1121 results about "Packet loss rate" patented technology

The invention discloses a data center intelligent operation and maintenance method and system based on an industrial internet of things, belongs to the field of image processing calculation, and aims to solve the problems of privacy protection deficiency, serious manual dependence and weak energy efficiency and safety. Full-link dynamic optimization is realized through an edge calculation layer and an adaptive clustering network, edge nodes are integrated with a wavelet transform noise filtering technology, dynamic denoising is performed on multi-band sensor signals of temperature, vibration, current and the like, heterogeneous device time sequence data are aligned in combination with a dynamic time warping algorithm, sampling frequency differences of multiple devices are eliminated, and the multi-band sensor signals are obtained. A weight formula is put forward, a cluster head is dynamically elected based on node residual energy, data similarity and a load coefficient, a transmission path of a high-delay area is optimized in combination with a multi-hop routing protocol, the packet loss rate and the signal strength are evaluated in real time, redundant coding is started for a high-packet-loss link or the high-packet-loss link is switched to a low-power-consumption mode. And synchronizing the dynamic topology state with the digital twin platform through an open platform communication unified architecture protocol.

The invention belongs to the technical field of data transmission, and particularly relates to an SD-WAN low-delay data transmission method and system based on edge computing, a plurality of edge computing nodes are deployed in an SD-WAN architecture, and the edge computing nodes are distributed on a network edge side, close to terminal equipment or a branch mechanism and have data preprocessing and local computing capabilities; monitoring network state parameters of each link in the SD-WAN in real time, wherein the network state parameters comprise delay, bandwidth, packet loss rate and computing resource utilization rate of edge nodes; routing to-be-transmitted data to the target edge node, and if the node has local processing capability, executing data preprocessing or caching operation; otherwise, forwarding the data to an adjacent edge node or a cloud data center; a distributed cache strategy is adopted, when network congestion is detected according to data access frequency and timeliness requirements, non-real-time data is temporarily stored in a local cache, and asynchronous transmission is executed after a link is recovered, so that the method has the effect of providing higher-quality and more reliable network service for a user.

The invention relates to the technical field of computer network security and data transmission, and discloses a computer network security data transmission method and device, and the method comprises the steps: obtaining real-time threat intelligence data, a real-time packet loss rate and a real-time transmission performance parameter, carrying out the encryption, and obtaining encrypted fragmented data; inputting the real-time transmission performance parameters into a path performance evaluation model to obtain a path performance score; sorting according to the path performance score to obtain a transmission path set; according to the transmission path set, matching to obtain a sequence corresponding relation; generating complete transmission data according to the sequence corresponding relation; performing anti-attack optimization according to the real-time transmission performance parameters to obtain a link connection strategy; analyzing the link quality according to the real-time packet loss rate to obtain a transmission link quality score; and performing dynamic adjustment according to the transmission link quality score, the link connection strategy and the complete transmission data to obtain real-time transmission data. According to the method, real-time and complete computer network security data transmission can be realized.

The invention provides a secure channel networking method based on edge computing power, and the method comprises the steps: obtaining the security parameters of a link between nodes in a network topology structure based on a selected networking strategy, including link encryption strength, an authentication mechanism and a key updating period, and combining the bandwidth and time delay of the link, obtaining an optimal secure communication route between the nodes through an edge calculation method; issuing the optimized node communication route to each edge computing node, and dynamically adjusting an authentication mechanism and a key management strategy of a secure channel between the nodes according to link security parameters including link encryption strength, a delay jitter threshold and a packet loss rate threshold; and according to the updated networking strategy, dynamically optimizing node roles and link redundancy, updating a network topology structure, and dividing edge nodes into three node function roles of computing nodes, storage nodes and forwarding nodes in combination with node computing capability, storage capacity and energy consumption level.

The invention discloses a deep reinforcement learning-based network traffic optimization scheduling method suitable for periodic traffic characteristics. The method comprises the following stages: collecting historical periodic traffic data; a network state is monitored in real time through an SDN controller, and historical periodic flow data including time delay, bandwidth utilization rate and load information are acquired; data preprocessing: performing cleaning, normalization and time sequence segmentation on the collected historical traffic data; constructing an LSTM model, constructing the LSTM model by using the preprocessed historical data, and defining an input feature and an output tag; training an LSTM model, training the LSTM model by using the training set data, and generating traffic prediction data of a next period; constructing a state space, fusing the predicted traffic data with the real-time traffic data, and constructing the state space; training a DQN model, and training a deep Q network (DQN) model by using the constructed state space; network topology and link state information are obtained, the current network topology is obtained through a topology discovery module of the SDN controller, and the bandwidth utilization rate, time delay and packet loss probability information of a link are collected in real time through a flow monitoring module; large flows and small flows are distinguished; scheduling the small flow by using the ECMP; and scheduling the large flow by using the DQN model.

