4385 results about "Multi agent technology" patented technology

The invention discloses a multi-agent collaborative task planning method, and is applied to the technical field of artificial intelligence. The method comprises the steps of decomposing a task into a plurality of sub-tasks through semantic recognition and generating corresponding semantic coding vectors; meanwhile, a preset agent resource library is called, and quantitative evaluation capability vectors of all agents in multiple skill dimensions are obtained; dynamically allocating the most adaptive target agent to execute the corresponding subtask based on matching calculation of the subtask coding vector and the agent capability vector; then parallelly driving the target agent to execute the subtasks, fusing processing results output by the target agent, and integrating to generate a task response text; and finally returning the response text to the user. According to the method, the task is split into the coding vectors corresponding to the sub-tasks through semantic recognition, and dynamic matching is performed in combination with the multi-dimensional capability vector of each agent, so that adaptation of task requirements and agent resources is realized, and the resource scheduling efficiency and execution reliability of a multi-agent system in a complex task scene are improved.

The invention provides a multi-agent collaborative question and answer enhancement method and system based on heterogeneous data knowledge, and belongs to the technical field of information management. The planning optimization intelligent agent carries out structured processing on the input complex natural language problem; a multi-modal retrieval mechanism on the heterogeneous knowledge source is constructed based on the problem disassembly and entity recognition result, and a multi-path recall agent passes through a semantic matching model of a double-tower structure; the abstract extraction agent performs semantic fusion and information extraction on the text segments and the knowledge graph sub-graphs output by the multi-path recall module; logic verification and quality evaluation are carried out on the answers generated by the abstract extraction module by the reflection iteration agent; and by setting a threshold mechanism and combining importance weights of the sub-questions, performing scoring and reflection optimization on the generated answers by utilizing a large language model LLM (Language Language Model). The method can effectively cope with cross-domain and multi-level complex question and answer tasks, and has good expandability and intelligent level.

The invention relates to the technical field of artificial intelligence, can be applied to service scenes such as pension service, financial science and technology and medical health, and discloses a multi-agent cooperative task processing method, device and equipment and a medium, and the method comprises the steps: obtaining a task instruction, analyzing a core target, and decomposing the core target into a plurality of subtasks; obtaining environment information, dividing task areas, and generating a cooperation framework in combination with agent capability and area weight; real-time states of the agents are obtained, and the optimal agents are matched based on the cooperation framework to generate a task allocation table; a task distribution table is issued to control the intelligent agent to execute the task and upload execution information; monitoring an execution process, and performing dynamic adjustment and updating a task allocation table when detecting path conflicts or equipment faults; and after the subtask is completed, obtaining environment completion state data, and comparing the data with a preset standard model for acceptance. According to the method, efficient task decomposition and intelligent distribution are realized by fusing task semantics, environment information and intelligent agent capability, and the cooperation stability is improved by introducing a real-time state perception and self-adaptive mechanism.

The invention relates to the technical field of multi-modal information processing, and provides a deep semantic collaborative fusion method for heterogeneous multi-modal data. The invention provides a dynamic adaptive fusion framework aiming at the problems that a modal interaction mechanism is rigid and semantic modeling is shallow in the prior art. The method comprises the following steps: carrying out feature coding and alignment on text, audio and video modal data to generate unified-dimension single-modal representation; dynamic interaction is realized through an enhanced multi-head gating fusion module, and double-path features are generated; and carrying out cross-modal depth modeling on the basis of a stacked Transform encoder, and outputting final fusion semantics. Wherein the multi-head attention path calculates cross-modal mapping by taking a text as a query vector and taking an audio / video as a key value vector; the gating path generates a dynamic weight through cosine similarity and a learnable temperature parameter; and the dual-path adaptive fusion adopts a balance factor alpha weighted combination. According to the method, the multi-modal data fusion precision and the system robustness are improved, and the method is suitable for government affair service, man-machine interaction and other scenes.

