207 results about "Long-term memory" patented technology

The invention discloses an agent long-term memory modeling method based on a memory network, and relates to the field of agents, and the method comprises the steps: storing vector data to a vector database, and storing structured metadata to a relational database; receiving an input request of a user, and executing vector similarity retrieval in the vector database to obtain semantic similar fragments; executing structured data query in the relational database to obtain structured metadata; the memory abstract is retrieved; forming a candidate data set; taking a filtered result as memory information, inputting the memory information into a large language model through a cue word project, and generating reply content; the input request of the user and the generated reply content are combined to form a new interaction record; processing the new interaction record and historical interaction records stored in a vector database and a relational database through a large language model to generate a memory abstract; aiming at the insufficient long-term memory emotion interaction coherence of the intelligent agent, the emotion interaction coherence is improved.

The invention discloses a Multi-agent-based psychological counseling platform and method. The counseling platform comprises an evaluation agent used for dynamically extracting psychological features based on dialogue behaviors of a visitor agent and updating results to a traits library module; the planning agent is used for constructing a personalized psychological counseling scheme based on the traits library module and dynamically adjusting a counseling process and a strategy according to counseling feedback; the consultant agent is used for carrying out dialogue interaction with the visitor agent according to the personalized consultation scheme and implementing emotion pacification and psychological intervention; the visitor agent is used for expressing psychological troubles, feeding back psychological state changes and promoting the consultation process; the historical dialogue long and short-term memory module is used for storing multiple rounds of psychological counseling interaction contents and comprises historical dialogue short-term memory and historical dialogue long-term memory; and the dynamic probability memory retrieval module is used for generating probability distribution based on the current consultation context and dynamically calling memory nodes from the historical dialogue long and short term memory module or the traits library module.

The invention provides a user dialogue generation method and system based on memory fusion, and relates to the technical field of artificial intelligence, and the method comprises the steps: building a user memory knowledge base based on a preset knowledge graph tool; based on the user memory knowledge base, reading current text data input by the current dialogue user and historical text data of the current dialogue user; obtaining short-term memory data, long-term memory data and scene memory data based on the user memory knowledge base, a preset large language model, a preset cue word engineering algorithm, the current text data and the historical text data; and generating a user dialogue based on the preset large language model, the preset cue word engineering algorithm, the short-term memory data, the long-term memory data and the scene memory data. According to the invention, the memory range of the user from recent interaction to long-term important information is fully covered, and the reliability of responding to the user is improved.

The invention discloses a universal agent evaluation method, device and equipment, a medium and a product, and belongs to the technical field of agent evaluation, and the method comprises the steps: obtaining basic parameters of an agent; based on the basic parameters, the agents are evaluated in five dimensions of long-term memory accuracy, task arrangement rationality, human setting consistency, tool calling effectiveness and content compliance, so that evaluation of the agents is achieved; wherein in the process of evaluating the intelligent agent in any one of the five dimensions, a corresponding evaluation set is constructed based on the basic parameters, the large language model is adopted to score the output of the intelligent agent on the evaluation set, and the score of the intelligent agent in the dimension is obtained. According to the method, the intelligent agent can be evaluated universally and accurately.

The invention relates to the field of artificial intelligence, natural language processing and knowledge graph modeling, in particular to a digital human continuous dialogue and memory enhancement method based on a knowledge graph. Comprising the following steps: receiving a dialogue text and a historical dialogue context input by a user in the current round, and processing through a multi-semantic channel to obtain a channel semantic representation vector and a semantic vector of a dialogue in the current round; performing language multi-dimensional scoring on each node in the global knowledge graph to obtain a multi-dimensional score of the node, and forming a knowledge graph sub-graph used by the current round of dialogue; for each activated node, calculating memory intensity; and performing weighted fusion on the node embedded representation vector, generating a semantic enhancement vector fusing the current context information and the historical memory content, and generating a natural language reply conforming to the current context. The technical problems that an existing digital human dialogue lacks long-term memory and context consistency in multiple rounds of dialogues, and a knowledge graph is difficult to dynamically update are solved.

The invention relates to a man-machine interaction method and device, computer equipment, a storage medium and a program product. The method comprises the following steps: acquiring interaction content input by a user; scene recognition is carried out on the interaction content through a large language model, and a scene label of the interaction is determined; determining a target long-term memory partition corresponding to the scene tag from a plurality of long-term memory partitions preset for the user; inputting target long-term memory content in the target long-term memory subarea and short-term memory content stored in a short-term memory area preset for the user into an intelligent agent; the intelligent agent is used for generating reply content for the interaction content according to the target long-term memory content and the short-term memory content. By adopting the method, effective association and collaborative calling of different scene memories can be realized, and the dynamic requirement of a user on coherent interaction in multi-scene switching is met.

