2028 results about "Network performance" patented technology

The invention provides a network topology intelligent generation method and system based on deep learning and topology analysis, and relates to the technical network intelligence field, and the method comprises the steps: obtaining historical topology data, carrying out the feature extraction based on time sequence division, constructing a graph neural network model, training, and evaluating the evolution trend and stability of a network topology structure. And constructing a deep reinforcement learning model to generate an optimization strategy, and carrying out iterative optimization until a network topology structure meeting requirements is generated. The network structure can be adaptively optimized, the network performance is improved, the operation and maintenance cost is reduced, and the network stability is enhanced.

The invention discloses an intelligent operation and maintenance management and control system for an information communication network, and belongs to the technical field of communication. The system comprises the following modules: an intelligent sensing and data acquisition module which is responsible for real-time acquisition of multi-source network data, dynamic monitoring of cross-domain equipment states and conversion of heterogeneous data into a unified standard; the network intelligent analysis module integrates network performance prediction, fault risk assessment, abnormal behavior identification and security threat monitoring, and provides comprehensive network operation and maintenance insight; the intelligent resource scheduling module is used for realizing dynamic allocation of cross-domain network resources and ensuring efficient and safe utilization of the resources through multi-dimensional optimization and a real-time load balancing strategy; the intelligent decision support module is used for continuously optimizing an operation and maintenance strategy based on machine learning, analyzing a long-term operation trend and providing suggestions for management decisions; and the open integration and collaboration module provides integration of an open API interface and a third-party system, supports cross-domain collaboration management, and improves the flexibility and expansibility of the system.

The invention relates to the technical field of integration of artificial intelligence security and network security, and discloses an AI dynamic security transmission system based on an SASE framework, which realizes security access control based on AI dynamic identity verification through an SASE integration access module, acquires and predicts network performance change in real time by using a network state sensing module, and transmits the network performance change to a network server. Equipment, environment and data content are subjected to multi-dimensional analysis by means of a security risk assessment module, a quantitative risk score is generated, and a transmission protocol, parameters and encryption strength are dynamically adjusted according to a network state and the risk score by means of a dynamic transmission optimization module and a self-adaptive encryption module; the problem that safety protection and transmission efficiency are difficult to cooperate in a traditional architecture is effectively solved, low-delay and high-reliability data transmission service can be provided for AI application in a complex network environment, meanwhile, self-adaptive dynamic protection of the whole data transmission process is achieved, and data safety is comprehensively guaranteed.

The invention relates to the technical field of data processing, and discloses a PTP network performance monitoring method and system based on big data analysis, and a medium. The method comprises the following steps: acquiring PTP message data, equipment state data and RTP stream data; preprocessing the data and calculating a clock error; constructing a time sequence database and an analysis model; real-time monitoring and anomaly detection are executed; performing performance scoring and fault positioning by applying machine learning; and carrying out network optimization and predictive maintenance. According to the PTP network performance monitoring method based on big data analysis, global state monitoring, intelligent anomaly detection, accurate fault positioning and predictive maintenance of the PTP network are achieved, and therefore the high-precision time synchronization requirement of a broadcast and television IP making and broadcasting system is guaranteed.

The invention relates to the technical field of communication data analysis, and discloses a communication network service health degree analysis method, system and device and a medium, and the method comprises the steps: obtaining real-time link state data, carrying out the quantitative analysis, and judging whether the link jitter exceeds a preset threshold value or not; when abnormality is detected, abnormal points can be positioned and the influence range of the abnormal points can be analyzed, so that the influence degree of nodes on the abnormal diffusion path can be identified; calculating a service quality index change value, and determining a starting point and a diffusion direction of chain degradation; by analyzing historical traffic data of the key nodes, a service traffic change trend and traffic dense area distribution characteristics are obtained. According to the method, the bandwidth allocation strategy is dynamically optimized, the bandwidth resources at the junction are reconfigured, and the regional service health degree is evaluated. And if the degradation is not effectively controlled, monitoring and detection mechanism parameters can be adaptively adjusted, and continuous analysis and control of link abnormity are realized, so that the network performance and the service quality are effectively improved.

