1360 results about "Network performance" patented technology

The invention relates to the technical field of integration of artificial intelligence security and network security, and discloses an AI dynamic security transmission system based on an SASE framework, which realizes security access control based on AI dynamic identity verification through an SASE integration access module, acquires and predicts network performance change in real time by using a network state sensing module, and transmits the network performance change to a network server. Equipment, environment and data content are subjected to multi-dimensional analysis by means of a security risk assessment module, a quantitative risk score is generated, and a transmission protocol, parameters and encryption strength are dynamically adjusted according to a network state and the risk score by means of a dynamic transmission optimization module and a self-adaptive encryption module; the problem that safety protection and transmission efficiency are difficult to cooperate in a traditional architecture is effectively solved, low-delay and high-reliability data transmission service can be provided for AI application in a complex network environment, meanwhile, self-adaptive dynamic protection of the whole data transmission process is achieved, and data safety is comprehensively guaranteed.

The invention relates to the technical field of communication data analysis, and discloses a communication network service health degree analysis method, system and device and a medium, and the method comprises the steps: obtaining real-time link state data, carrying out the quantitative analysis, and judging whether the link jitter exceeds a preset threshold value or not; when abnormality is detected, abnormal points can be positioned and the influence range of the abnormal points can be analyzed, so that the influence degree of nodes on the abnormal diffusion path can be identified; calculating a service quality index change value, and determining a starting point and a diffusion direction of chain degradation; by analyzing historical traffic data of the key nodes, a service traffic change trend and traffic dense area distribution characteristics are obtained. According to the method, the bandwidth allocation strategy is dynamically optimized, the bandwidth resources at the junction are reconfigured, and the regional service health degree is evaluated. And if the degradation is not effectively controlled, monitoring and detection mechanism parameters can be adaptively adjusted, and continuous analysis and control of link abnormity are realized, so that the network performance and the service quality are effectively improved.

The invention provides a self-adaptive network topology dynamic reconstruction method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning, and the method comprises the steps: obtaining the topology state information, service flow distribution information and historical reconstruction records of a current network; extracting topological correlation characteristics among nodes through graph convolution operation, and generating fusion state representation in combination with service flow information; inputting the fusion state representation into a deep reinforcement learning model to identify bottleneck nodes and redundant links, and outputting a reconstruction action candidate set; searching and evaluating the long-term cumulative income of the candidate actions through a Monte Carlo tree, and screening an optimal reconstruction action sequence; a graph coloring algorithm is utilized to allocate time slots and process resource conflicts, and a resource-feasible topology adjustment scheme is generated; and extracting a network evolution rule through tensor decomposition, and constructing a topological optimization association mapping graph. According to the method, the network bottleneck can be intelligently identified, the network topology structure is dynamically optimized, and the network performance and the resource utilization rate are effectively improved.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention discloses an intelligent load balancing method and system based on multipath fusion, and relates to the field of network load balancing. A distributed monitoring system is constructed to collect network performance indexes, and a Transform model is used to predict traffic; a hierarchical reinforcement learning architecture is adopted, a global strategy is generated according to a macroscopic network state, and flow distribution is optimized for a single path; kalman filtering and particle filtering are automatically switched according to the path stability; the flow is flexibly migrated based on a genetic algorithm; an index weight is calculated by using a Shapley value method and an entropy weight method, and path quality is evaluated; the distribution strategy is executed through the SDN controller, and the overall performance of the network is improved. According to the invention, network abnormity is quickly responded, the traffic migration efficiency is improved, and bandwidth waste is reduced; service differentiation scheduling is supported, the service quality is guaranteed, and the attack defense capability is enhanced; operation and maintenance efficiency is improved, fault positioning time is shortened, and efficient utilization of network resources and guarantee of service quality are realized.