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 discloses a rainstorm torrential flood disaster-causing element prediction method and system based on modal decomposition and deep learning, and solves the problems that a traditional model is insufficient in non-linear time sequence feature capture, and a physical model depends on complex data and is weak in generalization ability. Comprising the steps of collecting flow, flow velocity and water level data of an upstream site as input, and taking downstream disaster point data as output; preprocessing the data; a frost ice optimization algorithm is adopted to optimize variational mode decomposition parameters; a Fourier transform high and low frequency feature enhanced attention network is constructed, low-frequency and high-frequency components are divided, trend features are extracted through a fluctuation enhancement module, dynamic changes are captured through a multi-path difference calculation unit, self-perception attention is introduced to achieve feature weighted fusion and long-term memory, and a downstream hydrological state prediction result is output. By optimizing a modal decomposition and deep learning cooperation mechanism, the precision, robustness and generalization ability of sudden mountain torrent prediction are significantly improved, and the method is suitable for disaster emergency management in complex scenes of small and medium watersheds.

The invention belongs to the field of artificial intelligence and robot navigation, and discloses a visual language navigation method and system based on a cross-task incremental semantic memory graph, and the method comprises the steps that an intelligent agent executes a zero-sample visual language navigation task in a continuous environment; performing cross-modal alignment on the natural language instruction and environment observation based on a multi-modal large language model, selecting candidate waypoints and updating task progress; a semantic memory graph is constructed and dynamically updated, wherein the semantic memory graph is used for structured storage and cross-task multiplexing of scene semantic information and a spatial topological relation sensed by an intelligent agent in historical tasks; and performing global path planning and local dynamic fine tuning based on the semantic memory graph. According to the method, the problem of task-by-task forgetting in a traditional method is solved, environment understanding and task reasoning capabilities are improved by constructing structured long-term memory, and navigation precision and robustness are optimized through a global-local collaborative strategy.

The invention provides a communication protocol consistency test method and system. Key function points are extracted by analyzing a target protocol document; based on the key function points, a predefined first prompt template is applied to generate a test case prompt, a large language model is guided to generate a standardized test case corresponding to each key function point, and the first prompt template comprises k few-sample examples; dynamically retrieving a related code context from a pre-constructed long-term memory library through a retrieval enhancement generation mechanism, generating a code generation prompt by applying a predefined second prompt template based on the standardized test case and the related code context, and generating an executable test code based on the code generation prompt; and performing iterative optimization on the executable test code through the large language model, and generating a new code generation prompt based on a code execution result of the previous iteration and the retrieved related code context in each iteration. The test case can be automatically generated and dynamically adapted, and the test coverage rate, efficiency and flexibility are improved.

The invention discloses a VLA model autonomous generalization method, system and device and a medium, and belongs to the technical field of robot control. The method comprises the steps of obtaining visual observation data and natural language task description of a current environment of a robot, generating an action sequence and controlling the robot to execute tasks; track data in the task execution process are collected, and token-level dense rewards and structured reflection information are output; storing the trajectory data, the token-level dense reward and the structured reflection information into a double-memory block; extracting task priori from the long-term memory by using an experience-driven generator, and generating trajectory data containing a sub-target chain; updating the VLA strategy model by using a reinforcement learning algorithm based on the token-level dense reward; and updating the VLA strategy model by using a supervised fine tuning algorithm based on the high-quality track data set. According to the method, the technical problem of autonomous generalization of the VLA model is solved.

The invention specifically discloses a brain-like reinforcement learning method and system based on hierarchical experience playback, and relates to the technical field of reinforcement learning and brain-like computing. The method comprises the steps that S1, observation data are collected and preprocessed; s2, initializing an experience buffer pool, an actor network, a commentator network and a corresponding target network, and performing parameter initialization; s3, exploring noise is initialized, actions are selected from the actor network according to the current state and executed, and obtained experience samples are stored in an experience buffer pool; s4, obtaining a new sample from the experience buffer pool, and updating the short-term memory pool; s5, determining whether partial experience in the short-term memory experience pool is transferred to the long-term memory experience pool or not by using an attention discrimination module; and S6, updating parameters of the actor network, the reviewer network and the corresponding target network. By adopting the method, the experience utilization rate of the intelligent agent is improved, the reinforcement learning performance is improved, and the method has wide application potential in multiple fields.