The invention relates to the technical field of communication engineering, and discloses a power line carrier and wireless dual-mode fusion optimized communication method, which comprises the following steps of: 1, acquiring signal quality, noise and delay data of a power line and a wireless link in real time, and constructing a prediction model by utilizing historical data; step 2, dynamically adjusting a judgment threshold value of link switching in real time based on the prediction model, and ensuring that switching is triggered only when the signal change exceeds a preset range; step 3, based on the dynamic threshold, combining multiple indexes of signal-to-noise ratio, delay, packet loss rate and network load; according to the method, the necessity of link switching can be more intelligently judged by dynamically adjusting the judgment threshold of link switching and adopting a fuzzy logic decision mechanism, frequent switching during tiny fluctuation of signals is avoided by dynamic threshold adjustment, and a fuzzy logic system integrates multiple factors, so that the decision is finer, and the reliability of link switching is improved. And unnecessary delay and resource waste are reduced, so that the stability of a communication system is improved.

The invention relates to the technical field of software defined networks, in particular to an SRv6-based SDN (Software Defined Network) data center load balancing method and system, and the method comprises the steps: obtaining node real-time load and SRv6 routing available resource information through an SDN controller, constructing a load balancing model, and recognizing an overload node and a congestion link; generating a flow scheduling path in combination with segmented routing characteristics; in combination with network state information, monitoring the load condition in real time when the node is unbalanced or the link packet loss rate is gt; when 5%, a load balancing adjustment mechanism is triggered, unbalance types are distinguished, a related flow set is determined, optimization is performed by using a DQN algorithm, and a flow scheduling strategy is generated and dynamically adjusted by taking throughput maximization and unbalance degree minimization as targets; and issuing the scheduling strategy to the data center network equipment, updating the flow table through the Netconf protocol, and redistributing the flow. Therefore, the problems of low load balancing efficiency, insufficient dynamic adaptability, low resource utilization rate and the like in the prior art are solved.

The invention relates to the field of communication, in particular to a method for protocol optimization and dynamic routing for a network communication system. According to the invention, based on deep learning and big data analysis technologies, protocols in network communication are intelligently optimized. Through analysis of historical communication data, information such as a flow mode and a congestion condition in network communication is learned and predicted, so that communication protocol parameters are dynamically adjusted, and the efficiency and the stability of the network communication are improved. And meanwhile, a multi-objective optimization algorithm and a real-time network monitoring technology are adopted to realize dynamic routing selection. By monitoring network states such as bandwidth, delay, packet loss rate and the like in real time, an optimal routing path is calculated in combination with an objective function, and a data packet is transmitted along the path. By optimizing a data packet structure and a transmission mechanism of a protocol, redundant data is reduced, and the data transmission efficiency is improved. The method can effectively improve the transmission efficiency of network communication, reduce the time delay and packet loss rate, and improve the network stability and reliability.

The invention relates to a time delay optimized satellite network adaptive routing method and system, and belongs to the technical field of satellite communication. The method comprises the steps of multi-hop information age construction, multi-path routing, link state sensing, flow segmentation adaptive routing, dynamic routing decision and the like. A multi-hop information age model is adopted to consider the time-varying property of a link state, the real-time property of path information is accurately estimated, and the timeliness of data updating is ensured; a multi-path search algorithm predicts link occupation frequency according to ephemeris information to construct a plurality of disjoint transmission paths, so that the traffic congestion problem is relieved, and the link resource utilization rate is optimized; a flow segmentation adaptive routing algorithm adjusts the path flow distribution proportion, reduces the data packet loss rate and the information age, and guarantees the stability of time delay sensitive data transmission; according to the method, resource utilization is balanced in a high-dynamic satellite network, link congestion is effectively prevented, the overall transmission efficiency and reliability are improved, and a high-adaptability and efficient transmission scheme is provided for time delay sensitive services.

