79 results about "Network clustering" patented technology

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 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.

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 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 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 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.

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.

The invention discloses a computing resource scheduling method and system based on a computing power network cluster, and relates to the technical field of computing power network clusters, and the method comprises the steps: determining computing tasks matched with a plurality of computing power nodes according to the calculation amount of the plurality of computing tasks, the node load states of the plurality of computing power nodes and a resource scheduling system; and the matching accuracy of the plurality of computing power nodes to the computing task is ensured. Therefore, a calculation progress distribution diagram of the computing power network cluster is determined according to the calculation progress of the plurality of computing power nodes on the calculation task and the calculation modes of the plurality of computing power nodes; according to the computing progress distribution diagram of the computing power network cluster, the computing resources matched with the computing power nodes and the task states of the computing tasks, the optimization system of the computing resources is determined, the computing power of the computing power nodes in the computing process is dynamically optimized, further optimization of the computing resources is achieved based on the optimization system of the computing resources, and the computing efficiency is improved. And the computing power of the computing power node in the computing process is improved.

The present application belongs to the technical field of data processing, and particularly relates to a kind of geotechnical data structured conversion method.The method splits geotechnical log unstructured data into the minimum information unit of entity, attribute, relationship clue by information particle deconstruction, generates standardized entity and relationship through clue association network clustering and double-track reasoning model processing, constructs knowledge graph and maps into structured data;Then, the initial geological model is constructed using structured data, the core data is extracted and converted into an extended IFC format intermediate file containing geological special entities, and finally data fusion is realized by associating BIM model.The method realizes the structured conversion of unstructured data, breaks through the data format barrier and improves the efficiency of engineering collaboration.

The invention discloses construction and application of an interpretability model for virus molecular propagation network clustering. The construction comprises the steps of sorting a sample data set, preprocessing the data set, constructing a model, training, selecting an optimal model for interpretability analysis and the like. The method has the advantages that sequences and epidemiological information are converted into digital signals at the same time, comprehensive information fusion is carried out, and interpretable comprehensiveness and accuracy are achieved; meanwhile, an SHAP black box model is used for explaining a framework, an optimal model is selected for interpretability analysis, the SHAP value of each sample feature is calculated, contribution of the features to a model prediction result is quantified, and composition and features of the network are more effectively explained.

A communication method, device and apparatus based on wireless ad-hoc network. The method comprises: collecting real time network topological information of a plurality of candidate communication terminals; screening the target communication terminal and the communication terminals for a target network central node and a plurality of network sub-central nodes, determining current central communication terminal and initial communication terminals corresponding to the plurality of network sub-central nodes; generating a list of centrality and generating a forwarding sequence table according to the list of centrality, generating a data time slot scheduling table based on the data time slot scheduling information, sending or forwarding the list of centrality, the forwarding sequence table and the data time slot scheduling table in the target RF channel; sending management and control instructions in the target RF channel and controlling all the initial communication terminals in the network cluster to execute the management and control instruction.

The invention discloses a multi-dimensional resource oriented container scheduling and load balancing method and system in a computing network cluster, and the method comprises the steps: constructing a cluster resource state monitoring and index calculation module, and collecting the multi-dimensional resource use data of a physical node and a container in real time, calculating a resource ratio of node volume to container volume based on black box and white box monitoring modes; based on a calculation result, identifying overload nodes and selecting containers to be migrated, processing the overload nodes according to a descending order of node volumes, and selecting migration containers in each node according to a descending order of container volume-to-resource ratios; and selecting immigration nodes for the target container, investigating potential target nodes according to an ascending order of node volumes, finishing container migration on the premise of meeting capacity constraints, and iteratively executing until a cluster has no overload nodes or no executable migration. According to the method, efficient dynamic resource scheduling is provided for diversified workloads in the heterogeneous container cluster environment, the cluster resource utilization rate and scheduling efficiency are improved, and technical support is provided for large-scale containerization deployment.

