666 results about "G-network" patented technology

The dynamic bandwidth allocation and resource scheduling method based on the 5G network comprises the steps that S1, a base station collects service requests, network loads and channel information of user equipment of the 5G network, and the services are divided into real-time services, big data services, key services and conventional services; s2, the real-time service calculates the bandwidth by using deep learning; the big data service is distributed according to the idle bandwidth and the data volume proportion; key service main and standby links distribute bandwidths; conventional businesses come first and then are subjected to service distribution; s3, the base station maps bandwidth resources by using a multi-carrier and dynamic subcarrier algorithm, and optimizes signal transmission in combination with a beam forming technology; s4, processing service data according to a priority scheduling queue, and verifying signal transmission according to a channel quality adjustment coding mode; and S5, updating the data and evaluating the effect according to the user feedback, and re-optimizing the scheduling strategy as required. Through intelligent dynamic bandwidth allocation and resource scheduling, efficient, flexible and reliable transmission of different types of services is realized, and the network resource utilization rate and the service quality are improved.

The embodiment of the invention provides an environment monitoring method and system based on 5G. According to the method, a dynamic coupling sensing field is constructed in a 5G network coverage domain, an adaptive topology with nonlinear interaction of parameters is established, and when parameter abnormity is detected, differential geometric reconstruction of a 5G network slice control surface is triggered based on Riemannian curvature change. And constructing a space-time manifold space at the 5G edge computing node, decomposing and extracting a diffusion principal curvature direction, and iteratively solving a propagation rate of an abnormal parameter in combination with a geodesic equation to generate a diffusion leading edge envelope surface. And according to the main curvature distribution characteristics of the diffusion leading edge envelope surface, constructing a dynamic sampling optimizer, generating an exponential decay sampling function, and deploying a directional sensing wavefront. And carrying out conformal tightening processing on the sampling function and the geographic grid to generate an interactive holographic manifold, establishing an aging judgment criterion of an abnormal level based on curvature 2-form characteristics, and triggering a graded early warning pulse wave packet. According to the technical scheme provided by the embodiment of the invention, the real-time performance and precision of environment monitoring are improved.

The invention relates to a power edge cloud cooperative management method based on a swan OS, and the method comprises the following steps: S1, deploying intelligent edge nodes, setting an embedded AI acceleration unit, operating the swan OS on all the intelligent edge nodes, and achieving the seamless cooperation of equipment through the distributed characteristics of the swan OS; and S2, realizing self-organizing connection of edge devices by adopting a Mesh network technology, designing a direct communication path between the edge devices, and realizing rapid processing of local problems. S3, aiming at the real-time requirement of a power system, optimizing an MQTT / CoAP protocol stack, improving the efficiency and reliability of message transmission, and allocating special network resources for different types of power applications by utilizing the 5G network slice characteristics, S4, constructing a security architecture based on a zero-trust principle, enhancing the equipment access control and data transmission security, and S5, establishing a security architecture based on a zero-trust principle. And identity authentication and data integrity protection are performed by using a block chain technology, so that data tampering and counterfeiting are prevented. According to the method, the local distributed decision-making capability and the global cooperation capability are effectively improved.

The invention relates to a 5G network slice dynamic scheduling method and system based on multi-modal space-time perception and an event knowledge graph, and belongs to the technical field of mobile communication network resource management. According to the method, the change of a physical scene is sensed in real time by constructing a dynamically evolved event knowledge graph and designing a double-flow space-time cross network in combination with visual semantic analysis; dynamically adjusting the resource prediction model by adopting an event-scene dual-drive mechanism, dynamically adjusting parameters of the gated recurrent neural network through an elastic adjustment factor, and optimizing a multi-target resource allocation strategy based on a reinforcement learning algorithm; a two-stage resource scheduling mode is adopted, non-preemptive resource allocation of priority guarantee is implemented in an event triggering stage, and an optimization strategy of continuous adjustment is deployed in a steady-state stage. According to the method, the resource utilization efficiency and the service quality in a high-concurrency scene are remarkably improved, the method is compatible with an O-RAN standard interface, and the method is suitable for high-reliability and low-delay communication scenes such as smart cities and industrial internet.