The invention relates to a multi-agent scheduling method and system based on task intention matching. The method comprises the following steps that S1, tasks are received and analyzed; s2, intelligent agent matching candidates are obtained; s3, selecting an optimal agent by a scoring mechanism; s4, task assignment, execution monitoring and result acquisition; s5, performing reflection evaluation and strategy updating: dynamically updating an intelligent agent score, and adjusting a task label matching rule or other scheduling parameters so as to optimize a distribution decision of a subsequent task; and S6, result combination and output: when the task comprises a plurality of sub-tasks executed by a plurality of intelligent agents, the results are verified, sorted and combined. Compared with the prior art, the method has the advantages that the task target can be dynamically analyzed, the optimal agent can be intelligently matched to execute the task, and the scheduling strategy is continuously optimized through feedback after the task is completed, so that the task execution efficiency and effect of the multi-agent system are remarkably improved, and the accuracy, the adaptability and the intelligent level of task allocation are improved.

The invention discloses a data center machine room AI energy-saving control method and system, a digital twin model of a machine room operation state is constructed through a holographic perception and heterogeneous data fusion technology, centimeter-level monitoring of an equipment state and environmental parameters is realized, and the system integrates a laser radar array, an acoustic sensor and a gas sensor network. The time-space alignment of multi-modal data is completed by combining edge computing nodes, holographic mapping including thermodynamic characteristics, vibration characteristics and gas leakage risks is formed, historical temperature control strategy characteristics are extracted by adopting a variational auto-encoder based on a dynamic strategy generation mechanism of generative artificial intelligence, and a load trend is predicted by combining a long-short-term memory network. Constructing a self-adaptive strategy pool; the multi-agent reinforcement learning framework enables temperature control, equipment scheduling and power grid response to form game optimization, the strategy robustness in a complex scene is improved, and the system innovatively fuses power grid real-time electricity price and carbon transaction data so as to establish a multi-target decision system.

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention discloses a multi-agent dynamic arrangement method based on multi-modal analysis and adaptive retrieval, and relates to the technical field of artificial intelligence and information retrieval. Comprising the steps of S1, converting a text, an image, structured data and voice content input by a user into a unified multi-mode semantic representation, S2, converting the unified multi-mode semantic representation into a specific execution process, and S3, automatically scheduling a reasoning agent, a knowledge obtaining agent and an execution agent according to DAG nodes, task elements and available resources, and obtaining the task elements and the execution agent according to the reasoning agent, the knowledge obtaining agent and the execution agent. S4, after task process construction and agent arrangement are completed, dynamic retrieval, evidence convergence and strategy optimization are carried out on information requirements related to a user task, so that a reasoning agent obtains complete knowledge support with consistent context, and S5, knowledge evidence is combined with a task process, so that the task process is completed. The method comprises the following steps: step S6, implementing problem solving, strategy generation and task closed-loop execution through a reasoning agent, step S6, performing actual operation on a target task by an execution agent according to an executable instruction sequence output by the reasoning agent, and outputting a result, and step S7, performing result verification according to an output result returned by the execution agent, and the correctness, integrity and consistency of an output result are examined through rule verification, model evaluation and evidence alignment.

The invention provides a multi-modal knowledge extraction method and system based on multi-agent collaborative optimization, and relates to the technical field of knowledge extraction, and the method comprises the steps: carrying out the multi-modal deconstruction of an original document to be extracted; constructing a multi-modal agent, respectively executing feature extraction and preliminary knowledge extraction, and outputting a single-modal multi-component system; based on a cross-modal knowledge graph, mapping information of different modals to a unified semantic node, and establishing cross-modal association and analyzing a logic chain through a graph neural network and a causal reasoning module; dynamically allocating resources according to the importance of map nodes, and screening structured knowledge; and through confidence analysis and node traceability evaluation, an intelligent agent cooperation mechanism is optimized, and increment correction is carried out on a result. According to the method and the device, the technical problem of low knowledge extraction accuracy and efficiency caused by insufficient multi-modal knowledge collaborative mining capability due to knowledge extraction of literatures by adopting a single agent in the prior art can be solved, and the knowledge extraction quality and efficiency are improved.

The invention discloses a structured decision-making method based on multi-agent collaborative decision-making and reinforcement learning, and relates to the technical field of natural language processing, knowledge engineering and agent collaboration, and the method comprises the steps: receiving an original rule document, analyzing the document type, complexity and constraint conditions, and defining a task target and a success standard; and according to the task target, matching and scheduling the intelligent agent from the registered intelligent agent library, and further analyzing the capacity configuration of the intelligent agent for standby. Through the multi-agent cooperation and reinforcement learning technology, full-process automation of rule documents from input to structured analysis is realized, document types, complexity evaluation and constraint condition analysis can be automatically identified, and a clear task target and a success standard are generated; and the large language model generates a structured workflow according to task requirements and agent capabilities, so that the performability is ensured through logic verification, manual intervention is greatly reduced, and the processing efficiency and the system intelligence degree are improved.

