967 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.

A method may include accessing data associated with a failure of a 5G network component. The method may include providing the data associated with the failure of the 5G network component to a first machine learning model, configured to determine and output data indicating a root cause of the failure. The method may include providing data indicating the root cause of the failure to a second machine learning model, configured to determine and output data indicating one or more service providers and respective destinations associated with the root cause. The method may include generating a service ticket may include data indicating the root cause and the respective destinations associated with the root cause. The method may include transmitting the service ticket to the respective destinations of the one or more service providers associated with the root cause of the failure.

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 present invention relates to a delay guarantee method for a cloud PLC service in a 5G-TSN architecture, comprising: adding a service priority mapping function, a priority queue management function and a radio resource scheduling function in a 5G-TSN network, wherein a method for priority allocation of radio resources for a cloud PLC industrial control service comprises: collecting statistics about queue data volume, feeding back wireless channel quality, calculating required radio resources and allocating the radio resources. According to the present invention, a 5G network delay is reduced, intermediate cables and devices are reduced, the moving range of a device terminal is expanded, a cloud PLC service is given priority allocation of resources at a radio air interface, and in a multi-service hybrid transmission scenario, the proposed algorithm can obtain lower delay compared with an algorithm which does not provide guarantee. A virtual communication interface is flexibly connected to multiple protocols, multi-manufacturer device interconnection is supported, cloud cooperative control is achieved, the production efficiency is improved, a 5G-TSN network bears multiple services at the same time, delay deterministic guarantee is provided for industrial control services, and the deployment complexity of an industrial field network is reduced.

A Generative Adversarial Network (GAN) is used to generate synthetic network traffic data, such as for use in training Machine Learning (ML) models. In one embodiment, a new NWDAF analytic “SyntheticData” is defined. The analytic receives as input from a requesting network function (NF) at least an amount of network traffic data requested and the type of network traffic data requested. The SyntheticData analytic uses a GAN model to generate realistic synthetic network traffic data based on actual network traffic collected in the wireless communication network. The analytic sends to the requesting NF the specified amount of synthetic network traffic data of the specified type. In one embodiment, the synthetic network traffic data generation is implemented as a new logical function of an NWDAF: the Data Generator Logical Function (DGLF).

The invention provides a resource scheduling method and system for a converged network, an intelligent terminal and a medium, and particularly relates to the technical field of network communication, and the scheme comprises the steps: carrying out the priority division and sorting of target data collected in real time according to a preset priority evaluation strategy through employing a 5G network, and obtaining the target data with the marked priority; performing time synchronization processing on all the target data marked with the priorities by using the TSN equipment to obtain the target data after time synchronization; and according to the priority and the current network state, carrying out dynamic resource allocation on the target data after time synchronization, obtaining a resource allocation result and executing a resource scheduling operation. According to the scheme, priority scheduling and a time synchronization strategy are combined based on the 5G-TSN fusion network, priority processing and accurate time synchronization of key data are ensured, the reliability and effectiveness of data transmission can be improved, bandwidth allocation is dynamically adjusted according to the data priority, and the accuracy of data resource scheduling and the data transmission efficiency can be improved.

A method and system for configuring a fifth generation (5G) network may include utilizing software-defined networking (SDN) for separating a data plane from a control plane of a 5G network. The separated control plane may be run across a low earth orbit (LEO) system between an edge network and a core network of the 5G network such that the LEO system exclusively directs the control plane. A pathway for the data plane may be determined and generated by the LEO system exclusively using the control plane. In some examples, SDN control may be established exclusively on a LEO system based on a service request. A pathway for the data plane from a first location to a second location may be determined and generated based on the service request and the control of the control plane on the LEO system.