The invention provides a self-adaptive network topology dynamic reconstruction method and system based on deep reinforcement learning, and relates to the technical field of deep reinforcement learning, and the method comprises the steps: obtaining the topology state information, service flow distribution information and historical reconstruction records of a current network; extracting topological correlation characteristics among nodes through graph convolution operation, and generating fusion state representation in combination with service flow information; inputting the fusion state representation into a deep reinforcement learning model to identify bottleneck nodes and redundant links, and outputting a reconstruction action candidate set; searching and evaluating the long-term cumulative income of the candidate actions through a Monte Carlo tree, and screening an optimal reconstruction action sequence; a graph coloring algorithm is utilized to allocate time slots and process resource conflicts, and a resource-feasible topology adjustment scheme is generated; and extracting a network evolution rule through tensor decomposition, and constructing a topological optimization association mapping graph. According to the method, the network bottleneck can be intelligently identified, the network topology structure is dynamically optimized, and the network performance and the resource utilization rate are effectively improved.

The invention provides a node fault intelligent diagnosis method and system for an intelligent space sensor network, and the method comprises the steps: obtaining a real-time monitoring data set of all sensor nodes in a target sensor network, carrying out the state feature extraction of the real-time monitoring data set, generating a state feature set of each sensor node, and carrying out the detection of the state feature set of each sensor node; based on a preset fault diagnosis strategy model, carrying out multi-dimensional fault matching processing on the state feature set, determining a candidate fault type set of the sensing nodes, generating a node fault processing strategy set of the sensing nodes according to a strategy mapping relation between the candidate fault type set and a preset fault processing knowledge base, and sending the node fault processing strategy set to the fault diagnosis module; and performing node optimization operation on the fault nodes in the target sensor network based on the node fault processing strategy set, and feeding back optimized node operation parameters to a control terminal of the target sensor network. According to the method, the problem that the system stability is reduced due to traditional single-point repair is effectively avoided, and continuous and reliable operation of the overall performance of the network after fault repair is ensured.

The invention discloses a knowledge distillation method, electronic equipment and a computer readable storage medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the grouping of an attention map matrix of a teacher network based on the number of attention heads in an initial network structure of a student network, a high-dimensional attention space of a teacher network can be divided into subspaces with the same dimension as a student network, and attention map matrixes in groups are spliced, so that each group of spliced matrixes is aligned with an attention head of an initial network structure, a relatively regular corresponding relationship between the attention maps of the teacher network and the student network is realized, and the attention map splicing efficiency is improved. Therefore, the distillation loss can be calculated, and the knowledge of the complex teacher network can be accurately transmitted to the lightweight student network. Therefore, the problem that in a multi-head attention mechanism, the number difference between the teacher network and the student network hinders the student network to fully learn the teacher network knowledge can be solved, and the technical effects of improving the knowledge distillation effect and enhancing the student network performance are achieved.

The invention relates to the technical field of agricultural wireless sensor networks, and discloses an agricultural wireless sensor network topology reconstruction method based on an energy consumption balance model. The method comprises the following steps: acquiring multi-dimensional energy consumption data of each node, including node residual energy, communication load intensity and an environment interference coefficient; extracting a node energy health degree based on residual energy, generating a communication priority sequence according to communication load intensity, and analyzing an environment interference coefficient to generate an interference distribution thermodynamic diagram; inputting the result into an energy consumption balance evaluation model to generate a topology reconstruction strategy; and a self-adaptive topology adjustment map is constructed through a distributed optimization algorithm, and a node connection relation optimization scheme is output. The system is correspondingly provided with a multi-dimensional data acquisition module, an energy consumption state analysis module and the like. The problems of unbalanced energy consumption, non-uniform communication load, environmental interference and the like of the agricultural wireless sensor network can be effectively solved, the network performance is improved, the service life of the network is prolonged, and the development of precision agriculture is promoted.

