412 results about "Collaborative network" patented technology

The invention discloses a dynamic collaborative arrangement system and method based on an intelligent agent, and relates to the technical field of artificial intelligence. By integrating core modules of an agent communication protocol, knowledge base management, agent arrangement, security authentication, resource scheduling and the like, not only is a standardized agent cooperation framework and a distributed knowledge sharing mechanism provided, but also dynamic task arrangement and secure and controllable resource scheduling capability among agents are realized. The problem of how to effectively realize standardized communication and dynamic cooperation of agents among enterprises and unified management and shared utilization of knowledge resources in the prior art is solved, and particularly, the problem of how to construct an efficient, safe and extensible agent cooperation network in a multi-enterprise cooperation environment is solved.

The invention provides an AI-based post competency and recruitment post matching method, and relates to the technical field of big data processing, and the method comprises the steps: obtaining multi-source heterogeneous data, and extracting dominant skill entities and implicit ability entities of candidates; constructing a candidate ability evolution graph; analyzing a post dynamic demand by combining a time sequence prediction model, and forming a dynamic post portrait including a current demand, a hidden demand and a future evolution demand; calculating the structural similarity between a candidate map and a post portrait through a map neural network matching algorithm, and generating and sorting comprehensive matching scores in combination with adaptive dimensions such as core capability difference, future integrating degree and team integration potential; team cooperative effect simulation is introduced, the influence of candidate addition on a team capability structure and a cooperative network is predicted, and the model is optimized through a closed-loop feedback mechanism; the talent recognition accuracy and coverage range are remarkably improved, prospective strategic talent matching is achieved, and the matching efficiency is improved.

The invention relates to a multimodal transport end-to-end supply chain collaborative management method based on container logistics, and belongs to the technical field of supply chain management. The method comprises the following steps: virtually integrating scattered cargo owner demand, carrier transport capacity and transit point operation capability resources through a cloud computing platform to form a shared resource pool, and constructing a dynamic collaborative network; performing intelligent matching and scheduling on the shared resource pool through a contribution degree distribution mechanism to obtain a supply chain full-link state; dynamic routing optimization is carried out according to the full-link state of the supply chain, and optimal path selection is carried out on the transportation mode of container logistics based on the business process of multimodal transportation; a task is automatically decomposed through an intelligent contract and issued to a node, a carrier automatically uploads a voucher through an RFID gate after completing the node task, and the contract verifies the voucher and then triggers a next node task. Seamless connection and efficient collaboration among all nodes of the supply chain are achieved, and the transportation efficiency and reliability of container logistics are improved.

The invention relates to the technical field of underground pipe gallery intelligent monitoring, and discloses an underground pipe gallery data real-time processing system based on edge computing, which comprises a dynamic sensing primitive library abstracting pipeline pressure and video monitoring multi-source data into primitive units containing associated weights, the primitive recombination module dynamically adjusts the coupling relation between primitives through a function according to the rainfall environmental parameters, so that the association weight of the video texture and the pressure data is adaptively enhanced; according to the system, through a dynamic coupling mechanism driven by environment feedback, leakage gradual change characteristics which are difficult to capture by a traditional fixed threshold are effectively identified; the edge collaborative network realizes cross-node knowledge migration, and automatically triggers high-precision sampling of adjacent nodes when local abnormality occurs, so that a self-organizing diagnosis cluster is formed. According to the invention, through a composite architecture of dynamic primitive recombination and edge collaboration, the problems of response lag and high false alarm rate of a traditional monitoring system are avoided, and the technical span from passive monitoring to active prediction of the underground pipe gallery is realized.

The invention discloses a software multi-agent collaboration method and system based on a large language model, and the method comprises the steps: receiving natural language task description submitted by a user at the same time, carrying out the semantic understanding and intention recognition through a pre-trained large language model center, and generating a structured task element set; based on the structured task element set, the large language model center generates a task dependency graph through multiple rounds of reasoning, and the task dependency graph comprises a plurality of atomic subtasks, logic relations among the tasks and data flow constraints; according to a topological structure and resource demand characteristics of a task dependency graph, a double-layer graph attention network is adopted to dynamically match a professional agent with specific domain capability, and a distributed collaborative network is formed. Through the dynamic graph network scheduling and cross-domain semantic alignment mechanism, the problems that the multi-agent dynamic collaborative adaptation capability is insufficient and cross-domain semantic fusion is difficult are solved.

