113 results about "Network key" patented technology

The invention discloses a network key node identification and optimization method, system and device, and a storage medium, and relates to the field of key node identification and dynamic optimization of an electric power Internet of Things communication network, and the method comprises the steps: collecting the original data of the electric power Internet of Things, and obtaining a clean data cube in space-time alignment; constructing a dynamic space-time map, and further generating a dynamic weight matrix; evaluating the key nodes based on the dynamic weight matrix to obtain a node priority list; establishing a multi-target optimization model based on the node priority list in combination with a real-time network state; solving the multi-objective optimization model to obtain an optimal solution set, performing strategy matching and verification to obtain a verified optimization instruction set, and identifying and optimizing the network key nodes through the verified optimization instruction set; according to the method, through multi-dimensional quality portrait construction and space-time atlas analysis, network key nodes are scientifically identified and dynamically optimized, so that the stability, the safety and the performance of the network are improved.

The invention relates to the field of multimedia communication and network control, in particular to an intelligent network control method for low-delay video return and related equipment. The intelligent network control method comprises the following steps: acquiring network key indexes including bandwidth, delay, jitter and packet loss rate of a network link in real time, and providing real-time network environment data support for transmission strategy adjustment. According to the method, an intelligent control mechanism combining network state perception and video content feature recognition is constructed, so that the technical problem of low-delay video return in a complex network environment is effectively solved. Specifically, key indexes of a network link are collected in real time, and a lightweight CNN model is introduced to analyze the video content activeness, so that dual perception capabilities for a network environment and content features are formed, data support is provided for dynamic adjustment of coding parameters, and accurate balance between video quality and network adaptability is realized.

Technologies directed to detecting and controlling different types of ZigBee devices on different ZigBee networks reliably and efficiently. In one method of operating a first device, the method includes detecting first and second Zigbee networks. The method sends a first request to rejoin the first Zigbee network, the first request being encoded with the first network key. While rejoined as part of the first Zigbee network, the method detects a first set of devices part of the first Zigbee network. The method repeats this for a second set of devices part of the second Zigbee network. The method determines, using the device data, a subset of controllable devices located in proximity, and sends a message with the command to each in response to receiving a command. Each message is encoded with a respective network key and a respective link key specified in the device data.

The invention relates to the technical field of node identification, in particular to a network key node identification method fusing improved measurement and propagation influence, which comprises the following steps: setting an edge weight and a cross-layer dependency relationship of an infrastructure network; constructing weighted cross-layer local contact centrality, attenuation cross-layer path centrality and random walk centrality indexes; taking the maximum value of the three adaptive scores as an improved measure of the node; defining metric value standardization; calculating a propagation influence value, standardizing the measurement value, and carrying out weighted fusion on the measurement value and the propagation influence value to obtain a fusion score; carrying out propagation influence value convergence judgment by utilizing an LT linear threshold model; and arranging the nodes based on the fusion score value, and outputting a key node identification result. The method solves the problems that a traditional key node identification method only regards a multi-layer network as a plurality of isolated single-layer networks, lacks measurement of node heterogeneity in the multi-layer network, ignores a coupling relation between network layers and is difficult to be accurately applied to an urban multi-layer infrastructure network.

The invention discloses a public traffic composite traffic network key node identification method based on an improved K-shell algorithm, and the method comprises the steps: firstly extracting the line, geographic position, vehicle arrival interval and rated passenger capacity information of a subway and a bus station from multi-source data, and constructing a weighted composite traffic network considering the intra-layer transportation capacity and inter-layer transfer relation; node stripping is carried out based on a K-shell algorithm, and a shell fine position KP value is calculated in combination with a node removal sequence; further introducing a neighborhood structure overlap ratio, and constructing a node local propagation influence index; meanwhile, a comprehensive correction coefficient is constructed by considering the difference between intra-layer node strength and transfer attributes and the transportation capacity difference between subway and bus layers; and finally, a node comprehensive importance index IKA is formed and is used for sorting and identifying network key nodes. Finally, the recognition effect is verified through network efficiency, connectivity and SIR (susceptibility-infection-recovery) propagation simulation, and the result shows that the method is superior to a traditional centrality algorithm in the aspects of recognition precision and propagation influence measurement.

