115 results about "Agent architecture" patented technology

The invention relates to the technical field of data processing, and discloses an intelligent agent architecture-based education large model autonomous teaching planning method and system. The method comprises the following steps: inputting a cognitive ability matrix, a knowledge mastery degree vector and a learning preference vector into an improved Bayesian knowledge tracking algorithm, and adding an agent parameter consistency constraint term and a teaching planning coordination factor to obtain a learning track state vector; based on the learning track state vector, multiple agents cooperatively generate a recommendation content identification sequence, a difficulty level sequence and a time distribution sequence; and monitoring the knowledge mastery degree vector variation, and reordering the recommended content sequence when the knowledge mastery degree vector variation exceeds a preset threshold value to generate a teaching sequence. According to the invention, the Bayesian knowledge tracking algorithm is improved, the agent parameter consistency constraint term and the teaching planning coordination factor are fused, and coordination and unification of multi-agent teaching decisions in a communication-free environment are realized.

The invention provides a traffic corridor signal cooperative control method based on single agent reinforcement learning, and relates to the technical field of traffic management and control, and the control method comprises the steps: obtaining the real-time traffic state data of a traffic corridor comprising a plurality of signal intersections; acquiring a current signal control scheme of the traffic corridor; constructing a state vector according to the real-time traffic state data and the current signal control scheme; inputting the state vector into a pre-trained single-agent reinforcement learning model to obtain a corresponding action vector; based on the action vectors, phase division of all the signal intersections is synchronously adjusted, and a new signal control scheme is generated; according to the invention, signal timing of all signal intersections in a traffic corridor is cooperatively controlled by adopting a centralized single-agent architecture, so that the problems of system complexity and training instability caused by local observation, distributed decision and communication coordination among agents in a multi-agent scheme are fundamentally avoided.

The invention provides a multi-type micro-grid cross-layer collaborative scheduling method based on multi-agent reinforcement learning, and relates to the technical field of power system and micro-grid scheduling, and the method comprises the steps: firstly constructing a multi-type micro-grid layered collaborative architecture, a multi-agent reinforcement learning scheduling model, and a centralized coordination layer where global agents are deployed in a platform layer; and the local agents are deployed at each distributed micro-grid node of the platform layer, a cross-layer cooperative training mode is adopted to train the model, and finally, cooperative decision results of the global agents and the local agents are integrated to generate a global-local cooperative scheduling scheme. According to the method, global optimization and local flexibility can be considered, and the hierarchical multi-agent architecture adopts a multi-type micro-grid hierarchical collaborative architecture, so that the method can be suitable for multi-type micro-grid operation scheduling of a novel power system, and multi-type micro-grid collaborative scheduling considering global optimization and local flexibility is realized.

The invention discloses a computing power network resource scheduling method and system, and the method comprises the steps: responding to a service request, carrying out the coding of a network state through an edge attention graph neural network, and enabling the network to integrate an edge feature vector into attention weight calculation in message transmission, so as to sense a link state; based on a coding result, a scheduling decision of joint optimization calculation and network resources is generated through a self-adaptive multi-target reinforcement learning strategy network, and the reward function weight can be dynamically adjusted according to the network state; decisions are executed based on a distributed multi-agent architecture, and all agents realize global coordination through a message coordination mechanism. According to the invention, efficient, adaptive and extensible scheduling of multiple targets in the dynamic computing power network is realized.

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

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

The invention provides a code auditing method and device based on a multi-agent architecture, a medium and a product, and relates to the field of data processing, and the code auditing method comprises the steps of obtaining multi-source input data; performing distributed cooperative processing on the multi-source input data through the multi-agent architecture to obtain a plurality of processing results output by the multi-agent architecture; integrating the plurality of processing results through a multi-agent architecture to generate comprehensive judgment information; wherein the comprehensive judgment information is used for indicating a code auditing result; according to the comprehensive judgment information, a corresponding code auditing report is generated, and the method provided by the invention is used for achieving the technical effect of guaranteeing the security and compliance of code auditing.

