195 results about "Agent architecture" patented technology

The invention discloses an agent architecture based on a multi-modal large model, which comprises an external support module and an internal core module, and is characterized in that the external support module comprises an input module; an output module; a large model capability module; the internal core module comprises a sensing module; a memory module; a portrait configuration module; a planning module; a tool use module; and an action module. According to the intelligent agent architecture based on the multi-modal large model, the efficient, flexible and extensible intelligent agent architecture is innovatively realized, and the problem that most of existing intelligent agents are single-function artificial intelligence systems and are difficult to meet the requirement for multi-capability combination in a complex application scene is effectively solved.

A computing system for adaptive electronic message classification employs a multi-agent architecture comprising a media feature analysis system, a user context refinement system, and a response synthesis system. The media feature analysis system generates pillar scores including message type, intent, and link risk scores with associated confidence values using trained classification models. When pillar scores and confidence values do not satisfy predetermined threshold conditions, the user context refinement system dynamically constructs contextual prompts using the pillar scores and confidence values as input parameters. User responses generate score modification data that refines the pillar scores and contextual response data for recommendation generation. The response synthesis system generates refined classifications and personalized recommendations using the refined pillar scores and contextual response data. An orchestration system coordinates agent interactions using learned uncertainty points and implements asymmetric influence algorithms with variable weighting based on content and URL analysis concordance.

The invention discloses a multi-agent-based virtual power plant intelligent control method and system, and the method comprises the steps: dividing a virtual power plant into a plurality of sub-virtual power plants, deploying an agent in each sub-virtual power plant, collecting a local resource state through each agent, and predicting a load demand and the output of a distributed power supply, a hierarchical control unit is adopted to carry out collaborative optimization among the sub-virtual power plants according to a prediction result, and an upper-layer optimization control module constructs a linear programming model according to the prediction result and solves the linear programming model to obtain an initial scheduling scheme; and the lower-layer reinforcement learning control module performs local adjustment on the preliminary scheduling scheme according to a multi-agent depth deterministic strategy gradient algorithm to obtain a decision scheme. Based on a distributed control strategy of a multi-agent architecture, the fault-tolerant capability and reliability of the system are improved, a hierarchical control architecture is adopted, global optimization and local adjustment are organically combined, and efficient coordination and real-time adjustment capability of global resources are realized.

The invention discloses an energy management multi-agent collaborative optimization method and system based on a large model agent architecture. The system comprises a multi-agent architecture module, a model processing module, a data acquisition and management module, a system integration and interaction module and a collaborative optimization control module. The model processing module comprises a large model and a plurality of small models and is used for realizing task processing and analysis; the data acquisition and management module acquires and processes multi-source data; the system integration and interaction module realizes multi-platform integration and visual interaction; and the collaborative optimization control module ensures collaborative work and system optimization of the intelligent agents. The system has the advantages of autonomy and decentralized control, isomerism and modularization, intelligent collaboration and the like, instrument faults can be accurately diagnosed, energy management is optimized, production is intelligently controlled, enterprise energy utilization efficiency, instrument operation stability and the overall operation management level are improved, enterprises are assisted to achieve green and low-carbon transformation, and the system is suitable for popularization and application. The method is suitable for energy management and energy equipment maintenance scenes of various industrial production enterprises.

The invention discloses a network security situation analysis method and device based on multi-agent collaboration, and the method comprises the steps: firstly constructing an agent architecture and a route for network security situation assessment analysis based on an open source large language model, carrying out the role definition of each agent in the agent architecture through combining with a Prompt prompt word, and stipulating the communication format of the agent; based on domain knowledge, a dynamic rank learning LoRA algorithm is adopted to carry out fine adjustment on each agent in the agent architecture, based on a natural language rule base, a commander agent is utilized to coordinate a collusion agent and a simulated blue army agent, multi-dimensional analysis is carried out through reflection, inquiry and debate mechanisms, and a network security situation assessment result and trend prediction are generated. Through combination of dynamic LoRA fine tuning and a multi-agent debate mechanism, the problems that universality and field adaptability are difficult to balance and complex attack intention analysis is insufficient in a traditional method are solved, and the accuracy and prediction reliability of network security situation assessment are remarkably improved.

