122 results about "Context management" patented technology

The invention discloses a network data intelligent tool system and method based on a model context protocol MCP, and relates to the technical field of computer networks. The system comprises an AI analysis main body, an MCP Pcap tool engine, context management, a Pcap data source, various MCP Pcap tools, a Pcap tool gateway interface, a Pcap tool interface and a data packet interface. The invention provides an interaction mechanism of direct embedding and bypass data forwarding in tool calling. According to the method, direct embedded transmission of small-size data in tool calling is supported, separation of data transmission and instruction calling is achieved, the technical bottleneck that large-size Pcap data cannot be efficiently exchanged through a text channel is effectively overcome, efficient processing of the AI model on network packet capture data is achieved, and the data transmission efficiency is improved. And the efficiency and the automation degree of large-scale network traffic analysis are improved.

An auditory operating system designed to facilitate context-aware, audio-based user interactions, particularly with artificial intelligence agents or applications. An auditory operating system shell serves as the primary interface, managing and coordinating multiple specialized agents that handle specific domains like music streaming, scheduling, or home automation. Using natural language processing, the auditory operating system shell identifies the appropriate agent or application for a user's command or query and ensures task execution and context preservation across interactions. The auditory operating system shell enforces privacy and stability by controlling agents' and applications' access to data and system privileges. The auditory operating system shell also supports dynamic context management, enabling seamless handoffs between agents when user requests span multiple domains. This auditory operating system shell may reduce the need for users to memorize specific wake words or commands, as the auditory operating system shell may determine the user's intent from speech or contextual cues.

The invention relates to the technical field of databases, can be applied to the field of science and technology finance / digital medical treatment, and discloses a database migration method and device, electronic equipment and a storage medium. The method comprises the steps that database structured query language identifiers are defined, strategy classes corresponding to all databases are packaged, and a strategy context management container is created; receiving a target database type, dynamically loading a strategy type instance according to the type, and generating an executable query strategy chain according to the strategy type instance; when a database query request is received, matching a corresponding strategy type instance, and processing an original database query statement to generate a target database executable instruction; controlling a query strategy chain to execute a single-path or double-write-path mode through a common switch, and transmitting the instruction to a strategy type instance for execution; and analyzing a return result of the target database, performing conversion adaptation through the strategy type instance, and returning a conversion adaptation result. The method realizes automatic migration, reduces cost and risk, and improves system performance.

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 invention provides a background authority management system and method for adaptive risk control. Comprising a data acquisition module used for acquiring behavior data and environment data associated with an account and converting the data into an event stream; the context management module is used for associating the context attribute set with the event flow; the feature construction module is used for generating a multi-dimensional feature vector based on the event stream and the context attribute set; the adaptive risk assessment module is used for calling at least two heterogeneous models to perform parallel reasoning on the multi-dimensional feature vectors; the decision engine module is used for mapping the risk weight into an access control instruction and an audit instruction; the auditing module is used for recording auditing data according to the auditing instruction; and the authority management module is used for calculating an authority weight according to the attenuation parameter and judging and generating an authority recovery instruction or a maintenance instruction according to a threshold value. According to the method, event stream unification and context binding, real-time risk assessment and strategy linkage and permission adaptive recovery closed loop can be realized.

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.

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 provides a system for intelligent interaction of a software as a service platform. The system comprises a large language model LLM module, a voice processing module, a model context protocol MCP communication layer and a context management module. According to the system, function operation of the SaaS platform is directly driven through natural language interaction, combination of a menu or button control of a traditional graphical user interface (GUI), an LLM module and historical data of a context management module is not needed, the intention recognition accuracy is larger than or equal to 92%, and repeated correction of a user is reduced.

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 discloses a real-time voice stream dialogue interaction method and system based on a large language model, and belongs to the technical field of voice recognition, natural language processing and human-computer interaction. The method comprises the following steps: S1, segmenting a voice signal according to a 320 millisecond time window; s2, through double-channel parallel processing, generating a preliminary text hypothesis locally and uploading a frame to a server side to accurately reconstruct a text; s3, fusing to generate a final text with a confidence label; s4, inputting the text into a large language model with a context management mechanism to generate a natural language response; s5, performing speech synthesis output of rhythm perception; s6, updating a dialogue cache and synchronizing a language model state; s7, predicting the semantic trend in advance by adopting a delayed slow release mechanism; and S8, introducing round-level intonation / rhythm parameters for semantic adjustment. The method has the beneficial effects that the response delay is reduced, the recognition accuracy is improved by fusing the confidence label, and meanwhile, the continuity and naturalness of multi-round interaction are enhanced through context and intonation modeling.

