55 results about "Network performance optimization" patented technology

The invention relates to the technical field of network security, in particular to a GIS (Geographic Information System)-based network security protection system, which comprises a dynamic topology adjustment module, an intelligent threat identification module, a network security evaluation module, a global topology optimization module and an automatic recovery and redundancy design module, the global topology optimization module and the dynamic topology adjustment module are combined, long-term optimization of the whole network is realized on the global level, and the system can comprehensively evaluate each node and dynamically calculate the connection weight in combination with the multi-dimensional characteristics such as the traffic load, the security score and the geographic position of the node, so that the reliability of the network is improved. When a node fault or abnormity is detected, a redundant path can be automatically selected according to historical data and a geographic position, and the method is subjected to topology recovery, so that high-priority nodes can obtain better connection resources, and low-priority nodes can automatically allocate the redundant path, and network performance optimization and load balance are ensured.

The embodiment of the present application provides a method, device and system for obtaining an optical network performance optimization solution and diagnosing faults. The method includes: collecting operation logs, performance indicators and alarm information of various types of equipment in the optical network according to a pre-set data collection frequency, as well as collecting optical network topology data, network-level performance data, equipment configuration information and computer room environment data; cleaning and preprocessing all collected data; inputting all processed data into a pre-trained performance optimization model, a pre-built expert system and / or a pre-trained fault prediction model, generating an optical network performance optimization solution through the performance optimization model, and predicting the potential fault type, possible fault occurrence time and fault location of the optical network through the expert system and / or the fault prediction model. The embodiment of the present application can not only obtain a better optical network performance optimization solution, but also improve the fault location speed and accuracy.

The invention discloses an unmanned aerial vehicle calculation unloading method based on conditional value at risk and whale optimization. The method comprises the following steps: constructing an air-ground link channel state information estimation error uncertainty set, and constructing a chance constraint model; unmanned aerial vehicle position deployment, unloading decision and resource allocation are jointly optimized, and an air edge calculation unloading model is established; determining an association scheme of the user and the unmanned aerial vehicle; constructing a distributed robust opportunity constraint of task time delay under the worst condition, performing conservative estimation on the distributed robust opportunity constraint based on a conditional value-at-risk theory, and converting the distributed robust opportunity constraint into a second-order cone constraint problem; decoupling the second-order cone constraint problem into a computing resource allocation problem and an unloading decision problem; and solving to obtain a resource allocation and unloading decision scheme for minimizing the energy consumption of the unmanned aerial vehicle under the condition of satisfying multi-dimensional constraints in the hierarchical air edge computing network. According to the method, the problem of network performance optimization under the condition that channel state information estimation errors exist in an air-ground communication link is effectively solved.

The invention relates to the technical field of electric power communication and information, in particular to a substation wireless network performance optimization method based on the Internet of Things, which comprises a data acquisition module, a data transmission and preprocessing module, an intelligent analysis module, a strategy generation module, a strategy execution and feedback module and an effect evaluation and abnormity alarm module. The data acquisition module is used for acquiring operation data and environment parameters of a wireless network of the transformer substation in real time; and the data transmission and preprocessing module is used for performing low-power-consumption transmission, cleaning, normalization and feature extraction processing on the operation data and the environment parameters. According to the application, wireless signal quality (signal intensity, signal-to-noise ratio and the like), equipment operation states (CPU / memory utilization rate and connection number), service load characteristics (flow peak value and protocol type) and environmental parameters (electromagnetic interference intensity, temperature and humidity) are synchronously acquired through the data acquisition module, multi-dimensional data fusion is realized, and a more complete information basis is provided for subsequent analysis.

Embodiments of the present disclosure relate to network performance optimization, especially for a gNB performance optimization based on a reinforcement learning (RL) operation. The method comprises: obtaining current state information of a network environment and a reward associated with a reinforcement learning operation and related to a performance of the network environment; determining a predicted future state of the network environment based on the current state information; and generating, by using the reinforcement learning operation, a target configuration parameter set for the network environment based at least on the predicted future state of the network environment; and configuring the network environment using the target configuration parameter set.

