848 results about "Collaborative processing" patented technology

The invention discloses a multi-door-lock cooperative processing method and system, and the method comprises the steps: generating an assistance request IGMP multicast message according to a preset triggering operation detected by a first door lock B and read RFID identification information, and transmitting the assistance request IGMP multicast message to a router; according to the assistance request IGMP multicast message, recording interface information and validity period associated with the RFID identification information in a multicast forwarding table entry, and caching the multicast message; according to the preset triggering operation detected by the second door lock A and the same read RFID identification information, an assisted request multicast message containing the RFID identification information is generated, and the assisted request multicast message is sent to the router; according to the RFID identification information and the multicast forwarding table item, after the validity period is verified to be not expired, the message is directionally forwarded to the first door lock B, the first door lock B sends an assistance confirmation message to the second door lock A, and a robot calling instruction is triggered. According to the embodiment of the invention, low-delay and high-reliability door lock cooperative control can be realized.

The invention relates to the technical field of artificial intelligence, can be applied to service scenes of pension service, financial science and technology, medical health and the like, and discloses an instruction understanding and task execution method, device, equipment and medium, and the method comprises the steps: receiving a voice instruction and a text instruction, and carrying out the cooperative processing through an instruction understanding model, and generating a structured task description; collecting environment data to construct a real-time environment model; generating a task execution strategy by utilizing a task execution model based on the structured task description and the real-time environment model; controlling the intelligent agent to execute the task according to the task execution strategy, and dynamically adjusting the action in combination with real-time sensor information; task execution data and user feedback information are collected, and the instruction understanding model and the task execution model are updated. According to the method, multi-modal information is fused through structural description, an execution strategy is generated in combination with real-time environment perception, actions are dynamically adjusted, model self-optimization is further achieved through execution data and feedback, and the understanding, decision-making and adaptive capacity of an intelligent agent is improved.

The invention discloses a converter station intelligent gateway image recognition system based on a YOLOv3 target detection algorithm, and the system employs a three-stage cooperative processing architecture design, and builds seamless connection of a multispectral image collection layer, an edge calculation gateway layer, and a cloud operation and maintenance management platform layer. The invention further provides an equipment defect detection method based on the system, bimodal image data are collected through the visible light camera and the thermal infrared imager, preprocessing operation is carried out, equipment positioning and defect classification are synchronously executed by utilizing the improved YOLOv3 network, a structured detection result is output, temperature field analysis is carried out on an infrared thermal image, and the equipment defect detection result is obtained. And an abnormal heating area is identified, when defects are detected, multi-level risk response early warning is generated, and a defect diagnosis report is pushed to the cloud operation and maintenance management platform layer. Real-time image analysis of converter station equipment can be realized, the method is suitable for automatic detection of typical fault defects of the equipment, and the operation and maintenance efficiency of a power grid is improved.

The invention provides a multi-agent cooperative processing system and method for multi-scene legal complaint consultation, and relates to the technical field of artificial intelligence, and the method comprises the steps: receiving a legal or complaint consultation text inputted by a user, and generating structured consultation request data; automatically generating an evidence obtaining template, and forming a standardized evidence obtaining data packet; constructing a demonstration tree and outputting a multi-scheme decision matrix; performing multi-objective optimization by combining the success rate, the timeliness, the cost and the regional qualification index, and outputting a comprehensive recommendation result; generating a knowledge unit set, and updating a regulation index and agent prompt template library; predicting a re-complaint risk and generating a remedy and optimization suggestion; and outputting review result data, and reversely updating the review result data to the agent configuration strategy in the step of the knowledge construction module. According to the method and the device, the technical targets of multi-agent cooperative processing and dynamic optimization management in the whole legal complaint consultation process can be realized, and the technical effects of improving consultation processing efficiency, enhancing scheme recommendation accuracy, reducing manual participation cost and ensuring data compliance and risk controllability are achieved.

