148 results about "Network clustering" patented technology

To automatically onboard network functions to a credential vault, a orchestration processor actuates establishment of an cluster account for a network cluster, and actuates a cluster configuration of a processor of the vault to enable authentication of a network cluster. For each of a plurality of network functions associated with the network cluster, the orchestration processor generates an identifier, sets values for parameters of an initialization parameter set, actuates assignment of access permissions for a code address on a memory of the vault, actuates assignment of elevated access permissions for a credential address on the vault memory, and actuates association of the network function with a cluster account of the network cluster. The vault memory thereby defines credential addresses each corresponding to a respective network function.

The invention relates to a bio-inspired large-scale bee colony networking and formation control method, which comprises the following steps of: constructing a hierarchical clustering network by using a method based on maximum connectivity, and constructing routes in clusters and among the clusters; a hierarchical clustering network and grouping formation control are fused, and a message format is designed; the method comprises the following steps: constructing formation groups by network clustering; realizing pigeon flock formation simulation control in the groups; updating pigeon flock social levels by utilizing cluster heads; realizing starling formation simulation control among the groups; on the grouping level, a starling group mode, an obstacle avoidance mode and a following mode are realized. Compared with the prior art, the hierarchical clustering network architecture effectively reduces the routing overhead of the large-scale unmanned aerial vehicle cluster, and meanwhile, the control complexity and the control communication overhead of the large-scale unmanned aerial vehicle cluster are effectively reduced through the pigeon flock formation simulation control method and the starling formation simulation control method and the distributed architecture thereof.

The invention discloses an electric power, traffic and communication network collaborative electric power recovery method and system and a medium, and the method comprises the steps: inputting three-network comprehensive data of a target region into a deep classification-clustering model to obtain a three-network clustering result, and carrying out the calculation to generate a three-network node coupling tensor; determining a fault node set of the power network, and quantifying the cross-network influence of fault nodes by using a three-network node coupling tensor; based on the cross-network influence of the fault node, a chain risk value is obtained, and thus the weak level of the fault node is obtained; based on the weak grade of the fault node and the cross-network influence of the fault node on the traffic congestion rate and the communication delay, constructing a three-network cooperative power recovery model, and solving to obtain an optimal power recovery strategy; performing power recovery on the target area based on the power recovery optimal strategy; the power can be recovered through cooperation of power, traffic and the communication network, the recovery difficulty is small, the efficiency is high, and power failure economic losses and first-aid repair resource waste are reduced.

An embodiment method for managing at least one vehicle network including at least one electronic control unit (ECU) among a plurality of ECUs in a vehicle network management apparatus is provided, which may include, based on information of at least one default partial network cluster including at least one ECU among the plurality of ECUs according to at least one default partial network function provided by default in a mapping database, configuring a new partial network cluster by adding an ECU according to user customization to the at least one default partial network cluster, and storing information of the new partial network cluster in the mapping database.

The embodiment of the invention provides a network congestion processing method, device and equipment, a medium and a product. The method comprises the following steps: determining a first network path which is congested in a network cluster, and obtaining a first transmission multi-element group corresponding to the first network path; based on the real-time traffic information of a plurality of network paths in the network cluster, a second network path is decided from the plurality of network paths, and a second transmission multi-element group corresponding to the second network path is acquired; the transmission load of the second network path meets a light load condition; establishing a mapping relation between the first transmission multi-component system and the second transmission multi-component system; and when the to-be-transmitted first message with the first transmission multi-element group exists in the first network path, scheduling the first message to the second network path for transmission based on the mapping relationship. The embodiment of the invention is beneficial to improving the stability of the network cluster.

The invention discloses a storage battery real-time monitoring method based on edge calculation, and the method comprises the following steps: S1, collecting multi-source operation parameters of a storage battery, and constructing an operation parameter set; s2, processing the operation parameter set to generate a standardized state vector; s3, on the basis of a micro neural network model, generating graph structure reasoning characteristics are fused; s4, performing global search on a connection structure between the sub-network clusters by adopting a spider-now-monkey optimization algorithm, and updating the structure configuration of the micro neural network model; s5, on the basis of the graph structure reasoning features and the optimized sparse connection structure, forming a monitoring judgment result; s6, performing edge response control according to a monitoring judgment result, and generating a response record; and S7, based on the response record and the monitoring determination result, completing edge side dynamic optimization of the miniature neural network model. Sparse neural clusters and edge reasoning are adopted, and real-time monitoring and dynamic optimization control of the state of the storage battery are achieved.