The invention discloses a multi-agent-based gas insulated switchgear fault diagnosis method and system, and relates to the technical field of intelligent operation and maintenance of power equipment, and the method comprises the steps: obtaining signal data of target equipment, carrying out the feature extraction of the signal data, and constructing a multi-modal feature matrix; time delay features of acoustic and electromagnetic signals are extracted from the multi-modal feature matrix, a GIS propagation model is established, and the space coordinate position of a liberated power source is solved through a wave field inversion algorithm; combining the space coordinate position and the multi-modal feature matrix into a complete fusion feature vector, inputting the fusion feature vector into a dynamic Bayesian model, and outputting a fault type label and a corresponding confidence coefficient; migrating the dynamic Bayesian model based on a migration learning mechanism, and dynamically updating a classification threshold value; inputting the diagnosis history sequence into a time sequence prediction model, and predicting a future operation state; through multi-modal fusion and intelligent reasoning, GIS fault accurate positioning and prediction are realized, and the problems of low precision and poor adaptability of traditional diagnosis are solved.

The invention relates to the technical field of dispatching and emergency response, in particular to a supply chain multi-node real-time collaborative dispatching and emergency response system and method, and the system comprises a distributed data collection module which is used for obtaining the inventory data, logistics state and equipment operation parameters of each node in real time; the digital twin modeling engine is used for constructing a dynamic virtual mapping model of the supply chain network; the collaborative decision center generates a multi-objective optimization scheduling scheme based on a reinforcement learning algorithm; the emergency response trigger is used for automatically starting a graded emergency plan through abnormal mode recognition; according to the method, second-level response is realized through millisecond-level data synchronization and edge calculation, so that decision timeliness is improved, cross-node cooperation efficiency is improved by adopting multi-agent game and federated learning, and the punctuality rate of orders and the toughness index of the network are improved through multi-target Pareto optimization on the premise of controllable cost.

The invention relates to an intelligent agent system optimization method and device based on intelligent fault analysis and cross-generation knowledge inheritance, and belongs to the technical field of artificial intelligence. According to the method, interaction abnormal signals are captured in real time by deploying a lightweight log probe, and a tool benefit prediction model based on reinforcement learning is constructed to automatically generate an improvement proposal when the failure rate exceeds a threshold value; an agent genealogy map is established to realize automatic inheritance of a new agent on core memory and abandonment of failure knowledge, and a disastrous forgetting blocker is deployed to dynamically extract a functional module from a genealogy to deal with key capability degradation. Aiming at the problems of fault response lag, knowledge inheritance fracture, key capability degradation and the like in an intelligent agent system iteration process, the invention creatively provides a cooperation mechanism of an intelligent fault analysis layer and a cross-generation knowledge inheritance network, and the fault self-healing capability, version stability and service continuity guarantee level of the system are remarkably improved.

The invention relates to the technical field of electric power engineering, and discloses an electric power engineering multi-modal RAG system based on a knowledge graph and multi-Agent collaboration, and the system comprises a multi-modal dynamic knowledge base construction module which is configured to carry out the structural processing, multi-dimensional knowledge organization and dynamic optimization of electric power engineering multi-modal data; the self-adaptive retrieval strategy engine module is configured to construct a weight decision network based on deep reinforcement learning and execute multi-channel parallel retrieval and result fusion; the iterative self-reflection reasoning module is configured to generate a reasoning path in combination with the retrieval result and verify evidence validity from multiple dimensions; the MCP tool intelligent calling module is configured to integrate multiple types of standardized MCP tools; and the multi-Agent collaborative framework is configured to provide multiple types of Agents which are specific in function and have a cross-module interaction capability. According to the method, the question and answer accuracy, the reasoning depth and the result interpretability in the complex multi-modal scene of the electric power engineering can be remarkably improved.

The invention provides a large model agent collaborative scheduling method and system oriented to complex tasks, relates to the technical field of artificial intelligence, and comprises the steps of task decomposition, feature extraction, agent matching, dynamic scoring, scheduling scheme generation and optimization, execution monitoring, exception handling and the like to realize efficient collaboration of large model agents. According to the method, accurate matching can be carried out according to task characteristics and intelligent agent capabilities, the task completion efficiency and quality are improved, meanwhile, the dynamic adjustment capability is achieved, abnormal conditions in the execution process are effectively handled, and the system robustness is enhanced.