The invention relates to the technical field of medical equipment remote upgrading, and discloses an insulin pump program remote upgrading system and method based on a 4G network and a TCP protocol, and the system comprises a server side which is used for storing and managing a firmware upgrading package, verifying an insulin pump terminal, issuing the firmware upgrading package, and monitoring the online state and upgrading progress of equipment, the upgrading module is used for verifying and upgrading based on an issued firmware upgrading package through a 4G network; the upgrading process control module is used for monitoring a terminal power supply and a transmission breakpoint of the insulin pump in the upgrading process; and a network performance optimization module. By introducing a RaptorQ algorithm, redundancy dynamic adjustment is realized, the problem of a small number of data packets is solved by using a small amount of redundancy in a relatively stable network environment, when the network environment is relatively poor, the redundancy is improved to 20%, a large number of abnormal data packets are recovered by using redundant packets, retransmission is reduced, the influence of network fluctuation on upgrading is reduced, and the reliability and stability of data transmission are improved.

The invention discloses an unmanned aerial vehicle assisted emergency communication network performance optimization method and system. The method covers communication link optimization based on environment perception, information is collected through multiple sensors, an environment model is established by applying a machine learning algorithm, and the flight attitude and the communication frequency are dynamically adjusted according to the environment model. The distributed resource allocation utilizes a distributed algorithm and a game theory, takes a utility function Ui = w1Qi + w2Ri-w3Ei as a decision basis, and reaches Nash equilibrium through an iterative game; efficient energy management improves endurance by means of devices such as a solar panel and a wind energy collector in combination with an intelligent algorithm and an energy sharing mechanism. The system comprises a plurality of unmanned aerial vehicles equipped with various modules and corresponding control modules. The stability, the resource utilization rate and the cruising ability of the emergency communication network are effectively improved, and powerful communication guarantee is provided for emergency rescue.

The invention discloses a network flow prediction method based on a decoupling dynamic space-time diagram neural network D2STGNN. The method comprises the following steps: constructing a network flow data processing and representing module, and collecting and preprocessing flow data; then, a prediction model is constructed based on D2STGNN, a space-time framework is decoupled to decompose a network traffic space-time signal into a node-traffic association signal and a network-traffic time trend signal, and by means of a space-time local convolution layer, a gating circulation unit and a multi-head self-attention layer, an inter-node association process and a capture time sequence dependency relationship are respectively modeled; and meanwhile, a time attenuation mechanism is introduced, and the real-time dynamic capturing capability of the model on the network flow is enhanced in combination with adaptive attenuation parameter adjustment and a layered attenuation mechanism. And finally, predicting real-time network flow data by using the trained model, and evaluating a prediction effect through related evaluation indexes. According to the method, the change trend of the network flow can be efficiently and accurately predicted, and powerful support is provided for reasonable distribution of network resources, network performance optimization and the like.

The embodiment of the invention provides a measurement report processing method, a terminal and a network element, and the method comprises the steps that the terminal reports a measurement report to a target network element according to pre-configured target network element information, and other network elements obtain the measurement report from the target network element. According to the embodiment of the invention, the problem that the network node cannot obtain the required terminal measurement report to optimize the network performance in the related technology can be solved, the centralized collection of the terminal measurement report is realized, and the unnecessary forwarding process in the reporting process of the measurement report is avoided.

The invention discloses a network performance optimization method and device, and is applied to a server system of a multi-non-uniform memory access node architecture, and the network performance optimization method comprises the steps: obtaining non-uniform memory access node information loaded by an operating system kernel and non-uniform memory access node information where a network card is located; if it is judged that the non-uniform memory access node loaded by the operating system kernel is the same as the non-uniform memory access node where the network card is located, checking the interrupt request binding condition of the network card; according to the interrupt request binding condition of the network card, if the interrupt request of the network card is not bound to the CPU core of the non-uniform memory access node where the network card is located, binding the interrupt request of the network card to the CPU core of the non-uniform memory access node where the network card is located.