According to the transmission optimization method of the network cluster system and the related equipment, the network cluster system comprises a plurality of network clusters, each network cluster comprises a plurality of network nodes, and the method comprises the steps that firstly, the link state of a communication link between every two network nodes is acquired, and a link connection matrix is acquired; the link connection matrix is used for representing the connection state of each communication link; then, obtaining a network cluster scheduling model, inputting all link states and the link connection matrix into the network cluster scheduling model, and obtaining an output link optimization transmission rate sequence and an optimization flow distribution proportion of each network cluster; and finally, the transmission rate of each communication link in the corresponding network cluster is adjusted according to each link optimized transmission rate sequence, and the flow distribution of the corresponding network cluster is adjusted according to each optimized flow distribution proportion, so that the utilization rate of transmission resources can be effectively improved when information transmission between network nodes is performed in the network cluster system.

The invention relates to the technical field of academic network processing, and particularly discloses an academic network clustering method based on graph structure perception, which comprises the following steps: preprocessing an academic network and constructing a data set; based on the constructed data set, extracting features of the academic network through an enhanced graph auto-encoder, and constructing a reconstruction loss function; periodically updating an adjacent matrix of the academic network through a graph weight updating mechanism, and obtaining an optimal feature representation; a self-supervision mechanism is introduced, clustering soft distribution and auxiliary target distribution are obtained based on the obtained optimal feature representation, and a clustering loss function is determined; and integrating the reconstruction loss function and the clustering loss function, iteratively optimizing parameters of the structure perception depth map clustering model, and obtaining a clustering result after the parameters of the structure perception depth map clustering model are updated. The invention aims to solve the problems of intensive calculation and insufficient edge reliability in the existing academic network clustering method, and improve the efficiency and accuracy of the academic network clustering algorithm.

The present invention provides a geophysical identification method for mid-deep undercompacted low-velocity mudstone. By statistically analyzing data, representative P-wave velocity, S-wave velocity, and density of low-velocity mudstone and gas-bearing sandstone in well logging data are obtained. Combining geological information, a theoretical model of the superimposed relationship between low-velocity mudstone and gas-bearing sandstone is designed, and convolution forward modeling and prestack forward modeling are carried out on it. The post-stack and prestack seismic attributes of the model are calculated respectively. Then, sensitive attributes are optimized from these attributes, and the obtained sensitive attributes are used as learning samples, and the superimposed relationship is identified by probability self-organizing neural network clustering. The present invention adds prestack attributes to the optimization of sensitive attributes, making the fuzzy self-organizing neural network more accurate in identifying the superimposed relationship between gas-bearing sandstone and low-velocity mudstone, effectively distinguishing low-velocity mudstone from gas-bearing sandstone, and providing support for subsequent effective reservoir prediction.

The invention relates to the technical field of unmanned aerial vehicle ad hoc network cluster management and control scheme design based on intelligent street lamps and satellite communication, in particular to an unmanned aerial vehicle ad hoc network cluster management and control system based on intelligent street lamps and satellite communication. The system comprises an intelligent street lamp subsystem, a satellite communication subsystem, an unmanned aerial vehicle cluster node and a dynamic cooperative control platform. The innovative scheme comprises the following steps: (1) the multimode communication gateway realizes seamless conversion between a satellite protocol and a ground Internet of Things protocol; (2) a street lamp sensor generates a dynamic risk map to guide cluster obstacle avoidance; (3) supporting signal interruption emergency networking by a rapid clustering algorithm based on geometric topology; (4) task priority drives time slot distribution to improve communication efficiency; and (5) the satellite-street lamp-relay unmanned aerial vehicle multistage redundancy guarantee system is continuously controllable. According to the scheme, the communication reliability, the environmental adaptability and the cooperative capability of the cluster in a complex urban area are remarkably enhanced.

