1511 results about "Packet loss rate" patented technology

Flexible scheduling, self-healing, high-utilization strong stability for cellular network communications are discussed. Cellular network communication settings and strategies are developed with communication interval, transmit power data rate, channel delay, spectrum occupancy, source coding, receiving sensitivity, channel coding, signal-to-noise ratio, packet loss rate, channel occupancy rate, reception frequencies frequency band size of data package transmission frequencies, data package structure, modulation scheme, information coding scheme, antenna configuration.

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 provides a real-time streaming media transmission method and system based on a cloud game and a storage medium, and the method comprises the steps: monitoring network state parameters in real time, and generating network state data; dynamically adjusting the resolution, the frame rate and the bit rate of the video stream based on the network state; a redundant data packet is inserted, and the redundancy proportion is adjusted according to the packet loss rate and the jitter, so that the data reliability is enhanced; dynamically adjusting the transmission rate by adopting a congestion control algorithm; dynamically selecting an optimal server node according to a network condition and a server load; audio and video processing tasks are shared through edge computing nodes, and the load and transmission delay of a main server are reduced; setting a self-adaptive decoding buffer area, dynamically adjusting the size of the buffer area according to a network state, and reducing playing lagging; through real-time monitoring, dynamic adjustment and edge collaboration, smoothness is guaranteed, reliability is improved, delay is reduced, efficient load distribution and user experience optimization are achieved, and the method is suitable for real-time streaming media transmission in a cloud game scene.

The invention relates to the technical field of data transmission, and discloses an intelligent flight data transmission method and system based on satellite communication and a storage medium. The method comprises the following steps: carrying out real-time data acquisition and calculation on signal strength, bandwidth, delay and packet loss probability of an available satellite communication link to obtain a real-time link parameter table and a transmission channel allocation sequence; based on the real-time link parameter table, according to the priority information of the encrypted data packet queue and the transmission channel distribution sequence, performing multi-channel distribution and parallel transmission processing on the data packet to be transmitted to obtain a data packet transmission record; and carrying out statistical analysis and parameter calculation on the transmission success rate, delay time and retransmission times in the data packet transmission record to obtain transmission quality data and a system optimization parameter set. Under the condition of limited satellite communication resources, intelligent hierarchical transmission of flight data is realized, timely and reliable transmission of important data is ensured, and meanwhile, a real-time monitoring and optimization mechanism for data transmission quality is provided.

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 relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

The invention relates to the technical field of traffic management, in particular to a dynamic traffic scheduling method and system based on network configuration, and the method comprises the following steps: based on network configuration data, extracting network index data, network indexes including network delay, packet loss rate and bandwidth size, calculating a network quality score, evaluating a current network state, and obtaining network state information. According to the method, the key network indexes are extracted, the network quality score is calculated, and the current network state is comprehensively evaluated, so that network configuration can respond to network requirements and condition changes, and according to the real-time network quality, the bandwidth resources are dynamically adjusted, and a priority rating mechanism is introduced, the network quality is improved. The time sensitivity of different data contents is evaluated, it is ensured that important data can obtain enough transmission resources, the influence caused by network fluctuation is compensated by monitoring the network fluctuation in the data transmission process and adjusting bandwidth allocation in real time, and the continuity and efficiency of data transmission are ensured.

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 provides a private domain live broadcast bandwidth resource allocation and routing method and system based on reinforcement learning, and relates to the technical field of computer networks, and the method comprises the steps: collecting real-time data such as the number of users, geographical distribution, terminal types, content types, network delay, packet loss rate, bandwidth utilization rate and the like in a live broadcast room, and inputting the real-time data into a pre-trained reinforcement learning model; the model is based on a state space, features are extracted by using a deep neural network, bandwidth resource constraints are combined, and a bandwidth allocation and routing strategy is generated by adopting a strategy gradient algorithm; and according to the strategy, the software defined network controller adjusts bandwidth allocation parameters and routing table configuration in real time to realize differentiated bandwidth allocation and path optimization, establishes nearby access edge nodes based on user geographical distribution, and dynamically adjusts the path during congestion.

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 communication networks, and discloses a service channel time slot allocation method, which comprises the following steps of: 1, acquiring network data in real time from network equipment, the network data comprising bandwidth utilization rate, delay, packet loss rate and service flow type; 2, predicting future network flow fluctuation through a long-short-term memory network model based on historical flow data and real-time network data; and step 3, dynamically adjusting the allocation of time slot resources according to the network flow fluctuation prediction result, and preferentially allocating the time slot resources for high-priority services. According to the method, the dynamic adjustment of the time slot resources is realized by combining the flow prediction of the LSTM model and the Q-learning reinforcement learning algorithm, so that the system can flexibly allocate the resources according to the actual network load condition, the resource waste is reduced, the utilization efficiency of the bandwidth is improved, and the utilization rate of the overall network resources is remarkably improved especially under the condition of high network load.

