1079 results about "Network delay" patented technology

The invention relates to the technical field of network routing, and discloses a dynamic load balancing method and system for network routing, and the method comprises the steps: carrying out the periodic sampling and feature extraction of a processing load of a network node and a transmission load of a network link, and obtaining a load state vector and a network load degree; constructing a load balancing decision matrix, and performing comparison operation on the load balancing decision matrix and the multi-stage load threshold to obtain a load optimization instruction sequence; identifying a high-load source node set and a low-load target node set based on the load optimization instruction sequence, and performing load prediction modeling to obtain a task migration scheme; performing path optimization of reinforcement learning based on the task migration scheme to obtain a multi-routing path set; and carrying out load partition clustering and routing strategy distribution on the multi-routing path set to obtain a regional routing mapping table, and the method reduces the network delay, improves the resource utilization rate and realizes dynamic load balance.

The invention belongs to the field of intelligent edge systems and privacy enhancement computing, and particularly relates to a privacy protection-oriented robot large model cloud edge end collaborative reasoning and federated learning system, which comprises a cloud server layer used for deploying a large-scale pre-training model and executing complex reasoning and global federated learning coordination; the edge calculation layer is used for deploying an intermediate layer model and executing local data aggregation, privacy protection processing and intermediate feature calculation; the terminal equipment layer is used for deploying a lightweight model and executing data acquisition, primary processing and lightweight reasoning; the federated learning framework is used for optimizing the model; the privacy protection module is used for integrating data localization, differential privacy, homomorphic encryption, secure multi-party computing and a block chain verification mechanism; the adaptive allocation module is used for dynamically adjusting computing resources. According to the method, the problems of privacy leakage risk, computing resource limitation, network delay, insufficient data isolation and the like of the traditional AI service in a robot scene are solved, and efficient privacy protection and data security isolation are realized.

The invention relates to the technical field of prediction and alarm, in particular to a school computer room data operation monitoring and early warning method and system, and the method comprises the following steps: obtaining real-time data, such as CPU utilization rate, memory occupancy rate and network delay, through a sensor, generating a parameter sequence after standardization, extracting a difference sequence through a sliding window, and recognizing the output trend risk of a continuous rising interval. And extracting a behavior frequency to generate an abnormal coefficient, carrying out weighted analysis on the coupling degree to obtain an early warning threshold, and generating an early warning instruction if the growth rate exceeds the threshold and lasts for three time slices. According to the method, difference characteristics are extracted through a standardized time sequence and a sliding window, behavior state association is analyzed in combination with a periodic frequency, coupling degree dynamic early warning is calculated, a continuous deviation trend is screened, a real-time threshold range is formed, abnormal confusion is reduced, occupation of irrelevant alarm resources is reduced, risk identification capability is improved, and stable operation of equipment is guaranteed. And hidden danger accumulation occurrence probability is reduced.

The invention relates to the technical field of cloud computing resource migration, in particular to a cross-cloud resource migration method and system, and the method comprises the following steps: carrying out the proportional distribution of the processing capability of a virtual machine instance based on the processing capability of the virtual machine instance, the block storage read-write rate and the network delay time, carrying out weighted average on the block storage read-write rate and the network delay time, integrating the block storage read-write rate and the network delay time into a two-dimensional matrix, sorting and removing low-threshold nodes, and generating a preliminary resource optimization data set; according to the method, resource optimization accuracy is improved through proportional allocation of virtual machine processing capacity, weighted average block storage read-write rate and network delay and performance index matrix processing, performance weight difference and path parameter optimization performance dependency expression are combined, node transmission efficiency and priority are dynamically adjusted, transmission path elasticity is enhanced, and resource optimization efficiency is improved. The resource state of the target end is monitored and adjusted in real time, migration stability is ensured, the resource integration precision and service controllability of the multi-cloud environment are improved, and complex application requirements are met.

The invention relates to the technical field of software time synchronization, and discloses a software time synchronization method and system for multi-sensor data fusion, and the method comprises the steps: extracting temperature, load and drift frequency characteristics through principal component analysis based on the working state and historical drift data of a sensor, and constructing a confidence evaluation model to calculate the credibility of a timestamp; identifying an abnormal node group by using k-means and an isolated forest algorithm, analyzing a phase deviation fluctuation and network delay interaction effect, extracting a nonlinear drift feature in combination with a Prophet algorithm, and calculating a phase correlation value by using Hilbert cross-correlation; and dynamically adjusting node clock parameters and generating a calibration timestamp according to the network influence weight and the stability evaluation result. According to the method, the problem of time desynchrony caused by clock drift and network delay factors in a distributed system is effectively solved, and the overall time consistency and reliability of the system are improved.