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 embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention discloses an intelligent load balancing method and system based on multipath fusion, and relates to the field of network load balancing. A distributed monitoring system is constructed to collect network performance indexes, and a Transform model is used to predict traffic; a hierarchical reinforcement learning architecture is adopted, a global strategy is generated according to a macroscopic network state, and flow distribution is optimized for a single path; kalman filtering and particle filtering are automatically switched according to the path stability; the flow is flexibly migrated based on a genetic algorithm; an index weight is calculated by using a Shapley value method and an entropy weight method, and path quality is evaluated; the distribution strategy is executed through the SDN controller, and the overall performance of the network is improved. According to the invention, network abnormity is quickly responded, the traffic migration efficiency is improved, and bandwidth waste is reduced; service differentiation scheduling is supported, the service quality is guaranteed, and the attack defense capability is enhanced; operation and maintenance efficiency is improved, fault positioning time is shortened, and efficient utilization of network resources and guarantee of service quality are realized.

The invention relates to the technical field of network communication, and discloses an intelligent control method and system of a switch. The method comprises the following steps: receiving a heterogeneous data stream in a target network domain; performing data processing by using a preset flow feature aggregation model, and generating a flow dynamic pressure field distribution diagram and a protocol priority mapping matrix; constructing an adaptive scheduling threshold model, and generating a multi-target flow control instruction set; a cascade effect is simulated based on a network congestion chained conduction model, and collaborative routing parameters are optimized; and iteratively optimizing through a mixed integer programming framework, and outputting a network scheduling action sequence to a switch control engine. The system correspondingly comprises a data receiving module, a data processing module, an instruction generation module, a simulation optimization module and an instruction output module. According to the method, heterogeneous data can be effectively processed, congestion can be accurately predicted, flow control can be optimized, the overall performance of the network can be improved, and communication requirements in a complex network environment can be met.

The invention belongs to the technical field of air-space-ground integrated networks, and discloses an unmanned aerial vehicle track optimization and task offloading method for an air-space-ground integrated network. According to the method, the task unloading efficiency in the air-space-ground integrated network is optimized, optimization of energy consumption and time delay is focused on, various conditions of the air-space-ground integrated network are comprehensively considered, a decoupling communication mode is considered, and the influence of service interruption is reduced. And constructing an optimization model to minimize the weighted sum of time delay and energy consumption. In order to improve the collaboration of heterogeneous agents in a space-air-ground integrated network, based on a hierarchical reinforcement learning framework, a hierarchical learning-based two-stage Qmix algorithm is proposed, and the collaboration of a low-orbit satellite and an unmanned aerial vehicle is reinforced. According to Pycharm platform simulation and multi-scene verification, the method has remarkable advantages in average response time delay, energy consumption and QoS indexes, the network performance is effectively improved, and user requirements are met.

The invention discloses a cross-domain collaborative interface dynamic configuration method and device and a computer program product. According to the cross-domain collaborative interface dynamic configuration method, service flow priorities are determined by analyzing service layer data, and an initial interface configuration scheme is generated in combination with network state information. And when the initial scheme does not meet the transmission requirements, optimizing the interface parameters by adopting a dynamic programming algorithm, generating a new configuration scheme, and issuing the new configuration scheme to the target network management system. In the service switching process, network performance is monitored in real time, interface parameters are dynamically optimized and updated to a network management system, and intelligent management of service flow and efficient utilization of network resources are achieved. The method can adapt to different manufacturer devices and network levels, guarantees the service quality of cross-domain services, and improves the operation efficiency and reliability of an electric power communication network.