The invention relates to the technical field of 5G networks, in particular to a 5G network intelligent optimization method and system, and the method comprises the steps: obtaining 5G network signal data, recognizing an abnormal power spectral density region, carrying out the filtering extraction of the abnormal power spectral density region, and separating interference signals, interference types including narrowband interference, broadband interference and directional interference; extracting features from the obtained interference signals, and performing interference type identification according to a random forest algorithm; starting a corresponding anti-interference means according to the interference type, and generating operation state data in real time; a bee colony algorithm is adopted to simulate bee behaviors for iterative search, and a local optimal resource scheduling strategy is determined; and executing the resource scheduling strategy, feeding back an execution effect, and restarting the bee colony algorithm to determine a new resource scheduling strategy if the execution effect does not reach a set expectation. Therefore, the problems of lack of dynamic adaptive adjustment, single anti-interference means, lack of cross-base station collaboration and the like in the anti-interference aspect in the prior art are solved.

The invention relates to the technical field of power distribution operation and maintenance management, and discloses an intelligent power distribution operation and maintenance management system based on 5G transmission. The system comprises a 5G real-time acquisition module, a multi-dimensional feature fusion module, a dynamic topology generation module, an anomaly propagation analysis module and a strategy optimization feedback module. The 5G real-time acquisition module acquires operation state data streams such as current and voltage waveforms, an equipment temperature sequence and environment monitoring indexes of the power distribution equipment through a 5G network; the multi-dimensional feature fusion module is used for separating equipment state features, calculating mutual information amount and generating equipment state feature tensors; the dynamic topology generation module constructs an association intensity matrix according to the feature tensor, and generates a hierarchical connection path and a dynamic equipment topological graph; the abnormal propagation analysis module extracts a state fluctuation sequence, identifies an abnormal transmission path and marks a core propagation node; and a strategy optimization feedback module generates a maintenance strategy priority queue according to the dynamic topology map, and feeds back an execution result to update the dynamic topology map, so that the intelligence and accuracy of power distribution operation and maintenance management are improved.

The invention discloses an adaptive protocol stack reconstruction and transaction-level data control method for 5G communication, and aims to solve the technical problems of fixed protocol stack structure, coarse transmission control granularity and the like of an existing communication module. The method comprises the following steps: firstly, analyzing an application layer communication session initialization instruction, generating a communication task label, acquiring 5G network environment state data in real time, and constructing a network state feature vector; then, the communication task label and the network state feature vector are input into a protocol mapping decision map, a protocol reconstruction instruction is generated, loading and unloading of a protocol module in a protocol stack resource pool are controlled, a dynamically-adaptive protocol path is constructed, and a transaction identifier is allocated to the protocol path; and establishing a transaction-level data buffer area based on the transaction identifier, performing priority marking on the data packet, and executing differentiated dynamic flow control operation according to the network state feature vector and the priority label, thereby realizing high-reliability and low-delay 5G communication data management.

The invention provides an intelligent network connection automobile abnormity detection system and method based on big data analysis, and the method comprises the steps: obtaining metadata in the communication between a vehicle CAN bus and a cloud, the metadata comprising a protocol type and a load feature, carrying out the analysis of the load feature according to the protocol type, and extracting a key field of a data package; constructing a load feature matrix according to the key field, and normalizing the heterogeneous data to obtain a feature data set in a uniform format; a mahalanobis distance algorithm is adopted to calculate the distance between each data point in the feature data set and a preset base line, and if the distance exceeds a preset threshold value, abnormal traffic is judged, and an abnormal traffic mark list is generated; fingerprint features of a transmission link are obtained from the 5G network slices, the fingerprint features of different slices are subjected to standardization processing, and a cross-slice consistent fingerprint feature data set is generated; therefore, security threats in a complex Internet of Vehicles environment can be effectively handled, and the accuracy and reliability of abnormal traffic detection are improved.

The invention relates to the technical field of power network resource management, and discloses a power network resource intelligent management method and system based on 6G and deep learning. The method comprises the following steps: acquiring node data through a 6G technology to obtain a topological mapping table, preprocessing the data to obtain a feature tensor, training a model cluster by adopting a space-time diagram convolution and long-short-term memory network hybrid architecture, analyzing a network state based on a model, generating a scheduling scheme, and issuing a control instruction through 6G network fragmentation to realize intelligent management of a power network. According to the invention, through the combination of the 6G communication positioning integration technology and the space-time diagram convolutional network and long-short term memory network mixed architecture, the real-time data acquisition precision and intelligent analysis capability of the power network are improved. Based on a collaborative optimization mechanism of multi-source data fusion and a deep learning model cluster, the comprehensiveness of power grid state evaluation and the accuracy of resource scheduling are enhanced.