The invention discloses a high-dynamic mobile ad hoc network dynamic time slot allocation method and device, equipment and a storage medium, and belongs to the technical field of wireless communication and networks. The device is deployed between an upper-layer application module and a bottom-layer communication module of a communication node, and comprises a topology sensing module, a time frame construction module, an idle time slot distribution module, a GNSS module and an information interaction module. According to the method, neighbor nodes are determined among nodes through interaction information, and node topology change indexes and link states are calculated; the coordination node performs idle time slot allocation according to a request sent by the node in the control time slot, and dynamically adjusts a sensing period according to the current network topology change; and the node sends data in the information time slot according to the time slot allocation table, and carries out information interaction and topology perception according to the perception period. According to the method, the packet loss rate in the link unstable environment is effectively reduced, the data transmission reliability is higher, the method can adapt to link and topology dynamic changes, and the network throughput and the resource utilization rate are improved.

A low earth orbit satellite (LEO)-based communication system and method. The system comprises a low-orbit satellite constellation, a ground station, a user terminal and a network management and control center. The network management and control center comprises a track and link prediction module, a link quality prediction and evaluation module, a predictive resource pre-allocation and dynamic adjustment module and a space-ground cooperative control module. According to the communication system, active resource optimization and dynamic adjustment before actual switching are realized through track and link prediction, link quality prediction, predictive resource pre-allocation and dynamic adjustment and air-ground cooperative control. According to the method, the switching time delay can be greatly reduced, the packet loss rate is remarkably reduced, and the system throughput and the access success rate are also remarkably improved. Through predictive management, the performance, reliability and user experience of LEO communication are comprehensively improved.

The invention provides a file high-speed transmission method based on multi-link aggregation, and solves the problems of low transmission efficiency and insufficient stability of a traditional single link. The method comprises the following steps: detecting network link performance parameters, and screening available links; according to the number of links, the average bandwidth and # imgabs0 # limitation, dynamically segmenting a large file into adaptive data blocks, numbering the data blocks, and adding information such as a position to a head; an improved dynamic load balancing algorithm is adopted, link quality (including bandwidth utilization rate, delay, packet loss rate, stability and dynamic adjustment of weight along with a network environment) and data block priority (distinguished according to types and sizes and the weight is adjusted according to file characteristics) are synthesized, data blocks are distributed to different links for parallel transmission, and pre-distribution verification ensures link load safety; and recombining the data blocks by the receiving end according to the numbers to realize file recovery. The system significantly improves the transmission efficiency through multi-link aggregation and dynamic strategy adjustment.

The invention provides an intelligent short message link efficient detection method and system. According to the method, unmanned aerial vehicle cluster deployment is used for forming a dynamic communication topology, and the dynamic communication topology is used for monitoring the time delay mutation rate and the packet loss rate of a damaged short message link in real time and generating a state fluctuation sequence. When it is detected that the transmission time delay gradient exceeds an emergency communication threshold value, an abnormal marking mechanism is triggered, and abnormal association clusters and the space-time coupling relation of the abnormal association clusters are recognized by analyzing the data. Then, the information is bound with timestamps, and a block chain state change event is generated by using a multi-node parallel evidence storage technology; and adjusting the distribution density of the unmanned aerial vehicle according to a verification result, and generating a topology reconstruction factor so as to update a short message transmission path and optimize a communication network. According to the technical scheme provided by the invention, the stability and reliability of efficient detection of the short message link can be improved.

The invention provides a QoS routing optimization method and system, a computer and a readable storage medium, and the method comprises the steps: S1, obtaining a real-time network performance data set which comprises link bandwidth, delay and packet loss rate; s2, modeling a network topology into a graph structure based on the real-time network performance data set, and generating a network state feature matrix; s3, constructing a state space and an action space according to the network state feature matrix, wherein the action space comprises a plurality of candidate paths from a source node to a target node; s4, selecting a candidate path from the action space through a main network, evaluating the performance value of the candidate path by adopting a target network, updating the main network according to the performance value, and generating an empirical data set; and S5, optimizing a path selection strategy according to the empirical data set, and updating a routing table. According to the method and the device, the optimal path meeting the QoS requirement can be found for the data flow in different service environments in the dynamic network environment.

