179 results about "Agent architecture" patented technology

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 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 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 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 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 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.

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.

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.

The invention provides an intelligent yield management method and system based on a multi-agent network, and relates to the field of quality control and artificial intelligence, and the method comprises the following steps: a tutor agent carries out semantic understanding and task analysis on natural language interaction information, and routes decomposed sub-tasks to corresponding expert agents; the expert agent calls a corresponding tool to execute the task according to the subtask, and returns a task execution result to the tutor agent; and the tutor agent analyzes and integrates the task execution results, integrates the task execution results into final natural language response information, and returns the natural language response information to the user. The intelligent mode of yield management is fundamentally changed, the problems that a traditional yield management system is lack of autonomous execution capacity, low in intelligent degree and limited in expansibility are solved by constructing a multi-agent framework, and meanwhile the limitation of a single agent in the process of automatically executing cross-module and cross-function complex tasks is made up.

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 belongs to the related technical field of distributed workshop dynamic scheduling, and discloses a distributed heterogeneous workshop scheduling method and device based on greedy heuristic enhancement, and the method comprises the steps: inputting the state characteristics of a to-be-scheduled distributed heterogeneous workshop into a double-layer end-to-end strategy network; the double-layer end-to-end strategy network outputs an optimal workpiece to be processed, and then a final scheduling scheme is obtained; wherein the training environment of the double-layer end-to-end policy network is constructed based on a partial observable Markov decision process scheduling model; the state features are obtained based on a partial observable Markov decision process scheduling model; the partial observable Markov decision process scheduling model is obtained by modeling a dynamic distributed heterogeneous workshop scheduling problem by adopting a decentralized multi-agent architecture. According to the invention, the expansibility and the flexibility are improved.

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 relates to an ODRL strategy generation method based on a large language model multi-agent architecture, which is applied to a system of a multi-agent architecture in an orchestrator-worker mode, and comprises an orchestrator agent and a group of multi-category worker agents, the method comprises the following steps: receiving natural language strategy input of a user through an orchestrator agent, identifying a structure type of language logic of the natural language strategy, and selecting at least one worker agent to form a processing chain according to the structure type; and calling a worker agent in the processing chain, processing the natural language strategy, generating an ODRL strategy, and outputting the ODRL strategy.

The invention discloses an intelligent contract code reconstruction method, device and equipment and a readable storage medium, and is applied to the technical field of computers, and the method comprises the following steps: obtaining an intelligent contract code reconstruction request; obtaining a multi-agent architecture pre-constructed based on the intelligent contract code reconstruction workflow; the multi-agent architecture is a multi-agent architecture based on a large language model; and based on the smart contract code reconstruction request, performing smart contract code reconstruction by using a multi-agent architecture to obtain a reconstructed smart contract code. According to the method, based on the standard workflow of intelligent contract code reconstruction and in combination with a multi-agent architecture of a large language model, a universal intelligent contract code reconstruction workflow and multiple agents cooperate together, so that semantic understanding of a complex intelligent contract scene and implementation of a reconstruction target are improved, the method is an automatic reconstruction process, and the implementation efficiency is improved. A user does not need to customize a template, and the limitation that manual participation and implementation can only be achieved in a specific code mode is reduced.

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.