The invention discloses a generative AI endogenous communication network architecture and a hierarchical collaborative scheduling method, and relates to the technical field of communication networks, the communication network architecture comprises an intention extraction layer, a mapping adaptation layer and a network execution layer, the intention extraction layer processes multi-modal data by using a generative AI model to obtain a semantic demand representation of a user, and the mapping adaptation layer carries out mapping on the semantic demand representation of the user; the mapping adaptation layer maps the semantic demand representation into a service quality index by using a generative AI model, maps a real-time network state of a heterogeneous network in the network execution layer into a network performance index, and generates a transmission scheduling strategy based on the service quality index and the network performance index, and the network execution layer works based on the transmission scheduling strategy, so that the network performance of the heterogeneous network is improved. End-to-end deterministic transmission scheduling of data packets of a service is implemented. According to the invention, efficient deterministic transmission scheduling of intelligent computing services in a 6G network can be realized.

The invention discloses a 5G network real-time encryption technology based on artificial intelligence, and relates to the technical field of information security and encryption. Comprising the following steps: adjusting the initial encryption parameter through the security threat and the network environment to obtain a self-adaptive encryption parameter; a user and equipment are verified through a block chain technology, generation, distribution and updating of quantum keys are managed through an intelligent contract, and the quantum keys are updated through distributed nodes; and determining a final encryption protocol and algorithm through the encryption parameter and the updated quantum key. According to the method, the network flow mode is analyzed in real time through the AI algorithm, potential security threats are accurately recognized, encryption parameters are adjusted in time, meanwhile, generation, distribution and updating of the quantum keys are managed through the block chain technology and the smart contract, the security and non-tampering performance of the quantum keys are ensured, and the security of data transmission is enhanced.

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 discloses an adjustment method and system for 5G network slices, and relates to the field of 5G networks, and the method comprises the steps: obtaining historical demand information of a target service; determining an initial network slicing strategy based on the historical demand information; determining an initial network slice based on the initial network slice strategy; monitoring performance data of each initial network slice; obtaining priority information of a target service corresponding to each network slice; the initial network slice is adjusted based on the performance data and the priority information to determine an optimized network slice.

The invention relates to the technical field of positioning, in particular to a single Beidou positioning intelligent switching management system and method based on the 5G technology, and the method comprises the steps: obtaining key signal parameters of a 5G network and a Beidou system, and dynamically evaluating the communication quality of the 5G network and the Beidou system; obtaining the network load information of the 5G network and the Beidou system, and judging the congestion degree and response speed of the current 5G network and Beidou system; on the basis of evaluation and judgment results of the two steps, communication modes of the load are automatically switched in combination with the geographic position of the load; obtaining a load historical switching log and communication quality evaluation, analyzing a communication mode and communication preference of a user through a prediction model, predicting a communication demand of the user at a moment in the future, and formulating a communication switching strategy; based on the analysis of the pseudo-random noise code sequence of the 5G network and the pseudo-random noise code sequence of the Beidou network, the formulated communication switching strategy is optimized, and the Beidou network is prevented from being wrongly switched to the non-5G network.

The invention provides a distributed energy regulation and control method and system based on 5G network and block chain collaboration, and the method comprises the steps: building an energy block chain-based IES power transaction architecture which comprises a block chain node; docking the regulation and control object with a block chain node in an IES power transaction architecture, wherein the block chain node is used for storing the smart contract subjected to security verification; constructing a distributed energy optimization scheduling model considering the 5G network and a mathematical model of a cooperative scheduling mechanism; and the intelligent contract generates a scheduling instruction according to the distributed energy optimization scheduling model and the mathematical model of the cooperative scheduling mechanism, and the scheduling instruction value is issued to the power generation and utilization unit after passing constraint condition check.

The invention discloses a remote medical data acquisition method and system based on a 5G network, and the method comprises the steps: collecting 5G network state information in real time, calculating a network state information monitoring index, can dynamically adjust a data transmission strategy, and guarantees the timely and safe transmission of key medical data under a complex network condition; meanwhile, a cache and bandwidth cooperation factor and an intelligent cache strategy factor are introduced, the cache depth and bandwidth allocation are dynamically adjusted according to the network state and the medical data priority, intelligent cooperation management of bandwidth and cache is achieved, and continuity of medical data transmission is guaranteed; the constructed early warning model can detect network abnormity in time and trigger alarm, important data are processed preferentially according to an alarm level adjustment strategy, and the abnormity processing and data recovery capability is improved.