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 discloses a pulse neural network training method based on adaptive threshold integer activation neurons, and the method specifically comprises the following steps: inputting a neuromorphic data stream or a static image, generating a pulse sequence through time coding, and taking the pulse sequence as the input of a model; in the backbone network, performing down-sampling and extracting multi-scale features by using an SNN module based on LIF neurons activated by an adaptive threshold integer; the visual tasks are divided into image recognition, image detection and image segmentation, and are respectively accessed to different processing modules for processing and outputting; according to the invention, on the basis of a Meta-SpikeFormer element module, the spiking neural network based on LIF neurons activated by an adaptive threshold integer value is used, so that the memory overhead during training is reduced, the network performance of direct training is improved, the application range of the spiking neural network is expanded, and the network is better applied to image classification, recognition and segmentation tasks.

The invention relates to the technical field of computer networks, and discloses a method for locating reasons for network dial test quality index abnormity, which comprises the following steps of: multi-source data acquisition and network topology modeling: generating state information of network equipment and links by acquiring performance data of each equipment and flow data of the links in a network, and establishing a network topology model; establishing a network topological graph model; shortest path calculation and abnormal path identification based on the graph theory: calculating a shortest path from source equipment to target equipment by using a shortest path algorithm in the graph theory, and identifying abnormal links and equipment in the path; and load evaluation and bottleneck analysis based on an information theory. Through combination of multi-source data acquisition, graph theory optimization, information theory evaluation, game theory resource allocation, deep reinforcement learning and multi-target optimization, network paths and loads are dynamically adjusted, abnormal sources are accurately positioned, and network performance, stability and adaptive capacity are improved.

The invention relates to the technical field of artificial intelligence, can be applied to the fields of finance, medical treatment and the like, and provides a network fault processing method based on artificial intelligence, and the method comprises the following steps: carrying out the trend prediction and anomaly detection through a machine learning model based on real-time network performance indexes and historical alarm data, and obtaining a network fault detection result; generating an alarm set containing fault type inference; performing association analysis on the alarm set and network topology and change records, constructing a fault propagation map, and outputting a root cause node list sorted according to probability; generating an equipment parameterized repair script according to the root cause node list; executing the repair script and monitoring an execution state in real time to obtain a repair result; and verifying the repair result. Compared with the prior art, the method has the advantages that the end-to-end fault processing period is remarkably shortened, the manual intervention requirement is reduced, and the service continuity is guaranteed in a complex network environment.

The invention relates to a cloud computing extension cluster high availability method based on a dynamic fault domain and intelligent scheduling, and relates to the field of cloud computing extension cluster high availability. According to the method, hardware health data and network performance indexes of physical nodes are collected in real time, and the fault correlation degree between the nodes is calculated based on the hardware health similarity and a network topology attenuation factor; dynamically generating and updating a logic fault domain topological structure according to the fault correlation degree and a preset threshold value; based on the logic fault domain topological structure and a preset SLA strategy library, executing a virtual machine scheduling decision of multi-objective optimization so as to balance business service quality, fault domain risk and resource cost; and in response to the detected fault event, triggering a corresponding hierarchical migration process according to the service priority and the fault level. According to the method, closed-loop linkage of real-time reconstruction and intelligent scheduling of the dynamic fault domain in the cross-region cloud computing extension cluster can be realized, and the disaster tolerance capability and the resource utilization rate of the system are remarkably improved.

The invention discloses a regional traffic network dynamic risk assessment method under sudden earthquake influence, which belongs to the technical field of earthquake disaster assessment and comprises the steps of collecting data and preprocessing, constructing a traffic network model, constructing an earthquake vulnerability model and constructing a risk dynamic assessment system. According to the method, seismic parameters, social economy, a regional traffic network and real-time detection data after an earthquake are comprehensively considered when the earthquake occurs, the risk of the regional traffic network under the earthquake can be evaluated more comprehensively, a Monte Carlo simulation method is applied, an earthquake vulnerability model is combined, a damage scene of the earthquake to the traffic network is simulated, and the risk of the regional traffic network under the earthquake can be evaluated more comprehensively. Evaluating the damage probability and the function loss of the traffic facilities; calculating the connectivity of the traffic network model, and dynamically calculating the performance change of the traffic network after the earthquake based on the connectivity index and weight of the traffic network; and evaluating the toughness of the traffic network according to a network performance recovery curve under the recovery strategy, so that the performance change of the traffic network in the earthquake can be dynamically reflected in real time.