The invention relates to the technical field of deep reinforcement learning, and discloses a deep reinforcement learning decision optimization method and device, computer equipment and a medium, and the method comprises the steps: detecting whether a candidate network cluster is in a strategy evaluation state or not; if in the strategy evaluation state, obtaining an evaluation result corresponding to each training node output by the strategy evaluation node; training nodes in the candidate network cluster are optimized according to the evaluation result until the candidate network cluster meets a preset termination condition, and a target action decision network is obtained; and determining a target action node in the target action decision network, and taking an action strategy corresponding to the target action node as a target action strategy. According to the method, the problems that a deep reinforcement learning decision optimization scheme is low in training efficiency, local optimum and experience forgetting are likely to occur, the intelligent agent correlation utilization of a multi-node scheme is insufficient, and a single-node scheme is not suitable for multi-node training are solved.

The invention relates to a radio frequency identification network cluster head determination method and device, equipment, a medium and a product. According to the radio frequency identification network cluster head determination method provided by the invention, node state information corresponding to each node in a radio frequency identification network is acquired, wherein the node state information comprises energy data and centrality data corresponding to each node; performing fuzzy logic operation processing on the energy data and the central data to obtain a target opportunity probability; and according to the target opportunity probability, determining a target cluster head corresponding to each cluster in the radio frequency identification network, the target cluster head being used for communicating with other clusters. By adopting the method, the transmission efficiency of the radio frequency identification network can be improved.

The invention belongs to the technical field of data processing, and particularly relates to a rock-soil data structured conversion method. According to the method, unstructured data of rock and soil logs is split into minimum information units of entity, attribute and relation clues through information particle deconstruction, standardized entities and relations are generated through clue association network clustering and double-track reasoning model processing, and a knowledge graph is constructed and mapped into structured data; and constructing an initial geologic model by using structured data, extracting core data, converting the core data into an extended IFC format intermediate file containing a geologic special entity, and finally associating a BIM model to realize data fusion. According to the method, structured conversion of unstructured data is realized, a data format barrier is broken through, and the engineering collaboration efficiency is improved.

The present application relates to a cluster shared traffic threshold-based rate limiting method and apparatus, which are used for achieving effective bandwidth management and allocation among a plurality of nodes in a cluster and among a plurality of network card queues in the nodes. The method comprises: S1, a cluster manager issuing a cluster rate limiting threshold of a specified data stream to each node in a network cluster, and adding the forwarding process of each node into a multicast to share traffic information between the nodes, the cluster rate limiting threshold being acquired in the forwarding process of the node; S2, a node controller collecting statistics about the traffic of the specified data stream flowing into a network card per second, performing marking, sending out, by means of multicasting, a unique identifier of the node and network card traffic received per second to be shared to other nodes in the cluster, and on the basis of a multicast packet received from the cluster, calculating a node rate limiting quota threshold of the present node; S3, on the basis of each service core for processing traffic of queues in the forwarding process of the node and the bandwidth proportion of the total traffic of the nodes, a bandwidth allocation scheduler allocating a core rate limiting quota threshold of each node; and S4, on the basis of real-time data of the processed traffic of each queue in the nodes and the proportion of the total bandwidth of the nodes, a token bucket manager performing periodical and dynamical adjustment by means of a token bucket algorithm to obtain the proportion of each queue in a cluster rate limiting bandwidth, so as to limit the rate of the traffic passing through the queue.

The invention discloses a domain name configuration system, and relates to the technical field of communication. And the first configuration gateway node receives the domain name configuration information and transmits the domain name configuration information to H configuration gateway nodes in all the configuration gateway nodes contained in other network clusters. Therefore, fast propagation of the domain name configuration information among the network clusters is realized, the network time delay of the domain name configuration information reaching each network cluster is reduced, and a guarantee is provided for each network cluster to receive consistent domain name configuration information. A first configuration gateway node sends domain name configuration information to K control nodes included in a first network cluster, a first control node sends the domain name configuration information to X configuration release nodes included in the first network cluster, and the first configuration release node transmits the domain name configuration information to an authoritative server. Therefore, the transmission efficiency of the domain name configuration information in the network cluster is improved, the network delay of the domain name configuration information reaching the authoritative server is reduced, and a guarantee is provided for the authoritative server to receive consistent domain name configuration information.