The invention discloses a talent tag portrait intelligent analysis system based on big data, relates to the technical field of intelligent analysis, realizes unified access and processing of multi-source heterogeneous talent data through a vector module, and effectively solves the problems of tag expression inconsistency and semantic drift in combination with BERT embedding and tag semantic evolution mechanisms. Therefore, the accuracy of label normalization and portrait structure consistency is improved. According to the system, the label structure relation and the capability score are linked and fused through a graph construction module, a structurable original portrait vector is generated, a semantic collaboration network between labels is constructed, and deep semantic support is provided for portrait calculation. The matching module improves the man-post matching precision through vector alignment and similarity calculation of post portraits and candidate portraits, triggers a label offset analysis and reconstruction scoring mechanism when the matching value is insufficient, realizes dynamic optimization of the portraits, and enhances the self-learning and label compensation capabilities of the system.

The invention provides a region-level aviation flow prediction method based on Mamb-GCN, and belongs to the technical field of air traffic flow prediction, and the method comprises the steps: constructing a Mamb-GNC collaborative network model; historical flight path data of a target airspace is collected, the target airspace is divided into space grids, the number of aircrafts in each space grid is counted, and the time feature and the space feature of each aircraft are coded to construct a space-time tensor; constructing a dynamic adjacency matrix and a dynamic weight map based on the 8-neighborhood topology of the space grid; inputting the space-time tensor and the dynamic weight graph into a Mamba-GCN collaborative network model for training, and optimizing model parameters; and preprocessing the aviation trajectory data of the target airspace acquired in real time, and inputting the preprocessed aviation trajectory data into the trained Mamba-GCN collaborative network model to obtain an aviation flow prediction result. According to the method, the long-time dependence of the aviation flow in the time dimension and the grid correlation in the space dimension can be captured, and the prediction efficiency is high.

A collaborative autonomous vehicle sensor fusion system enables multiple vehicles to share multimodal sensor data for enhanced perception capabilities beyond individual vehicle limitations. Each autonomous vehicle captures multimodal sensor data, identifies safety-critical objects, applies priority-based compression based on safety criticality, and shares compressed data via vehicle-to-vehicle communication. An enhanced multi-vehicle AI deblocking network receives the compressed sensor data and enhances perception data for each vehicle using sensor data from multiple vehicles in the collaborative network. The system prioritizes reconstruction quality for safety-critical objects over non-safety-critical objects and enables detection of safety-critical objects occluded from individual vehicles through collaborative sensor fusion. The network fuses multimodal sensor data by identifying cross-modal correlations between different sensor types and uses these correlations to reconstruct sensor information that is degraded or occluded in individual vehicles, providing improved situational awareness for autonomous vehicle operation.

The invention provides a personalized commodity recommendation method and system based on user behavior data analysis, and the method comprises the steps: firstly constructing a user behavior sequence and an interest stability model, and then carrying out the correlation modeling of the user behavior sequence; the behavior transfer association degree of adjacent interaction behavior units and the commodity attribute dynamic association degree capable of being adjusted along with user interest stability are calculated in combination with an interest stability model, then a dynamic commodity collaborative network is constructed based on the commodity attribute dynamic association degree, and node importance parameters are updated according to user real-time interaction behaviors; according to the method, path optimization mining is carried out in a dynamic commodity collaborative network, a user potential behavior path set is generated, finally, the potential behavior path set is analyzed, and a commodity personalized recommendation sequence is generated in combination with path attribute association feature distribution and user current interaction behaviors, so that the recommendation accuracy and the personalized degree are greatly improved, and the user experience is improved. And the shopping experience of the user is effectively improved.

The invention discloses a hospital internal contract and project intelligent auditing method and system, and the method comprises the steps: inputting to-be-examined files into a multi-agent cooperation network model of an auditing vertical domain large model, carrying out the document format conversion of a plurality of to-be-examined files through a perception agent, and obtaining a plurality of text files; performing intention recognition according to the to-be-examined file, the audit scene and the audit type to obtain an audit rule list, and performing extraction, document slicing and assembly on the to-be-examined content according to the audit rule list to obtain an atomic file corresponding to each audit rule; performing risk judgment on the atomic file corresponding to each auditing rule by using the analysis agent to obtain a corresponding atomic result, performing summarization by using the summarization agent to obtain a risk prompt list, and performing summarization based on the risk prompt list to obtain auditing suggestions. According to the method, the files in different formats are processed through stepped task decomposition, and the processing results of the intelligent agents are integrated, so that the efficiency of the audit results is improved.