The invention discloses a large-scale unmanned aerial vehicle marshalling method for an emergency fire rescue task, and the method comprises the steps: carrying out the clustering analysis of task points of a rescue region through an FCM algorithm, determining the membership degree of a flight task, and generating a task region; on the basis of real-time environment change of a fire scene, a dynamic membership updating task allocation state is realized through a visual detection algorithm of fusing a convolutional neural network with an FCM; meanwhile, through 3DT route planning optimization based on the laser radar technology, a 3D model recombination map model is established, linear optimization is carried out by introducing time parameters, and optimal path planning is provided for the unmanned aerial vehicle in real time. In the rescue process, data are automatically transmitted in a segmented mode through the slicing technology of the 5G network, an autonomous Mesh centerless network topology structure is implemented when signals are intense, real-time signal optimization is conducted through a repeater of the high-altitude unmanned aerial vehicle, and high efficiency and flexibility of rescue are ensured.

The invention discloses a network intrusion intelligent detection system based on a 5G network, and relates to the technical field of network security. Comprising a traffic anomaly detection and analysis module, a data encryption tampering detection module, an anomaly analysis and evaluation module, an attack prediction and prevention module, a dynamic isolation and protection module, a reverse tracking and tracing module and a redirection and isolation protection module. According to the system, more efficient and more accurate intrusion detection and threat defense can be realized in a 5G network environment by integrating a plurality of advanced technologies and intelligent modules. Compared with a traditional rule-based detection method, the system can adaptively learn and identify a new attack mode, and the detection capability for zero-day attacks, variant attacks and advanced persistent threats (APT) is enhanced. Besides, by combining deep learning and reinforcement learning algorithms, the system can predict potential attacks in a dynamically changing network environment and automatically optimize a defense strategy, and the response speed and accuracy of the defense system are improved.

The invention relates to the technical field of industrial Internet of Things, and discloses a low-delay industrial Internet of Things data transmission system based on a 5G network. The system comprises a plurality of functional modules such as a data acquisition module, a data preprocessing module, a network transmission optimization module and a congestion control module. The data acquisition module acquires a heterogeneous data set, the preprocessing module performs standardization processing, the network transmission optimization module selects an optimal channel and node, and the congestion control module predicts flow and adjusts a transmission strategy. In addition, the system also has the functions of security encryption, heterogeneous data synchronization, dynamic spectrum allocation and the like. Through cooperative work of the modules, the system realizes low-delay and high-reliability transmission of industrial Internet of Things data, effectively processes heterogeneous data, guarantees data security, improves spectrum utilization efficiency and energy efficiency, enhances fault tolerance and compatibility of the system, and is suitable for various industrial scenes.

The invention relates to the technical field of electric power system communication, in particular to an electric power 5G network service quality monitoring method and system and a storage medium, and the method comprises the steps: responding to a protocol data unit session establishment request signaling, and obtaining signaling plane data and data plane performance data through a signaling plane probe and a data plane probe; associating the signaling plane data with the data plane performance data through the service quality flow identifier and the timestamp to generate a multi-dimensional spatial-temporal characteristic matrix; based on the multi-dimensional spatio-temporal characteristic matrix, a network quality prediction score is obtained through dynamic spatio-temporal causality graph network prediction; and when the network quality prediction score exceeds a preset score threshold, establishing a network resource configuration optimization model to obtain an optimal network resource configuration strategy. According to the method, the signaling plane data and the data plane performance data are subjected to correlation analysis, and the network quality is predicted by using the dynamic space-time causal graph network, so that the network resource configuration is optimized, and the service quality of the power 5G network is ensured.