The invention relates to the technical field of optical network operation and maintenance, and particularly provides an optical network fault prediction method and system based on MEMS characteristic embedding and digital twinning, and the method comprises the steps: collecting multi-physics field coupling parameters of optical network operation in real time through an MEMS multi-physics field coupling sensing unit which is disposed at an optical network key node and is embedded in a physical layer device; constructing an optical network cross-scale digital twin, inputting parameters to realize real-time synchronization and fault rehearsal of a physical entity and a virtual mirror image, and outputting a rehearsal result; constructing a fault prediction model based on an optical network physical mechanism, inputting parameters and rehearsal results to complete feature fusion and trend analysis, and outputting a fault prediction result; an intervention strategy is generated and subjected to twinborn virtual simulation verification; and after the verification is passed, driving the physical layer to execute intervention, and feeding back effect data to dynamically update twin model parameters. A closed loop of perception, modeling, prediction, intervention and feedback is formed, and the problems of prediction lag and low operation and maintenance efficiency in the prior art are solved.

The invention provides a centralized heating method and system based on room temperature feedback regulation, and the method comprises the steps: collecting and verifying real-time room temperature data, and generating a room temperature feedback signal; the signals are transmitted to a central control unit for sequential processing, and a temperature-time two-dimensional data matrix is generated; a preset dynamic compensation algorithm is used for calculating a heat demand correction value of each heat supply pipe network key node; the heat supply pipeline flow is dynamically adjusted according to the correction value; and verifying the effect through a closed-loop feedback mechanism and optimizing algorithm parameters. According to the method, a network is constructed to realize multi-node synchronous acquisition and verification, and heat distribution heterogeneity and fluctuation characteristics are captured in combination with matrix processing and interpolation completion. Compared with a traditional single-point sensor, according to the scheme, through two-dimensional mapping and anomaly detection, the adjustment response time is shortened to the sampling period level, the temperature deviation calculation precision is improved to + / -0.1 DEG C level, the overheating-underheating alternating phenomenon is solved, and accurate dynamic matching of the heat demand correction value is achieved.

The invention relates to a capacity expansion optimization method, system and equipment based on network performance marginal gain, and belongs to the technical field of network communication. The method comprises the following steps: constructing an undirected graph of communication network topology; defining a service requirement for each pair of nodes in the undirected graph, endowing each edge with a capacity value, and constructing to obtain an MCFP model; on the basis of the Lagrange duality theory, a duality model of the MCFP model is constructed, and a column generation algorithm is adopted to carry out double-model synchronous solution to obtain a maximum concurrency factor, optimal path flow and capacity constraint optimal duality variable; performing network key bottleneck identification according to the capacity constraint optimal dual variable and generating a plurality of capacity expansion candidate schemes; and comprehensively evaluating the capacity expansion candidate scheme by combining the network performance marginal gain, the influence degree of the link capacity change and the implementation cost, and selecting the optimal capacity expansion scheme for implementation. According to the invention, accurate load evaluation, bottleneck quantitative identification and efficient capacity expansion optimization of the communication network can be realized.

The present application discloses a quantum cryptographic network key relay dynamic routing method, device, and system. The method comprises: querying all accessible key relay paths in a quantum cryptographic network; selecting each relay node on the key relay paths to generate derived key pools for tasks assigned to the key relay paths; on the basis of the number of derived key pools generated by each relay node on each key relay path and a key generation speed, calculating a path weight of each relay node on each key relay path; and on the basis of the maximum path weight and the number of the relay nodes on each key relay path, determining an optimal key relay path. The present application solves the key generation rate bottleneck problem in key relay, involves a simple routing calculation method, and improves the concurrency performance.

The invention provides an automatic networking method and system for key equipment of a building automatic control system. The building automatic control system comprises a core equipment node and N key equipment nodes Ni, the method comprises the following steps: acquiring the current states of a core equipment node and a key equipment node Ni, and realizing automatic discovery and automatic networking of key equipment by adopting a corresponding method: when acquiring that the core equipment node is electrified earlier than the key equipment node Ni, executing an automatic networking method of the key equipment that the core equipment node is online earlier; when obtaining that the key equipment node Ni is randomly and sequentially electrified and the core equipment node is finally electrified, executing an automatic networking method of the key equipment that the key equipment is firstly online; when the key equipment Ni is restarted after successful networking is acquired, executing an automatic networking method of the key equipment in which the networked equipment automatically solidifies a network environment; and when the core equipment node is restarted after successful networking is acquired, executing the key equipment automatic networking method for reconnecting the networked key equipment after disconnection.