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 provides a context construction method and device for an agent and a storage medium, and the method comprises the steps: obtaining the current input information of the agent in one interaction round, and carrying out the analysis to generate a current semantic representation; determining a semantic association degree between the current semantic representation and the context of the current dialogue task, and calculating a long-term value score of the current semantic representation; under the condition that the semantic association degree is greater than a first preset threshold value, storing the current semantic representation into a short-term memory library; under the condition that the long-term value score is greater than a second preset threshold value, storing the current semantic representation into a long-term memory library; when the intelligent agent needs to generate a response for the current input information, searching target memory content related to the semantics of the current input information from the short-term memory library and the long-term memory library; and combining the target memory content with the current input information to form prompt information, and inputting the prompt information into a large language model to generate a response for the current input information.

The invention discloses an intelligent following robot object searching method and system based on graph structure memory. The method comprises the steps that the robot follows a user to move in real time, actively senses behaviors of the user and obtains an observation result; organizing a graph structure based on an observation result to form an initial pragmatic memory graph; forming structured memory based on the initial pragmatic memory graph, wherein the structured memory comprises continuously maintained and updated working memory and long-term memory for storing behavior habits of people; querying in the working memory or the long-term memory according to the question proposed by the user to obtain a query result; and outputting a voice prompt according to the query result. The system comprises an object observation module, a memory creation module, a structured memory storage module, a memory recall module and a reminding module. According to the method, the observation angle is flexible and active, the storage space is greatly saved, the query efficiency is improved, and based on graph structure memory, the method is closer to the memory and recall mode of human beings.

The invention provides a memory system of an AI agent and an updating method and a retrieval method of the memory system. The memory system comprises a time limit query cache module, a short-term memory module, a medium-term memory module and a long-term memory module, the time limit query cache module is used for storing data in a short time, and the short-term memory module, the medium-term memory module and the long-term memory module are used for storing data with different capacities and different representation modes respectively. Historical dialogue information is extracted and stored in a short-term memory, a medium-term memory and a long-term memory, and when a user inquires, related memory content is recalled according to inquiry content, so that cue word information is enhanced, and the complex task execution effect of the intelligent agent is improved.

The invention discloses a task planning method based on an intelligent agent and an intelligent system, and belongs to the field of artificial intelligence. Through the technical scheme provided by the embodiment of the invention, the main agent performs task planning on the target task based on the task description information of the target task, the first multi-modal information of the multi-modal sensor and the task reference information obtained through the long-term memory library to obtain the first task planning information, the process is combined with the task reference information, and the task planning efficiency is improved. And the accuracy of the obtained first task planning information is higher. The main agent sends the first task planning information to the plurality of sub-agents, each sub-agent executes the corresponding action based on the state information acquired from the short-term memory library and the first task planning information, and the state information is combined when the sub-agents execute the action, so that the actions executed by the plurality of sub-agents are not easy to generate conflicts, and the task planning efficiency is improved. And the completion effect of the target task is better.

The invention relates to an agent content storage method and device, electronic equipment and a medium, and is applied to a distributed storage system.The method comprises the steps that in response to a session request input by a user, dialogue content corresponding to the session request is obtained, and the dialogue content is stored to first-level equipment, the first-level equipment is used for storing the real-time content generated by the intelligent agent; when it is determined that the session is ended, the session content in the first-level device is migrated to a second-level device, and the second-level device is used for storing the short-term memory content and the group sharing data of the intelligent agent; updating the content of a third-level device based on the access information of the second-level device, wherein the third-level device is used for storing long-term memory and differential memory of the intelligent agent; and the specified content in the third-level device is synchronized to a fourth-level device, and the fourth-level device is used for storing the universal knowledge base and the group unified memory so as to efficiently realize the storage of the agent data.

The invention discloses an agent construction method and device based on a large model, a storage medium and equipment, and belongs to the technical field of artificial intelligence. Selecting a dialogue scene according to the current dialogue information and the scene description information; obtaining tool calling data from an intelligent agent corresponding to the dialogue scene; generating a short-term memory context according to the historical dialogue information, generating a tool context according to tool calling data, generating a file context according to a file, generating a long-term memory context according to the current dialogue information and the historical dialogue information, and forming a scene context; generating a candidate tool list sorted according to preferences according to the current dialogue information or the tool calling data; generating a system prompt word according to the scene context and the candidate tool list; and optimizing the intelligent agent according to the system cue word and the candidate tool list to obtain the intelligent agent based on user preference and context awareness. The intention and preference of the user can be recognized, answering is carried out according to the intention and preference of the user, and the accuracy is improved.