The invention discloses a low-carbon park capacity configuration and optimization method for photovoltaic and electric heavy trucks. A Monte Carlo sampling method is adopted to simulate the daily driving mileage and the charging starting time of the pure electric heavy truck, and a pure electric heavy truck charging quantity model is established; the method comprises the following steps of: carrying out scientific division on peak, flat and valley periods by adopting a self-organizing map (SOM) neural network clustering method, and optimizing time-of-use electricity price and the charging amount of the pure electric heavy truck by taking a fuzzy demand response mechanism based on a Logistic function as a basis and adopting a non-dominated sorting genetic algorithm (non-dominated sorting genetic algorithm-II); an upper layer system capacity configuration planning model with the minimum park equipment investment cost as the target and a lower layer low-carbon optimization scheduling model with the minimum system operation cost as the target are established, and a green power supply mode of photovoltaic power station-charging pile-pure electric heavy truck is established. According to the strategy provided by the invention, carbon emission and economic cost are reduced, and collaborative development of green low-carbon power supply and economic benefits is realized.

The embodiments of the present disclosure provide a network cluster data acquisition method and apparatus, the network cluster data acquisition method comprising: acquiring question text, the question text being for at least one network cluster; processing the question text on the basis of a target data processing model to generate response text, wherein the response text comprises answer text and step analysis text, the answer text is generated by the target data processing model on the basis of the step analysis text, and the step analysis text is generated by the target data processing model according to the question text and network cluster data corresponding to each network cluster. By using a target data processing model for data acquisition and processing, the present solution addresses the operational complexity and low efficiency of traditional methods, thereby achieving flexible multi-dimensional, multi-indicator responsiveness, and significantly improving the speed and accuracy of data acquisition.

The invention relates to the technical field of development, can be applied to financial network scenes, and discloses a cloud native network configuration method and device, equipment and a medium, and the method comprises the steps: storing full node information in a network cluster to a distributed key value storage system through a command line interface; performing self-defined plug-in connection with the distributed key value storage system in response to the operation and maintenance tool, and performing deployment marking on a target node in the network cluster to obtain a deployment label of the target node; and associating and deploying the cloud native network and the routing daemon process on the target node in the network cluster according to the deployment label of the target node, so that the target node realizes bidirectional transmission of routing information through component linkage. According to the process, the cloud native network configuration process needing manual operation can be converted into an automatic process capable of being repeatedly executed, so that the configuration process of the cloud native network adapts to the dynamic characteristics of the cloud native environment, and the flexibility of the cloud native network in the deployment stage is improved.

The application relates to a dynamic time sequence network clustering method and device based on contrast learning, electronic equipment and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: obtaining to-be-processed data; inputting the to-be-processed data into a pre-trained variational autoencoder to obtain dimension-reduced features corresponding to the to-be-processed data; wherein the pre-trained variational autoencoder is obtained according to a training method based on contrast learning; the training method based on contrast learning adjusts parameters of the variational autoencoder by using a loss function set based on contrast learning; and a clustering method is used for clustering analysis of the dimension-reduced features corresponding to the to-be-processed data. The pre-trained variational autoencoder is used for dimension reduction processing of the to-be-processed data, and then clustering analysis is performed. A variational autoencoder capable of considering changes in a dynamic time sequence network is proposed, the variational autoencoder can perform dimension reduction processing on data, effectively extract main features of the dynamic time sequence network, and perform clustering analysis.

This invention provides a traction system fault detection method based on PPDM and expert prompting learning, including the following steps: S1) Obtaining the fault-free dataset U from the simulation platform. N and noisy fault dataset U F S2) Construct a reconstruction model based on PPDM, including PDM and ICM; S3) Construct a domain generalization target network clustering model to obtain P meta ;S4) Change U N Input into PDM, based on P meta Get P i The extracted features are compared with P i Reconstructed into U f1 Input into ICM, and calculate J. th ;S5) P i As "K" and "V", U F The feature is designated as "Q"; S6) will use U F Input into PDM and cross-attention network to generate P m Feature extraction and P m Reconstructed into U f2 The input is fed into ICM, j is calculated, and compared with Jth. If j > Jth, then... th There is a fault, if j <J th No faults.