The invention provides a weighting network key node identification method based on layering. The method comprises the following steps: step 1, establishing a multi-layer network initial model based on a layering network thought; 2, selecting two layers of networks with the maximum similar modularity gain after combination to perform iterative combination, and obtaining a multi-layer network model by taking the similar modularity gains of any two layers of networks are non-positive as a loop termination condition; step 3, considering betweenness indexes of each network node in the multi-layer network model, measuring importance of each network node in the multi-layer network model, and dividing the network nodes into interactive nodes, first-level non-interactive nodes and second-level non-interactive nodes; and 4, evaluating the importance of the interactive nodes, the primary non-interactive nodes and the secondary non-interactive nodes by adopting three betweenness indexes. According to the method, multi-layer network modeling is carried out on the premise that the global attributes of the key nodes of the complex network have limitation, and the recognition accuracy of the key nodes in the network nodes of the multi-layer network is ensured.

The invention belongs to the technical field of network key node identification, and particularly relates to a social information network key node identification method based on reinforcement learning. Comprising the following steps: acquiring original graph data of a social information network to be identified, introducing a virtual node to be connected with all nodes and representing the virtual node as an undirected graph; generating an initial node feature matrix based on the adjacent matrix of the undirected graph; carrying out topological relation enhancement processing according to the adjacent matrix and the initial node feature matrix to obtain a weighted topological incidence matrix; performing channel-level dynamic calibration on the initial node feature matrix to obtain a weighted node feature matrix; performing hierarchical topological aggregation according to the weighted topological incidence matrix and the weighted node feature matrix to obtain three aggregation features; fusing the three aggregation features to obtain weighted fusion features; the weighted fusion features serve as agent input, a reinforcement learning method is adopted to train a key node recognition network, and a trained key node recognition network is obtained; according to the invention, the node identification accuracy is improved.

A wireless communication device. The wireless communication device comprises a radio transceiver; a first processor; a first non-transitory memory; and a subscriber identity module (SIM). The SIM comprises a second processor, a second non-transitory memory storing network keys that the radio transceiver uses to authenticate to a cell site, and an applet stored in the second non-transitory memory. When executed by the second processor, the applet sends a request to know where the wireless communication device is to the first processor, receives an answer from the first processor containing a cell global identifier (CGI) of a first cell site that the radio transceiver is attached to and containing a received signal strength of the first cell site, determines that the received signal strength of the first cell site is below a predefined threshold, and sends a refresh command to the first processor.

The invention discloses a subway network key station identification method considering cascade failure, an electronic device and a storage medium, and the method comprises the steps: simulating a cascade failure process after station failure based on a constructed subway weighted time-varying network and the initial state and degree value of each station in each time period, building a passenger flow distribution rule based on the consideration of origin and destination of passengers, and carrying out the identification of the subway network key station. And obtaining the metro weighted time-varying network after cascade failure caused by failure of each time period and each station. And based on the node failure rate, the network maximum connection rate and the network passenger flow intensity entropy after each station loses efficacy in each time period, calculating the comprehensive importance of each station in each time period, and identifying the key subway station in each time period based on the comprehensive importance. The method is used for assisting a public transport operator to identify key stations in each time period in a subway network, so that decision support is provided for emergency plan making under emergencies, and a scientific basis is provided for emergency operation scheduling and management of the subway network.

The invention discloses an unmanned aerial vehicle network intrusion detection method based on a lightweight Transform, belongs to the technical field of network technology security, and can improve the detection performance of unmanned aerial vehicle network traffic. Comprising the following steps: capturing network traffic data in real time through traffic monitoring equipment or software deployed on a network key node to obtain a network traffic sequence; performing standardization processing on the network flow sequence; increasing position information for the standardized network flow sequence by adopting rotation position coding; performing local feature aggregation and global feature enhancement on the encoded network traffic sequence by using a local aggregation attention unit; and extracting the enhanced features by using a lightweight feedforward neural network, processing the extracted features by using a stacked multi-layer lightweight Transformer encoder, outputting a detection result by using a SoftMax classifier, and detecting an attack type.

