184 results about "Network clustering" patented technology

Techniques are disclosed for a computing system comprising processing circuitry having access to a storage device, the processing circuitry configured to encode, by a network controller executing in a software defined network (SDN), one or more attributes with information identifying a network service, wherein the one or more attributes conform to a routing protocol. The processing circuitry is also configured to generate, by the network controller, an advertisement in a first network cluster executing within a container orchestration platform of the SDN, wherein the advertisement conforms to the routing protocol and includes the one or more attributes. The processing circuitry is also configured to 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.

The invention discloses an intelligent prediction method for fracture pressure of a compact conglomerate reservoir based on an improved Transform architecture, and relates to the technical field of combination of deep learning and oil extraction. Comprising the following steps: acquiring fracture pressure data of fractured wells in the same block and logging parameters related to the fracture pressure, matching the fracture pressure with the logging parameters, and preprocessing the data to obtain initial data; based on a CAE-SOM self-organizing mapping neural network, clustering the initial data to obtain sample categories under different geological conditions; based on a generative adversarial network, sample categories under any geological condition are expanded to obtain a training set; and in combination with an attention mechanism, training the Transform model, and predicting the fracture pressure of the block reservoir by using the trained Transform prediction model. According to the method, the limited fracture pressure data can be accurately utilized for prediction, so that a more efficient method is provided for providing powerful support for fracture design and segmented clustering optimization.

The invention discloses a micro-grid cluster multi-target scheduling method based on a self-adaptive multi-hybrid strategy bald eagle algorithm, which utilizes multi-micro-grid interconnection to realize electric energy coordination interaction and optimize internal resource distribution. On the premise that stable operation and installed capacity of multiple distributed power supplies are constrained, a micro-grid economic and environmental cost cluster multi-target scheduling model is constructed. And the management center determines optimization target parameters according to the obtained photovoltaic prediction data, basic load prediction data and real-time electricity price data of each micro-grid. And solving the scheduling model by using an improved Balde eagle algorithm which contains an adaptive adjustment factor and is combined with a multi-hybrid strategy to obtain an optimal solution and a solution set under the condition that sub-targets are minimum, reconstructing and optimizing a multi-objective function by using fuzzy membership satisfaction according to a result, and obtaining a power distribution scheme of the micro-grid cluster system. The method provided by the invention is higher in convergence speed and highest in precision, gives consideration to optimization between economic and environmental multiple targets, can effectively coordinate electric energy interaction between microgrids, and enables system operation to be more economical and environment-friendly.

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 the technical field of transaction data verification, in particular to a transaction data verification method and system. The method comprises the following steps: carrying out transaction data acquisition on block chain network nodes to obtain an original transaction data set; performing adaptive heterogeneous fragmentation on the original transaction data set to obtain a fragmented transaction data set; performing verification weight coefficient distribution on the fragment transaction data set to obtain a weighted fragment transaction data set; performing multi-level verification engine deployment on the verification nodes to obtain a layered verification node network; performing asynchronous message queue mechanism configuration on the hierarchical verification node network to obtain a parallel verification network cluster; and performing load balancing scheduling on the weighted fragment transaction data set based on the parallel verification network cluster to obtain a transaction verification task sequence. According to the invention, the throughput and transaction confirmation speed of the block chain system can be effectively improved.

The invention relates to management of a vehicle network, and particularly discloses an LIN-based network management method, electronic equipment and a wake-up manager. The method comprises the steps that when it is detected that a wake-up signal exists on an LIN bus, a specific LIN wake-up source is determined; determining corresponding PNC information according to a mapping relation between the LIN wake-up source and a local network cluster PNC; and waking up the network according to the PNC information. According to the embodiment of the invention, the network is awakened based on the LIN awakening source and can be awakened in a basic software layer, so that the network can be quickly awakened.