The invention relates to the technical field of network communication, and provides a hierarchical reinforcement learning scheduling and routing method for a multi-domain TSN, and the technical scheme comprises the steps: collecting the global state information of the multi-domain TSN, and carrying out the coding processing of the global state information to generate comprehensive state representation; performing a cross-domain routing decision, and outputting an inter-domain path and a time delay budget of a cross-domain flow; executing intra-domain scheduling, determining a sending sequence and a specific path of a domain flow, and generating gating list configuration; through hierarchical coordination and strategy optimization, a cross-domain routing decision and intra-domain scheduling are updated based on reward feedback of an intra-domain scheduling result; and deploying the finally updated intra-domain scheduling to a switch of the multi-domain TSN network for execution, thereby realizing deterministic transmission of the time-triggered flow. According to the invention, through a layered agent architecture, a hybrid neural network coding mechanism and a cross-domain collaborative optimization strategy, challenges of complexity, expandability, dynamic adaptability and the like of a joint routing and scheduling problem in a multi-domain TSN environment are effectively solved.

The invention discloses an industrial image anomaly detection method based on a multi-agent arrangement heterogeneous algorithm. The method comprises the following steps: S1, constructing an algorithm component library for shielding data representation differences among different architecture algorithms; s2, constructing a multi-modal large model system based on a multi-agent architecture; the multi-agent architecture comprises the following steps: receiving an image by using a visual expert agent, and outputting structured physical metadata and an unstructured visual suggestion text; the planner agent receives the physical metadata and the visual suggestion text, and generates an algorithm configuration tree based on an algorithm component library; the optimizer agent optimizes the algorithm configuration tree in the closed-loop feedback stage; s3, using the algorithm configuration tree to construct a reference model representing good product distribution according to the normal training sample images; s4, performing anomaly detection on the to-be-detected sample image by using the algorithm configuration tree to generate an anomaly judgment result; and S5, optimizing the algorithm configuration tree.

The invention relates to the field of sign language emotion interaction, and provides a multi-mode sign language emotion interaction system and method based on an intelligent agent architecture, and the system comprises a recognition module which is used for recognizing a current task type based on multi-mode data of a user, and generating a task scheduling instruction; the large language model module is used for generating feedback content by utilizing a large language model according to the task scheduling instruction; the emotion module is used for extracting keywords from the dialogue history and environment context information of the user, judging the emotion of the user through an emotion recognition algorithm, generating prompt words with humor / comforting elements by utilizing a large language model in combination with the emotion and the keywords of the user, and sending the prompt words to the user; according to the cue word, utilizing a large language model to select proper language content to generate feedback content with emotion; and the generation module is used for displaying the feedback content with the emotion to the user in a sign language form and / or a voice form through a digital person. According to the method, the defects that an existing system is fragmented and cannot be expanded, and situations cannot be understood are overcome.

Systems and methods are disclosed for automated generation, validation, and deployment of infrastructure-as-code (IaC) using a multi-agentic artificial intelligence / machine learning (AI / ML) architecture. An extraction agent parses multimodal artifacts (e.g., diagrams and / or configuration data) to derive or generate infrastructure set-up parameters. A coding agent generates IaC units based on the extracted parameters. A validation agent determines the syntactic and semantic compliance of the generated IaC, classifying the IaC as executable, non-executable, or identifying corrective actions. A deployment agent transmits executable IaC to target computing environments and manages automated provisioning of the IaC in the target environments. In cases of validation failure, error indicators are provided to a user (e.g., via an agentic chat bot) for clarification or correction, enabling iterative refinement.

The invention relates to the technical field of robot interaction, in particular to a guide robot interaction method and system based on a large language model and an intelligent agent architecture, and the interaction method comprises the steps: S1, carrying out the input processing of a user; s2, identifying and classifying user intentions; s3, service intelligent routing is carried out; s4, executing control; and S5, performing interrupt processing. The interaction system comprises a user interaction layer, a core processing layer, a back-end service layer and an execution layer.