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 provides a monitoring video transmission method and system based on wireless communication, and relates to the technical field of monitoring video transmission, and the method comprises the steps: obtaining and preprocessing network state data, the network state data comprising bandwidth, time delay and packet loss rate; establishing a network state prediction model based on the LSTM neural network, and predicting a future network state according to the network state prediction model; based on the future network state, a multi-objective optimization algorithm is adopted to perform transmission path selection; and for the selected transmission path, video coding parameters are adjusted, the coding parameters comprise a code rate, a resolution ratio and a frame rate, and a self-adaptive transmission protocol is adopted to dynamically adjust the transmission rate according to a real-time network state. According to the method, different paths can be dynamically selected, network congestion or degradation is avoided, and the video transmission quality and rate are adaptively and flexibly adjusted according to the current network state, so that stable transmission of the video quality under different network conditions is ensured.

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.

The invention discloses a self-adaptive data caching and synchronizing method, system and device for an edge gateway of the Internet of Things and a storage medium, and the method comprises the steps: obtaining network quality parameters, such as link bandwidth, time delay and packet loss rate, between nodes, detecting and solving instruction conflicts based on a vector clock mechanism, generating an instruction execution sequence, and carrying out the self-adaptive data caching and synchronizing of the edge gateway of the Internet of Things; local caching and incremental synchronization strategies are adopted for hotspot data, a data access mode is dynamically predicted in combination with a machine learning algorithm, and a cache updating period is optimized. The future data is predicted according to the data characteristics of the Internet of Things application, and the data cache updating period of each node is dynamically adjusted, so that each node can keep a relatively consistent updating rhythm when caching a hotspot data copy; therefore, the hot spot data copies of different nodes can be ensured to have similar accuracy and timeliness at the same time point, so that the data consistency is maintained.

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 disclosure relates to a 5G or 6G communication system for supporting a higher data transmission rate. Embodiments of the present disclosure provide a communication method, an electronic device and a computer-readable storage medium. For an uplink service, the method comprises steps of: by a user equipment, determining a remaining PDB of a data packet in a buffer, and reporting related information of the remaining PDB to a base station, so as to assist the base station to perform proper uplink scheduling for the User Equipment according to the related information of the remaining PDB. Accordingly, the packet loss rate of data packets is reduced, and the transmission efficiency is 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 provides a communication performance test method and system for a base station network side transmission path. The method comprises the following steps: determining a target monitoring node from a plurality of monitoring nodes deployed on a network side transmission path of a to-be-tested base station, and monitoring transmission delay data, packet loss rate data and bandwidth utilization rate data of a test data stream on a specific road section of the transmission path in real time; detecting periodic and non-periodic changes of transmission delay to obtain an analysis result; evaluating the influence of the packet loss rate data on the transmission performance to obtain an evaluation result; predicting the change trend of the bandwidth utilization rate data to obtain a prediction result; calculating a communication performance index of the specific road section by integrating the three results; and generating a communication performance test report according to a comparison result whether the communication performance of the specific road section reaches a predetermined standard. The communication performance of the base station network side transmission path is comprehensively evaluated by integrating the chaos theory, the fuzzy logic system and the deep learning algorithm, so that the communication performance testing efficiency is improved.

The invention belongs to the technical field of Internet of Vehicles, and particularly relates to an LTE and WiFi switching method using a deep learning network, which comprises the following steps: acquiring the strength, delay and packet loss rate data of LTE and WiFi signals in real time, judging the geographic position and motion state of equipment in combination with a GPS and an IMU, and selecting an optimal network switching strategy according to the current state by using a deep reinforcement learning algorithm. Switching operation is executed, non-inductive switching is guaranteed, the network state and the switching history are stored, and the DQN model is updated periodically. According to the technical scheme, the vehicle-mounted navigation system and the IMU are combined, the system can recognize a specific scene in advance and pre-judge network switching, through the DQN algorithm, the system has the self-learning capability and can adapt to different environments, double-network concurrent connection is adopted to ensure that a user does not perceive the switching process, the WiFi network is preferentially used to save LTE flow, and instability caused by signal fluctuation is avoided.

The invention relates to the technical field of fault detection for a wireless communication network, and discloses a fault detection method for the wireless communication network. By collecting data of base station signal strength, network delay, data packet loss rate and user equipment signal quality, the problem of insufficient fault detection caused by a single index is solved. Through multi-dimensional data acquisition, the system can comprehensively evaluate network performance, the fault detection accuracy is improved, and different fault types can be effectively identified especially in a complex fault mode. By storing the data according to the time sequence, the problem of time sequence chaos is avoided, and accurate detection of time-varying faults is ensured. The comprehensive data vector generated by each time of sampling comprises a plurality of network performance indexes, so that the fault detection and positioning capability is improved. Abnormal values are eliminated through data preprocessing, the data quality is ensured, an accurate basis is provided for subsequent analysis, and the reliability of a detection result is ensured.

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.