The invention discloses a road scene recognition method and system based on modal information evaluation, and belongs to the technical field of road scene recognition, and the method comprises the steps: constructing a scene recognition multi-task model based on modal information evaluation based on extracted multi-modal features; the multi-modal features comprise audio features and video image frame features; the scene identification multi-task model comprises a collaborative network and a backbone classification network, the collaborative network uses visual feature extraction VGG16-places365 as a modal quality evaluation network, and uses a BRISQUE algorithm based on a local normalized brightness coefficient as a teacher model to provide a learning reference label for the modal quality evaluation network; the backbone classification network uses a multi-modal DBN network to carry out unsupervised joint representation on visual and audio modal scene information. According to the method, the image quality is associated with the brightness information, and the modal information quality evaluation factor is provided to evaluate the video image frame quality, so that the modal information weight can be dynamically adjusted according to the illumination change, and the purpose of identifying the robustness of the model is achieved.

The invention discloses an automatic operation and maintenance method based on an agent technology and a collaborative network, an operation and maintenance agent responds to an alarm event and initializes an operation and maintenance task, a data agent is triggered to collect and preprocess multi-source heterogeneous operation and maintenance data, the operation and maintenance agent initiates a deep root cause analysis request according to the preprocessed data, and the deep root cause analysis request is sent to the collaborative network. Driving the code agent to generate and execute an analysis code, positioning a root cause and generating a repair scheme by the operation and maintenance agent based on an execution result of the code agent, verifying a repair effect after execution, and presenting an agent cooperation path and an evidence chain in an operation and maintenance process in real time by the visual agent through a preset visual protocol. A structured report is generated by the reporting agent. Dynamic code generation and safe execution are driven through a multi-agent collaborative architecture, end-to-end automatic root cause positioning and closed loop repairing are achieved, meanwhile, an evidence chain and a collaborative process are presented in real time by means of protocol visualization, and the operation and maintenance intelligent level and fault diagnosis transparency are improved.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The invention discloses an intelligent traffic light coordination control method and system for traffic flow optimization, and relates to the technical field of intelligent control. The method comprises the following steps: collecting traffic flow data of lanes in all directions in a target area in real time through heterogeneous sensor networks deployed at all intersections; identifying the traffic flow data to obtain tidal traffic flow features; performing dynamic intensity evaluation based on the tidal traffic flow characteristics to obtain a tidal direction intensity index; a multi-agent cooperative network is constructed, and a continuous intersection green light phase offset sequence is generated based on cooperative calculation of the multi-agent cooperative network; and when the tide direction intensity index is greater than a preset threshold value, inputting the continuous intersection green light phase offset sequence into a traffic signal lamp control system so as to start a tide green wave mode. The technical problems that in the prior art, traffic flow control is not flexible enough, and traffic lights cannot be intelligently adjusted according to the traffic flow condition are solved, and the technical effects of improving the road passing efficiency and reducing congestion are achieved.

The invention provides a robot positioning prediction method and device, a medium and equipment, and relates to the field of industrial robot motion control. According to the method, on one hand, a physical-data double-branch collaborative network is designed, meanwhile, a DH parameter method is used for predicting and obtaining a theoretical position coordinate of a target robot, an improved Transform network is used for predicting and obtaining a coordinate compensation value of the target robot, so that a theoretical value and the compensation value are decoupled, and the limitation of single model establishment is avoided; and then, the two are fused, and high-precision end point location prediction for the target robot is realized by combining priori knowledge of robot kinematics and nonlinear expression ability of deep learning. And on the other hand, a spatial physical information mixed loss function is provided, and a spatial topological structure output by the DH model is used to guide the distribution of predicted values of the whole position prediction model, so that the geometric rationality of target robot end point location prediction is improved.