The invention provides an AI agent generation method, a platform system, electronic equipment and storage equipment. The AI agent generation method comprises the following steps: acquiring a normalized task data set; performing feature extraction on the standardized task data set to construct task feature data; a hierarchical distributed architecture is adopted, initial parameters of the intelligent agent are optimized in combination with a genetic algorithm, the basic structure and the initial state of the intelligent agent are determined, and an initialized intelligent agent architecture is obtained; performing reinforcement learning training and optimization on the initialized agent architecture based on the task feature data to generate an AI agent; wherein the agent architecture is repeatedly executed in a simulation environment by using a near-end strategy optimization algorithm and task feature data, and strategy network parameters of the agent architecture are continuously adjusted according to reward feedback of task completion to obtain a trained and optimized AI agent. According to the invention, the trained intelligent agent can quickly adapt to the change of the environment, and the strategy of the intelligent agent can be adjusted in time.

The invention provides a report generation method and system based on a multi-agent architecture, and the method comprises the steps: firstly receiving and analyzing a report generation instruction, obtaining a theme demand, a framework specification and an initial reference material, then starting a multi-agent cooperation framework, generating an agent task distribution table, and generating an agent task distribution table; and the resource retrieval agent executes network resource directional retrieval according to the task allocation table to generate an associated resource set, and the content extraction agent performs structured conversion on the associated resource set and the initial reference material to obtain a structured content unit with chapter codes. The method comprises the following steps: performing module classification and logic series connection on a structured content unit by a report integration agent in combination with a framework specification to generate a report first draft, and finally performing content verification and optimization on the report first draft by a multi-agent collaborative framework to generate a final report text conforming to the framework specification, thereby realizing automation, intellectualization and high efficiency of report generation. And report quality is improved.

The invention belongs to the technical field of power systems, and particularly relates to a transmission and distribution cooperative scheduling optimization method based on multi-agent deep reinforcement learning. The method comprises the following steps: firstly, establishing a transmission and distribution cooperative scheduling distributed framework, and respectively establishing distributed optimization models taking the minimization of the operation cost of a power transmission network and the minimization of the operation cost of each active power distribution network as targets; secondly, converting a transmission and distribution collaborative scheduling problem into a Markov game model with a multi-agent benefit balance characteristic, and designing a collaborative multi-agent architecture; then interacting with a transmission and distribution interconnection environment through each agent to obtain historical interaction information, storing the historical interaction information into an experience pool, and training by using an MADDPG algorithm to realize iterative updating and optimization of an Actor network and a Critic network of each agent; and finally, realizing local optimization control of the power transmission network and each active distribution network by using an Actor network.

The disclosed technology provides for an improved approach to AI code generation. In various embodiments, the disclosed technology provides for an AI agent architecture platform for generating, revising, testing, and debugging code using a customizable team of agents with specific tasks.

The invention discloses an industrial design automation method based on an artificial intelligence large model and an intelligent agent, relates to the technical field of industrial design, and solves the technical problem that industrial design automation is difficult to realize at present. The method comprises the following steps: automatically matching a large model according to input industrial product related information; extracting a design optimization problem through a problem analysis agent based on a large model, and decomposing the design optimization problem into optimization tasks; a target function or a simulation code is generated through the target quantification agent, and an interface is provided; mapping from intelligent body fitting simulation input to output is built through the agent model, and an interface is provided; packaging the interface through an optimization packaging agent and executing an optimization task; according to the embodiment of the design automation, a hierarchical agent architecture for industrial design automation by utilizing an artificial intelligence large model is obtained through generalization. According to the invention, high automation of industrial design can be realized.