The invention discloses an intelligent scene type CPR visual training system based on an MR technology, which belongs to the technical field of non-special measurement for specific variables and comprises a training request sensing module, an intelligent scene automatic generation module, a sensing data acquisition module, a visual display and voice prompt module and a network performance evaluation management and control module. According to the method, the performance parameters of the sensing source nodes built in the simulation human body model are acquired and analyzed in real time, so that the data feedback precision in the training process is improved, and the data feedback precision in the training process is further improved; the problem of poor CPR visual training experience caused by lack of real-time performance monitoring and feedback mechanisms in the prior art is effectively solved.

The present disclosure relates to a system (102) and method (500) for analyzing network performance parameters. A user interface module (212) enables reception of comprehensive network speed test requests that specify multiple network performance parameters for analysis. A test execution module (214) triggers coordinated operation of multiple testing units (302a, 302b, 302c) to perform simultaneous network speed tests across different platforms. A data collection module (216) systematically aggregates the generated performance information from all testing units. One or more processors (202) transform the collected information through advanced processing algorithms to generate standardized performance data for each network parameter. An analyzing module (218) performs multi-dimensional analysis of the processed data based on specific attributes to provide comprehensive network performance insights. The integrated approach enables automated cross-platform testing, unified data collection, and sophisticated analysis of network performance characteristics, offering significant advantages over conventional single-platform testing methods.

The invention provides an intelligent detection method and system for a Mura defect in an OLED display panel, and relates to the field of defect detection, and the method comprises the steps: obtaining a multi-scale feature image and a fusion feature image based on an original image of the OLED display panel; performing feature enhancement and feature fusion on the multi-scale feature image and the fusion feature image to obtain an initial prediction image and a coding feature image; edge refinement and image segmentation are carried out on the initial prediction image and the coding feature image to obtain a fine prediction image; and carrying out end-to-end multi-objective optimization training by adopting a mixed loss function so as to comprehensively optimize the network performance. According to the method, feature enhancement and network optimization are carried out in a mode of combining a three-module fusion decoder architecture and a mixed loss function, so that the network model can accurately detect Mura defects, and the problems of missing detection and error detection are prevented.

Disclosed are a power line communication method and apparatus, comprising: a first device generating a physical layer protocol data unit (PPDU) frame, the PPDU frame comprising a frame control field, a training symbol field, and a load symbol field, the load symbol field being used for modulating and transmitting a load symbol, the training symbol field being used for bearing a training symbol, and the load symbol field being used for receiving the training symbol; the training symbol field is used for channel estimation of multiple input multiple output (MIMO) and extended bandwidth, the frame control field comprises a variable region field, the variable region field comprises a frame identification field and a frequency band field, the frame identification field is used for indicating a frame type of a frame start (SOF) frame included in the PPDU frame, and the frequency band field is used for indicating an available frequency band of a training symbol and a load symbol; and the first device sends the PPDU frame to the second device. According to the embodiment of the invention, through the redefined PPDU frame, the power line communication supports the characteristics of MIMO, large bandwidth and the like, so that the overall network performance is improved.