The invention relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention relates to a distributed outbound system optimization method based on cloud computing. The method comprises the following steps: disassembling an outbound task into fine-grained subtasks including dialing, recording, interaction and recording; generating a unique identifier for each subtask; based on a distributed task scheduler; according to node real-time performance and network delay, dynamically distributing tasks, and introducing a task priority rule; monitoring a task execution state in real time through a distributed tracking system; recovering and redistributing abnormal and failed tasks; fragmenting the call-out data according to regions or customer groups, and distributing the call-out data to different nodes; processing the call record and the client feedback by using a stream processing framework comprising Apache Flink; adding a call task including Kafka into a distributed task queue; setting a dynamic priority queue, and adjusting the priority according to the task timeliness; setting an overtime threshold value for the call task, and monitoring a task state in real time; and when the task is overtime or failed, triggering a retry strategy including adjusting the call period and switching the node.

The invention relates to the technical field of multi-path information processing, and provides a multi-path optimization method, device and system for network routing, and the method comprises the steps: obtaining network node information, obtaining a plurality of available paths based on the network node information, evaluating all available paths, and obtaining network bandwidth data and an initial weight; adjusting the initial weight to obtain an initial path weight and a path priority table, distributing and identifying data packets of network flow by using the path priority table, obtaining a flow data packet with a path identifier, and adjusting the initial path weight after obtaining a flow load log and network delay data based on the flow data packet; and generating a flow scheduling scheme to update the path priority table. Through obtaining network node information and utilizing a bandwidth evaluation algorithm, initial path evaluation reflecting network conditions is generated, and through real-time data feedback of a dynamic monitoring module, a flow scheduling scheme capable of carrying out adaptive adjustment at any time is generated, so that the defects of the prior art in the aspect of dynamic adaptability are improved.

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 provides a method, a device and a system for switching master and standby control centers of a smart home. In the scheme, real-time operation state data of each control center in the smart home and the number of times of downtime in a first preset time period are acquired, and based on the real-time operation state data and the number of times of downtime, a long-term short-term memory network model is used to score each control center to obtain a final score of each control center; sorting all the final scores from large to small, and determining the control centers corresponding to the final scores ranked in the front preset number as candidate control centers; an improved PBFT protocol is adopted to carry out consensus election on the candidate control hub, a target control hub is elected, the main control hub is switched to the target control hub for operation, and the target control hub meets at least one of the following preset conditions: the highest network communication efficiency, the shortest network delay and the lowest power consumption. The problem that the switching efficiency of the main and standby control centers in the smart home system is low is solved.

The present invention relates to the technical field of information processing, and provides a time-sensitive network end-to-end time synchronization method, device and storage medium, including obtaining the timestamp of the source end, performing time calibration on the time synchronization signal according to the timestamp of the source end, collecting the time of the target end after obtaining the calibrated time synchronization signal, obtaining the timestamp of the target end, analyzing the timestamp of the source end and the timestamp of the target end, obtaining the network delay value, combining the local clock information of the source end and the target end, and obtaining the corrected timestamp of the target end, and adjusting the local clock of the target end according to the corrected timestamp of the target end and the timestamp of the source end. By obtaining the timestamp of the source end and performing time calibration and analysis, and correcting the timestamp of the target end, the accuracy and stability of the end-to-end time synchronization are improved, and the problems of insufficient synchronization accuracy and unstable delay in complex network environments are improved.

The invention discloses a block chain fragment optimization method and system, and particularly relates to the technical field of block chains. The method comprises the steps of generating an initial fragmentation scheme by obtaining a global fragmentation topological structure of a target block chain network; based on an initial fragmentation scheme, analyzing cross-chain interaction frequency and internal node connectivity between fragments, calculating a cooperation coefficient of each fragment, and identifying a high-frequency interaction subchain to form a fragment collaborative optimization subset; then, sub-node grouping analysis is carried out on high-frequency interaction sub-chains in the optimization subset, and reconstruction candidate fragments are screened out by combining in-chain transaction confirmation time and resource consumption; carrying out load state evaluation on nodes for reconstructing the candidate fragments, and optimizing load distribution through node migration simulation and inter-chain communication prediction to form an optimal fragment migration scheme; the migration scheme is applied to the target fragment, the consensus protocol path is adjusted in real time, the optimization effect is evaluated based on interaction data, fragment resource allocation can be dynamically optimized, network delay is reduced, and the overall performance is improved.