A method for MCCH acquisition in a 5G wireless network (1000) by a UE (100) includes: receiving, by the UE, a MCCH change notification from the gNodeB over a PDCCH. Further, the method includes detecting whether at least one of the UE is interested to receive a new MBS and the UE is already receiving an MBS. Further, the method includes determining the start of the new MBS session for the new MBS by reading the first bit of the MCCH change notification in response to detecting that the UE is interested to receive the new MBS, or determining the modification to the MBS session configuration of the ongoing MBS session by reading the second bit of the MCCH change notification in response to detecting that the UE is already receiving the MBS.

The application discloses a 5G network slice access control method based on service type, which comprises the following steps: 1) in a 5G network supporting multiple network slices, the type of a newly arrived service is judged, and the service is divided into a time delay sensitive service and a non-time delay sensitive service; if the service is the time delay sensitive service, step 2) is executed; if the service is the non-time delay sensitive service, step 3) is executed; 2) a bearing slice of the time delay sensitive service is selected; 3) a bearing slice of the non-time delay sensitive service is selected; 4) the next service needing transmission is waited for, and step 1) is executed. The application can minimize the time delay of the time delay sensitive service, realize the relative fairness of the time delay of various services, minimize the transmission waiting time of the non-time delay sensitive service, and improve user perception.

The invention relates to the field of network communication, and discloses a multi-source heterogeneous network data cooperative transmission method based on dynamic multi-dimensional evaluation and intelligent disaster recovery, which comprises the following steps: collecting system network parameters of a 5G network and a long-distance wired network in real time through a software definition interface module; performing weighted evaluation on the acquired system network parameters based on a dynamic link selection engine, the evaluation dimensions including real-time bandwidth, transmission delay and current link traffic, and generating an optimal communication link combination scheme; carrying out fragmentation and protocol adaptation on the data by adopting a general packaging framework and an intelligent label technology; distributing multi-path parallel transmission according to a priority strategy; at a receiving end, data recombination and disaster recovery supplementary transmission are realized through network coding and multi-path cooperation; and the evaluation weight is dynamically optimized in combination with reinforcement learning. According to the invention, the problems of rigid link selection, low protocol conversion efficiency and insufficient disaster tolerance in the prior art are solved.

The invention relates to the technical field of network capability scheduling, in particular to a 5G network dynamic security capability scheduling method based on deep learning. The method comprises the following steps: in a 5G network operation process, continuously collecting state parameters of network slices, and constructing a security situation data sequence; performing feature correlation analysis on the security situation data sequence, extracting security feature factors reflecting security situation changes, and generating a security risk judgment signal according to the security feature factors; when the safety risk judgment signal reaches a preset triggering condition, determining the scheduling priority and the distribution proportion of the safety protection unit according to the risk intensity and the resource occupation distribution; according to the invention, through 5G network dynamic security capability scheduling, security capability adaptive scheduling based on dynamic situation awareness is realized, and risk determination precision and resource allocation coordination are significantly improved.

The invention relates to the technical field of communication networks, in particular to a dynamic resource allocation method and system for an industrial 5G network. The method comprises the following steps: acquiring industrial terminal data; identifying network congestion nodes based on industrial terminal data; detecting a link congestion fault according to the network congestion node, and determining a bandwidth utilization rate; allocating spectrum resources based on the bandwidth utilization rate; performing frequency spectrum slicing on the frequency spectrum resources, and determining a flow load amount; adjusting load balance according to the traffic load, and generating a load resource report; dynamically adjusting time slot allocation by using a load resource report, and determining a task priority; task scheduling simulation is executed according to the task priority, and if congestion occurs, a congestion snapshot is stored; reducing scheduling conflicts according to the congestion snapshots, releasing bandwidth resources, and determining available network slice capacity; power resources are dynamically adjusted based on available network slice capacity. The bandwidth utilization rate and the task scheduling efficiency of the industrial 5G network are improved based on the communication network technology.

The invention relates to the technical field of network slice resource allocation, in particular to a 6G intelligent load balancing and fault self-healing method based on AI and network slices, which comprises the step of building an AI decision-making layer model architecture, a self-adaptive optimization mechanism, a resource dynamic scheduling mechanism and a strategy execution guarantee architecture. According to the method, the network load trend is predicted in real time through the AI technology, the resource allocation between the slices is dynamically adjusted, and the problem of low resource utilization rate caused by static allocation and periodic adjustment is solved. Meanwhile, an automatic fault detection and recovery mechanism is designed, when a node fault is detected, standby resource takeover can be quickly triggered, session continuity can be kept, the service interruption time is remarkably shortened, and the requirements of a 6G network for high reliability and low time delay are met.