The invention provides an urban virtual power plant operation method and device based on an intelligent agent architecture, relates to the field of artificial intelligence, and solves the problem that in the prior art, operation strategies related to a virtual power plant are mostly based on static rules and manually set optimization models. And rapid and efficient response is difficult to realize in a multi-participant, multi-constraint condition and dynamic electricity market environment. The method comprises the following steps: collecting multiple pieces of first data in an urban energy system in an urban virtual power plant; performing arrangement and task scheduling on the first data on the basis of data types required by the plurality of agents to execute tasks, determining the first data required to be processed by each agent, and calling the plurality of agents to process the first data required to be processed; and generating a regulation and control decision and a market transaction strategy of the urban virtual power plant based on the processing results output by the plurality of agents and the operation constraint conditions. The method is used in the operation process of the urban virtual power plant.

The invention discloses a task processing method and device based on causal agent cooperation, equipment and a medium, and the method comprises the steps: constructing a dual-agent architecture comprising a general agent and a causal agent, and determining a chain-type solution of a to-be-processed task through the general agent; performing intervention type analysis, intervention effect analysis and chained causal reasoning analysis on the chained solution through the causal agent to judge whether the chained solution is matched with the to-be-processed task, and if not, updating the chained solution through the universal agent until the chained solution is matched with the to-be-processed task; generating a thinking chain of each sub-task in the chain solution through the universal agent, verifying the process logic rationality of each thinking chain through the causal agent, and updating the thinking chain which does not pass the verification through the universal agent until the verification is passed; a subsequent task sequence of a current subtask in the chain solution is determined through a causal agent, and anti-factual reasoning of a result effect is carried out through a general agent according to the subsequent task sequence. And the task processing stability is improved.

The present invention provides an integrated, context-aware, security system that provides an adaptive endpoint security agent architecture model for a continuously monitoring and recording activity across an enterprise, specifically monitoring activity on endpoints, and subsequently detecting and blocking any malicious processes that may otherwise invade the enterprise and cause issues. The endpoint security agent architecture exposes a well-defined, public interface to the event data generated by the endpoint security agent in the form of a custom programming language by which a user can define the logic that the endpoint security agent executes in response to event data to perform detection of and response to suspicious activity.

The invention relates to a water-light storage area power grid optimization method based on federated and layered multi-agent reinforcement learning. The method comprises the steps of constructing a federated and layered multi-agent model, defining states, actions and reward spaces of layered agents, implementing federated reinforcement learning offline training with privacy enhancement, deploying the model and executing online distributed optimization. Through the federal learning framework, it is ensured that private data of all the energy main bodies are locally calculated, the privacy leakage risk caused by data collection in a traditional centralized method is avoided, and a technical basis is provided for building collaborative trust among multiple main bodies. Meanwhile, the designed hierarchical intelligent agent architecture decouples a complex power grid system into subproblems with clear hierarchies, and when the scale of the system is expanded, new energy units can be accessed in a modular mode, so that the overall maintenance and upgrading difficulty of the system is reduced, and the expandability of the system is improved.

The invention provides an automatic consumption tag analysis system and method based on multi-agent collaboration, and the system comprises a data processing agent which is used for collecting data from a social media platform and carrying out the data preprocessing; the label identification intelligent agent is used for extracting consumption labels of different dimensions from the preprocessed data; the sentiment analysis agent is used for carrying out context modeling and sentiment tendency recognition and binding the recognized sentiment tendency to the corresponding consumption label; the label normalization agent is used for performing clustering and normalization processing on all consumption labels bound with emotional tendencies to generate a structured multi-layer label atlas; the central scheduling agent is used for scheduling other agents, generating a label analysis result by using the multi-layer label atlas and sending the label analysis result to the user; according to the invention, based on a multi-agent architecture, structured analysis is carried out on user tags, behavior attributes and consumption intentions in social media contents, so that high-precision and high-efficiency intelligent consumption insight is realized.