The invention relates to the technical field of visual positioning, in particular to an improved bolt pose estimation method. The method comprises the following steps: S1, creating a virtual bolt data set for training; s2, using a deep learning-based method FPCC instance segmentation network to extract features of each point, and using a non-maximum suppression mode to search for a center point for each instance; s3, the point with the highest score is set as the geometric center point of each instance, and the remaining points are clustered to the nearest geometric center based on a network clustering algorithm, so that the instances are segmented; and S4, after the instance is segmented, performing cylinder fitting, re-estimating parameters of a cylinder by using an improved RANSAC algorithm, obtaining a direction vector of an axis of the cylinder, and determining a central axis direction of the bolt. Based on an FPCC instance segmentation network, a new bolt pose estimation method is provided in combination with an SAC-IDBSCA coarse registration algorithm, an NDT fine registration algorithm and an improved RANSAC algorithm, the FPCC instance segmentation network provided by the invention can still realize rapid and effective segmentation of bolts under a high-degree stacking condition, any training of manual annotation data is not needed, the algorithm is simple, the algorithm is easy to implement, and the method is suitable for large-scale popularization and application. The improved RANSAC algorithm provided by the invention can effectively compensate the negative influence of the traditional algorithm on the algorithm effect due to improper threshold selection.

The invention provides a salt marsh vegetation long-term change monitoring method, which belongs to the technical field of salt marsh vegetation monitoring, and comprises the following steps: establishing a multi-level monitoring grid with main monitoring point spacing of 500m and auxiliary monitoring point spacing of 100m, and configuring a multi-source sensor to obtain environmental data; a wavelet transform technology is used to separate seasonal change and long-term change trends, a matrix equation is calculated through seasonal change amplitude to dynamically adjust sampling frequency, and an ecological network connectivity analysis algorithm based on a graph theory is constructed to calculate a network clustering coefficient and a shortest path length to evaluate habitat fragmentation. A fuzzy C-means clustering algorithm is adopted to process the boundary fuzziness of a vegetation community based on multi-dimensional environmental factors, a multi-scale matrix mapping hierarchical processing system is established to realize pixel-to-landscape scale information fusion, and the technical problem that the monitoring result is not accurate enough due to insufficient monitoring precision of long-term change of salt marsh vegetation is solved.

The invention discloses an underwater acoustic sensor network clustering method, system and device and a medium, and relates to the technical field of underwater acoustic communication, and the method comprises the steps: obtaining the current network state characteristics of each node of a target underwater acoustic sensor network, inputting the current network state characteristics into an underwater acoustic sensor network clustering model, and obtaining a current optimal clustering scheme; based on the current optimal clustering scheme, electing cluster head nodes through a dynamic role rotation mechanism, and generating an adaptive network topology structure; performing performance evaluation on the adaptive network topology structure to obtain a group of performance indexes; and if the performance index does not meet the performance threshold value, or the confidence coefficient of the current optimal clustering scheme output by the underwater acoustic sensor network clustering model is lower than a confidence coefficient threshold value, starting a complete optimization learning process. According to the technical scheme, the technical problem that it is difficult to balance multi-target performance online and adaptively and carry out continuous self-optimization to adapt to the dynamic underwater environment through an existing clustering method is effectively solved.

The invention discloses a bandwidth allocation method and device, electronic equipment and a nonvolatile storage medium. The method comprises the following steps: acquiring a network data link bandwidth of a target network cluster at a first moment; the shared data link is divided into a plurality of different data planes based on the service requirements of different network flows of the target network cluster, each data plane corresponds to the service requirement of one network flow, and the shared data link is a network data link commonly used by all network flows in the target network cluster; and performing network data link bandwidth allocation on each data plane based on a token bucket algorithm and the network data link bandwidth at the first moment, the token bucket algorithm being used for adjusting the network data link bandwidth of each data plane. According to the method and the device, the technical problems that the key service has no response and bandwidth resources cannot be fully utilized due to the fact that network traffic of multiple services mixedly uses a shared data link in the prior art are solved.