The invention belongs to the technical field of intelligent short message service scheduling, and particularly relates to an intelligent short message scheduling system and method based on 5G network slices. According to the method, the short message service processing efficiency in the 5G network slice environment is effectively improved through accurate prediction and intelligent scheduling of the short message service traffic, the change trend of the short message service traffic can be accurately captured through smooth processing and exponentially weighted moving average processing of the traffic change rate time sequence, and the short message service processing efficiency is improved. Reliable data support is provided for subsequent scheduling decisions, meanwhile, through confidence evaluation and resource utilization evaluation, the predicted demand of short message service flow and the resource utilization condition can be comprehensively considered, the scheduling reasonability and effectiveness are ensured, in addition, the intelligent scheduling mechanism can automatically adjust resource allocation according to the scheduling priority, and the scheduling efficiency is improved. The sending requirement of the high-priority short message service is met, and the processing speed of the short message service and the user experience are further improved.

The invention discloses a seamless switching method and system for a high-speed terminal of a ground-non-ground convergence network based on trajectory prediction and resource pre-reservation, and belongs to the technical field of space-ground convergence communication. The core of the method is that a switching area is accurately judged through bidirectional motion prediction of a terminal and an NTN node; a dynamic switching window is calculated in a self-adaptive mode by combining factors such as terminal speed and network time delay; intelligently selecting an optimal target link by using an AI enabled link scoring model; and cooperative pre-reservation and uplink and downlink synchronization of resources are completed before the window. The method has the advantages that the problem of failure of traditional RSRP judgment in an NTN dynamic environment is solved, the switching success rate and prediction accuracy in a high-speed moving scene are remarkably improved, the service interruption time and signaling overhead are greatly reduced, the continuity and reliability of communication are effectively guaranteed, and the method is suitable for large-scale popularization and application. The method is suitable for high-dynamic service scenes such as intelligent network connection vehicles and unmanned aerial vehicles in a 6G network.

A communication system is disclosed in which a number of procedures may be performed between user equipment, a base station, and / or the core network in the context of network slicing. The procedures support, for example efficient configuration of paging areas, roaming, access restriction, mobility, setting up of dual mode connectivity, connection re-establishment, efficient paging and slice selection.

Certificate management using Automated Certificate Management Environment (ACME) protocol in a 5G network. In an embodiment, a network function (NF) of the 5G network operates an ACME client to obtain a digital certificate from a certificate authority via ACME protocol for secure communication by the NF. The ACME client is configured to send a certificate request to the certificate authority indicating an NF instance identifier, receive an ACME challenge from the certificate authority, and write the NF instance identifier as encrypted or a previously-issued certificate to a server under a security domain of the NF in response to the ACME challenge.

To provide a method for monitoring each terminal in a control plane of a 5G network.SOLUTION: A field for a trace context information element containing a trace ID is provided in messages of the N2 interface between an AMF and a (R)AN, and the N4 interface between an SMF and a UPF. In addition, a SUPI is assigned to the span attribute in the AMF or the SMF. Since the trace ID and SUPI are included in messages between the control plane and the user plane, it becomes possible to recognize one transaction on a terminal basis.SELECTED DRAWING: Figure 2

The invention discloses a data hierarchical aggregation and query method and system for privacy protection in a 6G network, and the method comprises six stages: a system initialization stage, a data uploading and edge aggregation stage, an edge data preprocessing stage, an edge classification query request stage, a central data preprocessing stage, and a central node classification query request stage. The system initialization stage, the data uploading and edge aggregation stage, the edge data preprocessing stage and the center data preprocessing stage only need to be executed once, and then the edge classification query request stage and the center node classification query request stage can be responded unlimitedly. According to the method, the privacy protection problem in the data hierarchical aggregation and query process of the AI service in the 6G network is effectively solved.