The invention provides an intelligent teaching method and system fusing a learning track and an attention mechanism, and belongs to the technical field of education, and the method comprises the steps: obtaining a question text inputted by a user and historical learning behavior data of the user; using a current problem encoder to construct an input problem text into a current problem semantic vector; using a user memory encoder to construct the historical learning behavior data into a user memory vector sequence, and updating a user memory state based on the user memory vector sequence; based on the semantic vector of the current problem and the updated user memory state, a context fusion module of a dynamic memory attention mechanism is adopted to generate a memory context vector, and meanwhile, a double-flow user behavior encoder is adopted to obtain double-flow representation; and fusing the double-flow representation and the memory context vector, and obtaining a final teaching answer through a decoder and an output generator. According to the invention, deep modeling and long-term memory integration of the learning track of the user are realized, so that more efficient and personalized teaching services are provided.

The invention discloses a hierarchical visual language navigation memory enhancement system in a cross-floor scene, relates to the technical field of intelligent navigation, and adopts the technical scheme that the hierarchical visual language navigation memory enhancement system comprises three core parts, namely a hierarchical visual semantic model construction module, a memory enhancement algorithm module and a navigation system integration module. The hierarchical visual semantic model construction module is used for constructing a basic visual feature layer, a floor semantic layer and a cross-floor semantic association layer; the memory enhancement algorithm module comprises a short-term memory module, a long-term memory module and a memory fusion and update strategy module; and the navigation system integration module is used for integrating the hierarchical visual semantic model and a memory enhancement algorithm into the system. Three-dimensional space characterization misalignment and path planning error accumulation can be effectively avoided, the accuracy, environment adaptability and decision-making efficiency of cross-floor navigation of the intelligent agent are remarkably improved, and the method has important technical innovation value and application prospects.

The invention discloses a hierarchical city operation agent memory management method and system, and relates to the technical field of artificial intelligence and smart cities. The method comprises the steps of collecting multi-modal task data of smart city operation; estimating the occurrence intensity of the multi-modal task data in the time dimension to obtain a task intensity factor; calculating the dynamic weight of the memory entry in combination with the task intensity factor, the time attenuation factor and the industry key factor; realizing hierarchical evolution of short-term memory, medium-term memory and long-term memory according to the dynamic weight of the memory entry; establishing a unified embedding and indexing mechanism for the multi-modal task data, and storing the multi-modal task data in the same memory knowledge graph; and performing scoring by integrating the semantic similarity, the dynamic weight and the knowledge graph centrality, and performing memory retrieval and sorting according to a scoring result. According to the invention, the hit rate, timeliness and accuracy of agent emergency disposal and operation and maintenance decision can be improved.

The invention discloses a dialogue data storage method based on a big language model and a related device, and relates to the field of data storage, and the method comprises the steps: calling a memory encoder to obtain dialogue data of a user and an agent, employing the big language model to carry out key information extraction operation on the dialogue data, and obtaining key information, carrying out integrity verification and rationality verification on the key information; if the verification is passed, carrying out importance evaluation on the key information to obtain an importance evaluation value; and respectively storing the key information in a long-term memory database, a short-term memory database or a working memory database according to the importance evaluation value. The purpose of reliable storage of the dialogue data is achieved based on the large language model.

The invention discloses a multi-agent-based college entrance examination consultation method and system, and belongs to the technical field of large language model application. The problems that in the prior art, a traditional college entrance examination consultation system based on a large language model is limited in complex problem processing capacity, insufficient in personalized service capacity and lack of expert knowledge are solved. The method comprises the following steps: S1, constructing a local knowledge base containing objective data and expert knowledge extracted through a specific process; s2, designing a knowledge retrieval tool for accessing a network and a local knowledge base; s3, introducing a long-term memory mechanism to store, apply and update personalized information of the user in a cross-session manner; and S4, constructing a multi-agent collaborative architecture, analyzing user intentions according to user personalized information, scheduling expert agents, and integrating results to realize college entrance examination consultation. According to the method, the accuracy and comprehensiveness of answering are effectively improved, continuous personalized services are provided, and the method can be applied to online college entrance examination consultation.