The invention relates to the technical field of coal mine communication, and discloses a mine signal blind area ad hoc network cluster system which can support multi-person talking, is low in cost and can quickly recover communication after a disaster, the mine signal blind area ad hoc network cluster system comprises a relay unit and a terminal unit, and the relay unit comprises a plurality of relay devices arranged at intervals. At least one relay device is in wired or wireless connection with the ground node to receive and transmit emergency signals, each relay device adaptively adjusts radio frequency parameters, and adjacent relay devices are connected through a LoRa single-channel link and communicate through a Mesh protocol; the terminal unit comprises a plurality of carry-on terminals which are connected with the nearest relay device or the strongest-signal relay device through a LoRa single-channel link, and each carry-on terminal comprises a transmitting mode and a receiving mode; and when receiving the emergency signal or a random access request sent by the portable terminal, the relay device forwards the emergency signal or the personnel sound signal to an adjacent relay device, or sends the emergency signal or the personnel sound signal to the portable terminal in a receiving mode in a broadcast mode.

This invention provides an energy-saving clustering routing method for wireless sensor networks based on improved multi-target ant colony optimization, comprising: Step 1, initialization; Step 2, determining the set of active sensor nodes in the current round; Step 3, completing network clustering; Step 4, maintaining the Pareto optimal path solution set; Step 5, repeating steps 2 to 4 until the maximum number of iterations is reached or the solution set converges, outputting the Pareto optimal path solution set as the routing scheme for network data transmission. This invention improves network energy efficiency and extends network lifetime by constructing a globally collaborative energy management framework that integrates sleep scheduling, cluster head election, and routing optimization, while ensuring the real-time performance and reliability of data transmission.

The embodiment of the invention discloses a terminal data management method and device, electronic equipment and a storage medium. Through a multi-terminal cluster management triggering mechanism, monitoring data, installation position data and equipment operation state data are classified, only core data are transmitted and matched with terminal identifiers, the transmission efficiency is improved, and redundant data occupation is reduced; secondly, a standby switching design of a main communication module and a virtual operator network cluster communication module is adopted, so that the problem of data interruption caused by link instability in a complex environment is solved; and meanwhile, the core data are uniformly reported to the multi-terminal management server, so that multi-terminal uniform management and control and real-time state control are realized, the problems of low reliability and poor management efficiency of the existing scheme are solved, and the reliability and management efficiency of terminal data management are improved.

The invention relates to the technical field of complex network analysis and data mining, and provides a scientific research cooperation analysis method for edge crossing attribute network clustering, and the method comprises the steps: obtaining original data, and carrying out the preprocessing of the original data; an undirected empowerment cooperative network is constructed and processed to obtain a core sub-graph, and the maximum connected component LCC of the core sub-graph is extracted; constructing a node multi-dimensional feature vector by using a natural language processing technology; using an ECV algorithm to determine an optimal community number; constructing a topological feature matrix and an attribute similarity matrix by adopting an ANCA algorithm, and fusing the topological feature matrix and the attribute similarity matrix; hyper-parameters of the ANCA algorithm are adjusted and optimized in a grid search mode, and community division and analysis are carried out. The method has an extremely important application value and a wide social prospect in optimizing resource configuration, evaluating team influence, promoting academic communication and cooperation and guiding development of a scientific research direction.

The invention relates to a large-scale heterogeneous multi-attribute equipment scheme clustering decision-making method and a medium. The method comprises the following steps: uniformly converting heterogeneous attribute data in a digital twin decision-making information system into binary connection numbers; determining an attribute weight by adopting an MEREC method, and constructing an attribute non-compensable value based on the attribute weight and the binary connection number; generating a scheme similar network according to the attribute non-compensable value; performing network clustering operation on the scheme similar network, and outputting a scheme clustering result; and based on the scheme clustering result, calculating the comprehensive target distance of the scheme in the cluster by adopting the grey target theory, and fusing the global value of the scheme and the comprehensive target distance to generate a final scheme score. According to the method, the problems of heterogeneous information fusion distortion, attribute non-compensatory mechanism deficiency and low large-scale scheme clustering efficiency in a digital twinborn decision scene are solved by constructing heterogeneous data unification-attribute non-compensatory mechanism modeling-network clustering-intra-cluster decision.