The invention relates to a cloud computing extension cluster high availability method based on a dynamic fault domain and intelligent scheduling, and relates to the field of cloud computing extension cluster high availability. According to the method, hardware health data and network performance indexes of physical nodes are collected in real time, and the fault correlation degree between the nodes is calculated based on the hardware health similarity and a network topology attenuation factor; dynamically generating and updating a logic fault domain topological structure according to the fault correlation degree and a preset threshold value; based on the logic fault domain topological structure and a preset SLA strategy library, executing a virtual machine scheduling decision of multi-objective optimization so as to balance business service quality, fault domain risk and resource cost; and in response to the detected fault event, triggering a corresponding hierarchical migration process according to the service priority and the fault level. According to the method, closed-loop linkage of real-time reconstruction and intelligent scheduling of the dynamic fault domain in the cross-region cloud computing extension cluster can be realized, and the disaster tolerance capability and the resource utilization rate of the system are remarkably improved.

The invention discloses an intelligent scene type CPR visual training system based on an MR technology, which belongs to the technical field of non-special measurement for specific variables and comprises a training request sensing module, an intelligent scene automatic generation module, a sensing data acquisition module, a visual display and voice prompt module and a network performance evaluation management and control module. According to the method, the performance parameters of the sensing source nodes built in the simulation human body model are acquired and analyzed in real time, so that the data feedback precision in the training process is improved, and the data feedback precision in the training process is further improved; the problem of poor CPR visual training experience caused by lack of real-time performance monitoring and feedback mechanisms in the prior art is effectively solved.

The present disclosure relates to a system (102) and method (500) for analyzing network performance parameters. A user interface module (212) enables reception of comprehensive network speed test requests that specify multiple network performance parameters for analysis. A test execution module (214) triggers coordinated operation of multiple testing units (302a, 302b, 302c) to perform simultaneous network speed tests across different platforms. A data collection module (216) systematically aggregates the generated performance information from all testing units. One or more processors (202) transform the collected information through advanced processing algorithms to generate standardized performance data for each network parameter. An analyzing module (218) performs multi-dimensional analysis of the processed data based on specific attributes to provide comprehensive network performance insights. The integrated approach enables automated cross-platform testing, unified data collection, and sophisticated analysis of network performance characteristics, offering significant advantages over conventional single-platform testing methods.

The invention provides a method, a device, equipment and a medium for carrying out resource configuration on a multi-mode network, and the method comprises the steps: obtaining the network state information of each network mode in the multi-mode network, the network state information comprising network request information and network environment index information; based on the network state information, running a preset configuration strategy code to generate a configuration strategy for the multi-modal network, and completing optimization of resource configuration of the multi-modal network according to the configuration strategy; wherein the configuration strategy code is generated by performing nested iterative optimization on the basis of a pre-constructed database comprising a plurality of data samples by utilizing a large language model; the network state information and network state input data of a code predictor used for screening codes in the configuration strategy code generation process are from the same network environment. Therefore, the corresponding configuration strategy can be adaptively generated according to the change of the network environment so as to optimize the resource configuration of the multi-modal network, and the network performance and the resource utilization efficiency are improved.

The invention discloses a LoRa-based ship environment multi-parameter intelligent wireless monitoring system, which is characterized in that environment state data and ship operation auxiliary data are acquired through a data acquisition module, and a data processing module is used for preprocessing and standardizing the acquired data to obtain a standardized data frame; the environment safety quantification module calculates a single parameter safety score, a comprehensive safety index and a safety situation level SL, the intelligent LoRa communication module dynamically selects a communication strategy according to the SL and the current link quality and generates and sends a safety data packet, the LoRa gateway receives the safety data packet, redundancy filtering is carried out, receiving metadata is added, a final data record is formed and uploaded, and the LoRa gateway sends the final data record to the intelligent LoRa communication module. And the data center performs visual display and data storage on the final data record, and generates a network optimization instruction based on the network performance data, so that the problem that transmission reliability, real-time performance and low power consumption are difficult to consider in ship environment monitoring is solved, and the intelligent level of ship safety management is improved.