The invention discloses a ship management method and system based on digital twinborn scene construction. The method comprises the following steps: acquiring multi-source scene data of a target water area in real time, and constructing a corresponding ship management digital twin model; performing integrity evaluation on the constructed digital twinborn model, and performing defect compensation according to an evaluation result to obtain an optimized digital twinborn model; further collecting ship density and weather condition data of each sub-region of the target water area, constructing a network transmission delay prediction model, analyzing related data based on the model, and generating a network delay coefficient distribution diagram of the target water area; and according to the delay distribution diagram, determining the real-time updating lag amount of each subunit of the optimization model, and correcting the ship position information in the digital twin model. According to the method, the real-time performance and accuracy of the digital twin model can be remarkably improved, and the ship operation management efficiency and safety in a complex water area environment are enhanced.

The invention discloses a vehicle-mounted controller firmware upgrading method, which relates to the technical field of intelligent networked automobiles, and comprises the following steps of: accurately describing a dependency relationship between versions by introducing a directed acyclic graph, providing a clear topological sorting basis for an upgrading process, and improving the upgrading efficiency. The system function conflict and disorder caused by disordered upgrading sequence are fundamentally avoided; a distributed consensus protocol is utilized to ensure that each controller achieves state coordination before upgrading, so that the overall robustness of a multi-controller cluster is effectively improved when the multi-controller cluster faces network delay or individual node abnormity, and upgrading failure or version bifurcation caused by the fact that part of nodes are not ready is avoided; through the combination of the group-level binding token and the atomic switching mechanism, the controller group with a strong function dependency relationship can be ensured to be effective and updated as a unified transaction unit, and the safety and reliability of cross-domain function linkage are remarkably enhanced.

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 relates to the technical field of operation and maintenance management, and discloses a cloud operation and maintenance management method, system and equipment for a charging station, and the method comprises the steps: monitoring the WiFi signal strength, 5G network quality and wired network delay parameters of each floor of a stereo garage in real time, and building a network environment database; when the charging pile detects that the SOC change amplitude exceeds a set threshold value, an optimal target transmission path set is screened out; carrying out differential bit coding on the collected charging data to generate a charging data frame, and sending the charging data frame to a cloud server in a multi-path concurrent mode; the cloud server receives and gathers the charging data frames from different floors of the stereo garage, and analyzes the charging load distribution condition of the whole garage; and calculating a power distribution scheme of the charging pile of each floor based on the whole garage charging load distribution condition, and issuing a scheduling instruction to the charging pile of the corresponding floor, thereby improving the intelligent operation and maintenance management level and resource utilization efficiency of the stereo garage charging station.

A system (100) for optimizing the allocation of computing resources, comprising: a data acquisition unit (102) comprising a retrieval processor (102a) configured to retrieve historical performance data, which is then stored in a data storage module (104); a centralized processing unit (106) comprising a dedicated processor and a graphics processing unit (GPU) for performing a plurality of resource allocation functions, the centralized processing unit further comprising: a prediction module (106a) configured to implement machine learning algorithms to analyze historical performance data and generate workload forecasts; a performance monitoring module (106b) configured to track real-time system metrics, including CPU utilization, memory consumption, and network latency; a resource allocation module (106c) configured to dynamically adjust the computing resources based on the workload forecasts and real-time system metrics; a container orchestration module (106d) configured to manage the deployment and scaling of microservices across distributed containers; and an administrative user interface module (108) comprising a user display (110) and a visualization module (112), wherein the visualization module (112) generates representations of performance metrics and the user display (110) is configured to display system performance metrics and enable configuration of scaling policies.

A player monitoring system for compensating for network delays comprises a server that is configured to receive sensor data from multiple sensors at dispersed locations. In some embodiments, a server is configured to determine a delay associated with one network path carrying sensor data to the server and a delay associated with another network path carrying sensor data to the server. Based on the delays, the server is configured to synchronize events detected by the sensors at the dispersed location, thereby permitting more precise and accurate gameplay decisions for a dispersed sporting event.