The application provides a sensor network clustering routing method based on coverage area division and energy consumption balance, and the method comprises the following steps: obtaining a network structure of sensor nodes; performing area division on the network structure according to a pre-set sink node, so as to obtain at least two node areas; calculating a distance factor and a residual energy factor of nodes in each node area according to the node areas obtained by division; calculating a cluster head selection threshold of each node according to the distance factor and the residual energy factor of each node and the node area where each node is located; determining a cluster head in a current period according to a pre-set selection rule and the cluster head selection threshold of each node, and the remaining nodes are ordinary nodes, and clustering the ordinary nodes according to the cluster head; and performing a fusion task for the nodes after clustering until the next period, so that the network structure is processed in a hierarchical manner, the sensor nodes can be effectively managed, the network energy consumption is reduced, and the network resources are optimized.

The invention relates to the technical field of vehicle communication, in particular to an intermittent control method for an intelligent traffic unmanned vehicle communication network, and the method comprises the steps: carrying out the precise modeling of the unmanned vehicle communication network of an intelligent traffic system through employing a positive complex network model, so as to obtain a state space model, and then assuming an isolated node, and communicating with other network clusters to realize information interaction. And secondly, by designing an observer and a periodic intermittent control protocol thereof, the unmanned vehicle communication network of the intelligent traffic system can normally achieve synchronization without real-time communication, so that the stability of the unmanned vehicle communication network under various actual conditions is ensured, and the control efficiency of each unmanned vehicle is improved.

The invention discloses a large model optimization method based on multi-round diagnosis and treatment experience learning. The method comprises the steps that variable-length multi-modal time sequence modeling and unified embedded representation learning are carried out on dynamic domain experience through a heterogeneous tense multi-graph expression model; the disease course evolution is constructed into a heterogeneous, multi-modal, multi-scale and uncertainty weighted signal graph network cluster with a time sequence concept; carrying out strategic random walk resampling with a time concept on the network cluster; and performing large model optimization according to the sampling and multi-round diagnosis and treatment experience. According to the method, through a multi-modal domain experience unified representation modeling technology based on heterogeneous multi-graph progressive embedding learning, higher-quality feature expression is learned from multi-modal input data.

The utility model provides a computing system, network equipment, a distributed network cluster system and a PCB computing board card, and the computing system comprises a mainboard which comprises a first expansion bus interface; the DPU waste chip acceleration card is connected to the first expansion bus interface, the DPU waste chip acceleration card comprises an independent CPU subsystem and an acceleration engine, the CPU subsystem is normal, the acceleration engine has a fault, so that the DPU waste chip acceleration card is used as a CPU daughter card of a single board, and the DPU waste chip acceleration card is connected to the first expansion bus interface. The CPU subsystem and the acceleration engine have power supply and clock systems that are independent of each other. According to the technical scheme provided by the invention, the inherent CPU subsystem in the DPU waste piece can be reused as a computing node of other equipment, so that the waste is reduced.

The present invention provides a Tor network attack and defense experimental platform based on multi-granularity simulation, which is to mirror the Tor node to a Docker container, and according to the network topology information uploaded by the user, use the container node of the Docker container mirror and the Tor node to form a simulated private Tor network, the simulated private Tor network can effectively generate background nodes that conform to the real network distribution, and then use the Master node to realize the construction of the simulated private Tor network cluster, and realize the maximum resource utilization of the simulated private Tor network cluster through the cluster scheduling strategy algorithm. The Tor network environment built by the present invention has the participation of real Tor applications, the traffic generated in the Tor link is the traffic of the real Tor protocol, and the traffic in the network can be captured, which is convenient to support the tracing attack and defense experiments.

Techniques are disclosed for a computing system comprising processing circuitry having access to a storage device, the processing circuitry configured to: generate, by a network controller executing in a software defined network (SDN), an advertisement in a first network cluster executing within a container orchestration platform of the SDN, wherein the advertisement conforms to a routing protocol and comprises information identifying a network service executing in the first network cluster, wherein the network service exposes a backend of a network application to the container orchestration platform of the SDN; and broadcast, by the network controller and to a second network cluster executing within the container orchestration platform of the SDN, the advertisement in accordance with the routing protocol.