The invention relates to the field of artificial intelligence, discloses a full-duty investigation method and system based on a large model hierarchical agent architecture, and aims to solve the problems of efficiency bottleneck, knowledge blind areas, data stream solidification and the like in existing full-duty investigation. The method comprises the steps that a central task planning agent receives an instruction and decomposes and dynamically reconstructs a task; the agent modules in each field refine tasks and cooperatively analyze multi-source data; the central evidence management agent constructs a global evidence chain; the central report generation agent generates a report. The system comprises a central task planning module, a plurality of fields, a central evidence management module and a central report generation agent module. According to the technical scheme, intelligence, efficiency and depth of full-duty investigation can be remarkably improved, existing problems are solved, and through dynamic task reconstruction, knowledge fusion, evidence verification and full-link traceability, conclusion objectivity and reliability are improved, and systematic risks are reduced.

The invention discloses a cross-system collaborative to-do task query and processing method and system based on an AI Agent architecture, and particularly relates to the technical field of artificial intelligence and information system integration. According to the method, to-be-handled task data in each heterogeneous service system is automatically collected through a distributed deployment AI Agent module, and standardized processing is carried out through a unified task modeling module; identifying and combining repeated tasks by using a semantic understanding and deduplication module based on natural language processing, and generating a global unified task view; distributing tasks according to a dynamic strategy through an intelligent task routing and distribution module; realizing cross-system operation and real-time state synchronization by means of a task execution and state synchronization module; and finally, providing an aggregated task list and an operation interface for a user through a unified interaction interface. According to the system, unified integration, intelligent processing and collaborative management of multi-source heterogeneous tasks are effectively realized.

This invention discloses a secure voice-based intelligent customer service system and interaction method based on a multi-agent architecture. The system includes an ASR (Automatic Speech Recognition) module, multi-layered security barriers, a main retrieval agent, a specialized retrieval agent cluster, a parallel retrieval and result aggregation module, an LLM (Limited Language Modeling) synthesis engine, and a TTS (Text-to-Speech) module. Through multi-agent system architecture and methodological design, this invention achieves modular processing, parallel retrieval tasks, and closed-loop security control. By enabling independent calls between input, output, agents, and inference stages via multi-layered security barriers, the system effectively mitigates risks such as prompt word injection, indirect injection attacks, harmful content generation, and information leakage. Sensitive information identification, access permission verification, and content compliance review are embedded in each stage of input, retrieval, information synthesis, and output to prevent sensitive data leakage and meet the application requirements of high-security business scenarios (such as finance, telecommunications, and government services).

The invention discloses a semiconductor valve fluid simulation method based on deep reinforcement learning fusion, and relates to the technical field of semiconductor equipment manufacturing, and the method comprises the steps: collecting real-time working condition data, and dynamically calibrating the helium detection inner leakage rate, the flow coefficient CV and the adaptive threshold of the inner surface roughness in combination with the service degradation characteristics of a valve; a perception-decision double deep reinforcement learning agent architecture is established, a perception agent extracts multi-physics field high-dimensional features through a convolutional neural network and performs noise reduction, and a decision agent drives a simulation model to adaptively adjust structural parameters by adopting an improved double-strategy reinforcement learning algorithm; starting multi-physics field simulation and double-agent closed-loop iteration until a simulation result meets a self-adaptive threshold value requirement; and introducing a multi-target degradation compensation mechanism based on an iteration result, reversely correcting valve service degradation characteristic related parameters, and outputting a valve simulation optimization scheme. According to the invention, the problem of low simulation precision in the prior art is solved, and intelligent and accurate optimization of the semiconductor valve structure is realized.