The invention relates to the technical field of computer vision and image recognition, in particular to an image recognition method based on edge computing, which comprises the following steps: dynamically capturing an original image through a plurality of edge nodes, rejecting redundant regions through a multi-modal perception triggering mechanism, and establishing a cooperative processing group. Illumination equalization, noise filtering and resolution self-adaptive compression tasks are distributed according to dynamic role election, a standardized preprocessed image is generated, a lightweight convolutional neural network is operated in parallel to extract a dual-channel feature vector, and after entropy coding lossless compression and equipment identity tag and time sequence stamp attachment, the dual-channel feature vector is transmitted to a cloud end by adopting a lightweight encryption protocol. The cloud end analyzes the data packet, reconstructs a feature topological graph based on space-time relevance, loads a depth residual error recognition model to execute feature fusion and classification decision, feeds back and updates the weight of an edge node model, solves the problems of low collaborative efficiency and feature distortion, and improves the efficiency and precision of image recognition.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a target event processing method, device, equipment and medium based on language model collaboration.The method comprises the steps that a target event is monitored and recognized, multi-source data associated with the target event are collected, the multi-source data are fused to generate fused data, and the fused data are sent to a server; and inputting the fusion data into a pre-training language model to generate a decision scheme, performing decision coordination and matching based on the decision scheme, distributing a processing agent, executing a matched decision task by using the processing agent, and generating a decision result. According to the invention, through fusion of multi-source data and pre-training language model deep reasoning and combination of an intelligent agent dynamic cooperation mechanism, intelligent perception, rapid response and cooperative processing of complex events are realized, and the adaptive ability and processing efficiency of the system in response to unknown faults and complex tasks are improved.

The invention provides a real-time intelligent agent dynamic scheduling method and system composed of multiple large models, and is applied to the technical field of data processing. The method comprises the following steps: performing dynamic scheduling expansion around a plurality of large-model real-time intelligent agents, firstly receiving task request data of a real-time service scene, performing grouping according to task types, priorities and resource requirements, pre-configuring a scheduling strategy, and generating a pre-scheduling queue and resource pre-planning information; and in combination with the target configuration information, scheduling task information after secondary optimization is obtained through multi-dimensional optimization such as task splitting and priority rearrangement. Dynamically adjusting and generating a target scheduling instruction based on real-time system resources and a model operation state, and cooperatively processing the target scheduling instruction with the large model capability adaptation parameters to form scheduling execution information; and finally, issuing to model nodes according to preset service quality requirements and resource constraints, generating information such as adaptive parameters and priority rules through feature extraction, association processing and the like, and supporting multi-model cooperation to efficiently complete real-time service tasks.

The invention discloses an efficient processing method and system for online troubleshooting of the information department, and relates to the technical field of automatic operation and maintenance, and the system comprises a multi-modal data acquisition module, a dynamic knowledge graph construction module, a reinforcement learning driven self-healing module, an edge co-processing module and a cross-domain privacy calculation module. According to the method, the limitation of traditional single-dimensional monitoring is broken through, multi-source heterogeneous data such as server performance, container state and network flow are fused, a high-timeliness and high-consistency decision information base is constructed through unified time sequence alignment and semantic standardization, and a panoramic view is provided for dynamic analysis; based on an incremental learning mechanism and a causal inference engine, a service topology dependency relationship is updated in real time, a deep root cause is accurately positioned, and a cross-system and cross-level complex fault combination is solved.

The invention discloses a remote monitoring cooperation system based on intelligent network connection, and relates to the technical field of cooperation control, and the system comprises a protocol analysis and normalization module which analyzes an original protocol message uploaded by a heterogeneous terminal, and generates standard event metadata; the arrangement scheduling module injects an execution token into a linkage execution sequence generated based on the standard event metadata and the strategy library, issues an action instruction, and triggers a remedial sub-process along a rollback edge when feedback is abnormal; the parameter intelligent setting module is used for generating an operation portrait based on the execution track log and obtaining a strategy parameter set based on the operation portrait; the intelligent cooperative processing module executes digital signature verification, certificate rolling rotation, deterministic transmission control and video adaptive adjustment according to the strategy parameter set; according to the invention, through organic combination of strategy analysis, arrangement scheduling, parameter intelligent setting and intelligent cooperative processing modules, automatic response and efficient control during coupling of multiple types of events in an intelligent network connection remote monitoring scene are realized.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention relates to the technical field of edge computing, and discloses an intelligent edge computing co-processing system based on an integrated circuit, which comprises a heterogeneous computing cluster module, a hardware computing unit set, a multi-core control processor based on RISC-V, a programmable pulse tensor computing array and a reconfigurable engine oriented to streaming processing, according to the intelligent edge computing cooperative processing system based on the integrated circuit, zero-delay data exchange is realized through silicon intermediate layer integration of the heterogeneous computing cluster module and a snakelike data channel, and traditional bus arbitration delay is eliminated through real-time operation code analysis and optimal optical communication path mapping of the hardware task routing matrix (TRF) module; the priority path distribution of the task scheduling subsystem is synchronously coordinated with the time-sensitive task, so that the collaborative efficiency of the computing unit is improved in multiple dimensions, the non-blocking transmission of the high-priority task is ensured, and the effect of enhancing the overall processing capability and response speed of the intelligent edge computing is achieved.