The invention relates to a network slice optimization method for a 5G private network, and the technical scheme comprises the steps: constructing a time sequence performance model in a slice operation process, and monitoring tiny changes including end-to-end time delay, throughput and packet loss rate; then extracting a short-period fluctuation mode of the index, and comparing the gradient with a historical stability threshold value; judging whether the index is in a performance critical state or not by identifying an index drifting behavior which continuously jitters but does not default yet; non-equal-ratio responses including jitter improvement and delay decline after each time of resource adjustment are recorded; then trend fitting is carried out on the data, and whether a performance platform period or a critical jump point exists or not is judged; delimiting three intervals of resource investment in the model: an inactive area with invariable investment, a high-sensitivity area with rapid performance change and a rising saturation area with stable performance and continuous resources, wherein the three intervals include an inactive area with invariable investment, a high-sensitivity area with rapid performance change and a rising saturation area with stable performance; and identifying the current response area of the slice through the model, determining that the regulation strategy does not take equal proportion input as the core, and taking the optimal response area as a resource allocation interval to make a choice.

The invention relates to the technical field of mobile communication transmission, and discloses a heterogeneous network multilink parallel mobile communication transmission method and system. A dynamic link aggregation module of the system collects link state parameters including signal strength, delay jitter and packet loss rate from a plurality of heterogeneous network interfaces in real time; a flow distribution decision module calculates available bandwidth weight of each link according to the data and generates a dynamic flow distribution strategy; the data fragment packaging module splits to-be-transmitted data into a plurality of data fragments according to the strategy, and adds a sequence identifier and a link identifier to each fragment; the multi-channel concurrent transmission module sends corresponding data fragments in parallel through a specified heterogeneous network link; and the receiving recombination verification module sorts the recombination data fragments according to the sequence identifiers, and detects the data integrity through a verification mechanism. According to the system, a set of complete multi-link parallel transmission solution is formed, and the problems existing in a traditional single link and an existing multi-link transmission technology can be effectively solved.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention relates to the technical field of satellite telemetering and remote control, and discloses a port task processing method for a satellite measurement, operation and control system, which comprises the following steps of: 1, detecting all available network interfaces through a port scanner, and establishing a multi-path communication channel comprising a public network, a private network and a satellite link; step 2, acquiring packet loss rate, delay, bandwidth and jitter parameters of each path in the multi-path communication channel in real time, and updating the parameters based on a preset period; and step 3, generating a comprehensive score of each path according to the packet loss rate, the delay, the bandwidth and the jitter parameters. According to the method and the device, the technical scheme of dynamic scoring and switching of the multi-path communication channel is adopted, and the technical effect of automatically selecting the optimal path according to the real-time network performance is achieved; the problems of high transmission delay, large packet loss rate and incapability of adaptive adjustment caused by network fluctuation are solved.

The invention relates to the technical field of Internet of Things routing management, in particular to a big data-based routing optimization method, which comprises the following steps of: acquiring spectrum data of an Internet of Things routing management area, performing correlation analysis on a spectrum state corresponding to a historical success rate and retransmission times of communication between nodes, and quantifying a probability of successful communication at a future target time. According to the method, the multi-band historical communication success rate and retransmission frequency data are dynamically acquired, the interference risk of the target time period is analyzed and predicted in combination with the time sequence, an interference avoidance strategy based on static channel assessment is upgraded to a probability-driven pre-judgment mechanism, and the anti-interference capability of a transmission path in a complex electromagnetic environment is improved. According to multi-protocol link characteristics of Bluetooth, WiFi, 2.4 G and the like, parameters such as delay, throughput, packet loss rate, flow cost and the like of a single path are synchronously quantified, a unified performance cost file of a heterogeneous network link is established, and end-to-end performance modeling of cross-protocol path combination is realized.

The invention discloses a reliable communication system based on a publishing and subscribing mode in a satellite-ground weak network environment, and aims to solve the problems of high round-trip delay, limited link bandwidth, high instantaneous packet loss rate, easiness in blocking and switching influence of links and the like in a data transmission process between a low-orbit satellite and a ground station. A publishing and subscribing model is introduced into an application layer to realize service logic decoupling, deep fusion with an underlying enhanced QUIC transmission protocol is realized, and mechanisms such as message persistence, breakpoint resume, forward error correction and connection migration are combined, so that stable, orderly, complete and safe transmission of service data can still be ensured under the conditions of link instability, quality fluctuation and communication interruption, and the service data transmission efficiency is improved. And high availability and continuity of a satellite-ground communication task under a weak network condition are realized.