The invention provides a wireless sensor network cooperative charging optimization method based on node attributes, and relates to the technical field of wireless sensor networks, and the method comprises the steps: carrying out the network clustering of each sensor node in a wireless sensor network, and determining a cluster head node and a common node of each cluster; respectively distributing independent mobile charging equipment for the cluster head nodes and the common nodes; receiving a charging request of a to-be-charged sensor node, acquiring an energy state and position information of the to-be-charged sensor node, and establishing a charging planning mathematical model; solving the charging planning mathematical model to obtain an optimal charging path scheme of each mobile charging device; and controlling each mobile charging device to charge each to-be-charged sensor node according to the optimal charging path scheme. According to the invention, the charging path of the charging equipment is dynamically optimized while the node energy supply is ensured, the charging efficiency is improved, the charging cost is reduced, and the service life of the network is prolonged.

The application discloses a kind of combination clustering scene reduction method and system based on potential decision result, the uncertainty factor of each node in power system is expressed as multidimensional curve input domain in the form of time sequence curve;Based on multidimensional curve input domain, the power supply planning problem solving model under different time scale scene is constructed, considering operation checking, multidimensional curve input domain is converted into potential decision result domain for clustering analysis;SOM neural network clustering is carried out to potential decision result, and preliminary clustering result is obtained;SOM and k-medoids combination clustering method is used to finally classify preliminary clustering result, and finally the typical scene used in operation checking is obtained.The application is used to cluster the most representative scene set in power supply planning typical scene operation checking, to reduce the complexity of solving model to the greatest extent under the condition of guaranteeing the reliability of checking result, improve calculation efficiency.

This application discloses a method, apparatus, electronic device, and storage medium for transmitting startup files, relating to the field of computer technology. The method includes: when the node to be deployed in a network cluster is in the basic system environment, obtaining the node status of other cluster nodes, and then determining the number of peer service nodes based on the node status of the other cluster nodes. When the number of peer service nodes meets the peer service conditions, it indicates that there are enough nodes in the network cluster to provide peer upload services. Each first file request is sent to the corresponding peer service node. The node to be deployed can obtain the first file block returned by the peer service node using the peer service download mode. In this way, the upload bandwidth of all peer service nodes in the network cluster is effectively utilized, thereby effectively improving the download speed of startup files and enhancing deployment efficiency.

The present invention relates to quality of service (QoS) of services. The present invention provides a QoS handling system for one or more services, the QoS handling system comprising a plurality of QoS handling nodes arranged in a hierarchical tree structure having at least two levels of hierarchy. The highest level of hierarchy comprises one or more root nodes, each root node being associated with a set of service instances of the services. The lowest level of hierarchy comprises a plurality of leaf nodes, each leaf node being associated with one service instance, each leaf node being derived from at least one root node, and the service instances of the set of each root node being associated with the leaf nodes derived from the root node. Each leaf node can apply a local QoS policy to the associated service instance of each leaf node, and each root node can apply a first common QoS policy to the set of service instances of each root node.

The invention provides an academic network graph clustering method based on structure perception and prototype constraint, and the method comprises the steps: obtaining high-dimensional data of an academic network, searching a corresponding feature matrix and an adjacent matrix, carrying out the normalization smoothing of the feature matrix, and obtaining an adjacent matrix; performing triple evaluation verification on the academic network high-dimensional data by using the processed feature matrix and the adjacency matrix to generate a corresponding verification view, performing soft orthogonality on the verification view to obtain an orthogonal view, calculating comparison loss and KL loss between the two views, determining a plurality of pieces of key distribution information of the academic network high-dimensional data, and determining the academic network high-dimensional data. An academic network clustering result is generated in the academic network, similar contents in related recommended papers are identified by using each piece of key distribution information and marked, and a report is recommended and displayed, so that a user can be helped to rapidly mine core resources and associated information in the academic network, the efficiency and accuracy of academic research are effectively improved, and the user experience is improved. And powerful technical support is provided for academic communication and innovation.