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 provides a geophysical recognition method for medium-deep under-compacted low-speed mudstone, which comprises the following steps of: obtaining representative longitudinal wave velocity, transverse wave velocity and density of low-speed mudstone and gas-bearing sandstone in logging information through statistical analysis of data, and designing a theoretical model of a superposition relationship between the low-speed mudstone and the gas-bearing sandstone in combination with geological information, so as to obtain the geophysical recognition result of the low-speed mudstone and the gas-bearing sandstone. Performing convolution forward modeling and pre-stack forward modeling on the model, respectively calculating to obtain post-stack seismic attributes and pre-stack seismic attributes of the model, performing sensitive attribute optimization on the attributes, taking the obtained sensitive attributes as learning samples, and identifying the superposition relationship by using probability self-organizing neural network clustering. According to the method, the pre-stack attributes are added into sensitive attribute optimization, so that the fuzzy self-organizing neural network can more accurately recognize the gas-bearing sandstone and low-speed mudstone stacking relation, the low-speed mudstone and the gas-bearing sandstone can be effectively distinguished, and support is provided for subsequent effective reservoir prediction.

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.

The invention discloses a computer network system and a computer networking method for network service discovery. The computer network system includes processing circuitry accessible to a storage device, the processing circuitry configured to encode, by a network controller executing in a software defined network (SDN), one or more attributes with information identifying a network service, where the one or more attributes conform to a routing protocol. The processing circuitry is further configured to generate, by the network controller, an advertisement in a first network cluster executing within a container orchestration platform of the SDN, where the advertisement conforms to a routing protocol and includes one or more attributes. The processing circuitry is further configured to broadcast, by the network controller, an advertisement to a second network cluster executing within the container orchestration platform of the SDN according to the routing protocol.

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 embodiment of the invention provides a network cluster data acquisition method and device.The network cluster data acquisition method comprises the steps that a problem text is acquired, and the problem text aims at at least one network cluster; the question text is processed on the basis of a target data processing model, an answer text is generated, the answer text comprises an answer text and a step analysis text, and the answer text is generated by the target data processing model on the basis of the step analysis text; the step analysis text is generated by a target data processing model according to the problem text and network cluster data corresponding to each network cluster. According to the scheme, based on data acquisition and processing of the target data processing model, the problems that a traditional method is complex in operation and low in efficiency are solved, then multi-dimensional and multi-index flexible response is achieved, and the data acquisition speed and accuracy are remarkably improved.

The invention relates to an unmanned aerial vehicle ad hoc network clustering method and system based on adaptive node degree difference. According to the method, the multi-channel characteristic of the large-scale unmanned aerial vehicle ad hoc network is considered, the node similarity introducing the multi-channel constraint condition and the adaptive node degree difference is defined for clustering, and the uniformity of the node connection condition can be reflected through the adaptive node degree difference during clustering, so that the clustering result is more balanced, and the clustering efficiency is improved. The problem of excessive competition or waste of resources caused by excessive maximum clusters or minimum clusters is avoided; furthermore, the connection edges among the nodes in the unmanned aerial vehicle ad hoc network after clustering are circularly processed, the modularity increment brought by cluster merging is dynamically calculated, and the optimal node combination is selected for cluster merging, so that the community structure and the overall performance of the unmanned aerial vehicle networking can be continuously optimized, the cluster scale deviation is reduced, and the optimal clustering result is realized.

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 provides a depth-first underwater acoustic sensor network clustering routing method, and relates to the technical field of signal transmission. The method includes: acquiring depth data of a target area; performing height division according to the depth data to obtain a node network layer; performing sub-layer division according to the height of each layer of the node network layer to obtain an upper sub-layer and a lower sub-layer of each layer; determining a cluster head of each layer; and according to the network layer and the sub-layers where the current source node is located, selecting the cluster head of the upper sub-layer in the current network layer or the cluster heads in the two sub-layers of the upper network layer to communicate, and determining an optimal path for information transmission. According to the method, the problem that the service life of the grid is shortened due to increase of network hops and energy consumption in an underwater routing method in the prior art is 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.