The invention provides an intelligent unmanned aerial vehicle cluster collaborative operation system, and belongs to the field of unmanned aerial vehicle collaborative operation, and the system comprises a task planning module which is used for receiving a task demand of an unmanned aerial vehicle cluster, decomposing the task demand into subtasks, and determining the importance of the subtasks; the collaborative decision-making module is used for calculating the comprehensive capability of each unmanned aerial vehicle and generating a task allocation scheme according to the comprehensive capability and the importance degree of the subtasks; the resource management module is used for determining total task resources according to task requirements of the unmanned aerial vehicle cluster and generating a resource allocation result based on the task allocation scheme and the total task resources; and the execution module is used for guiding the unmanned aerial vehicle cluster to execute the task based on the task allocation scheme and the resource allocation result. According to the method, task requirements are decomposed, the sub-task collaborative network diagram is constructed, the sub-task importance is calculated, and dynamic sorting is performed according to the sub-task importance, so that optimal distribution of tasks is realized, and the overall operation efficiency of an unmanned aerial vehicle cluster is improved.

The invention provides a multi-protocol equipment fault positioning diagnosis method and system based on AI big data, and relates to the technical field of network equipment fault diagnosis, and the method comprises the steps: generating a fault feature tensor through multi-protocol data adaptation processing and multi-graph collaborative network feature interaction and aggregation, and positioning a fault root through combining a fault incidence matrix and a causal reasoning decision tree. And matching evaluation is carried out in the double-layer adaptive fault feature library, and finally a diagnosis report is generated. According to the invention, standardized processing of cross-protocol fault data, accurate positioning of a fault source and reliable evaluation of a diagnosis result can be realized, and the accuracy and efficiency of fault diagnosis are improved.

The invention discloses a fresh commodity after-ripening regulation and control planning method oriented to maturity grading, and relates to the technical field of food science and biologication.The method comprises the steps that physiological parameters of fresh commodities are collected in real time based on a multi-modal sensor, and the physiological parameters comprise the epidermis pigment content, the ethylene release rate, the fruit hardness and the respiration intensity; the collected physiological parameters are input into a multi-objective optimization model based on deep learning, the model fuses a metabolic kinetic equation and an LSTM time sequence prediction algorithm, and a regulation and control parameter combination adaptive to the current mature stage is output; according to the regulation and control parameter combination, the temperature, the relative humidity and the O2 / CO2 concentration gradient of the controlled atmosphere storage equipment are synchronously regulated through a distributed control system, and a plant hormone antagonist is injected through an ultrasonic atomization device; and constructing a supply chain collaborative network based on a block chain technology, binding a grading result with logistics path planning, and adjusting a temperature control strategy and a shelf life countdown parameter of the cold chain transport vehicle in real time through an edge computing node.

The invention discloses a data security risk assessment method based on a big data model, and relates to the technical field of data security, and the method comprises the steps: collecting and preprocessing multi-source data, collecting security-related data from network equipment, a server and an application system, carrying out the preprocessing, carrying out the adaptive feature extraction, and carrying out the data security risk assessment. The feature importance is evaluated by calculating the mutual information amount of features and risk tags, a standardized feature vector set is constructed, multi-model collaborative analysis is performed, feature vectors are input into a cascade collaborative network composed of an anomaly detection model, a threat recognition model, a correlation analysis model and a prediction model, and a risk risk is obtained. Through cross-model feature transmission and a bidirectional information feedback mechanism, deep collaborative analysis and multi-model deep fusion decision making are carried out, a weight is calculated according to historical accuracy of each model, a comprehensive risk score is calculated by adopting dynamic gating deep fusion, and a dynamic threshold value is calculated based on a sliding time window. And the risk is divided into three levels of high risk, medium risk and low risk.

The invention discloses a radar detection blind area wind field fine reconstruction method based on anemograph collaborative networking, and the method comprises the specific steps: firstly, separating wind speed measurement deviation caused by vibration displacement in real time, eliminating vibration interference, carrying out the automatic diagnosis and restoration of data based on machine learning, and optimizing the integrity of wind speed data and the measurement continuity; secondly, an ultrasonic anemograph array optimization arrangement method oriented to global wind field sensing is established, time-space consistency calibration of multi-sensor data is achieved, and the precision and reliability of wind speed measurement are improved; and finally, carrying out data-physical fusion bridge flow field high-resolution intelligent reconstruction, and improving the data integrity of a complex flow field area. The reconstruction method effectively cooperates with the wind measurement laser radar and the anemograph, the advantages of high-frequency single-point measurement and wide-area space scanning of the wind measurement laser radar and the anemograph can be exerted, high-precision reconstruction of the global flow field of the bridge is expected to be achieved, and reliable support is provided for guaranteeing wind resistance safety of a large-span bridge.