The system (100-2) and method for identification of high ranked neighbor cells in a telecommunications network provide an efficient and accurate approach to selecting neighboring cells with superior performance for handover purposes. The system (100-2) leverages performance metrics, algorithms, and dynamic adaptation techniques to determine the suitability and ranking of potential neighbor cells. By considering factors such as signal strength, signal quality, interference levels, load balancing requirements, and operator-defined policies, the system (100-2) evaluates the performance of neighbor cells and assigns them scores or rankings to identify the higher ranked neighbor cells. The system's dynamic adaptation ensures that the rankings remain up to date and responsive to changing network conditions. The present system (100-2) and method optimize handover decisions, enhance network performance, and provide users with seamless connectivity and improved quality of service.

The invention provides a low-orbit satellite communication terminal network performance dynamic tuning method and system, and relates to the technical field of low-orbit satellite communication. Comprehensive data such as relative movement of a satellite and a terminal and signal transmission loss are acquired; preprocessing the integrated data, inputting the preprocessed integrated data into a time sequence prediction model containing LSTM and Transform, generating a signal quality and intensity index sequence and a link stability level in a future preset time window, and carrying out dynamic optimization based on the generated indexes, including adjusting a terminal modulation mode according to the signal quality; combining the link stability and the signal strength to aggregate the link, and optimizing the switching opportunity and path; allocating resources according to the signal strength and the service characteristics; and optimizing the energy consumption according to the relative motion and the business characteristics. Through accurate prediction and multi-dimensional adjustment and optimization, the communication efficiency and reliability of the low-orbit satellite communication terminal network are improved, energy consumption and delay are reduced, and good communication quality is guaranteed.

The invention relates to the technical field of network switching, in particular to a heterogeneous network dual-mode self-adaptive switching method and system. According to the method, the multi-dimensional environment sensing data and the initial signal intensity are acquired, the signal intensity is calibrated in a dynamic environment, the dynamic response of the network state to real-time change is realized, and the connection state characteristic and the side network consumption index of the dual-mode network are acquired by combining the calibrated signal intensity. The switching demand can be evaluated on the basis of the data, the switching demand can be judged more accurately through the multi-dimensional evaluation mechanism, the limitation that a traditional method only depends on signal strength is avoided, and the switching demand can be evaluated by generating an optimized directed time sequence graph and a network performance data set. According to the technical scheme, the equipment can make a scientific and reasonable switching decision according to the real-time network state snapshot data and the preset optimization time sequence graph, and the connection requirements of the equipment can be dynamically adapted under the variable network conditions, so that the optimal network experience is kept, and the network connection quality is improved.

The invention relates to an Internet of Vehicles channel selection method based on a multi-agent depth deterministic strategy gradient algorithm, and belongs to the technical field of communication. The method comprises the following steps: establishing an Internet of Vehicles system model, and setting local and global environment variables, a vehicle action space and a reward function of the Internet of Vehicles system model; establishing a dynamic channel model of the Internet of Vehicles based on the system model of the Internet of Vehicles, and determining a state space and steady-state probability distribution under multi-channel combination; a graph neural network GNN is introduced into a multi-agent depth deterministic strategy gradient algorithm, and a GNN-Critic network is established to process dynamic vehicle input information; the intelligent body vehicle learns a channel selection strategy by using a global environment state so as to complete centralized training; and after training is completed, the intelligent body vehicle observes local channel state distribution and executes channel selection. According to the method, the channel utilization rate can be remarkably improved, the vehicle can make a better channel selection decision according to the global environment information, the channel idle time is reduced, and the overall network performance is improved.

The invention relates to the technical field of wireless communication, and particularly discloses a high-voltage transformer remote metering monitoring system based on 5G network slicing, which senses network performance in real time by constructing a multi-dimensional network state matrix, performs path rehearsal and resource allocation calculation by using a digital twinning technology, and realizes remote metering monitoring of a high-voltage transformer. Generating a candidate transmission path set with optimal time delay consistency and a corresponding wireless resource block allocation scheme; calculating a path resource allocation factor and combining with network load prediction to generate an optimal resource scheduling strategy; and finally, converting the strategy into a specific network control instruction, establishing a special transmission channel with deterministic service quality, and implementing real-time monitoring and maintenance.