The invention provides a network performance optimization method and system in a remote calibration system based on an ant colony algorithm, and the method comprises the following steps: forming basic data based on a basic model of the remote calibration system; scheduling a central network node and an edge network node by using an ant colony algorithm, and obtaining an optimal solution of a network performance optimization scheme of the remote calibration system through an iteration process; and adjusting and optimizing network performance parameters of the remote calibration system according to the optimal solution to realize optimization of the network performance of the remote calibration system. According to the method, the synchronous dependency relationship of each node is fully considered, and self-adaptive and dynamic network performance optimization is realized by applying the ant colony algorithm. According to the method, the network delay of the total task is reduced under the network load constraint condition of the edge network nodes, the situations of task congestion and resource waste are reduced, the method is high in convergence speed and high in efficiency, the obtained result has good stability, and the performance and efficiency of the remote network are improved on the whole.

The invention discloses a power grid hybrid network performance optimization model considering construction cost, and belongs to the technical field of intelligent power grid communication. According to the model, a parameter system including communication equipment, terrain and a global system is defined, a function with the purpose of minimizing the total cost of a life cycle and multivariate constraint conditions of transmission distance, retransmission times, delay, reliability and flow velocity are constructed, and optimal equipment and transfer device configuration is solved by adopting a node traversal and iterative optimization algorithm. Cost and performance collaborative optimization of a honeycomb, Wi-Fi and ZigBee heterogeneous hybrid communication network under a complex terrain is realized, planning accuracy and reliability are remarkably improved, and construction, operation and maintenance cost is reduced.

The application belongs to the technical field of communication, and particularly relates to a network performance testing method, a network performance testing device, a computer readable medium, an electronic device and a computer program product. The method comprises the following steps: establishing a network performance testing channel for communication connection with a specified network element, wherein the specified network element is a functional network element specified in a core network of a communication system; and performing data communication with the specified network element based on the network performance testing channel, so as to obtain network performance parameters of communication traffic. The application can improve the accuracy of network performance optimization.

The application discloses a network management method based on 5G embedded network management software, and solves the technical problem of insufficient intelligent level of some existing 5G embedded network management software. The method comprises the following steps: collecting data by using a Scrapy framework and a Selenium tool; storing data by using an HDFS system; pre-processing data by using a Spark SQL module; analyzing and processing data by using a reinforcement learning algorithm; learning and predicting network states according to data by using a supervised learning algorithm; verifying and detecting fault points based on the prediction of network states by using the supervised learning algorithm; and issuing an alarm notification and recovering from a fault based on the verification and detection result of the fault points. In the aspects of network fault prediction and network performance optimization management, the application introduces a supervised learning algorithm in a machine learning algorithm to predict network faults, and introduces a reinforcement learning algorithm to adjust network configuration, so as to optimize network performance. Compared with the prior art, the application improves the intelligent level of network management.

The invention particularly relates to a machine room network performance optimization system based on big data, and relates to the technical field of network performance optimization. A phase difference module which reflects and controls the path difference; a total phase in-phase implementation module; a reflection amplitude control module; and a dynamic monitoring and optimizing module. According to the invention, a full-link optimization system from signal characteristic analysis to phase and amplitude fine control is constructed, a reflection and control path difference module is combined with a machine room space to flexibly adjust a path difference, an active compensation phase is introduced by a total phase in-phase implementation module, and phase deviation of metal reflection and dielectric loss is accurately counteracted; the incident wave and the reflected wave are strictly superposed in the same phase at the receiving end, a phase foundation is built for signal gain, the superposition enhancement effect is theoretically developed into stable engineering practice, the problems that the signal enhancement effect is unstable and is easily counteracted by interference in traditional optimization are solved, and high reliability of data transmission is guaranteed.