The invention provides a time sequence remote sensing image crop classification method based on semantic and boundary joint learning. The core innovation of the method lies in a multi-scale spatial-temporal feature joint optimization mechanism, high-level coarse-grained semantic features and low-level fine-grained boundary features are respectively captured by constructing a multi-level spatial-temporal feature extractor, and semantic and parcel boundary features are interacted through a semantic boundary collaborative network, so that the spatial-temporal feature joint optimization is realized. And end-to-end high-precision crop remote sensing classification is realized. Besides, by constructing a semantic and plot boundary information enhancement module, the learning ability of fine-grained plot boundary features is enhanced, so that spatio-temporal feature learning of crop types on corresponding plots is guided, the crop extraction precision is improved, and the phenomena of missing detection and false detection are effectively reduced. The method provided by the invention is high in expansibility, a time sequence feature extraction network can be freely replaced, and an effective technical means is provided for realizing accurate and automatic crop type remote sensing classification.

The invention relates to the technical field of artificial intelligence, and provides a marketing data generation method and device based on portrait data, equipment and a medium, marketing association data can be collected and purified based on a three-level data gateway, feature fusion is carried out by using a star-shaped collaborative network constructed based on a dynamic weight mechanism and a federated learning mechanism, and the marketing data generation efficiency is improved. The problems of data dimension limitation and data island are solved; scene recognition is performed based on a marketing data graph constructed by a secondary scene classification tree including a gift scene, and the problems of low utilization efficiency of unstructured data and insufficient crowd portrait granularity are solved; the marketing strategy is generated by using the target engine matched with the scene, so that the problems of scene engine deficiency and gift scene adaptation imbalance are solved; and generating the target marketing data according to the target marketing strategy and the marketing data graph. The problems of low operation efficiency and insufficient content accuracy are solved.

The invention relates to the technical field of power distribution, in particular to a dynamic power distribution method and system for an automobile charging pile, and the method comprises the steps: extracting the historical load data of the charging pile through a sensor, converting the data into a time series data set in a unified range, and carrying out the window sliding fitting through employing a convolutional neural network based on the time series data set; the future load demand of each charging pile is predicted, meanwhile, a graph structure network of a power transmission relation is constructed, node power distribution information is fused, a charging pile cooperation relation is established, the relation between the power demand of a single charging pile and the power grid bearing capacity is evaluated by adopting a genetic algorithm, power output is adjusted, the output ratio is calculated, and a reasonable power distribution scheme is formed; and finally, monitoring a power grid load and a charging pile operation state in real time, constructing a graph structure of a charging pile cooperation network based on a geographic position, changing independent management of the isolated charging piles into networked cooperation management, and enhancing reasonable resource allocation among the charging piles.

The invention relates to the technical field of smart home and mobile terminals, and discloses a smart home anti-theft system based on a smart mobile terminal, which comprises a dynamic behavior learning module, a multi-modal sensor collaborative network, a dual-channel verification unit and a cross-platform emergency linkage engine. According to the smart home anti-theft system based on the smart mobile terminal, a false alarm scene is effectively eliminated and the false alarm rate is reduced through a multi-modal sensor collaborative network and a dual-channel verification unit, and a personalized security policy is dynamically generated by adopting a mixed learning algorithm and an edge-cloud collaborative computing architecture, so that the privacy data of a user is protected; through the cross-platform emergency linkage engine, hierarchical alarm and multi-dimensional response are supported, the intrusion event processing efficiency is improved, it is ensured that an evidence chain cannot be tampered, judicial evidence obtaining standards are met, and the problem that evidence of a traditional system is prone to being lost or tampered is solved.

The invention provides an environmental protection equipment operation state collaborative management and control method and system based on the Internet of Things, and relates to the technical field of environmental protection equipment management and control, and the method comprises the steps: collecting environmental protection equipment operation parameters, storing the parameters in a distributed time sequence database, constructing a group control collaborative network, and generating a group control decision model based on hierarchical segmentation and federated learning. The operation parameters are optimized through a multi-level pheromone guided hybrid group algorithm, and an optimal scheduling instruction is generated and distributed; according to the invention, efficient cooperative operation of environmental protection equipment is realized, energy consumption is reduced, and overall operation efficiency and anti-interference capability of the system are improved.