The invention provides a multi-agent cooperation method, system and device and a storage medium, and relates to the technical field of multi-agent collaboration.The method comprises the steps that initial role allocation is conducted on multiple agents, one agent is an observer, the other agent is a coordinator, and the other agents are all executors; a strategy network based on deep reinforcement learning is introduced according to the running state of the multiple agents to dynamically adjust role allocation of the multiple agents, and a role allocation strategy is dynamically adjusted according to task completion rewards, role conflict punishment and resource conflict rewards; the coordinator constructs a task priority and a dependency relationship based on the task graph or the task dependency tree, and dynamically allocates tasks according to the state, the capability vector and the task adaptation degree of the executor; conflicts are recognized through resource contention detection, task overlapping detection and behavior conflict detection, and the conflicts are coordinated. According to the invention, multi-agent responsibilities are layered, and the task completion efficiency is improved through task allocation and conflict detection and coordination.

The embodiment of the invention discloses a multi-agent collaborative data visualization analysis method and device and a medium, and relates to the technical field of visualization analys.The method comprises the steps that a natural language analysis request input by a user is received, task splitting is conducted on the natural language analysis request through a preset task planning agent, and the task splitting result is obtained; generating corresponding task plan information, obtaining real-time operation data of a preset functional agent cluster, carrying out agent distribution on the plurality of sub-tasks based on the real-time operation data and the task plan information, determining a target functional agent corresponding to each sub-task, and sending the target functional agent to a server; the target function agent comprises any one of a data analysis agent and a visual display agent; and executing the corresponding data analysis subtask through the data analysis agent to obtain a data analysis result, performing agent cooperative verification on the data analysis result, and after the verification is passed, generating visual analysis data corresponding to the data analysis result through the visual display agent.

The invention discloses a reinforcement learning-based virtual power plant response optimization scheduling system and method, and relates to the technical field of virtual power plant intelligent scheduling. The system comprises an environment modeling module, an intelligent agent module, a multi-agent coordination module and a self-adaptive optimization module which are respectively used for constructing a multi-dimensional state space and a layered action space, generating and optimizing an action strategy based on an Actor-Critic network, executing a scheduling instruction through a layered multi-agent structure and realizing conflict consensus, and dynamically adapting to state space change in combination with incremental learning and meta-learning mechanisms. The system and the method have the advantages of fine state modeling, efficient action response, adaptive strategy updating, stable agent coordination and the like, and can keep the continuity, the stability and the optimality of a scheduling strategy in an operation environment in which multi-source heterogeneous power resources participate in scheduling cooperatively, market rules change frequently and load fluctuation is violent.

The invention discloses a retrieval generation method and system based on multi-agent collaboration, a terminal and a medium, and relates to the field of artificial intelligence. Performing semantic analysis on the input word embedding converted by the natural language query instruction through a query analysis agent, and determining a semantic intention vector; performing reinforcement learning and meta learning on the semantic intention vector through a strategy construction agent, and determining a retrieval strategy; performing semantic enhancement on the semantic intention vector according to knowledge graph node embedding to obtain a semantic enhancement vector; determining a data channel according to the semantic enhancement vector, a retrieval strategy and a real-time system load, and calling the data channel for retrieval to obtain candidate documents; and generating a target answer according to each candidate document based on an adaptive reflection feedback mechanism in combination with an auto-encoder and a generative adversarial network. The problems that the prior art depends on a fixed retrieval strategy, has limitation when facing complex query, multi-round interaction and cross-modal data fusion, is easily interfered by noise and is not accurate enough in semantic matching are effectively solved.

The invention discloses a human abnormal behavior monitoring method based on a large-model multi-agent, which is executed by a modular multi-agent system deployed on a back-end server, obtains information through a monitoring camera, and comprises the following steps: obtaining a video stream from the monitoring camera by a sensing agent and extracting human body posture features; analyzing the key frame by a scene understanding agent by using a visual large model, and constructing a time sequence dynamic scene graph; the core reasoning agent evaluates the scene semantic conformity based on the pre-trained large model and performs abnormal preliminary judgment; performing fine-grained classification, interpretation generation and risk assessment on the abnormal behaviors; and the report and action agent generates an alarm and records event data. According to the invention, through multi-agent cooperative work and a large model technology, efficient and accurate monitoring of human abnormal behaviors is realized, and the intelligent level of the monitoring system and the abnormal behavior identification accuracy are improved.