The invention relates to the technical field of energy storage, and discloses a global protection method, device and equipment for an energy storage system and a medium, and the method comprises the steps: obtaining temperature data and environment data monitored by a global sensing network; a three-level multi-agent framework is adopted, a particle swarm optimization algorithm is utilized to output a cluster global decision and an equipment control instruction, protection modes are switched, a corresponding protection strategy is determined, and the protection modes comprise a normal operation protection mode, a fault early warning protection mode and an emergency isolation protection mode; according to the invention, a global sensing network is used for monitoring parameters, a monitoring blind area is eliminated, full-dimensional parameter monitoring is realized, a three-level multi-agent architecture is used for outputting a global decision and an equipment control instruction, compliance is guaranteed from the decision level, a multi-level protection mode is switched, and the protection mode is dynamically adapted. Safety and efficiency are balanced, and the hierarchical protection device executes a protection strategy, so that global protection is realized, and global safety protection capability is improved.

The application relates to the technical field of satellite intelligent access, and discloses a multi-satellite intelligent access method, system and equipment based on AI prediction, which comprises the following steps: collecting satellite orbit data and user terminal position data of a multi-satellite system, and performing satellite position prediction through an orbit prediction neural network to obtain satellite predicted positions and communication window information; each intelligent agent perceives communication environment parameters and constructs an environment perception vector; a multi-agent strategy network is driven based on the satellite predicted positions and the environment perception vector to generate a first access strategy; each intelligent agent performs resource conflict processing according to the first access strategy to obtain a second access strategy; the application adopts a multi-agent architecture to replace traditional centralized control, each intelligent agent has independent decision-making capability and realizes cooperation through a distributed negotiation protocol, single-point failure problems are avoided, robustness and scalability are improved, and resource competition and access conflicts are effectively reduced.

This invention discloses a method, apparatus, equipment, and medium for coordinated operation of liquefied natural gas (LNG) receiving terminals based on multi-agent collaboration, relating to the field of artificial intelligence technology. The method includes: constructing a hierarchical collaborative multi-agent architecture comprising equipment agents, process agents, and safety agents; real-time collection of multi-source state data, which is then fused through a state representation layer to generate an operational profile containing a unified state ontology for the entire station; collaborative decision-making by process and equipment agents based on the unified state ontology and preset rules to generate a coordinated operation sequence; risk verification of operation instructions by the safety agent using a constraint shielding mechanism, followed by issuance to multiple execution devices for coordinated execution; real-time collection of execution feedback to update the operational profile and triggering a readjustment of the operation sequence within the architecture. This invention solves the problems of fragmented data representation, poor scheduling flexibility, and lack of dynamic safety verification in existing technologies.

The invention provides a physical intelligence detection method and system driven by chemical information, and relates to the technical field of physical intelligence and chemical sensing. Comprising a physical sensing module, a chemical sensing module, an intelligent acquisition module, a multi-modal fusion analysis module, a tool execution module and a global optimization module, the physical sensing module is used for positioning a target, the micro chemical sensing module is used for acquiring multi-modal spectral information, the intelligent acquisition module is used for optimizing parameters and preprocessing data, the multi-modal fusion analysis module is used for realizing cross-modal feature coupling and large model aided decision making, and the body execution module is used for completing sampling and response. And accurate positioning, molecular-level identification and intelligent decision making of the target substance in a complex environment are realized.

The invention discloses an AI-GIS system based on a multi-agent fine-tuning language model, and belongs to the field of artificial intelligence and geographic information science. According to the system, firstly, a task disassembling corpus and a tool matching corpus are constructed, and corresponding base models are finely adjusted according to the task disassembling corpus and the tool matching corpus, so that a task disassembling agent and a tool matching agent are obtained; designing a tree-shaped multi-agent framework, and associating an external application program interface of the GIS tool; when the AI-GIS system runs, a complex GIS task is automatically decomposed into sub-tasks, a proper GIS tool is matched for each sub-task, and finally the function of intelligent construction of the GIS workflow is achieved. According to the method, the use threshold is greatly reduced, operation can be carried out without professional knowledge, the processing efficiency and accuracy of the method exceed those of a mainstream scheme, the system is light in weight, easy to expand and low in resource requirement, a complete closed loop from task input to result output can be finally achieved, and available achievements can be directly delivered.