The invention discloses an intelligent abnormity early warning system for an industrial Internet of Things edge node, which comprises an edge sensing layer, an edge intelligent layer and a cloud edge collaboration layer, and is characterized in that the edge sensing layer is used for data acquisition and security preprocessing; the edge intelligent layer is used for intelligently analyzing the data transmitted by the edge sensing layer and constructing a dynamic space-time diagram neural network model and a dynamic edge knowledge graph; a digital twinning threshold engine is set, when the digital twinning threshold engine detects abnormity, reverse reasoning is carried out along the knowledge graph, and self-adaptive early warning is carried out; and the cloud edge collaboration layer is used for carrying out collaboration processing on early warning triggered by the digital twin threshold engine. According to the invention, the spatial-temporal features of the equipment group are learned through the set dynamic spatial-temporal diagram neural network; the digital twin threshold engine generates a dynamic threshold based on physical simulation and adapts to environment and load changes; the edge layer realizes lightweight real-time analysis; and a full-link embedded chaotic encryption and national cipher SM4 algorithm is adopted.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a root cause analysis method and device based on a space-time dependency graph, equipment and a medium. Comprising the steps of constructing a space-time dependency graph, generating a diagnosis path blueprint, identifying a fault source and a root cause entity type, generating a graph query statement, executing query and performing cause and effect verification, and outputting a root cause analysis report. And the causal reasoning and root cause positioning of the system state change are realized by fusing the graph structure information and the natural language processing capability. Through cooperative processing of a language model and a graph data structure, fault symptom information and system structured state data are deeply fused, a path is generated in the graph structure, and a causal relationship is verified, so that the ability of understanding a complex system state evolution chain is improved, and accurate identification and diagnosis of root causes are realized. And the accuracy and the automation level of root cause analysis are obviously enhanced.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of data center operation and maintenance, financial science and technology, medical treatment and health and the like, and discloses a task processing method and device based on a domain knowledge base, equipment and a medium. Cleaning and formatting to generate standardized task list data, inputting the standardized task list data and a domain knowledge base into a language model to generate structured task list data, and matching task list types based on the structured task list data to generate a task list execution plan, and determining an execution mode according to the task list execution plan and executing to generate a task list execution state. Through cooperative processing of the domain knowledge base and the language model, standardized and structured processing of task list data is realized, and in combination with task type matching and execution mode control, processing automation and accuracy are improved, manual participation is reduced, and response speed and efficiency are improved.

The invention relates to the technical field of chemical enterprise production management, in particular to a chemical enterprise safety production informatization management system which comprises a sensing network layer architecture, a digital twin management platform, a risk management and control and decision center, a process closed-loop management center and an emergency cooperative processing platform. According to the chemical enterprise safety production informatization management system and method, through construction of a chemical knowledge graph, on the basis of a dynamic risk matrix and an AI prediction model library, equipment remaining service life prediction, process tiny anomaly capture and accident consequence simulation processing are realized; meanwhile, a root cause analysis module is used for associating low-level alarm and traversing a knowledge graph to automatically infer a fault root, physical equipment and a virtual model are bound through an entity-model mapping engine, the model state is dynamically updated in combination with a real-time rendering technology, and technological parameter optimization, accident deduction and scheme verification can be completed in an analogue simulation engine; and the management intuition and the decision-making scientificity are effectively improved.