The invention provides a method, a device, equipment and a medium for carrying out resource configuration on a multi-mode network, and the method comprises the steps: obtaining the network state information of each network mode in the multi-mode network, the network state information comprising network request information and network environment index information; based on the network state information, running a preset configuration strategy code to generate a configuration strategy for the multi-modal network, and completing optimization of resource configuration of the multi-modal network according to the configuration strategy; wherein the configuration strategy code is generated by performing nested iterative optimization on the basis of a pre-constructed database comprising a plurality of data samples by utilizing a large language model; the network state information and network state input data of a code predictor used for screening codes in the configuration strategy code generation process are from the same network environment. Therefore, the corresponding configuration strategy can be adaptively generated according to the change of the network environment so as to optimize the resource configuration of the multi-modal network, and the network performance and the resource utilization efficiency are improved.

A bidirectional autoencoder learns or approximates an identity mapping as it trains a single network with a version of the new bidirectional backpropagation algorithm. Ordinary unidirectional autoencoders find many uses in image processing and in large language models. But they use separate networks for encoding and decoding. Bidirectional autoencoders use the same synaptic weights for encoding and decoding. The forward pass encodes while the backward pass decodes. Bidirectional autoencoders improved network performance and significantly reduced memory usage and used fewer parameters. Simulations compared unidirectional with bidirectional autoencoders for image compression and denoising. The models trained on the MNIST handwritten-digit and CIFAR-10 image datasets. The performance measures were the peak signal-to-noise ratio and the index of structural similarity. Bidirectional autoencoders outperformed unidirectional autoencoders and still reduced the number of trainable synaptic parameters by about 50%.

The invention discloses a LoRa-based ship environment multi-parameter intelligent wireless monitoring system, which is characterized in that environment state data and ship operation auxiliary data are acquired through a data acquisition module, and a data processing module is used for preprocessing and standardizing the acquired data to obtain a standardized data frame; the environment safety quantification module calculates a single parameter safety score, a comprehensive safety index and a safety situation level SL, the intelligent LoRa communication module dynamically selects a communication strategy according to the SL and the current link quality and generates and sends a safety data packet, the LoRa gateway receives the safety data packet, redundancy filtering is carried out, receiving metadata is added, a final data record is formed and uploaded, and the LoRa gateway sends the final data record to the intelligent LoRa communication module. And the data center performs visual display and data storage on the final data record, and generates a network optimization instruction based on the network performance data, so that the problem that transmission reliability, real-time performance and low power consumption are difficult to consider in ship environment monitoring is solved, and the intelligent level of ship safety management is improved.

The system (100-2) and method for identification of high ranked neighbor cells in a telecommunications network provide an efficient and accurate approach to selecting neighboring cells with superior performance for handover purposes. The system (100-2) leverages performance metrics, algorithms, and dynamic adaptation techniques to determine the suitability and ranking of potential neighbor cells. By considering factors such as signal strength, signal quality, interference levels, load balancing requirements, and operator-defined policies, the system (100-2) evaluates the performance of neighbor cells and assigns them scores or rankings to identify the higher ranked neighbor cells. The system's dynamic adaptation ensures that the rankings remain up to date and responsive to changing network conditions. The present system (100-2) and method optimize handover decisions, enhance network performance, and provide users with seamless connectivity and improved quality of service.

The invention provides a low-orbit satellite communication terminal network performance dynamic tuning method and system, and relates to the technical field of low-orbit satellite communication. Comprehensive data such as relative movement of a satellite and a terminal and signal transmission loss are acquired; preprocessing the integrated data, inputting the preprocessed integrated data into a time sequence prediction model containing LSTM and Transform, generating a signal quality and intensity index sequence and a link stability level in a future preset time window, and carrying out dynamic optimization based on the generated indexes, including adjusting a terminal modulation mode according to the signal quality; combining the link stability and the signal strength to aggregate the link, and optimizing the switching opportunity and path; allocating resources according to the signal strength and the service characteristics; and optimizing the energy consumption according to the relative motion and the business characteristics. Through accurate prediction and multi-dimensional adjustment and optimization, the communication efficiency and reliability of the low-orbit satellite communication terminal network are improved, energy consumption and delay are reduced, and good communication quality is guaranteed.