The invention provides a tax AI agent construction method based on a large language model. The method comprises the following steps: constructing a tax knowledge base, and based on the constructed tax knowledge base, executing large language model training and optimization to obtain a large language model; constructing a tax AI agent based on a large language model by using the designed agent architecture; collecting a standard session generated by the tax AI agent for the standard consultation, and training and optimizing the tax AI agent according to the generated standard session until multiple threshold parameters are met; deploying and applying the tax AI agent, and continuously optimizing and updating the tax AI agent according to the business session generated by the collected tax AI agent for the non-standard consultation and according to the generated business session. Therefore, efficient and low-cost tax consultation service can be provided.

The invention provides a geographic knowledge complex question-answering method based on a space-time agent architecture. The geographic knowledge complex question-answering method comprises the following contents: natural language analysis: accurately understanding intentions of users and information contained in questions; multi-modal geographic entity recognition: processing non-text data contained in user input; generating a WKT format and injecting a prompt; knowledge graph retrieval enhancement; in the spatial computing tool chain arrangement, an LLM generates a problem solving scheme after combining multi-modal information, WKT geometry and related data resources associated with a knowledge graph, tools or functions needing to be called and an execution sequence of the tools or the functions are listed, and the series of tools adopt a model context protocol; in answer generation and response, the method can effectively solve the problems that in the prior art, when complex geographic questions and answers are processed, question input recognition is incomplete, reasoning steps are not accurate, and the answer basis is not real, and more intelligent and reliable question and answer support is provided for the field of natural resource planning and the like.

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.

A system and method for accuracy-enhanced processing of query data within a multi-digital agent architecture may include receiving unrefined query data including textual data; retrieving historical user dialogue data from a computer database associated with the unrefined query data; transforming the unrefined query data to refined query data based on the retrieved historical user dialogue data; generating at least one digital agent classification inference using the refined query data; routing the refined query data to a digital agent based on the at least one digital agent classification inference; generating, by the digital agent, a digital response to the unrefined query data; and returning the digital response to an interactive graphical user interface.

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 belongs to the technical field of artificial intelligence, and relates to an adaptive questioning method of a multi-agent collaborative inference system based on a large language model. According to the method, a layered multi-agent collaboration architecture is established, and the multi-agent collaboration architecture comprises a supervisor agent and a plurality of working agents; the supervisor agent allocates reasoning tasks for each working agent; and in the process of executing the reasoning task, the working agent actively puts forward a question to the supervisor agent through a self-adaptive questioning mechanism, and receives knowledge or guidance fed back by the supervisor agent. Through organic combination of a Supervisor-Worker multi-agent architecture, a self-adaptive question decision mechanism and Actor-Critic reinforcement learning optimization, the agent system driven by a large language model is endowed with a new ability of'thinking and good questioning ', the limitation of traditional fixed scripts or single-round dialogues is broken through, and the method has the advantages of being simple in structure, convenient to operate and high in practicability. Therefore, the intelligent agent can autonomously seek information and correct thinking in a complex and unknown task environment.

An architecture includes multiple agents that communicate through structured dialectical exchanges to transform business requirements into executable applications. The agents are configured to collaborate and communicate through a series of iterative dialogues, where each agent contributes its specialized expertise to the process. The structured exchanges allow the agents to challenge, refine, and validate each other's outputs to automatically generate an application.