The invention relates to the technical field of terminal monitoring, in particular to an intelligent security terminal collaborative monitoring system with edge computing capability. Comprising a multi-mode sensing unit; the edge calculation processing unit is used for distributing CPU and FPGA operation tasks by using a dynamic characteristic distillation technology and a hardware load feedback technology on the basis of multi-mode perception data; a distributed cooperative communication unit; and an intelligent decision-making unit. Visual, voiceprint and environmental physical signals of a security area are collected through the multi-modal sensing unit, unified time stamps and spatial coordinate association are added for multi-dimensional signals by means of the data synchronization module, and cooperation of the feature distillation module and the model optimization module in the edge calculation processing unit is combined. According to the method, multi-modal features are mapped to a unified feature space, high-value features are screened, and redundant convolution kernels are identified and removed, so that effective cooperative processing of multi-modal security and protection data can be realized, and the processing efficiency of edge-side sensing data is improved.

The invention relates to the technical field of multi-modal large models. By providing the multi-modal large model information co-processing method and device, the equipment and the medium, the method comprises the following steps: carrying out cross-modal privacy perception permission label binding processing on input multi-modal data, and generating a standardized data stream with a dynamic permission label; performing dynamic task allocation processing on the standardized data flow through a federated learning driven adaptive task scheduling engine, and generating a multi-modal intermediate result including a cross-modal interaction data flow; performing causal reasoning monitoring processing on the cross-modal interaction data flow through an interpretable security agent to generate a real-time security situation report; and performing semantic alignment processing based on a knowledge graph on the multi-modal intermediate result to generate a fusion cognition result so as to realize the technical effects of improving cross-modal privacy association perception capability, enhancing causal reasoning capability of security monitoring, optimizing multi-model cooperation efficiency and improving fusion decision accuracy.

The invention relates to the technical field of supply chain management, in particular to a multi-dimensional data visualization supply chain process optimization method and system based on big data, and provides a supply chain dynamic optimization thought based on multi-source heterogeneous data fusion and intelligent decision closed loop. By constructing an end-to-end multi-source heterogeneous data real-time cooperative processing and intelligent decision closed-loop architecture, dynamic integration, intelligent analysis and automatic response and update optimization of full-link data of the supply chain are realized, so that the real-time analysis capability and dynamic adjustment capability of the supply chain are remarkably improved.

Disclosed are a data query method and system, and a related device, relating to the field of artificial intelligence. The method comprises the following steps: a data query system acquires input data; obtains, on the basis of the input data, metrics to be queried as requested by a user; uses, on the basis of said metrics, a multi-agent collaborative processing strategy to confirm a data query result, the multi-agent collaborative processing strategy being used for indicating a manner of implementing data retrieval on the basis of at least one of a graph model and a vector database; and presents the data query result, wherein the graph model expresses, in a graph-based form, correspondences between the metrics and between the metrics and database table data, and the vector database expresses, in a vector-based form, descriptive texts and computational formulas of the metrics. In this way, even if the expression of input data differs from the expression of fields in the database, or if metrics to be read are not present in the database, corresponding SQL statements can still be obtained by means of the graph model or the vector database, thereby reducing the error rate of natural language query, and meeting the query requirements of users.

The invention discloses a method and equipment for constructing a multi-role cooperative processing task based on a large language model. The method comprises the steps that the identity of a large language model is endowed to be a universal role agent, the universal role agent creates a plurality of task role agents based on the content of a task instruction, and the identities of the task role agents are different; updating the identity of the large language model as a task splitting agent, splitting the task into a plurality of sub-tasks by the task splitting agent based on the obtained identity of each task role agent, and allocating the plurality of sub-tasks to the corresponding task role agents; each task role agent executes the corresponding sub-task to obtain a sub-task result and submits each sub-task result; and updating the identity of the large language model as a task result summarization agent, and summarizing each subtask result by the task result summarization agent to obtain an execution result corresponding to the task. By means of the mode, multi-role cooperative task processing can be achieved, and the execution quality and the execution efficiency are improved.

The invention provides a chart analysis method and device, electronic equipment and a storage medium, and belongs to the technical field of artificial intelligence, and the method comprises the steps: extracting visual features of a to-be-analyzed chart image; and inputting the visual features into a multi-modal large language model to obtain a structured analysis result of the chart image output by the multi-modal large language model. According to the method and the device, the first task adaptation module consisting of the plurality of first expert modules and the first gating network is arranged in the multi-modal large language model, and the chart analysis tasks are dynamically allocated to different first expert modules for cooperative processing by utilizing the first gating network, so that the comprehensiveness and the accuracy of chart analysis are improved; moreover, the first expert module is constructed based on the low-rank adaptation technology, and only a small number of parameters need to be updated during training, so that the general world knowledge learned by the multi-modal large language model in the pre-training stage can be effectively reserved while the chart analysis capability is improved, and the problem that the world knowledge is forgotten is avoided.