The invention provides a task risk early warning method and system based on project management data, and relates to the technical field of project management early warning. According to the method, the task risk index weight is automatically adjusted to adapt to process states and external environment changes in different stages of a project, the accuracy and flexibility of risk assessment are remarkably improved, and good dynamic adaptability is achieved; performing grading evaluation on tasks in each stage of the project by means of a quantitative comprehensive risk value, determining a management priority, and assisting a project team to focus on key risks; a task cooperation relationship is visually presented by constructing a task cooperation network, a center node and a fragile link are accurately identified, and a risk propagation path is effectively grasped; and real-time monitoring and graded early warning are realized in combination with a dynamic risk early warning rule, so that the risk can be found and dealt with in time.

The invention discloses an explainable appearance feature learning and quantitative representation method for aerodynamic parameter prediction, and belongs to the technical field of aerodynamics and artificial intelligence crossing. The method comprises the following steps: constructing a collaborative network architecture comprising an aerodynamic prediction module, an airfoil concept learning module and a quantitative distillation agent module; while high-precision and high-efficiency aerodynamic parameter prediction is realized, a prediction result can be decomposed into the sum of quantitative contributions of different airfoil profile concepts, so that a direct explainable basis is provided for the prediction result, and expert users are assisted in understanding and verifying the prediction process of the model.

The invention provides a method and a system for evaluating the biological health condition of cultivated land soil based on exploratory factor analysis, and relates to the technical field of evaluation of the biological health condition of soil. The precise analysis of the complex interaction relationship between the soil microorganism function and the environmental factor is realized; through multi-dimensional index fusion and deep mining of a factor model, key biological function factors are effectively discriminated and quantified, and the scientific evaluation capability of soil ecological functions is improved; in combination with causal path analysis, the scheme can reveal the influence of environmental pressure on a microbial regulation and control mechanism, and intelligent attribution and traceability of abnormal health conditions are realized; based on dynamic fusion and grading judgment of function and environment scores, the scheme supports accurate health condition evaluation and hierarchical intervention, the early warning capability and decision scientificity of soil management are enhanced, and the agricultural sustainable development guarantee level is remarkably improved.

The invention discloses a power distribution network data high-frequency acquisition and low-delay interaction method and system, and the method comprises the steps: firstly, constructing a communication cooperative network model, taking the minimum end-to-end delay of power distribution service data and the weighted sum of network operation cost as a target, converting an optimization problem into a form suitable for real-time solving through employing a Lyapunov optimization method, and carrying out the real-time solving of the optimization problem; and further based on the optimization problem, constructing a Markov model, improving a loss function of a deep Q network (DQN) to adapt to the acquisition requirements of distribution network services, and designing a corresponding network learning training method to optimize terminal scheduling and data compression decisions. And finally, a transmission power and bandwidth distributed optimization method based on an alternating direction multiplier method (ADMM) is adopted, so that the efficiency of network resource allocation is further improved. According to the technical scheme, an optimal data acquisition strategy with low time delay, low cost and balanced scheduling is realized, and the method is suitable for high-frequency data acquisition scenes such as a smart power grid and an industrial internet of things.

The invention discloses an intelligent networked unmanned ship cluster control method and system, and relates to the technical field of intelligent control and cluster collaboration, and the method comprises the steps: constructing a multi-layer distributed unmanned ship cooperative control network structure, and dividing nodes according to the task attributes of unmanned ships. The method comprises the steps of establishing a connection topological graph in a cluster according to initial function distribution, calibrating nodes through the connection topological graph, collecting the energy state, the load capacity, the communication quality and the task pressure of each unmanned ship, establishing a multi-dimensional state matrix, and calculating a comprehensive decision value of each node according to the multi-dimensional state matrix. And switching node operation through an adjacent topological relation and a comprehensive decision value, synchronizing the updated node state and the connection diagram to an unmanned ship cluster, and carrying out self-adaptive distribution and network structure optimization. According to the method disclosed by the invention, the management orderliness of a cluster structure and the task cooperation efficiency among the nodes are improved by constructing a three-layer cooperative network of the control nodes, the execution nodes and the sensing nodes.