The invention relates to the technical field of traffic regulation and control, in particular to an intelligent traffic regulation and control method based on the 5G cloud computing terminal technology, and the method comprises the steps: obtaining real-time traffic data, and transmitting the real-time traffic data to an edge computing node through a 5G network; preprocessing the real-time traffic data, and uploading the data to a cloud; the method comprises the following steps: constructing a dynamic graph neural network to analyze global traffic, recognizing a congestion sub-graph through node clustering, outputting a congestion probability, judging whether the traffic is congested according to the congestion probability, if so, scoring driving behaviors of vehicles in a congested area, analyzing the congested area to recognize a congestion type, and if not, judging whether the traffic is congested or not. And according to the congestion type and the driving behavior score, generating a differentiated regulation and control strategy, issuing the differentiated regulation and control strategy to each execution device through a 5G network, and guiding the vehicle to drive out of the congested road section by executing the differentiated regulation and control strategy. Therefore, the problems of lack of driving behavior analysis, lack of pertinence of regulation and control strategies and the like in the prior art are solved.

The invention relates to the technical field of distribution network management, in particular to a distribution network management electricity meter state analysis method and system. According to the technical scheme, the distribution network management electricity meter state analysis method comprises the following steps that S1, multiple layers of edge computing nodes are deployed in a power distribution network, first-level nodes are deployed in a power distribution station, second-level nodes are deployed in an electricity meter concentrator, and computing resource distribution of the edge nodes is dynamically adjusted based on electricity meter distribution density and real-time loads; s2, through a collaborative acquisition mechanism of the first-level node and the second-level node, acquiring operation data of the electric meter in real time; and S3, transmitting the classified and marked data by adopting a multi-modal compression strategy: compressing the periodic monitoring data by using difference value coding, and extracting the sudden abnormal data by using a feature abstract. By constructing a multi-level edge collaborative acquisition mechanism and a 5G network slice transmission channel, the real-time bottleneck of traditional manual inspection and fixed-period acquisition is broken through.

Systems and automated processes are described to provide collection of wireless network status data, such as a 5G network, and to automatically respond to queries regarding the performance of the network. Systems and automated processes may, in response to a request for network performance information, obtain performance and static data from network data sources, analyze the static data to generate validated site data, apply a KPI formula to the performance data to generate KPI data, and generate a network performance report based on the validated site data and KPI data. In addition, the systems and automated processes may use an appropriate machine learning model from a library of models to determine recommended actions to increase network performance and may automatically implement such actions. A top offender system may be implemented to analyze the status data to determine the network elements having the largest negative impact on network performance.

The invention discloses a low-delay slice resource intelligent allocation method for a 5G network, and the method comprises the steps: collecting the connection state and performance index of a network node in real time, dynamically adjusting the collection frequency according to the fluctuation of a network load, and generating structured topological data; calculating a node degree distribution entropy value based on the topological data, generating a virtual resource topological graph, and predicting resource requirements of network nodes; constructing a dynamic confidence interval by using the virtual resource topological graph and the entropy change rate, and designing a resource pre-allocation strategy; network burst traffic is detected, resource allocation is rapidly adjusted through incremental updating of the graph neural network, and it is ensured that the reconfiguration delay is lower than 1 millisecond; according to the invention, through topology perception driven by the graph neural network, prediction guided by entropy and an adaptive allocation strategy, low-delay resource allocation of the uRLLC slice is realized, resource waste is avoided, and strict performance requirements of a 5G network are met.

The invention discloses a 6G network intelligent collaborative dynamic flow identifier distribution method and system, belongs to the technical field of mobile communication, and aims to solve the technical problem of how to realize intelligent collaborative dynamic flow identifier distribution. Comprising the following steps: customizing a flow identification feature dimension through a management layer; an identification template and an identification strategy are self-defined through the management layer, an identification template instance is created, and the identification strategy and the identification template instance are issued to the decision-making layer; the method comprises the following steps: collecting network situation data through a decision layer, selecting related data from the network situation data based on a selected flow identifier feature dimension to carry out network overall evaluation to obtain a network evaluation result, reading each part field of an identifier from an identifier template instance based on the network evaluation result and an identifier strategy, dynamically generating a flow unique identifier, and carrying out flow overall evaluation on the flow unique identifier. Issuing the generated flow identifier to an execution layer; the flow identifier is analyzed through the execution layer, mapping from the identifier to the physical resource is realized, and resource configuration is dynamically adjusted.