The invention discloses a delivery task cooperative processing method, device and equipment based on a quadruped robot and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: analyzing a delivery task through an upper computer, combining the real-time dynamic information of an office scene, and employing an improved binary particle swarm optimization algorithm to optimize the task distribution of multiple robots, and efficient collaborative distribution in an intelligent office environment is realized. The robot receives a distribution scheme sent by the upper computer, a multi-level navigation strategy is started by utilizing environmental perception data, a topological map and a D * Lite algorithm are fused to carry out path planning, and dynamic obstacle avoidance and efficient distribution are ensured. The target path planning information is monitored in real time through the upper computer, dynamic analysis is carried out through the scene perception algorithm, the scene relevance result is obtained, the collaborative distribution strategy is executed to complete the distribution task, the task response speed and the distribution efficiency are improved, interference to the office environment is reduced, and the work efficiency is improved. And automation and intelligentization of robot distribution in an intelligent office scene are realized.

The invention discloses a satellite-ground cooperative processing method and system based on edge computing, and the method comprises the steps: constructing a satellite-ground integrated cloud edge-end cooperative architecture which comprises a satellite terminal layer, a ground edge layer and a cloud computing layer, connecting a satellite end with a ground cloud center through a distributed foundation edge service station, and achieving the unified scheduling of satellite-ground resources and task cooperation; establishing a task processing position decision model based on task data volume, link bandwidth and computing power, dynamically selecting a satellite end, an edge station or a cloud center for processing in combination with response time constraint and energy consumption cost, and optimizing time delay and energy consumption; the method comprises the following steps: constructing a geoscience sky intelligent learning framework, performing ground center offline training to optimize a deep learning model, performing pruning quantification to generate a lightweight model, issuing the lightweight model to a satellite end, performing on-line reasoning by the satellite end, and collecting and returning data to form a closed-loop learning process; an offline-online cooperation mode is introduced, high-real-time tasks are preferentially processed online, non-real-time tasks are cached and downloaded in batches, and a strategy is dynamically adjusted to improve the system efficiency and the resource utilization rate.

The invention provides a multi-robot dieless forming and laser shock peening combined machining system and method, and relates to the technical field of manufacturing and machining. A thin-wall workpiece to be machined is placed on a workpiece clamping platform, a central control system controls a vacuum chuck array to generate negative pressure of a corresponding area according to the size and the shape of the workpiece, the central control system introduces a three-dimensional model of the workpiece, the motion trails of a forming robot and a forming and strengthening robot are planned based on the technological requirements of forming and strengthening, and the forming and strengthening process is completed. Determining a sequence area of dieless forming and a corresponding area of laser shock peening; then collaborative processing is carried out, the collaborative processing comprises a forming stage and a strengthening stage, that is, every time a forming robot completes forming of one sub-area, a forming strengthening robot immediately strengthens the sub-area, and the central control system carries out collaborative processing according to the forming precision and strengthening effect fed back by the visual positioning module; and parameters of the subsequent forming area and laser energy of the strengthening area are dynamically corrected till machining of the whole workpiece is completed.

The invention provides a drainage basin level dam monitoring data standardized integration and cooperative processing method and device, and the method comprises the steps: building a four-dimensional unified data model, and carrying out the standardized mapping of space, time, equipment and business dimensions of multi-source heterogeneous monitoring data; deploying a self-adaptive protocol analyzer, automatically analyzing an equipment document based on a natural language processing technology, generating a protocol conversion rule, and realizing dynamic conversion of various industrial protocols; performing local cleaning, compression, encryption and time sequence feature extraction on the standardized data through an edge computing node to generate a data packet in a unified format; a transmission path is dynamically optimized according to channel quality by using an intelligent routing selection model based on Q-learning, and multi-mode hybrid transmission of a data packet among LoRaWAN, 5G and Beidou short messages is realized. Standardized integration and intelligent cooperative processing of multi-source heterogeneous dam monitoring data in a drainage basin are achieved, information islands are effectively eliminated, the comprehensive operation and maintenance cost is reduced by 30% or above, and the security situation awareness timeliness is improved to the minute level.