The invention discloses a pulse time-driven network performance optimization system and method, and the system comprises a sparse scrambling coding module which is used for carrying out the sparse scrambling coding of input data, so as to convert the input data into a pulse sequence with the determined time distribution in the coding time, and obtain a coded pulse sequence; the decoding module is used for inputting the coded pulse sequence into the pulse neural network for reasoning, and continuously monitoring pulse activity of each category of channel of an output layer of the pulse neural network in the reasoning process; when the first pulse of any category of channel is detected, generating category prediction based on the pulse time, the pulse count or the combination thereof of the first pulse; and the coding and decoding driving training module is used for carrying out combined optimization of weights and thresholds on the network or carrying out conversion from an artificial neural network and carrying out threshold calibration on the network based on category prediction. Therefore, by adopting the embodiment of the invention, the recognition robustness of the network on the data set can be obviously improved.

The embodiment of the invention discloses a network performance optimization method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring an optimization request; if the optimization request is a network optimization request, determining an initial optimization scheme through a large model; analyzing the network optimization request through a large model to obtain N sub-requests; the N sub-requests are sent to a second server, so that the second server calls M small models corresponding to the N sub-requests through a wireless intelligent management and arrangement function layer; receiving a first optimization scheme determined by the first small model according to the corresponding first sub-request and the current network state data; the first sub-request is any one of N sub-requests, comparing the initial optimization scheme with N first optimization schemes, and determining a target optimization scheme; and sending the target optimization scheme to the wireless network equipment, so that the wireless network equipment performs network optimization according to the target optimization scheme. An optimization scheme is provided through fusion of the large model and the small model, and the network optimization effect is improved.

This invention discloses a group-connected BD-RIS-assisted uplink communication system and a method for maximizing transmission rate, relating to the field of wireless communication technology. The method first establishes a channel model between the user, base station, and BD-RIS, derives the decoded signal expression for the user at the base station, and calculates the user's signal-to-interference-plus-noise ratio (SNR) and achievable rate. Then, it constructs an optimization problem with the goal of maximizing the system's sum and rate, with the optimization variables being the equalizer, user transmit power, and BD-RIS phase shift matrix. Finally, it employs an alternating optimization strategy, sequentially optimizing the equalizer based on the minimum mean square error criterion, transforming the non-convex problem into a convex problem to solve for the transmit power, and optimizing the phase shift matrix using the BFGS quasi-Newton method, iterating until the system performance converges. This invention overcomes the design limitations of traditional reconfigurable smart reflectors, fully leverages the control advantages of BD-RIS, effectively improves the sum and rate of the uplink communication system, and provides a new solution for future wireless network performance optimization.

The invention discloses a network management method based on 5G embedded network management software, and solves the technical problem that part of existing 5G embedded network management software is insufficient in intelligent level. Comprising the following steps: acquiring data by using a Scrapy framework and a Selenium tool; storing data by using an HDFS system; a Spark SQL module is used for preprocessing the data; using a reinforcement learning algorithm to analyze and process the data, and using a supervised learning algorithm to learn and predict the network state according to the data; the network state is predicted based on a supervised learning algorithm, and a fault point is verified and detected; and based on the verification detection result of the fault point, sending out an alarm notification and carrying out fault recovery. In the aspects of network fault prediction and network performance optimization management, a supervised learning algorithm in a machine learning algorithm is introduced to predict network faults, a reinforcement learning algorithm is introduced to adjust network configuration so as to optimize network performance, and compared with the prior art, the intelligent level of network management is improved.

Disclosed is data processing system having at least one server communicably couplable to at least one display apparatus, wherein at least one server has at least one processor configured to obtain quality of service information from network traffic regulator of communication network, wherein communication network is between at least one server and at least one display apparatus, obtain media processing latency information from at least one rendering application executing at the at least one server, and obtain client device operational information from at least one display apparatus; process quality of service information, media processing latency information, and client device operational information together to generate streaming configuration parameters; and control at least one rendering application to generate images for displaying at the at least one display apparatus and send images via communication network to at least one display apparatus, according to generated streaming configuration parameters.