Described herein are techniques for secure orchestration and publication control among agents (e.g., distributed agents), such as non-human identities (NHI), using cryptographic certificates, trust rules, and / or an Information-Centric Networking (ICN) architecture. In an example, a framework establishes identity for human and non-human identities-such as AI agents, services, and autonomous workloads—via cryptographically signed publications and / or collections. Trust policies can be defined and enforced through signed, verifiable trust rules, enabling access control, provenance validation, and / or policy delegation across federated domains. The disclosed techniques can enable multi-agent systems (MAS), zero-trust enforcement, and / or secure cross-domain communication using ICN-named role-based certificates and programmable trust shims. The disclosed techniques can also enable decentralized validation and selective replication of data while maintaining traceability and fine-grained control of agent behavior.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a linkage method and system of an apartment network and intelligent equipment, and the method comprises the steps: collecting network operation data and equipment operation data of a target apartment in real time; based on the equipment operation data, determining currently achievable candidate service scenes of the target apartment; fusing the network operation data and the equipment operation data into a joint operation map of the target apartment through the space-time diagram attention network; a multi-agent depth deterministic strategy gradient algorithm is adopted to carry out network resource allocation and equipment control decision making on the joint operation map, scene adaptive optimization is realized in combination with candidate service scenes, and a linkage strategy of a target apartment is obtained; and generating a decision instruction based on the linkage strategy, and adopting the decision instruction to realize remote control of the network equipment and the intelligent equipment. Through deep fusion of the space-time diagram attention network and multi-agent reinforcement learning, dynamic collaborative management and control of the network and equipment are realized, and the resource utilization rate and the service response speed are improved.

The invention relates to the technical field of vehicle scheduling, in particular to a multi-terminal vehicle scheduling system based on reinforcement learning. The system comprises a heterogeneous data fusion module, a resource allocation module, a hierarchical reinforcement learning module, an optimization feedback module and a man-machine cooperative control module. Data of vehicle operation, operation tasks, environment monitoring and the like are collected and uniformly packaged into a structured data set, an upper-layer manager model generates a global scheduling instruction set based on a PPO algorithm, and a lower-layer worker model outputs a specific vehicle control instruction based on multi-agent reinforcement learning. The system also evaluates and optimizes a historical scheduling execution effect through an NSGA-II algorithm, selects a Pareto optimal solution set, and realizes continuous iteration of a scheduling strategy. The man-machine cooperative control module supports visual display and manual intervention operation, and improves the adaptability and controllability of the system in a complex operation scene.

The invention discloses a public opinion risk assessment method and system based on multiple agents and a large language model, and belongs to the technical field of network information security. The method comprises the steps of obtaining multi-source public opinion data; reasoning in combination with a language model to obtain a multi-dimensional semantic vector, and clustering to form a plurality of topic clusters; the emotion opposition level, the credibility and the text quantity increment score of each topic cluster are generated based on an intelligent agent, a comprehensive risk value is obtained through weighted fusion, and high-risk topics are screened through a double-threshold retention mechanism; and generating a knowledge graph according to the high-risk topic, determining an associated entity, a propagation link and an intervention node, implementing an intervention strategy, and generating a public opinion intervention report. According to the method, the monitoring problem of multi-source heterogeneous public opinion data can be effectively solved, the accuracy and timeliness of risk assessment are improved, full-link automation from risk identification to accurate intervention is realized, and efficient support is provided for public opinion management and control of governments, enterprises and other mechanisms.

The invention relates to the technical field of data processing, and particularly provides an operation intention recognition method, system and device based on multi-mode fusion and a medium, and the method comprises the steps: synchronously collecting interaction data of at least two modes of a user, the modes comprising at least two of gestures, voice and eye gaze; carrying out alignment processing on the interaction data, wherein the alignment processing comprises time synchronization and space mapping to a unified coordinate system; recognizing structured semantic information from each piece of aligned modal data, wherein the structured semantic information comprises a gesture type, a voice text and a fixation point coordinate; and based on a preset semantic rule and context memory, performing semantic association and anaphora resolution on the structured semantic information to obtain an operation intention. The method effectively overcomes the inherent defects of unnatural single-mode interaction, easy ambiguity and poor fault tolerance.