A system for optimizing one of a 5G New Radio (NR) network or a 4G Long Term Evolution (LTE) network operation, includes: an artificial intelligence (AI) engine configured to: a) implement at least one of the following: i) collection of raw training data set; ii) training of an AI agent; iii) inference generation triggered by UE; iv) using at least one of network key performance indicator (KPI) report and network operator inputs; and v) network KPI collection after application of inferences; andb) at least one of infer and apply at least one of i) network-wide optimal per-gNB or per transmission / reception point (TRP) Synchronization Signal (SS) burst set, and ii) associated channel state information reference signal (CSI-RS) configurations for downlink (DL) reference signal transmissions, thereby enhancing at least one of network transmission power efficiency and spectral efficiency.

Systems and methods for making telecommunication network predictions on a granular geographic area-scale. The method can include receiving network key performance indicators (KPIs) associated with a plurality of user equipment (UEs) and then parsing the network KPIs into a plurality of geographic area-based categories corresponding to network KPIs obtained by cell towers or UEs located within a given geographic area, like a zip code. The method can further include combining the geographic area-based categories into clusters determined based on one or more similarities in the network KPIs of each, and predicting a most-influential one of the network KPIs within at least one of the clusters via a machine learning (ML) model. In response, the method can then cause an indicator to be generated at a user interface, the indicator indicating the most-influential one of the network KPIs within the at least one of the clusters.

The invention discloses an electric power communication network key node identification method based on a graph neural network, and belongs to the technical field of intelligent power grid analysis. The method comprises the following steps: firstly, constructing an electric power communication network topological graph, generating an adjacent matrix from the topological graph, standardizing the adjacent matrix to generate a first input matrix, and encoding one-hot of nodes to obtain a second input matrix; inputting the first input matrix and the second input matrix into a trained GCN auto-encoder to obtain a node potential representation; and inputting the node potential representation and the adjacent matrix into a trained GNN node ranking prediction model, outputting node importance scores and sorting, and selecting the first k nodes as key nodes. During model training, a BA model is adopted to generate a synthetic network, an SIR propagation model is adopted to obtain the real importance of nodes, and a listMLE loss function and an Adam optimizer are combined to train an auto-encoder and a prediction model. The method can automatically extract the topology deep features, is high in calculation efficiency and generalization capability, and improves the key node recognition precision.

The invention relates to the technical field of industrial Ethernet, and particularly discloses an Ethernet-based intelligent electric meter data acquisition system, which abandons passive congestion monitoring, and actively calculates a predictive congestion level capable of pre-judging a congestion trend by analyzing a change speed and an acceleration of a network key index time sequence. And then, the system deeply fuses the prediction level and the task priority, and dynamically generates a dynamic scheduling table which allocates an exclusive uploading time slot and an adaptive sampling rate to each ammeter. And finally, the electric meters perform ordered peak shifting data uploading according to the dispatch table. According to the scheme, the synchronous and concurrent data flood flow is converted into the asynchronous time division data flow, so that the formation of a data acquisition storm is avoided fundamentally, and the acquisition integrity and timeliness of key data are ensured under high network pressure.

The invention discloses a key negotiation synchronization method and system for a complex satellite network, and the core of the key negotiation synchronization method is that chaotic system synchronization and neural network mutual learning synchronization are organically fused, and a dual synchronization architecture is constructed; specifically, a local field of each hidden unit of the TPM is transformed and then input into improved Logistic chaotic mapping, and then output bits of the hidden units are determined after output of the chaotic mapping is transformed through an arcsine function, so that a bidirectional feedback coupling relation is established between state updating of a neural network and evolution of a chaotic system. According to the design, the fast synchronization characteristic of the chaotic system and the mutual learning capability of the neural network are mutually promoted, the synchronization convergence speed of key negotiation is remarkably increased, meanwhile, the attack resistance of the system is enhanced by utilizing the unpredictability of chaos, and finally, on the premise that the safety is guaranteed, the key negotiation efficiency is improved. And the efficiency and the reliability of the key negotiation process of the complex satellite network are effectively improved.

