields of natural language processing and human-computer interaction. The method includes: firstly, unifying multimodal input into a standardized semantic representation; then, performing semantic parsing by fusing historical context and external knowledge; encapsulating the parsed state changes as events and publishing them to a distributed log; independently consuming events asynchronously, and atomically updating the global state through version control and optimistic locking mechanisms; improving read / write performance using multi-level caching and incremental compression; and simultaneously, maintaining a graph-based long context in a streaming manner and optimizing resources through lifecycle management. This invention, through unified semantic representation, event-driven state management, long context modeling, and optimistic concurrency control, constructs a closed-loop collaborative system, significantly improving the concurrency processing capability, contextual coherence, and response robustness of dialogue systems.

The invention discloses a video compression method and device, computer equipment and a storage medium, and the method at least comprises the steps: collecting video data, and carrying out the preprocessing to obtain a standard video sequence; performing framing processing and classifying video frames; performing multi-modal feature extraction to obtain feature data including spatio-temporal features, depth features and motion features, and generating an entropy value for quantifying video content complexity; determining a coding strategy for the video frame based on the video frame type and the entropy value, the coding strategy comprising a quantization parameter; and encoding according to the encoding strategy and outputting a compressed code stream. The method has the advantages that video frame classification and context management can be effectively realized on the basis of multi-modal features, local texture details and long-distance space-time dependence are considered, space-time features, depth features and motion features are effectively integrated to generate entropy representing content complexity, different coding and dynamic bit allocation strategies are realized on the basis of frame types and the entropy, and the coding efficiency is improved. And the balance between the compression efficiency and the complexity of video compression is ensured.

This invention discloses an intelligent analysis method, apparatus, device, and medium for big data warehouses. The method includes responding to user analysis requests and obtaining target resource location information corresponding to the user's analysis requests from a preset initial entry layer; wherein the initial entry layer stores resource location information of the big data warehouse; constructing a current context based on the user analysis requests and target resource location information; checking whether the current context meets the sufficiency conditions for analyzing the user's analysis requests based on a preset requirement coverage check strategy, to obtain a check result; if the check result is determined to be satisfactory, simplifying the current context according to a preset context management strategy to obtain a minimum effective context; performing analysis and reasoning based on the minimum effective context and outputting the analysis results. This invention significantly improves the accuracy and efficiency of big data warehouse analysis by actively exploring data and dynamically constructing a minimum effective context.

The invention discloses a conference room intelligent reservation method based on a DeepSeek large language model, and the method comprises the steps: S10, natural language understanding based on domain knowledge injection and output constraints: S101, dynamically assembling prompt words; s102, performing intention and entity joint extraction on cue words and user query by utilizing a DeepSeek model; s103, performing semantic disambiguation and information completion; s104, carrying out structured output and verification; s20, performing intelligent reservation based on multi-objective optimization and hybrid decision, wherein the intelligent reservation comprises multi-dimensional conference room matching, conflict detection and active solution and priority scheduling; and S30, performing context management and personalized learning based on deep representation learning. According to the method, the complex and fuzzy reservation intention of the user is accurately understood, the resource utilization rate of the conference room is improved, and the conversion from passive alarm to active multi-mode solution providing is realized.

The application discloses a kind of based on multistage ASR cooperation's reproductive cycle of promoting and recording device and method, it is related to medical information and artificial intelligence cross technical field, including interface learning module, adaptive learning module, context management module, user interaction module, core configuration module, voice wake-up module, voice recognition module, instruction set model, free text model, text proofreading module and interface operation module;The present application overcomes the existing medical record system in the monitoring of assisted reproductive ovulation cycle The deficiencies of low voice input efficiency, high cost of third-party system integration, lack of context memory and adaptive learning ability, with the ability of collaborative processing multiple types of voice input, AI dynamically adapts heterogeneous system interface, context memory and adaptive learning.

The invention discloses a data filter execution system based on a chain of responsibility mode and an implementation method thereof. The system comprises a filter chain manager, a filter execution engine, a context management system, an exception handling and recovery center and a monitoring and auditing module. The filter chain manager is responsible for dynamically configuring the filter and managing the dependency relationship of the filter; the execution engine executes the filters in sequence and supports front check and state monitoring; the context management system is used for creating and maintaining a FilterContext object for packaging the key information; the exception handling and recovery center provides multi-level exception capture and data consistency guarantee; and the monitoring and auditing module is responsible for collecting performance data and recording an execution track. Through dynamic configuration, efficient execution, intelligent management and comprehensive monitoring, the reliability, consistency and expansibility of data processing are remarkably improved.