The invention relates to the technical field of intelligent agricultural robots, and provides a multi-agent collaborative anti-collision picking method based on digital twinning and deep reinforcement learning, which comprises the following steps: constructing a digital twinning model of a picking scene, and generating environmental geometric parameters, agent kinetic parameters and fruit position parameters through three-dimensional point cloud reconstruction; acquiring environment state data in real time and inputting the environment state data into the digital twin model for space-time alignment processing to generate synchronous state data; based on the synchronous data, a collaborative strategy containing a collision avoidance priority matrix, a path planning sequence and a task allocation weight is generated through a deep reinforcement learning network; an action instruction set is generated according to the strategy, and multiple agents are controlled to execute a picking task after virtual-physical space bidirectional verification of the digital twin model. According to the invention, efficient collision avoidance and accurate picking of multiple agents in a dynamic environment can be realized, and the picking efficiency, safety and system robustness are improved.

The invention discloses an intelligent agent automatic arrangement method and system based on a large language model, and relates to the technical field of artificial intelligence. The method comprises the following steps: decomposing a natural language instruction of a user into a structured subtask sequence by utilizing a first large language model; based on the agent portrait library, matching and allocating agents for each sub-task to generate an initial execution plan; task execution is scheduled and monitored in real time through an event-driven architecture; when abnormity is monitored, a self-adaptive adjustment mechanism is triggered, the affected plan part is re-planned, and an updating instruction is issued. According to the invention, efficient, flexible and robust multi-agent automatic arrangement is realized.

The invention relates to an intelligent operation and maintenance method and apparatus based on a knowledge graph and a large model, and an electronic device. The method comprises the steps of collecting multi-source runtime data of a Kubernetes cluster; constructing a knowledge graph with time dimension based on the resource change event, recording termination time in response to graph relationship failure, recording starting time in response to a newly added relationship and not setting the termination time, and associating the entity with the performance index and the log data; responding to the diagnosis request, scheduling a specialized agent by a coordination agent through multi-agent cooperation to retrieve associated information from multi-source data, and iteratively integrating to generate a structured context; and inputting the generated structured context information into a large model reasoning service to output a fault root cause diagnosis and solution. The technical problems that information dispersion and relevance are weak, root cause positioning is difficult and time-consuming, comprehensive context sensing ability is lacked and expert experience is excessively relied on are solved.

The invention relates to a multi-agent cooperation strategy generation method, device and equipment and a medium, and the method comprises the steps: separating acoustic spectrum features and text semantic features of conference voice through environment perception processing, solving a cross-modal information conflict problem, and generating an accurate semantic understanding result; identifying the essence of the problem based on task analysis, associating the responsibility field, and constructing a classifiable problem point set; calling an agent capability library to dynamically match problem requirements, and generating a candidate agent list; quantifying a problem influence range and a decision time limit through weighted emergency scores, and generating a priority-sorted agent sequence; screening and confirming a core problem point and a primary agent; and finally, generating an executable cooperation scheme through multi-agent collaborative optimization. According to the method, the problems of incomplete feature extraction, task allocation delay and resource conflict in the prior art are solved, and the operability and decision-making efficiency of a cooperation strategy are remarkably improved.

The invention discloses an enterprise multi-modal data intelligent processing system fused with an RAG technology and an intelligent processing method of the enterprise multi-modal data intelligent processing system, and relates to the technical field of enterprise-level multi-modal data intelligent processing. And the data processing module is configured to respectively process the structured data and the unstructured data through the dynamic heterogeneous encoder and output unified semantic representation by adopting a cross-modal adversarial alignment mechanism. According to the enterprise multi-modal data intelligent processing system fused with the RAG technology, the problem of enterprise multi-modal data splitting is solved through dynamic adversarial semantic alignment and a stepped fusion mechanism. Semantic gaps are eliminated through self-adaptive convergence of cross-modal features in a hidden space, deep association of heterogeneous data is achieved based on concept mapping and credibility arbitration of an ontology network, key information of unstructured data is accurately extracted and converted into structured knowledge, and the accuracy of cross-modal association analysis and decision reliability are improved.