The invention provides a dynamic adaptive encryption protocol selection mechanism. The mechanism comprises a network condition monitoring module, a data sensitivity evaluation module, an encryption protocol dynamic selection module and a performance feedback adjustment module. Indexes such as network bandwidth and delay are monitored in real time, and the network condition is evaluated; evaluating a sensitivity level of the data to be transmitted; dynamically selecting the most suitable encryption protocol from the encryption protocol library according to the network condition and the data sensitivity; encrypting and transmitting data by using the selected encryption protocol; and monitoring the transmission performance, and adjusting an encryption protocol selection strategy according to feedback. The method can automatically judge network and data conditions, dynamically select an encryption protocol, optimize network performance and resource utilization efficiency on the premise of ensuring data security, improve transmission efficiency and reduce calculation overhead, and has the advantages of automation, security, performance optimization, efficient resource utilization and the like; the method is suitable for the field of network communication with high requirements for safety and performance.

The invention relates to a DAS noise modeling method based on a conditional diffusion model, and belongs to the technical field of distributed optical fiber sensing data processing. DAS noise data is acquired and analyzed, a DAS-conditional diffusion model is constructed, and model training and data generation are carried out. According to the method, the statistical characteristics and conditional constraints of the DAS noise are fused in the diffusion process, so that the physical consistency and diversity of the generated noise are effectively improved, and the problem of lack of high-quality mark data in the DAS system is solved. Experiments show that the generated noise and the real noise are highly similar in time-frequency domain and statistical characteristics, the performance of the denoising network based on the extended training set is remarkably improved, high-quality data support is provided for tasks such as DAS data denoising and signal enhancement, and the application capability of the DAS technology in a complex environment is enhanced.

The invention relates to an industrial Internet of Things secure communication method and system based on OpenHarmony, and the method comprises the following steps: collecting node features of to-be-communicated equipment, carrying out the routing selection through combining network performance parameters, and generating a main and standby transmission path combination. And performing fragmentation encryption on the industrial production data sent by the source equipment to form encrypted fragmentation data. And sending the encrypted fragmented data through the main transmission path, and synchronously executing integrity verification to obtain a verification result. Whether transmission abnormity occurs in the main path or not is judged according to the integrity verification result, if yes, switching to the standby transmission path is triggered, encrypted fragmented data corresponding to the abnormity is retransmitted, it is ensured that the data are complete and reliable, and finally the data are recombined into complete industrial production data at a receiving end. The technical problems that a rapid detection and response mechanism for abnormal behaviors is lacked in the data transmission stage, and it is difficult to switch a standby path in time and complete data retransmission when transmission abnormity occurs are solved.

The invention provides a hybrid resource network port management method and a port management system, and the method comprises the steps: determining the number of available continuous port segments, the length of each available continuous port segment and the total number of available ports in a logic port pool of hybrid resources, and further combining the constraint length of the continuous port segments, measuring the discrete degree of available ports in the hybrid resources to obtain a port fragmentation index, and judging whether the hybrid resources meet a preset integration condition according to the port fragmentation index, if so, determining a to-be-integrated port section in the logic port pool, migrating services in the to-be-integrated port section to an available continuous port section, and if not, migrating services in the to-be-integrated port section to the available continuous port section. According to the method and the device, available ports in the ports are combined, and a continuous port interval obtained after combination is written into a logic port pool, so that port integration is realized, idle ports can be sorted out through service migration when the ports of hybrid resources are distributed dispersedly and discontinuously, and the problems that new connection is refused and the requirement of a large port cannot be met due to port fragmentation are avoided; and network performance is improved.