This invention relates to the field of computer complex network optimization technology, and discloses a method for mining key nodes in a network based on a discount strategy and an improved discrete crow search algorithm. The method includes: first, preprocessing the citation network to convert it into an adjacency matrix, and then reversing the network to obtain a reverse network; then using the LRDiscount algorithm to initially screen the nodes in the reverse network to obtain a candidate node set. C Next, the candidate node set is optimized according to the local optimization process of the improved discrete crow search algorithm. C Optimization is performed; finally, the optimal set is selected from the optimized node set, and the node influence is evaluated to obtain the final result. k Key seed nodes. Compared with existing technologies, this invention combines the influence discount strategy of network nodes with an improved discrete crow search algorithm. It spreads influence by updating node positions in the process of mimicking crow search, and finds key nodes by the marginal gain generated by individual crows walking in the citation network.

The application discloses a kind of third party construction fixed and mobile intelligent monitoring device, it is related to safety monitoring technical field, specifically includes: acquisition positioning unit, pipe network mapping unit, intelligent fusion evaluation unit and early warning disposal unit;Acquisition positioning unit carries out continuous image acquisition to gas pipe network key protection area by fixed monitoring equipment, and supplementary acquisition is carried out to fixed monitoring blind area or temporary construction area by mobile monitoring equipment;Pipe network mapping unit maps the space-time collection information corresponding to the collection image to the gas pipe network protection range, determines the correlation between construction target and gas pipe network;Intelligent fusion evaluation unit identifies construction target and its behavior state, generates construction risk grade;Early warning disposal unit corrects risk position and revises construction risk grade when meeting preset early warning condition, generates alarm information, the application can improve third party construction target positioning accuracy and risk early warning reliability.

The invention discloses a multimode communication method and a multimode communication system based on 2.4 GHz and SUB-1GHz networks. The technical problems of performance contradiction, poor network expansibility, high deployment cost and insufficient equipment cooperation caused by a single network frequency band in the prior art are solved. The method comprises the following steps: S1, constructing a communication architecture fusing an SUB-1GHz network and a 2.4 GHz MESH network; s2, the sewing equipment performs authentication with the gateway through the SUB-1GHz network, and the gateway distributes a 2.4 GHz network key after the authentication is passed; s3, dynamically maintaining a routing table between the devices based on an SUB-1GHz network, and carrying out data relay transmission through a 2.4 GHz MESH network; s4, when the new equipment accesses the network, authentication is carried out through the SUB-1GHz network of the adjacent equipment; and S5, when the equipment fails, copying the parameters of the same model to the failed equipment through the adjacent equipment. The method has the advantages of high speed, wide coverage and low cost expansion.

The invention relates to the technical field of sequential network processing, in particular to a sequential network key node identification method based on spectrum normalization propagation accumulation. Interactive node pairs in each continuous time period are extracted, a weighted adjacent matrix is constructed based on a full-time-domain node union set, and it is ensured that interaction time sequence characteristics and node integrity are considered; the adaptive propagation gain coefficient is calculated by means of the cumulative adjacent matrix spectral radius, the overall interaction strength of the network can be adapted without manual parameter adjustment, the propagation effect is prevented from being excessively amplified or underestimated, and the calculation stability is guaranteed; according to the method, propagation states are iteratively calculated in different time periods and accumulated to obtain a total propagation state, the transmission and accumulation strengthening characteristics of node influence in time sequence evolution are accurately described, and the recognition precision of key nodes is effectively improved.

The invention relates to an intelligent internet of things non-intrusive sensor controller. The system comprises a data acquisition unit, a data processing unit, a communication transmission unit and a remote operation and maintenance control unit, collecting first pipeline data, second signal data and third terminal data, and performing processor edge calculation, result screening and abnormal value elimination on the three types of data to obtain regular data; judging the battery voltage and the switching value according to a preset threshold value, and generating an alarm identifier; classifying and caching the regular data and the alarm identifier to obtain a standardized data frame; acquiring the data frame by adopting a modulation mode; synchronously transmitting the second signal data and generating a gateway core jump packet, and simultaneously completing terminal power-on and obtaining cloud platform instruction information; corresponding data parameters are adjusted according to the instruction, a cloud platform control instruction is received by means of an alarm identifier, a relay is driven to act after the instruction is transmitted through a LoRa channel, and the equipment state is read in real time based on regular data; and low-power-consumption data acquisition and remote control in a scattered scene are realized.

