980 results about "Network performance" patented technology

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 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 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 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 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.

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.

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.

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.

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

The invention discloses an urban sewage pipe network system operation and maintenance effect evaluation system and method, and belongs to the technical field of sewage pipe network operation and maintenance management. According to the method, through four core models of data quality scoring, improved AHP-entropy weight method combination weighting, pipe network toughness approximate quantification and public security risk Bayesian updating, robust evaluation of data incomplete and error scenes is realized; and in combination with a core-maneuvering-wide area three-layer three-dimensional perception network and a four-step progressive data governance process, outputting a system health index with a confidence interval, designing a double-layer performance payment function associated with data quality, and generating a data short board diagnosis report. According to the method, the problem that efficiency quantification is difficult in the landing of an effect-based payment mechanism is solved, an enhanced closed loop of evaluation, data improvement and accurate operation and maintenance is constructed, a scientific quantification basis is provided for performance management of the sewage pipe network, and the method is suitable for operation and maintenance evaluation of the urban sewage pipe network under various data conditions.

Aspects of the subject disclosure may include, for example, accessing device usage data that can be generated when end user devices connect with the network; and analyzing the device usage data according to identification information of the deployable cell site, which enables determining or estimating a location of the deployable cell site. In one embodiment, this analysis can be based on device usage reports and network performance data. A performance report and / or location map can be generated for an area associated with the deployable cell site. Other embodiments are disclosed.

The invention discloses a multipath relay transmission method and system for underground confined space construction data, and belongs to the technical field of wireless communication network routing, and the method comprises the steps: obtaining a signal quality parameter of a communication link and a distribution density parameter of an ad hoc network node, and combining the load state of the node and the information of a neighbor node, decision calculation is executed on a plurality of relay nodes in parallel, relay decision vectors corresponding to the relay decision vectors are generated, a plurality of candidate transmission paths are formed through evaluation, construction data blocks to be transmitted are fragmented and distributed to the corresponding optimal candidate transmission paths to be sent in parallel, all received data fragmented sub-blocks are verified and recombined, and the construction data blocks to be transmitted are obtained. And recovering a complete construction data block. A distributed relay decision based on network state feature fusion, data adaptive fragmentation based on path stability and a multipath parallel transmission and feedback optimization mechanism are adopted, so that the data transmission reliability in an underground complex environment can be improved, the transmission delay is reduced, and the dynamic optimization of network performance is realized.

The invention discloses a medical image steganography method and system based on a reversible deep network, and aims to solve the defects that in the prior art, the steganography capacity is limited, and a multi-modal medical image scene is difficult to adapt. The method designed by the invention comprises the following steps: receiving an original medical image and secret information to be embedded; normalization and spatial alignment are carried out on the medical image; inputting a multi-scale feature extraction network, extracting low-layer texture features, middle-layer edge features and high-layer semantic features, and splicing in channel dimensions to form fusion features; the fusion feature and the encrypted secret information are spliced and then input into a reversible neural network encoder to generate a steganographic image; reversely extracting fusion features and encrypted information through a reversible neural network decoder, and reconstructing an original medical image and secret information; optimizing the network performance by adopting a structure-aware joint loss function; the method can be applied to safe storage of medical images, multi-center cooperation and medical block chain evidence storage.

Example embodiments of the present disclosure relate to methods, devices, and computer storage medium for communication. The method comprises: receiving, at a terminal device from a network device, at least one of the following parameters associated with a protocol data unit (PDU) set: a first parameter indicating whether all PDUs in the PDU set are needed by an application layer; a second parameter indicating whether the PDU set is to be discarded when an additional PDU set associated with the PDU set is lost or discarded; a third parameter indicating a discard timer for the PDU set; or a fourth parameter indicating a reordering timer for the PDU set, and processing the PDU set based on the received at least one of the parameters. In this way, packet processing with PDU set granularity is supported, and PDU-set based handling is also supported. Therefore, communication quality and network performance are improved.

The invention discloses a wireless communication network intelligent optimization method and system based on neuron collaboration, and relates to the technical field of wireless communication. The method comprises the following steps: mapping a communication node into a bottom layer sensing neuron and constructing a neural state variable set; constructing a node neural situation function based on the variables; when the local threshold value is exceeded, excitation pulses are generated and uploaded to middle-layer convergence neurons; the middle layer carries out pulse space aggregation and extreme value search, and outputs a selection strategy and an adjustment strategy; and reporting to a top layer to execute whole network neural synchronization index analysis, and optimizing the wireless communication network. The technical problems of low spectrum resource utilization rate and unstable network performance caused by the fact that a traditional wireless communication network cannot realize high-efficiency spectrum allocation and dynamic topology reconstruction under user mobility change are solved, and the purposes of realizing local quick response and global collaborative optimization by constructing a layered neural network and improving the network performance are achieved. And the spectrum resource utilization rate is improved, and the network dynamic adaptive capability is enhanced, so that the user service quality is guaranteed.