The invention relates to the technical field of communication routing, in particular to a network connection automatic tuning system and device based on an SRv6 protocol. According to the method, the state data of network equipment is collected in real time, key network indexes such as delay, bandwidth, packet loss rate and hardware performance are combined, the health condition of network connection is dynamically evaluated, and path optimization is carried out in combination with an SRv6 protocol. By automatically calculating a path scoring matrix, the system can automatically select an optimal network path according to the priority of network transmission tasks, and intelligent adjustment and optimization are realized under the conditions of dynamic traffic and complex tasks; the invention provides a path calculation and scheduling mechanism based on an SRv6 protocol, the priority of a network path is automatically adjusted according to different types of data transmission tasks, and the selection of an optimal path is ensured. Meanwhile, the system has an automatic switching mechanism, and can automatically switch to an alternative path when the network path has a fault, thereby improving the reliability and fault-tolerant capability of the network.

The invention belongs to the technical field of intelligent unmanned systems and edge intelligent reasoning, and discloses an unmanned aerial vehicle emergency inspection system integrating low-altitude communication and edge calculation. The system is composed of a link state monitoring and scoring module, a reasoning calculation migration and model loading module, a task graph modeling and dynamic segmentation module, a multi-unmanned aerial vehicle task collaboration and trajectory continuation module, a flight state fusion and path adjustment module, a redundancy criterion driven fault-tolerant control module and a result consistency verification and data caching module. According to the method, the communication link scoring function fusing the packet loss rate, the bandwidth, the time delay and the signal-to-noise ratio is constructed, the comprehensive quality evaluation of different low-altitude communication links is realized, and the reasoning model switching and task migration strategy is driven by the scoring result, so that the communication adaptation capability of the system in a complex, dynamic or emergent scene is remarkably improved, and the system performance is improved. Compared with an existing communication mechanism depending on a fixed link or a preset priority.

The invention discloses a network optimization method based on an online conference, which relates to the technical field of real-time audio and video transmission, and comprises the following steps: in a transnational network, deploying monitoring probes at nodes participating in the online conference for monitoring network parameters such as network delay, bandwidth, packet loss rate and jitter in real time; the method comprises the following steps: processing network parameters and predicting abnormity by using a statistical analysis algorithm and a time sequence model to obtain network condition information, and then combining global network topology and using a reinforcement learning Q-Learning model and a graph neural network GNN topology prediction algorithm; according to the method, the network condition is monitored and predicted in real time, the optimal transmission path is output by using the reinforcement learning Q-Learning model and the GNN topology prediction algorithm, the bandwidth fluctuation is predicted through the LSTM, the packet loss rate is predicted based on the Bayesian network, the data transmission strategy is optimized, the network delay, the packet loss rate and the jitter are effectively reduced, and the network performance is improved. And the smoothness and the stability of the conference are improved.

The invention provides an optical fiber communication line fault feedback system, which relates to the technical field of optical fiber communication fault diagnosis, and comprises a data acquisition module, a feature extraction module, a feedback module and a fault feedback module, according to the method, the attenuation rate, the reflectivity, the packet loss rate, the temperature, the humidity, the vibration signal, the electromagnetic pulse signal and the radio frequency interference signal of the optical fiber communication line are obtained; compared with the prior art, the method comprehensively considers the influence of communication related parameters and environment related parameters, achieves the comprehensive evaluation of the optical fiber communication line, and improves the evaluation accuracy of the optical fiber communication line. Therefore, the accuracy of subsequent fault diagnosis and fault classification is improved.

The invention relates to the technical field of computers, and provides a game acceleration method, a game accelerator and a storage medium. The method comprises the following steps: in response to a game acceleration request initiated by a game client for an external service game, sending a network state detection packet set to an acceleration node group according to a preset detection period, and receiving a network state data packet set fed back by the acceleration node group; determining a current cycle packet loss rate and a next cycle packet loss rate corresponding to each acceleration node based on the network state detection packet set and the network state data packet set; determining a target transmission path and a target transmission mode based on the packet loss rate of the current period and the packet loss rate of the next period; and transmitting the original game data packet to a target game server through the target transmission path and the target transmission mode to complete game acceleration. According to the method, the network delay and the packet loss rate when the game user plays the outer clothing game can be effectively reduced, so that the game experience of the user is improved.