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 an agent context management system and device based on importance sampling, and belongs to the technical field of artificial intelligence and agents, the agent context management system and device based on importance sampling comprise a global context manager used for maintaining and managing eight types of context information; the task stack manager is used for receiving a new request and performing task splitting; and the recursive execution engine is used for executing the task in a recursive mode and controlling the recursive depth. Eight types of context information are maintained through a global context manager, splitting and storage of complex tasks are achieved through a task stack manager, a recursive execution engine provides an efficient task execution mechanism and controls the recursive depth, an importance sampler selects related contexts based on a joint probability model, and the task execution efficiency is improved. And the context compressor combines the sampling fragments into a final context, and the task scheduler coordinates the working process of each component, so that a complete agent collaborative context management system is constructed.

The invention discloses an automatic code debugging and optimizing system and method based on an AI agent, and relates to the technical field of artificial intelligence and automatic programming, the system comprises a coordinator module, a plan generation module, a code generation module, a test verification module, a deployment operation module, a project summary module, a language model module and a context management module; the coordinator module supports to select and call an Agent for specific task execution according to the current task process, and the plan generation module is configured to automatically generate a development task plan according to the requirements of a user and transmit a task list to the code generation module. Through the exception retry mechanism and the task backtracking mechanism, the system can automatically detect errors in the code generation process and repair the errors, and the tedious process of traditional manual debugging is avoided. The system optimizes the code generation process by backtracking the historical task and adjusting the generation strategy, and the quality of the final code is ensured.

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 discloses a three-dimensional modeling method based on voice interaction and a large language model, and relates to the technical field of computer intelligent modeling. According to the method, on the basis of dialogue context management, a closed-loop'voice / text input-modeling script generation-execution monitoring-error feedback and repair 'process is constructed based on a Rhinoscriptsyntab library in combination with RAG (Retrieval Enhanced Generation) and an automatic error correction mechanism, so that a three-dimensional model can be incrementally modified in multiple rounds, and object attributes can be tracked; therefore, flexible response to complex modeling requirements is realized in the Rhino environment. According to the method, a designer can quickly generate and edit the three-dimensional model through a natural language instruction, so that the design efficiency is remarkably improved, the cognitive burden of software operation is reduced, and the continuity of creative thinking is kept.

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 provides a retrieval enhancement generation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing.The method comprises the steps that multi-source heterogeneous data are received in real time through a streaming processing engine, vectorization coding is conducted on the multi-source heterogeneous data, and dynamic knowledge vectors are generated; incremental index updating is triggered based on a preset time window, the dynamic knowledge vector is utilized to update a layered storage dynamic knowledge base, and the dynamic knowledge base comprises a real-time data storage layer and a historical knowledge storage layer; selecting a mixed retrieval strategy according to the query type, and jointly retrieving real-time data in the dynamic knowledge base and a historical knowledge base to obtain related data; and related data obtained by retrieval is combined with streaming context management to generate an initial result, and the initial result is optimized and output through a dual-check mechanism. Compared with the prior art, the second-level dynamic updating of the knowledge base can be realized, the timeliness and accuracy of the generated content are remarkably improved, and the problem of generation deviation caused by knowledge lag is effectively solved.

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.

The invention relates to the technical field of voice interaction, and discloses a complex scene-oriented precise voice interaction system, which comprises an acoustic feature extraction module for extracting acoustic feature vectors from voice signals and generating acoustic feature representations for suppressing dialect differences; the self-adaptive semantic understanding module is used for generating an initial semantic vector based on the acoustic feature representation, performing semantic enhancement, performing weighted fusion on the historical dialogue semantic vector by combining the dynamically adjusted semantic weighted fusion weight, and generating a user intention representation; the semantic decoding module is used for decoding the user intention representation to generate a precise intention instruction; and the context management module is used for generating a dialogue state vector according to the user intention representation, and generating dialogue context representation by calculating the correlation degree between the dialogue state vector and a historical dialogue semantic vector so as to dynamically adjust the semantic weighted fusion weight. According to the method, the accurate voice recognition rate can be improved, and the context continuity is guaranteed, so that complex and changeable real scene requirements can be better met.

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.