The invention relates to the technical field of underwater communication, in particular to a collision avoidance method suitable for digital underwater acoustic communication, which is characterized in that a neighbor propagation delay mapping database is maintained at each communication node, and inter-node propagation delay information is acquired through passive monitoring and active detection. When a node wants to send data, combining delay mapping to predict a channel occupation time slot, and judging whether the sending is conflicted with an RTS / CTS handshake stage, a data transmission stage and an ACK (Acknowledgement Character) confirmation stage which are carried out by a neighbor; if a conflict risk exists, the node delays to send the RTS and then initiates handshake until the security window is finished, so that collision caused by hidden nodes and exposed nodes is avoided, meanwhile, parallel transmission under the conflict-free condition is allowed, the channel space-time multiplexing efficiency is improved, the network throughput can be remarkably improved, the data packet collision rate is reduced, and the network performance is improved. And the node access fairness is improved.

The embodiment of the invention provides a training method and device for a poor-network-quality root cause recognition model, equipment and a medium, and belongs to the technical field of communication network operation and maintenance. The method comprises the following steps: performing feature extraction on first network performance time sequence data through an initial encoder to obtain a first poor communication quality feature, and updating the initial encoder according to the first poor communication quality feature and a first poor quality root cause category label to obtain a reference encoder; performing feature extraction on the second network performance time sequence data through a reference encoder to obtain a second communication poor quality feature, and determining a predicted poor quality root cause category of the second network performance time sequence data based on the second communication poor quality feature through an initial classifier; and calculating cross entropy loss according to the second poor-quality root cause category label and the predicted poor-quality root cause category, and updating a reference encoder and a classifier according to the cross entropy loss to obtain a network poor-quality root cause identification model. According to the embodiment of the invention, the accuracy of network poor-quality root cause identification can be improved.

Systems, computer program products, and methods are described for advanced congestion control using multiple congestion indicators in a networking environment. An example system may include an intelligent agent configured to learn congestion control policies. The agent may interact with real-world or simulated environments replicating real-world benchmarks. Congestion indicators such as telemetry information, packet drop metrics, congestion notification packet rate, pause frame rate, port utilization metrics, and / or the like form a comprehensive state representation of the network, enabling congestion state of the network environment. The intelligent agent evaluates these conditions using a reward function to optimize network performance. The intelligent agent may then implement a behavioral policy in response to the captured congestion indicators, thereby changing the congestion state of the network environment.

The invention discloses a reinforcement learning routing optimization method considering a dynamic underwater sound environment and node mobility. The method comprises the following steps: performing parameter initialization on an underwater sensor network; monitoring and updating underwater environment parameters and node positions in real time, and dynamically updating a network topology connection state; during data transmission, constructing a multi-dimensional state vector, selecting a routing action by adopting an epsilon-greedy strategy, and generating a routing decision; if the data packet transmission fails when the routing action is executed, ending the iteration, otherwise, calculating a multi-target reward value; updating the Q value table to optimize the routing decision; and monitoring network performance indexes in real time, adaptively adjusting reinforcement learning parameters of the intelligent agent for next iteration, and outputting an adaptive routing selection model until an iteration termination condition is met. According to the method, time-varying channel and node mobility challenges can be intelligently dealt with, and multi-objective optimization balance of energy consumption, delay and stability is realized while the data delivery rate is remarkably improved.

The present invention discloses a method for an intra-stack zero-copy Tx path in a shared-memory-based communication mode and pertains to the field of data communication technology, in which copying from shared memory to packet buffers is replaced with writing references to data in the shared memory into the packet buffers, eliminating creating a copy of the data in the packet buffers. An original Tx path in a network protocol stack is remodeled into a zero-copy Tx path without any modification to the application side while maintaining transparency and loose coupling of the network protocol stack to upper-layer applications. This eliminates data copying within the network protocol stack and data replication in LLC memory, thereby reducing unnecessary LLC occupancy and improving the network protocol stack's network performance in large-file transfers under heavy load conditions.

The invention discloses a wireless network data reliability transmission system, which is used in the technical field of data transmission and comprises a slave gateway deployment and data aggregation module. A scheduling protocol and priority management module; a hierarchical caching and breakpoint resuming module; and a trend prediction optimization decision module. According to the invention, in combination with priority-based scheduling, link quality evaluation, data caching and retransmission mechanisms, reliable transmission of data with different priorities in a wireless network can be ensured, and normal transmission can be recovered through an efficient data recovery mechanism when the network is unstable; through real-time environment state estimation and prediction, communication parameters and resource scheduling can be dynamically optimized, network performance is effectively improved, and communication delay is reduced. The hidden state of a communication environment is estimated in real time through comprehensive analysis of link quality, event records and cache state data, future hidden state changes are predicted through a time sequence convolutional network, and potential performance degradation risks are recognized in advance.