The invention belongs to the technical field of network environment diagnosis, and particularly relates to an eBPF network performance monitoring and diagnosing method based on a SW platform. Comprising the following steps: analyzing a network performance monitoring demand of the SW platform to determine a performance index and a diagnosis target of a network monitoring tool; by developing an eBPF tool based on an SW operating system kernel, related data in a network transmission process are captured in real time, and the related data are displayed and stored; performing deep analysis and anomaly detection on the network performance monitoring data by combining the characteristics of the SW platform so as to generate a data analysis result; and checking and positioning potential network problems according to the data analysis result. The invention aims to solve the problem of network performance monitoring under the SW platform, provides a method for capturing and analyzing the network abnormity in real time, and improves the network monitoring performance and the abnormity diagnosis efficiency under the SW platform.

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 is applicable to the technical field of power distribution network monitoring, and provides a power grid monitoring-oriented power adaptive congestion control method, which comprises the following steps of: acquiring a network performance index and a power grid operation parameter of a power grid monitoring data transmission network, and determining a congestion state of a current network through a preset congestion evaluation model; a 5G ad hoc network node connection strength coefficient, a new energy monitoring data priority label, a west-to-east transmission channel power flow fluctuation coefficient and a regional vulnerability index are used as decision variables, and the minimum weighted transmission delay, the packet loss rate and the maximum new energy consumption responsivity are used as objective functions. Generating a control strategy corresponding to the current network congestion state according to the hybrid intelligent optimization model fusing reinforcement learning and a genetic algorithm; executing the control strategy to dynamically adjust the network resources; monitoring the execution effect of the control strategy, and feeding the result back to the model to update the parameters; and therefore, the requirements of multi-target optimization and differential service quality in a complex power grid monitoring scene can be met.

The invention discloses a fault-tolerant neural network optimization method based on automatic architecture search. The method comprises the following steps: introducing a plurality of operators and diversified neural network unit structures in a search space construction process, and constructing a neural network search space supporting multi-dimensional balancing of accuracy, calculation overhead and fault-tolerant capability; in the multi-target architecture optimization process, topological structure coding is performed on a neural network, three performance indexes are introduced in a combined manner based on a Bayesian optimization theory, an agent model is constructed to simulate network performance, and a candidate architecture is generated by using an acquisition function and a multi-target optimization algorithm; in the architecture evaluation and iteration process, candidate architectures are trained through a weight-shared super network, key indexes of the candidate architectures are evaluated, an architecture pool is updated, an optimal compromise is obtained through iteration, and a final neural network architecture is given; the method has high efficiency, flexibility and universality, can be used for automatically exploring the fault-tolerant neural network architecture required by the safety key field, and can adapt to multi-scene and multi-requirement fault-tolerant neural network optimization design.

A density perception image defogging method in combination with physical prior comprises the following steps: S1, acquiring a fog-containing image, marking a fog area and a fog-affected area, constructing a data set D1 through size unification, normalization and multi-dimensional data enhancement, and dividing the data set D1 into a training set and a verification set according to a proportion; s2, a density perception defogging network is constructed, the density perception defogging network comprises an encoder sub-network, a middle layer sub-network and a decoder sub-network, core modules are a gridding atmospheric light attention module and a space detail enhancement module, and a GAAB integrates a multi-scale density perception convolution module, a grid perception atmospheric attention module and a feature fusion module; fog density features can be accurately captured, and physical prior can be fused; s3, inputting the data set into a defogging network to generate a multi-scale feature map; s4, carrying out training optimization on the network by adopting a combined loss function of L1 loss and perception loss; s5, evaluating the network performance through the verification set; according to the invention, adaptive defogging in different fog concentration scenes is realized, and the definition of the defogged image is effectively improved.

Embodiments of the present disclosure relate to methods, devices, apparatuses, and computer readable medium for small data transmission (SDT). A first device, in response to first data to be transmitted via a SDT corresponding to a first set of resources for a random access procedure and a second set of resources for a configured grant procedure, determine a target resource for at least a part of the first data based on a logical channel restriction on the SDT; and transmit, to a second device, at least the part of the first data on the target resource. In this way, the SDT procedure can be performed with logical channel restrictions, which allows a better isolation of services through specific resource selection. The communication efficiency and network performance can also be improved.