The invention discloses a visible light-infrared image restoration fusion method based on a hybrid expert model, and aims at the characteristics that a visible light image is susceptible to noise, haze and blurring, and an infrared image is susceptible to stripe noise, the invention provides an image restoration fusion model based on a hybrid expert mechanism. The model adopts a two-stage cooperative processing architecture: in the first stage, a degradation perception gating mechanism and a multi-expert cooperative module are used for guiding a Transform module to realize high-quality restoration of a bimodal image, and restoration features are extracted; in the second stage, network parameters in the first stage are multiplexed and frozen, and image fusion is realized through a feature pre-fusion technology and a fusion perception gating mechanism. According to the method, the specific degradation problem of the multi-modal image can be effectively solved, the processing tasks are dynamically allocated through the gating multi-expert system, the quality of the fused image is remarkably improved, and an efficient solution is provided for multi-modal image processing in the fields of security and protection, medical treatment and the like.

The invention provides a flow and form decoupling cooperative processing method, and relates to the technical field of business flow management, and the method comprises the steps: constructing a layered architecture, separating a flow control layer from a form service layer, defining a flow node jump rule by the flow control layer based on a BPMN standard, and dynamically defining a form structure by the form service layer through JSON Schema; a form identifier is declarated in the process definition file, a corresponding form template is matched through a dynamic mapping service according to context information, and the context information comprises a process instance state, a user role, a device type and a service type; establishing a data sandbox isolation mechanism, generating a unique data space identifier for each process instance, and managing data access authority through a multi-dimensional isolation strategy of a tenant level, a service level and a process instance level; deploying an event bus communication mechanism, triggering two-way communication between a process engine and a form engine through a predefined event type, and ensuring the consistency of cross-system operation by adopting a transaction compensation mechanism.

The invention relates to a manipulator multi-task cooperative control method and system based on artificial intelligence. The method comprises the following steps: firstly, receiving a multi-task request group sent by an external system, and analyzing to obtain task intention description and execution constraint conditions of each task; then environment state data and real-time operation parameters of the manipulator are collected through a multi-sensor system, and an operation capability evaluation result is generated; inputting the information into a pre-trained multi-task collaborative planning model, and generating a multi-task collaborative control strategy through collaborative processing; according to the strategy, action sequence instructions of all joints of the manipulator are generated and sent to a driving control system; and finally, collecting feedback data in real time by utilizing multiple sensors, dynamically adjusting an action sequence instruction, and ensuring ordered execution of multiple tasks. According to the method, efficient cooperative control over multiple tasks of the manipulator is achieved, and the task execution efficiency and stability are improved.

A latent transformer architecture with latent attention mechanisms and expert processing systems for federated deep learning is disclosed. The system operates entirely within latent space, eliminating traditional embedding and positional encoding layers while maintaining full attention capabilities. Input data is compressed into latent vectors via variational autoencoder encoding, then processed by a latent attention module that computes query, key, and value matrices directly from latent representations. The architecture incorporates expert processing systems including gated latent expert networks for sparse computation and latent mixture of experts for collaborative processing. In the gated approach, a routing network selectively activates specialized expert modules based on latent vector characteristics. The mixture approach enables all experts to contribute through weighted combination, facilitating distributed computation and enhanced model expressiveness.

The invention discloses a multi-agent collaborative application big data management system and method. The system comprises a data acquisition unit, a data preprocessing unit, a multi-agent collaborative processing unit, a data storage unit and a data scheduling unit. The data acquisition unit is used for acquiring multi-source heterogeneous original big data and transmitting the multi-source heterogeneous original big data to the data preprocessing unit; and the data preprocessing unit is used for executing preprocessing operation including de-duplication and format standardization on the original big data. The invention relates to the technical field of big data management. According to the multi-agent collaborative application big data management system and method, through a multi-agent collaborative algorithm, the system can realize a self-adaptive data classification and optimization strategy, a data processing mode is dynamically adjusted, the processing efficiency problem of a traditional static rule during data mode fluctuation is improved, and the data processing efficiency is improved. The system adopts a dynamic resource scheduling strategy based on a data access demand and a system load, efficiently distributes calculation and storage resources, and facilitates priority processing of high-priority tasks.