The invention relates to the technical field of computer network communication and intelligent control, and discloses a switch configuration method and system based on AI adaptive congestion control, and the method comprises the steps: collecting the multi-dimensional feature data of a network in real time through a data plane programmable pipeline of a switch, and transmitting the multi-dimensional feature data to an AI intelligent decision engine through an encryption channel; based on the multi-dimensional feature data, executing congestion prediction, strategy generation and interpretability analysis through an AI intelligent decision engine to obtain a congestion control regulation and control strategy; converting the congestion control regulation and control strategy into an executable switch configuration command, and issuing the executable switch configuration command to switch hardware to dynamically adjust congestion control parameters of the switch; and acquiring the network performance index after configuration execution, calculating the profit value of the congestion control strategy, and updating the model weight of the AI intelligent decision engine based on the profit value. According to the invention, the adaptability of the network to dynamic loads and complex scenes is effectively improved, delay and packet loss are reduced, and the operation and maintenance complexity is reduced.

Systems and methods are disclosed for optimizing network routing and data processing through dynamic route optimization. The invention features a dynamic route optimization engine that uses real-time performance metrics such as latency, bandwidth availability, memory utilization, CPU load, and throughput capacity. It allows for rule-based configurations, enabling custom parameters for transaction processing, data retrieval, and user authorization. Integration with network monitoring tools provides continuous visibility into network performance. The system supports multi-cloud and hybrid cloud environments, dynamically selecting optimal paths based on real-time conditions and regulatory compliance. Predictive analytics anticipate network congestion, allowing proactive routing adjustments. Adaptive algorithms learn from past conditions to optimize performance continuously. Scalability is achieved through automated updates to routing tables and security groups. The invention reduces latency and ensuring efficient data transfer, providing a cost-effective and reliable solution for modern network management, addressing a long-standing need for adaptability and efficiency in network routing.

The invention provides a link determination method and device, electronic equipment and a storage medium, and relates to the field of mobile communication. The method comprises the following steps: determining a target service type corresponding to service data of a terminal; determining a first prediction result based on the terminal state parameter and the network performance parameter of the first link, the first prediction result being used for indicating the transmission performance which can be reached when the first link transmits the service data of the target service type; determining a second prediction result based on the terminal state parameter and the network performance parameter of the second link, the second prediction result being used for indicating the transmission performance which can be reached when the second link transmits the service data of the target service type; and determining a target link from the first link and the second link based on the first prediction result and the second prediction result. According to the application, the selected target link can meet the service requirement of the target service type, so that the network service quality can meet the service requirement.

The invention relates to a dynamic routing optimization method and device, computer equipment, a readable storage medium and a program product. The method comprises the following steps: acquiring a topological space feature vector and a predicted time sequence feature according to a node load and a link time delay of a network topology, fusing the topological space feature vector and the predicted time sequence feature to obtain a target fusion feature, and according to the target fusion feature, obtaining a target fusion feature; determining a target routing action from all routing actions in the routing action space, executing a traffic forwarding operation corresponding to the target routing action, obtaining a network performance parameter after the traffic forwarding operation is executed, obtaining a target fusion reward value based on the network performance parameter and the state prediction parameter, and sending the target fusion reward value to the routing action space; and updating model parameters of the decision network model according to the target fusion reward value, and performing dynamic routing optimization based on the updated decision network model. By adopting the method, the communication reliability can be improved.

The invention discloses a software and hardware cooperation system and method for satellite network routing simulation, and mainly solves the problem that an existing simulation scheme is difficult to consider high fidelity of protocol behaviors and high performance of data forwarding in a high dynamic satellite network. The system comprises a software control plane unit, a hardware data plane unit, a satellite network simulation upper computer and a switch, and the switch is bidirectionally connected with the software control plane unit, the hardware data plane unit, the satellite network simulation upper computer and the switch and serves as a data exchange platform of the system. The satellite network simulation upper computer is used for system configuration management, operation state monitoring and result visualization; the software control plane is used for executing simulation calculation and routing decision of a satellite network routing protocol and generating a standardized forwarding table item; and the hardware data plane unit performs high-speed and deterministic table lookup forwarding according to the standardized forwarding table item, and simulates a satellite resource constraint environment. According to the method, the integrity of protocol simulation details is guaranteed, meanwhile, high-efficiency forwarding performance and non-interruption updating in a high-dynamic environment are achieved, and the method can be used for routing protocol verification, network performance testing and satellite-borne algorithm evaluation of large-scale low-orbit satellite constellations.

Provided are a spatial reuse method and apparatus. The method includes: receiving a radio frame and determining whether the radio frame carries a trigger frame configured to trigger uplink multi-user transmission according to indication information of the trigger frame in a physical signaling field of the radio frame; when the trigger frame is determined to be carried in the radio frame, receiving, by a station, the trigger frame, and acquiring first spatial reuse information of the trigger frame; at an end of a radio frame reception process, determining whether to perform first spatial reuse according to the first spatial reuse information. The present disclosure solves the problem in the related art of low spatial reuse efficiency when a station performs spatial reuse on a radio frame and achieves a technical effect of improving network performance.