The invention relates to the technical field of servo motor control, and particularly discloses an intelligent control system and method for a servo motor, and the method comprises the steps: collecting the position, speed, current and acceleration data of the servo motor, and constructing a state vector; constructing a reference disturbance observer based on the theoretical parameters, and outputting an observation state, a disturbance value and an observation residual error; a multi-scale time sequence feature tensor is constructed by using the observation residual error and the state vector, and future disturbance is predicted through a CNN-Transform network; carrying out adaptive fusion on the neural network prediction result and the output of the reference observer; performing security constraint on disturbance compensation through a constraint filter and an amplitude limiter; and injecting the constrained compensation signal into a control system, and continuously optimizing the network performance through online incremental learning. According to the method, a traditional observer and a deep learning technology are fused, precise prediction and effective suppression of complex nonlinear disturbance are realized, and the control stability of the servo motor is remarkably improved.

The present invention relates to the automatic adaptation of deep neural networks to data and / or concept changes through a method that: reveals a variety of design principles (e.g., interconnection of learning blocks, network size, etc.) of deep neural networks for a variety of learning tasks (e.g. image and language processing). The method evolves neural networks constrained by discovered design principles; trains and validates neural networks; hosts a production environment where validated neural networks can operate on production data; monitors production data and network performance; reports different signs of obsolescence; and addresses signs of obsolescence by retraining neural networks with recent production data, replacing obsolete deep neural networks with new models designed by neural architecture research, and / or discovering new design principles to refactor the entire structure, which significantly improves the robustness of machine learning operations in production environments.

The invention provides an optimization method for proposal node selection of a consensus algorithm of a carbon Popingness block chain, and belongs to the technical field of carbon Popingness block chains. The optimization method comprises the following steps: starting a new era through a node, counting candidate node information, calculating a mathematical mean value and a standard deviation of a consensus efficiency score to identify an outlier node; evaluating the network performance of candidate nodes by applying a carbon popularity performance prediction model and a dynamic memory adjustment function, generating a proposal node list after removing outlier nodes, performing transaction verification and state verification by verification nodes, executing a multi-round voting verification process by a consensus algorithm, and adding consensus participation points for participation nodes, and after the block is submitted, each node sends a score list to the next height proposal node and writes the scoring data into the block head part, and through the complete node selection and consensus verification process, the technical problems that the proposal node selection efficiency is low and the nodes with abnormal performance cannot be effectively excluded in the block chain consensus algorithm are solved.

The invention discloses a low-altitude unmanned aerial vehicle communication network dynamic modeling and multi-dimensional evaluation method and a related device, and relates to the technical field of low-altitude communication networks, and the method comprises the steps: constructing a time sequence network model of a low-altitude unmanned aerial vehicle communication network according to the obtained real-time state data of an unmanned aerial vehicle and a preset link connection condition; constructing a communication link dynamic model based on the time sequence network model and the acquired environment data; processing the sequential network model and the communication link dynamic model based on a preset communication evaluation index to obtain a multi-dimensional evaluation index data set; processing the multi-dimensional evaluation index data set by using a preset dynamic evaluation algorithm to obtain an optimized instruction set; the optimization instruction set is used for optimizing the network performance of the low-altitude unmanned aerial vehicle communication network. The application can improve the utilization rate of network resources and ensure the reliability of the cooperative task of the unmanned aerial vehicle.

A method includes recording a first bitrate of a data transfer session between a user endpoint device and a network element of a terrestrial mobile network, wherein the user endpoint device is locked into a radio frequency band of the terrestrial mobile network, recording a second bitrate of the data transfer session, during a test interval in which a satellite of a non-terrestrial network that shares the radio frequency band with the terrestrial mobile network is executing a function that generates a data transmission to generate full-bandwidth noise signals, calculating a difference between the first bitrate that is recorded and the second bitrate that is recorded, and initiating, in response to the difference exceeding a threshold, an action to modify a parameter of at least one of the terrestrial mobile network or the non-terrestrial network to reduce an effect of channel interference resulting from the sharing of the radio frequency band.