According to the intelligent auxiliary learning method and system based on the model context protocol and the cognitive state modeling, decoupling of a model end and a tool end is achieved through the model context protocol, an error type-tool dependency topological graph is constructed through the cognitive state modeling, tool dynamic screening and pruning based on deterministic rule constraints are achieved, and the method and the system have the advantages that the method and the system are easy to implement. The Token consumption is reduced, and the accuracy of model reasoning is improved at the same time. In the aspect of safety control, semantic firewall middleware is introduced into the system, output streams are monitored in real time, illegal behaviors directly giving code answers are intercepted through text semantic analysis and code abstract syntax tree comparison, and a model is forced to turn to a thought guide mode. The system also includes hierarchical context compression to maintain long term memory, adaptive difficulty knowledge retrieval based on user cognitive states, and a mechanism to utilize code sandbox to assist verification of model reasoning logic correctness. According to the method, the behaviors of the large language model can be effectively regulated and controlled, and safe, efficient and personalized intelligent auxiliary learning is realized.

The invention provides an instruction perception memory device for video understanding. The instruction perception memory device comprises a text-visual memory library module and a cross attention module, the text-visual memory library module is used for storing and retrieving cross-modal features and supporting video analysis, the text-visual memory library module is integrated with a multi-modal large language model, and video data are processed in an incremental mode, so that the limitation of a memory and a context length is overcome; and the cross attention module is used for fusing the text and the visual features and generating cross-modal representation. By introducing a text-visual memory library and a cross attention module, early fusion and long-term memory management of video and text information are realized. The fine-grained time dependency relationship in the video can be effectively captured, and the performance of the model in a long video understanding task is improved, so that the aim of improving the accuracy and efficiency of video understanding is fulfilled.

Methods for developing and managing long-term memory solutions for large language models (LLMs) within a context of providing agents of the LLM as a service are disclosed. Following task-related communications between LLM agents and users of the service, information pertaining to domain knowledge, user preferences, and success or not in completing the requested task is distilled into data samples by a reflections agent of the service. The data samples are then stored into a long-term memory database that is accessible by LLM agents in the future, such that the agents can recall information of previous interactions in order to more efficiently perform new tasks for users.

The invention provides a live broadcast long video editing method and device based on memory perception collaboration, and relates to the technical field of image processing and video editing. Voice, subtitles and picture actions are analyzed in a linkage mode on a unified time grid, low-complexity characterization of a long video is achieved through an efficient perception module, and the effect of editing the long video is achieved. Cross-fragment core characters, events and semantic clues are precipitated through long-term memory of an entity center, and current perception is fed back through memory retrieval so as to enhance consistency and robustness; on the basis, a three-mode fusion wonderness score is generated, interval optimization and boundary refinement with duration budget constraint are executed, and finally, an editing decision list is output and quickly spliced into a high-quality finely-cut video. According to the invention, the problems of large quantity of long-time live broadcast materials, low efficiency of manual reviewing and editing and easy leakage of wonderful fragments are solved, and the later production efficiency and the quality of the live broadcast content are remarkably improved.

The invention discloses a retrieval enhancement generation method and system based on long-term memory and multi-source knowledge iterative fusion. The method comprises the following steps: firstly, after user inquiry is received, triggering long-term memory database retrieval, external knowledge base retrieval and large language model internal knowledge generation in parallel to construct a multi-source candidate knowledge base, and improving result correlation by adopting a self-adaptive retrieval method based on a dynamic top-k value; secondly, inputting user query and multi-source knowledge into the large language model for iterative fusion, and generating a final answer through conflict detection, fusion processing and confidence evaluation; and finally updating the long-term memory database. The method can improve the accuracy and stability of the generated answers in a multi-round interaction and complex task scene, reduces the risk of answer one-sidedness, inference incompleteness or fact deviation, enhances the multi-source knowledge utilization ability and long-term learning ability of the system, and improves the user experience. Therefore, the comprehensive performance and reliability of the system in applications such as intelligent question answering, information retrieval assistance and text generation are obviously expanded.

The invention discloses an autistic child emotion regulation system integrating electroencephalogram feedback and art, and relates to the technical field of medical intelligent rehabilitation. An emotional state decoding module; a dynamic generation type art interaction guiding module; a closed-loop feedback regulation and control module; and an adaptive optimization module. According to the method, deep fusion of physiological signal monitoring and artistic emotion expression is realized, scientificity and effectiveness of emotion regulation intervention of the autistic children are remarkably improved, participation willingness and compliance of the children are effectively improved, personalized iteration and accurate regulation of the intervention process are realized, limitation of traditional single-mode intervention is broken through, and the method is suitable for popularization and application. Not only is the objectivity of emotion recognition and the pertinence of art guidance enhanced, but also an individualized rehabilitation path is formed through long-term memory map construction, and an intelligent, quantifiable and sustainable new emotion regulation normal form is provided for autistic children.