The invention discloses a parameter adjustment method and device, a storage medium and electronic equipment, and relates to the technical field of communication, and the method comprises the steps: obtaining an operation parameter of target access network equipment in an area where network performance optimization needs to be carried out; determining network parameters and state information of at least one cell covered by the target access network equipment; performing global optimal analysis on the target access network equipment according to the network parameters and the state information to obtain target operation parameters corresponding to the target access network equipment; and adjusting the operation parameter of the target access network equipment to the target operation parameter so as to optimize the network performance of a target area where at least one cell in the area is located. According to the invention, the global optimal analysis is carried out on the target access network equipment according to the network parameters and the state information to obtain the corresponding target operation parameters, so that the accuracy of parameter adjustment is improved; by adjusting the operation parameter of the target access network device to the target operation parameter, the network performance optimization effect of the target area is improved.

The invention provides a lossless network performance guarantee method and system for real service flow simulation enabling, and belongs to the technical field of network communication, and the method comprises the steps: deploying a flow probe to collect service flow data, mapping the service flow data to a feature space of a preset dimension, carrying out the clustering of sample points in the feature space, outputting a flow mode label of each type, and carrying out the clustering of the sample points in the feature space; calculating statistical characteristics of the service flow data of each label; extracting a time sequence feature of the service flow data under each label, and splicing the statistical feature and the time sequence feature to obtain a feature vector; a special traffic simulation model is pre-trained for each label, and a traffic simulation framework is constructed in combination with model selection logic; and inputting the feature vector of each flow mode into a flow simulation framework, outputting simulation flow, evaluating the simulation flow, and quantifying an evaluation result. According to the invention, through real service flow simulation, the lossless network performance is accurately evaluated, the resource configuration is optimized, and the QoS guarantee capability of the key service is improved.

The invention provides a container network performance tuning method, and relates to the technical field of network performance optimization, and the method comprises the steps: obtaining container network configuration data and container network performance data through monitoring the container network performance in real time; dynamically adjusting the network resource allocation data and the network resource limitation data of the container in combination with the network resource data, and performing primary network performance optimization on the container; constructing a container network performance optimization strategy, determining a secondary network performance optimization target of the container, and performing secondary network performance optimization on the container; performing an automatic network performance test, and performing optimization adjustment on the container network performance optimization strategy according to a test result; and the efficient tuning and high-quality tuning of the container network performance are realized.

The invention provides an HTTP request performance diagnosis method and system based on a dynamic Agent mechanism and a storage medium, and relates to the technical field of computer network performance optimizing.The diagnosis method comprises the steps that S1, a dynamic Agent program is accessed to a target server through an Attach mechanism of a JVM tool script; s2, an Agent program acts as a service method of javax.servlet.http.HttpServlet, a specific HTTP request is intercepted, and a background monitoring thread is started to execute the following operations: setting an execution time threshold value, and marking the HTTP request exceeding the time threshold value as an STUCK state; performing thread stack sampling on the STUCK state request at a set frequency; collecting a method execution completion event, and constructing flame map data; s3, generating two result files: head.txt, and recording the total consumed time of the request and a unique identifier UUID; and uurid.txt, storing the flame image data obtained by sampling; s4, calling a flame graph generation tool to generate a flame graph in an SVG format; s5, positioning the performance bottleneck by analyzing the stack width and the sample number of the flame pattern; the width of the flame map is positively correlated with execution time consumption, and the widest stack is optimized preferentially.

The invention belongs to the technical field of intelligent power grid and vehicle network interaction, and discloses an MA-enhanced mobile edge cloud computing network performance optimization method, which constructs an MA-enhanced WPB-MEC system, improves the communication performance in the vehicle network interaction scheduling optimization process, and improves the network performance. Calculating the total calculation bit of the WPB-MEC system according to the transmitting covariance matrix at the AP, the network time resource allocation, the transmitting power and the calculation frequency of the WDs and the position of the MAs; a block coordinate descent algorithm is designed, a time allocation scheme in a specified transmission block, a transmission covariance matrix at an AP, the calculation frequency of WDs, the transmission power and the position of MAs are optimized, an optimization problem is established, and the total calculation bit of the system is maximized. The computing capability of the network is remarkably improved, the communication capability of vehicle-network interaction scheduling optimization is enhanced, and the communication performance during vehicle-network interaction is improved.