Embodiments include methods for a network data analytics function (NWDAF) of a communication network. Such methods include obtaining, from an operations administration maintenance (OAM) network function (NF) of the communication network, the following information for each of one or more target NF instances in the communication network: an identifier of the target NF instance: a NF type associated with the target NF instance: resource usage by the target NF instance; and resource configuration of the target NF instance. Such methods also include computing analytic(s) related to deviation in processing load or capacity of the target NF instances, based on the information obtained from the OAM NF and on a capacity dimensioning model for each target NF instance. Such methods also include sending, to a consumer NF of the communication network, a message including the computed analytic(s). Other embodiments include complementary methods for the OAM NF and the consumer NF.

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.

An apparatus and method for time synchronization in 5th generation (5G) networks are presented. Time sync Reference Unit (TRU) instances are used to calculate synchronization deltas between Radio Units (RUs) and Distributed Units (DUs) and time offsets between antennas of an RU. Time synchronization data are collected from the antennas with reference signals synchronized using precision time protocol (PTP) signals, deltas calculated, and averaged to determine a synchronization offset that is applied to adjust the timing of the RUs and DUs. An artificial intelligence (AI) model is used to process timing data, accounting for environmental and network variables. The synchronization offsets are published for application adjustments, and feedback is collected to refine the AI model and enhance future synchronization accuracy.

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 invention discloses a 5G + TSN fusion network asynchronous flow optimization scheduling method with high schedulability, which comprises the following steps: S1, in a 5G and TSN fusion network, firstly, a controller of a center collects overall information of the network through a network control protocol, the information comprises channel conditions and frequency resources in the 5G network, and the channel conditions and the frequency resources in the 5G network are transmitted to a server; the communication requirements of the network topology, the link rate and the flow of the TSN network are met; and S2, running an asynchronous traffic optimization scheduling model of the 5G + TSN hybrid network through the central controller, performing routing decision and transmission scheduling on the traffic collected in the previous step in the hybrid network to obtain a scheduling result, and minimizing the consumption of network resources while meeting the transmission requirement of hard real-time traffic. According to the invention, the schedulability of the TSN gateway is obviously improved, the application range of an asynchronous deterministic access mechanism is expanded, and end-to-end traffic scheduling is realized.

The invention discloses an unmanned aerial vehicle traffic jam and personnel gathering detection method based on AI identification, and the method comprises the following steps: S1, obtaining a real-time video and three-dimensional space data of a target region, and constructing a data set; s2, preliminarily processing the data set, and extracting key features; s3, the extracted key features are transmitted to a cloud server in real time through a 5G network, and an AI model is constructed and optimized; s4, performing multi-target detection and classification on the received key features to generate a multi-target detection result; s5, performing global scheduling optimization by adopting an intelligent scheduling algorithm of fuzzy logic control and particle swarm optimization; s6, identifying possible equipment faults in advance by using a graph neural network and an adaptive lifting algorithm; s7, monitoring the flight state and the recognition result of the unmanned aerial vehicle in real time; and S8, displaying the identification result and the analysis report through a visual interface. According to the invention, AI identification and intelligent scheduling are combined, unmanned aerial vehicle path planning and maintenance are optimized, and the method has the advantages of high efficiency, safety and high adaptability.

The invention provides a converged network slice resource scheduling method and system, an intelligent terminal and a medium, and particularly relates to the technical field of network communication, and the scheme comprises the following steps: obtaining an original data flow in a pre-constructed 5G-TSN converged network; performing slicing operation on the 5G-TSN fusion network based on a preset rule to obtain a plurality of different types of network slices; classifying the original data streams based on the network slices to obtain a plurality of sub-data streams; calculating the priorities of all the sub-data streams according to a preset resource scheduling model, and sorting the priorities to obtain a priority sequence of the sub-data streams; and generating a resource allocation strategy based on the priority sequence, and executing a resource scheduling action according to the resource allocation strategy. According to the scheme, by generating the cooperative scheduling strategy of the 5G network slice and the TSN switch for the resources, low-delay transmission of the time-sensitive data flow can be ensured, and the data flow is ensured to be transmitted according to the priority, so that the timeliness, reliability and stability of the system are improved.