The application relates to a heterogeneous network key node identification method and device based on decoupling causal interaction, which comprises the following steps: designing an intention decoupling graph encoder, introducing a generation mechanism of a variational graph autoencoder, mapping node features of a heterogeneous network into a plurality of mutually independent Gaussian distributions, performing differentiable sampling on the node features through reparameterization sampling, and physically separating the node features; based on a contrast learning mechanism of mutual information maximization, regarding each layer of the heterogeneous network as a view, using the contrast learning mechanism to compare and learn mutual information of decoupled representations of the same node under different views, and filtering random noise edges appearing only in a single layer; constructing a structural causal model, identifying mixed factors based on the structural causal model, estimating the weight of the edge by adopting an inverse propensity weighting strategy, and performing causal readjustment on the weight of the edge to identify key nodes that truly have structural control power. The application has the effects of realizing semantic decoupling, structural denoising and causal correction, and improving the accuracy and robustness of network key node identification.

The invention discloses a regional network risk management and control method and system, and relates to the technical field of risk management and control analysis, and the system comprises an information collection module which is used for collecting basic information and login information corresponding to a regional network key node at a collection time node; the analyzing and processing module is used for analyzing and processing the basic information and the login information corresponding to the key nodes of the regional network, performing security analysis on the regional network based on an analyzing and processing result, and formulating a management and control strategy according to a security analysis result; the early warning module is used for executing the control strategy and outputting a warning signal based on the regional network key node; and the communication module is used for outputting notification information to a worker of the management and control area network. The method and the device have the effect of improving the security of the regional network.

In one non-limiting example, a method includes, at an activator device, selecting an environmental powered device from a plurality of environmental powered devices supportable by an access point. The method also includes sending, from the activator device to the selected environmental powered device, an identifier of the activator device and an identifier of the selected environmental powered device, where at least the identifier of the activator device is encrypted and sent to the selected environmental powered device in a request to activate the selected environmental powered device, and then receiving, at the activator device from the access point, an authorization token that can be used to authenticate the activator device and the selected environmental powered device for subsequent data. In some alternative examples, a freshness parameter can be used for encryption. In other alternative examples, an encryption key for the selected environmental powered device can be generated from a home network key set.

The invention provides a communication network key generation method and device, equipment, a medium and a product. The method comprises the steps that a first network element receives a security context of a terminal sent by a second network element; sending a first AS SMC request to the terminal, the request carrying first capability information used for indicating whether the first network element supports wireless key distribution; receiving first AS SMC completion information sent by the terminal according to the first AS SMC request, the first AS SMC completion information carrying second capability information used for indicating whether the terminal supports wireless key distribution; and when both the first network element and the terminal support wireless key distribution, the first network element negotiates with the terminal to generate an encryption and / or integrity protection key of an access layer, and performs a second AS SMC process based on the key. Therefore, the security of the communication network is improved, the first network element can directly negotiate the generation of the key with the terminal, the modification on the network side is small, the influence on the user experience caused by the long wireless key distribution time is reduced, and the improvement of the user experience is facilitated.

The invention discloses a backbone-level destroy-resistant communication system and method based on heterogeneous biplanes, and belongs to the technical field of communication networks. The system comprises a broadband transmission plane serving as a high-speed primary plane; the narrowband transmission plane is used as a heterogeneous backup plane; and the dual-plane cooperative transmission equipment is deployed at a user access node and a network key node, and integrates the functions of service analysis processing, network state monitoring, protocol conversion, dual-plane access adaptation and intelligent routing decision. According to the invention, physical redundancy is realized by constructing heterogeneous biplanes, and intelligent routing and seamless switching of service awareness are realized by using biplane cooperative transmission equipment. The service is preferentially transmitted through the broadband transmission plane in a normal state; when the broadband transmission plane is damaged, key emergency services such as anti-seismic command and scheduling can be automatically identified, and seamless switching to the narrowband transmission plane is realized, so that the continuity of the key services is guaranteed in an extreme environment, and the overall survivability and the survivability of network communication are greatly improved.