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 discloses an agent building workbench based on a multi-agent framework, and relates to the technical field of artificial intelligence. The system comprises an agent building module, a workflow configuration module, a release and integration module and an enterprise WeChat access module. The agent building module is used for quickly creating an agent according to business requirements and configuring parameters, a function module and a knowledge base of the agent at the same time; the workflow configuration module constructs a task allocation mechanism based on an open source multi-agent architecture, and the workflow configuration module is used for supporting configuration of a multi-agent workflow through a graphical interface and a programming interface, and embedding an open source tool and a self-research tool in the workflow to realize modular processing of tasks; by constructing the Roles intelligent agent construction workbench, the problem that an existing intelligent agent development tool is insufficient in flexibility and expansibility is solved, an efficient intelligent agent construction and management platform is provided for enterprise users, intelligence and automation of the service process are achieved, and the method has wide market application prospects.

The invention discloses a multi-agent resource scheduling method based on a large language model in an industrial scene, and the method comprises the following steps: 1, setting initial scheduling parameters and user input of a multi-agent scheduling system; 2, the planning agent performs intention recognition, feature value extraction and coding; and 3, the scheduling agent receives the scheduling coding information, selects a scheduling algorithm tool and generates a scheduling scheme. And 4, the fault detection agent receives the initial scheduling scheme and receives key parameters of the working agent. And 5, the fault detection agent judges that a fault exists and optimization exists, if yes, the scheduling agent is fed back, and the step 3 is executed again, and if not, an optimal scheduling result is output. According to the method, a multi-agent architecture driven by a large language model is adopted, and a dynamic algorithm tool selection mechanism and a prompt project are introduced. Minimization of task total time consumption and maximization of fault detection accuracy are taken as optimization objectives, and adaptivity of industrial scene resource scheduling and system reliability are remarkably improved.

The invention relates to the field of data assessment, and discloses an enterprise risk assessment method based on a multi-agent risk assessment algorithm MA-ERC, and the method comprises the following steps: S1, constructing an enterprise multi-dimensional risk knowledge graph; s2, designing a risk assessment algorithm MA-ERC to assess initial risks of the enterprises and quantify propagation risk values among the enterprises; s3, constructing an MNF-GNN model to carry out enterprise risk assessment; s4, constructing an enterprise risk assessment interpretable graph EG; according to the method, a multi-agent architecture based on a large language model and a risk propagation algorithm based on a graph are combined, the risk propagation process between enterprises is more accurately and reasonably simulated, and the assessment precision of a risk assessment model can be effectively improved through the calculated propagation risk value; and a new thought is provided for the combination of the multi-agent based on the large language model, the knowledge graph, the graph neural network and other technologies.

The present invention provides an integrated, context-aware, security system that provides an adaptive endpoint security agent architecture model for a continuously monitoring and recording activity across an enterprise, specifically monitoring activity on endpoints, and subsequently detecting and blocking any malicious processes that may otherwise invade the enterprise and cause issues. The endpoint security agent architecture exposes a well-defined, public interface to the event data generated by the endpoint security agent in the form of a custom programming language by which a user can define the logic that the endpoint security agent executes in response to event data to perform detection of and response to suspicious activity.

Embodiments described herein provide a method for building a hierarchical structure of a plurality of neural network models for performing a task. The method includes the following operations. A task instruction is received via a data interface. A first neural network model generates a first sub-task from the task instruction. A second neural network model is selected from the plurality of the neural network models based on the first sub-task. A first connection is built via a first API, between the first neural network model and the second neural network model. The first neural network model generates a first sub-task package in a format compliant with the second neural network model. A first output is received via the first connection from the second neural network model that executes the first sub-task package. The first neural network model generates a second sub-task based on the task instruction and the first output.

The invention discloses a multi-agent PCB (printed circuit board) design document automatic extraction and table completion method. According to the method, the table area in the PCB design document is automatically extracted by training the YOLOv11 model, and missing table lines are complemented by using an image processing algorithm, so that the extraction precision of table data is improved. A multi-agent architecture is adopted, text extraction and table data extraction and combination are carried out respectively, and a clear document is finally generated and output. According to the method, the defects of an existing OCR technology in table extraction can be overcome, especially the accuracy problem under the influence of table line missing or image noise can be solved, and the method has high practical value and innovativeness.