The invention relates to the technical field of wireless communication networks, in particular to an eSIM global operator dynamic OTA switching system based on an AI strategy, and the system comprises an environment data collection module which collects the bandwidth, the packet loss rate, the network stability and the switching success rate at an equipment end, and generates local network state data; the behavior and tariff modeling module collects communication behavior and tariff information and generates user preference data; the AI strategy generation module outputs an OTA control instruction based on a graph neural network and an attention mechanism; the OTA strategy synchronization module issues and analyzes an instruction to generate an execution parameter packet; and the double-link switching execution module completes switching and rollback configuration. According to the invention, by fusing graph neural network strategy modeling, a terminal AI and OTA cooperation mechanism and a double-link redundancy switching process, intelligent, low-delay and high-reliability dynamic switching of an eSIM operator is realized.

The invention relates to the technical field of communication control, and discloses a communication control method and system based on a dual-mode communication module, the system integrates two communication modes of 4G and 5G, and flexible and reliable real-time data transmission is realized. The core is that a communication quality evaluation module is introduced, and 4G and 5G communication quality is scored by analyzing data such as signal strength, data transmission rate and packet loss rate. The system constructs a fault prediction module, and predicts the type and probability of future communication faults by using historical communication quality scores and equipment operation state data. And the maintenance early warning module carries out early warning on the maintenance demand of the communication equipment in combination with the evaluation and prediction results. And the maintenance scheme generation module automatically generates a maintenance scheme according to the early warning result, triggers an emergency notification when the maintenance demand is high and provides detailed steps. According to the invention, the stability and reliability of communication service are improved, and the maintenance process of communication equipment is optimized.

The invention relates to the field of broadband alarm, in particular to a broadband user-based optical modem power failure and fiber break alarm real-time monitoring method, which comprises the following steps of: S1, monitoring and recording the online state, the light attenuation level, the flow information, the packet loss rate, the network delay and the log of an optical modem under OLT (Optical Line Terminal) equipment in real time; s2, automatically identifying an abnormal mode and a potential fault according to a model established by a machine learning algorithm and a deep learning algorithm; s3, automatically diagnosing problem reasons according to the collected data, and optimizing the knowledge base in combination with an expert system; s4, performing deep mining and analysis on historical fault data, and predicting a potential fault risk based on the historical data and a real-time monitoring result; and S5, continuously updating and optimizing a fault detection and diagnosis model through a self-learning algorithm model, and uniformly monitoring and managing all data acquisition, analysis and alarm modules. The fault detection accuracy and timeliness can be improved, and potential fault prediction and preventive maintenance are realized.

The invention provides a network traffic scheduling optimization method based on deep learning. The method is applied to the technical field of communication networks, and comprises the following steps: S1, collecting flow data, link state, delay and packet loss rate indexes deployed at network nodes in real time, and constructing a sample database; s2, according to the sample database, constructing a traffic prediction model fusing a retrieval enhancement diffusion model and a mixed linear expert model to perform multi-modal traffic prediction; s3, constructing a state space of a reinforcement learning agent according to a real-time network state and the future traffic prediction result; s4, performing strategy optimization based on a multi-objective optimization mechanism; and S5, deploying the trained and optimized model to a network controller or an edge computing node to realize real-time sensing and dynamic scheduling control of network resources. According to the method, the traffic prediction precision is remarkably improved, the scheduling strategy is updated and optimized in time according to the network environment change, and the comprehensive performance of the network is improved.

The invention provides a self-adaptive adjustment method for sound tone of a live broadcast person based on a network model, which relates to the technical field of audio signal processing and comprises the following steps of: determining an accurate starting position and an accurate ending position of packet loss through a sudden change point of a short-time energy function and an abnormal change point of a zero-crossing rate; therefore, the packet loss interval is repaired by pertinently utilizing the fundamental frequency track characteristic and the formant characteristic. The method provided by the invention can solve the problems of fundamental frequency track breakage and unnatural voice caused by the contradiction between acoustic parameter decoupling repair and real-time processing constraint in a high packet loss rate scene in a traditional voice repair method, thereby realizing high-precision and low-delay self-adaptive repair of packet loss voice, and improving the voice recovery efficiency. And the sound quality and the user experience in the live scene are obviously improved.