The application discloses a communication auxiliary system, a master control device and a method, and belongs to the technical field of communication. The system adopts a mediation double-agent architecture, the master control device is logically connected between a communication terminal and a headset device, and meanwhile, isolated physical connections are established by simulating hands-free (HF) device specifications and audio gateway (AG) device specifications. The master control device is internally provided with an uplink data decision module and a memory bus with a first buffer address and a backup buffer address. When a local trigger event (such as touch, action or external entity button pulse) is captured, the decision module forcibly switches the memory data reading pointer of the digital audio by using the bottom-layer hardware atomic operation in an instant, and replaces the headset pickup source with an external high-definition pickup source. The whole switching process does not trigger the bottom-layer audio routing redistribution (Audio Policy) of the communication terminal operating system, and the absolute continuity of the Bluetooth bottom-layer SCO synchronous link frame sequence is maintained. The system realizes the high-response seamless migration and injection of the external pickup source without invading the protocol stack of the host device, supports the double-track solidification of the multi-dimensional voice shunting of the communication link, and significantly improves the communication and pickup efficiency in modern mobile and complex scenarios.

The application discloses a terminal service handling system and method fusing a large language model and an agent architecture, and the system comprises an agent scheduling layer and a user interaction layer, an artificial intelligence capability layer, a tool service layer and a data layer connected with the agent scheduling layer respectively; the user interaction layer comprises a multi-modal input interface; the agent scheduling layer comprises a plurality of constructed agents; the artificial intelligence capability layer comprises a large language model, a multi-modal model and a vector model; the tool service layer comprises a plurality of service handling tools for executing specific service operations; and the data layer comprises a knowledge base, a service database and a vector database for storing and retrieving power service related data; the application constructs a multi-agent collaborative system to realize intelligent scheduling, task allocation, exception handling and result checking of a service process.

The invention discloses an adaptive decision-making large model agent architecture and an implementation method, and belongs to the technical field of artificial intelligence and intelligent system crossing. The adaptive decision-making large model agent architecture comprises a multi-level decision-making reasoning mechanism used for realizing cross-task adaptive and dynamic environment decision-making optimization; the environment perception and fusion module is used for processing the multi-mode environment information and outputting unified features; the resource optimization and real-time performance improvement module is used for reducing model resource consumption and accelerating reasoning; the multi-level decision reasoning mechanism, the environment perception and fusion module and the resource optimization and real-time performance improvement module are sequentially connected through data interfaces to form a perception-decision-execution closed-loop process. The method can improve the decision precision of the large model agent, reduces the resource consumption, is suitable for multiple fields of intelligent customer service, automatic driving, smart home and the like, is high in adaptive capability, is high in environment compatibility, and is low in deployment cost.

The invention provides an automatic test method based on an Agent architecture large model and a computer program product, which can automatically generate a test case which takes different constraint combinations as a test coverage target and covers all stations, all driving directions and continuously divided time slices. The test case driving target urban rail transit scheduling optimization model initiates full-station multi-driving-direction passenger demand pre-calculation in continuously divided time slices, the number of required trains corresponding to the passenger demands is obtained, and a train number set is determined according to the number of required trains; the test case and the train number set are used as input to drive the train number operation scheduling simulation to obtain the scheduling decision variable of each train number, and finally, whether the test passes is determined through the execution of constraint verification, so that the test efficiency and accuracy are remarkably improved, the test can meet complex constraints and a complex rail transit network, and the test efficiency is improved. The method has an efficient automatic problem positioning and feedback mechanism, and is a core technology for improving the rail transit dispatching intelligence and optimization capability.