The invention relates to the technical field of power supply, and particularly discloses a peak and frequency regulation heat supply method for a multi-element energy storage coupling coal power unit, which is used for solving the problems that only active frequency is optimized, electric heating coordination and charge state constraint are lacked and time delay compensation is insufficient in the prior art. The method provided by the invention comprises the steps of system modeling, controller design, coupling constraint, adaptive parameter adjustment, delay compensation and distributed consensus. According to the method, a dual-domain electric power-thermal interconnection model is constructed, a cooperative controller based on delay compensation distributed model prediction control is designed, electric-thermal cooperative penalty and charge state constraint are introduced into a target function, a prediction window and weight parameters are adaptively adjusted, and network delay is identified and compensated online. And a consensus algorithm is adopted to realize multi-region power-heat collaborative optimization.

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 discloses a real-time interaction and intelligent management method for a multi-dimensional data space, and relates to the technical field of data interaction management, and the method comprises the following steps: I, collecting multi-source data, including structured data, unstructured data and spatio-temporal data, in real time through a diversified collection terminal, and dynamically adjusting the sampling frequency based on a data entropy value, carrying out cross-device space-time synchronization calibration and edge end lightweight processing on the acquired data; iI, preprocessing the data flow in real time, fusing the multi-modal data, and the like. According to the method, the sampling frequency is dynamically adjusted based on the data entropy value, and in combination with edge end lightweight processing, invalid data transmission is reduced, and the key data acquisition efficiency is improved; hot data is stored in a time sequence database and a memory database, real-time monitoring of an emergency scene and second-level response of emergency command are supported, dynamic capacity expansion and shrinkage of computing resources are matched, edge-cloud end collaboratively distributes tasks, network delay is reduced, and system stability and response speed in a high-concurrency scene are guaranteed.

The invention discloses an intelligent and automatic transformer substation information acquisition system, which relates to the technical field of information acquisition, and is characterized in that an information change coefficient and an information priority coefficient are acquired and substituted into a logistic regression model to obtain an information potential evaluation coefficient, the information potential evaluation coefficient is analyzed with a preset potential threshold to obtain a comparison result, and the transformer substation information is acquired according to the comparison result. Dividing to obtain a real-time acquisition potential information set and a non-real-time acquisition potential information set, substituting into a default score combination mechanism, determining an actual classification acquisition result of each piece of information, obtaining a network state when each piece of information is uploaded, obtaining a network delay degree and a bandwidth utilization rate from the network state, and substituting into fuzzy logic for fuzzy reasoning to obtain a real-time acquisition potential information set and a non-real-time acquisition potential information set; according to the method, the reasonability result of the default score combination mechanism is determined, then the network delay degree and the bandwidth utilization rate when each piece of information is uploaded are substituted into set parameters in the default score combination mechanism, the score combination mechanism is determined and adjusted, and the information redundancy and the system burden are reduced.

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 relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a cross-platform information interaction method, device and equipment and a medium, and the method comprises the steps: obtaining original information sent by a source platform, and analyzing the original information into a standardized data structure according to a preset data format; extracting multimedia content elements from the structure and performing format conversion to generate standardized multimedia content; packaging the structure and the content into a to-be-transmitted data packet; acquiring network delay and bandwidth parameters of the target platform, and selecting a transmission path based on the parameters; compressing the to-be-transmitted data packet and sending the compressed to-be-transmitted data packet to the target platform through the selected path; and the target platform receives and analyzes the compressed data packet and presents the original information content. Information format compatibility is achieved through a standardized structure and content packaging, transmission efficiency is improved through network parameter perception and path selection, complete presentation of information is guaranteed in combination with compression processing and terminal adaptation, and stability and consistency of cross-platform interaction are enhanced.

The invention provides a fault simulation test method and device, electronic equipment and a storage medium. In the fault simulation test method, when a test instruction sent by a user is received, test parameters are configured based on the test instruction; wherein the test parameters comprise a test type, a fault parameter and a test target. And based on the test type and the fault parameter, configuring a network rule through a flow controller, and generating a fault simulation scene. And injecting the fault simulation scene into the test target, and carrying out performance test on the test target. And monitoring performance indexes of the test target in the test process, and generating a test report of the test target. Therefore, by utilizing the method provided by the invention, different types of network delays or faults, such as delay increase, packet loss and connection interruption, can be accurately injected into the target server, so that the stability and the performance of software or service under bad network conditions are effectively evaluated, and the flexibility and the expandability of the system are ensured at the same time.