The invention discloses a directed ring causal structure discrimination and estimation method for network intelligent operation and maintenance, relates to the technical field of network intelligent operation and maintenance, and provides a solution based on high-order cumulant matrix analysis for causal discovery and effect estimation requirements of a feedback loop in a network system. A unified cumulant matrix construction principle is established, so that cumulant total orders of main diagonal elements and auxiliary diagonal elements of the matrix are the same; on the basis, the system firstly utilizes rank conditions of a forward three-order cumulant matrix and a reverse three-order cumulant matrix to realize loop detection; and after the existence of the feedback loop is confirmed, constructing a mixed third-order cumulant matrix, constructing a solvable equation about a causal coefficient by utilizing determinant properties, directly completing loop effect estimation, and realizing integration of loop discrimination and effect estimation. According to the method, a complex intra-ring causal effect estimation problem is converted into an algebraic problem which can be solved directly, and accurate causal support is provided for network performance optimization, resource scheduling and safety protection.

The invention relates to a model training method and device and a processor readable storage medium, and a center node carries out model training in cooperation with a first distributed node according to node data of the first distributed node, so that the first distributed node can complete a model training task under the cooperative help of other nodes. The effects of improving the network performance of the whole distributed network and optimizing the network and application service experience are achieved.

The application discloses a kind of time synchronization network performance optimization method, device, equipment and medium, the method is applied to synchronous network probe monitoring system, comprising the following steps: when time synchronization network performance appears deterioration fault, probe equipment reports time synchronization network performance deterioration fault information to probe network management equipment;Probe network management equipment prompts operation and maintenance personnel to carry out fault maintenance to fault position, and issues first switching instruction to probe equipment, and probe equipment is switched from measurement mode to time source mode according to first switching instruction;Probe network management equipment receives fault solution receipt, then issues second switching instruction to probe equipment, and probe equipment is switched from time source mode to measurement mode according to second switching instruction;Probe equipment continues to measure and obtains the time synchronization network performance of access equipment, to complete the optimization of time synchronization network performance.The method can realize the automation and reliability of synchronous network fault root location process.

The invention discloses an image recognition method for handwritten digits and a terminal, and the method comprises the steps: setting algorithm parameters, such as momentum and learning rate, for a digit recognition model according to an obtained sample data set of the handwritten digits, calculating an adaptive learning rate and related parameters on a model training data set through gradient second-order information parameters, and carrying out network weight training; in the training process, a verification data set is used for evaluating a loss function value to judge whether training is completed or not, whether the adaptive learning rate is adjusted or not is judged in the verification stage, and if the adaptive learning rate is adjusted, adaptive learning rate parameters are updated through operations such as calculation of a negative gradient of the loss function value and normalization according to network performance optimization requirements; and finally, selecting a test data set for network prediction, and taking the weight corresponding to the minimum loss function value as the final weight of the model. Therefore, the advantages of the stochastic gradient descent optimization algorithm fusing the adaptive learning rate and the second-order information in improving the training efficiency and prediction accuracy of the digital recognition model are fully displayed.

The invention provides a network performance optimization method and device for a space-ground integrated internet, and the method comprises the steps: determining a candidate ground station set based on the subscription user historical flow data of the space-ground integrated internet and a predictable satellite-ground connection relation of the space-ground integrated internet; the predictable satellite-ground connection relation is a link relation which can be predicted by the space-ground integrated internet and is formed by a satellite and a ground station; according to a pre-constructed service quality constraint and a pre-constructed cost model, determining a target ground station set to be rented in the candidate ground station set, so that the ground station renting cost of the space-ground integrated internet for network performance optimization based on the target ground station set is minimum; and based on the target ground station set, network performance optimization is carried out on the space-ground integrated internet. The ground station lease cost of network performance optimization of the space-ground integrated internet is minimized on the premise that the network performance requirement of the space-ground integrated internet is met.