The invention discloses a center-edge type multi-expert joint scientific data processing agent platform, which is characterized by comprising an edge expert agent architecture and a center agent system, the edge expert agent architecture is used for accessing required tools, knowledge, data and professional scientific models, constructing an edge expert agent and accessing the edge expert agent to the central agent system; the central agent system comprises a multi-expert agent management subsystem for large device scientific data fusion processing and a task management subsystem for large device scientific data fusion processing; the multi-expert agent management subsystem is used for registering and managing the edge expert agents and calling the corresponding edge expert agents to intelligently process professional scientific data according to the received subtasks; and the task management subsystem is used for performing task decomposition on a user task to obtain a plurality of sub-tasks and appointing corresponding edge expert agents to be sent to the multi-expert agent management subsystem.

The application provides a full-process dynamic task planning AI agent scheduling method and system for SEO, relates to the technical field of agent scheduling, and preconfigures rule configuration information after building an agent core architecture, connects the preconfigured rule configuration information to a plurality of LLM models and a full-network information collection interface, and obtains preparation architecture building information; the preparation architecture building information is used for creative task analysis through a retrieval hub, creative task analysis data is obtained, the creative task analysis data is analyzed for task allocation through the retrieval hub, task allocation analysis data is obtained; the keywords, outline and content are sequentially generated and verified and analyzed through the multi-agent architecture building information, and agent scheduling data is obtained, the application effectively solves the contradiction between the plurality of LLM models and task adaptation and the problem of scheduling hub computing power overload under high concurrency, breaks the vicious circle of the two, does not require manual intervention, and greatly improves the full-process automation level of SEO content creation.

The invention discloses an intelligent number asking method and system based on an intelligent agent architecture. The method comprises the steps that a user sends a natural language number asking request to an intelligent agent module through a front-end client; the intelligent agent module receives a natural language number asking request of a user, and analyzes the natural language number asking request in combination with a session context to obtain an intention containing a tool demand; the agent module calls a corresponding tool according to the intention and a priority strategy, preferentially executes a data query tool to obtain basic data, then triggers a data extraction, computational analysis or drawing generation tool as required, and updates a tool execution result to a session context in real time; and the agent module summarizes execution results of the tools, generates answers containing text analysis, structured data or image resource paths, and pushes the answers to the front-end client through the streaming interaction module. According to the method, full-process automatic processing of data query-statistical analysis-visual presentation is realized through natural language interaction, the use threshold of non-technical users is reduced, and the data analysis efficiency is improved.

The invention provides an intelligent interview evaluation method based on multi-agent and cross-modal fusion. The method comprises the following steps: collecting multi-modal data, and carrying out preprocessing and data alignment on the multi-modal data; based on the aligned multi-modal data, through a weighted attention model, mapping three types of features of texts, voices and videos to a unified representation space, realizing information complementary fusion among modals, and processing the fused features by adopting a lightweight optimization strategy; task processing in each interview process is completed by adopting a modular multi-agent architecture design and a distributed cooperative scheduling mechanism; in the question-answer interaction process, a dynamic question-chasing generation and manual priority question insertion mechanism is introduced to realize a hybrid decision; and after the question and answer interaction is finished, performing multi-dimensional capability evaluation, and generating an interpretable report. According to the invention, response delay accumulation of a traditional serial architecture in a high-concurrency scene is effectively avoided.

The application discloses a state self-driven multi-agent crop growth report generation method and system, first, a multi-agent cooperative communication mechanism is constructed, a unified event-driven model and a communication pipeline are built, information routing and state synchronization between heterogeneous agents such as planning, querying and data analysis are realized, and asynchronous cooperation and data intercommunication of a multi-agent network under complex logic are guaranteed. Secondly, a state self-driven agent architecture is realized, that is, a self-driven core based on a finite state machine is implanted into the agent, so that each node can autonomously complete state flow, action decision and local task execution according to real-time context environment and task feedback mechanism. Finally, automatic generation of a structured document is realized, special report agents are used to logically aggregate and template rendering of multi-dimensional analysis data and knowledge fragments output by the cooperative network, and finally, a structured analysis report with standard layout and rigorous content is output.