The invention is suitable for the technical field of data processing, and provides a distributed data consistency control method and system, and the method comprises the steps: constructing an enhanced vector clock containing a fuzzy membership parameter and a time window deviation value; a dynamic fuzzy inference engine is built in combination with factors such as network delay jitter; calculating conflict event logic precedence relation probability distribution by utilizing an engine; generating an execution sequence hypothesis by integrating the multi-dimensional information; selecting an optimal scheme based on a dynamic confidence threshold; and resources and consistency are balanced through a three-level conflict processing mechanism. According to the method, the problem that a traditional vector clock cannot process conflict event sorting is solved, the accuracy and adaptability of distributed data consistency control are improved, and efficient collaboration of the system is guaranteed.

The invention discloses an optimal signal selection intelligent network switching management method based on a super SIM multi-network, and relates to the field of intelligent network switching management, and the main scheme is as follows: analyzing and calculating operation state data to obtain a network delay score, a downloading rate score and a network stability score in a current network environment, and carrying out comprehensive analysis to obtain an optimal signal selection score; obtaining a current network state score; judging whether to execute a network switching instruction or not by evaluating the current network state score; the coverage degree data, the network state data and the switching cost data are analyzed and calculated to respectively obtain coverage matching degree indexes, network state indexes and switching cost-benefit ratios of different operators; switching priority indexes of different operators are obtained through comprehensive analysis, the switching priority is determined, and the problem that switching decisions and requirements are disjointed due to the fact that switching is triggered only based on signal strength or a single rate threshold and core indexes such as network delay and stability which affect user experience are not fused is solved.

The invention discloses a control method, system and equipment of a remote rehabilitation robot, a medium and a product, and belongs to the technical field of robots. The method comprises the steps of obtaining multi-modal data of a patient end, obtaining a time delay prediction value through prediction of a long short-term memory network model, dynamically adjusting admittance parameters of an admittance model according to the multi-modal data and the time delay prediction value, and finally controlling the remote rehabilitation robot according to the dynamically adjusted admittance parameters. According to the method, the admittance parameters of the admittance model are dynamically adjusted in real time based on the time delay predicted value predicted by the long-short-term memory network model and the electromyographic signals of the patient in the multi-modal number, dual adaptation of the network state and the physiological features of the patient is achieved, the admittance parameters are dynamically adjusted through the electromyographic signals, and the change requirement of the complete rehabilitation cycle of the patient is met; admittance parameters are dynamically adjusted through the time delay prediction value reflecting the network time delay state, and the time delay disturbance requirement in the remote communication environment is met.

The invention provides a multi-operator card AIOT management method and system based on the Internet of Things, and the method comprises the following steps: carrying out API butt joint, calling a mobile CMNet API, a Unicom WorAPI and a Telecom CTWing API, and obtaining the state, flow and signal intensity data of an Internet of Things card; a heterogeneous protocol is converted into a unified JSON format through a protocol conversion gateway; network delay and base station loads are monitored in real time, and network scores of all operators are calculated based on the base station loads, the network delay, the signal strength, the tariff cost and the historical stability; and judging whether a switching trigger condition is met or not, and when the switching trigger condition is met, determining to switch the network based on the network scores of the operators, and executing a network switching process. According to the method, the network condition is monitored in real time, and the score of each operator network is calculated based on multiple factors, so that the available network can be quickly switched to when a single operator network has a fault or a poor signal, and the stability of the Internet of Things system is improved.

The invention discloses a seamless switching method and system for a high-speed terminal of a ground-non-ground convergence network based on trajectory prediction and resource pre-reservation, and belongs to the technical field of space-ground convergence communication. The core of the method is that a switching area is accurately judged through bidirectional motion prediction of a terminal and an NTN node; a dynamic switching window is calculated in a self-adaptive mode by combining factors such as terminal speed and network time delay; intelligently selecting an optimal target link by using an AI enabled link scoring model; and cooperative pre-reservation and uplink and downlink synchronization of resources are completed before the window. The method has the advantages that the problem of failure of traditional RSRP judgment in an NTN dynamic environment is solved, the switching success rate and prediction accuracy in a high-speed moving scene are remarkably improved, the service interruption time and signaling overhead are greatly reduced, the continuity and reliability of communication are effectively guaranteed, and the method is suitable for large-scale popularization and application. The method is suitable for high-dynamic service scenes such as intelligent network connection vehicles and unmanned aerial vehicles in a 6G network.