61 results about "Burstiness" patented technology

The invention belongs to the technical field of network test equipment, and discloses network response test equipment and a test method thereof, and is characterized in that the network response test equipment comprises the following modules: a distributed multi-mode dynamic sensing probe used for capturing service flow characteristics and microsecond-level performance indexes of a network link in real time; the extensible data middle table is in data connection with the probe and is used for establishing a unified spatio-temporal index for cross-level performance data from a physical layer to an application layer; and the causal atlas analysis engine based on artificial intelligence is used for positioning a network fault root cause according to the cross-level performance data. According to the invention, an evaluation standard is automatically calibrated according to historical business load characteristics through an extensible data-in-station dynamic baseline threshold mechanism, so that a test environment completely fits complex scenes such as video burst frame transmission, database synchronization ACK, high-frequency transaction chain and the like, the simulation distortion of a traditional tool to business traffic burst and multi-protocol mixed characteristics is eliminated, and the test efficiency is improved. And the test result is ensured to accurately map the long-term stability requirement.

The invention discloses a cloud task fault prediction method based on a sparrow search and gating cycle unit network. The method comprises the following steps: S1, obtaining a cloud task fault prediction standardized data set; s2, constructing cloud task fault prediction features; s3, obtaining an initial multi-scale gating circulation unit network model; s4, obtaining a sparrow search algorithm initial population matrix; s5, outputting a cloud task fault prediction optimal hyper-parameter vector; s6, obtaining cloud task fault prediction training completion model parameters; s7, deploying parameters of the cloud task fault prediction training completion model to a cloud computing platform reasoning environment, generating a cloud task fault probability sequence, comparing the cloud task fault probability sequence with a cloud task fault risk threshold value, and outputting a cloud task fault early warning signal when any cloud task fault probability exceeds the cloud task fault risk threshold value; and S8, sending the cloud task fault early warning signal to a cloud computing platform scheduling module. According to the invention, resource waste and service interruption risks caused by sudden task faults are effectively reduced.

The invention provides a historical load analysis-based power supply path dynamic generation and evaluation method, which comprises the following steps of: acquiring historical load data of a commercial district and topological structure data of a power distribution network, extracting load space distribution characteristics, and determining a dynamic change trend of load space distribution by adopting a clustering algorithm; performing time sequence analysis on the offset distance of the load center and the change rate of the power utilization density, and identifying the change period of the load in the commercial district and the sudden characteristics of sudden increase or sudden decrease of the load; key section distribution is identified according to the section identification standard, the load matching degree of the topological structure of the power distribution network is evaluated according to the key section distribution, and the load distribution balance and the line bearing capacity between topological nodes are calculated; and identifying weak nodes and areas with unbalanced load distribution according to the load distribution balance among the topological nodes and the line bearing capacity, and adjusting the line layout according to the identified weak nodes.

The invention discloses an industrial data transmission optimization management system based on service link quality, and relates to the technical field of data transmission. Weak delay oscillation and a periodic packet loss rule are reserved through multi-scale processing, so that the system can discover precursor anomalies which are difficult to capture by a traditional monitoring mechanism; minute-level even second-level early warning of link disturbance is realized, and data congestion caused by sudden quality reduction is avoided; a dynamic discrimination mechanism driven by a precursor score is utilized, so that a scheduling decision is converted from passive response to active adjustment, and the probability of cascade failure caused by mass mutation is greatly reduced; an interference spectrum adaptive strategy enables the system to adaptively match the optimal coding mode and the redundant structure when facing narrow-band noise, periodic interference or transient impact, so that the anti-interference capability and the bandwidth utilization efficiency are improved; priority adjustment is carried out in combination with a link risk trend and a state evolution rule, and the protection timeliness and resource allocation efficiency of the key link are remarkably improved.

The invention discloses a flow behavior perception-based photoelectric double-plane cross-layer interconnection scheduling method, which comprises the following steps of: acquiring communication service data flow in a distributed training task, and generating flow behavior time sequence characteristics; performing flow behavior time sequence feature modeling based on the flow behavior time sequence features; identifying flow behavior time sequence characteristics of different types of communication service data streams, and dividing the communication service data streams into lightweight control streams, periodic synchronization streams and sudden transport streams; the method comprises the following steps: acquiring a network state of a link in a data transmission plane, establishing a transmission path reachability constraint, allocating capacity for the link, detecting a link capacity occupation value and a link capacity upper limit, and generating a scheduling strategy; the scheduling strategy is converted into a mapping instruction, and the data transmission plane executes distribution of a transmission path of the communication service data flow according to the scheduling strategy; according to the invention, data stream differential transmission and cross-layer cooperative control based on photoelectric biplanes are realized, the training iteration time is remarkably shortened, and the utilization rate of network resources is improved.

The present application relates to the technical field of semantic processing, and more particularly to a word element aggregation and distribution method and system based on multi-level cache and semantic preheating, which obtains historical word element request interaction logs, analyzes the access characteristics of different business scenarios and context fragments; in combination with semantic dependency relationships, identifies regular repetition and sudden hotspot characteristics of word element access, determines a preloading target set; real-time acquisition of context semantic data of the current inference request, analysis of word element prediction demand parameters to be generated for output; fusion of word element prediction demand parameters and preloading target set, dynamic generation of word element aggregation parameters and distribution scheduling strategy. The present application realizes forward-looking preheating and accurate scheduling of word element resources by deeply mining historical rules and real-time semantic intent, effectively solves the problems of weak semantic perception, low hit rate and response lag of traditional cache strategies, significantly reduces the inference delay of large models, and improves the throughput and stability of the system in high-concurrency scenarios.

The application relates to a time division multiple access time slot allocation method based on a manta ray algorithm and belongs to the technical field of communication networking. The application divides a time frame into an information collection period and a time slot allocation period, and further divides the time slot allocation period into a dedicated time slot period and a dynamic time slot period. In the information collection period, a central node collects time slot application requests of other nodes, judges whether the nodes can continue to apply according to relevant criteria, and gives the nodes priorities according to the traffic volume and the service importance of the nodes. In the dedicated time slot allocation period, high-priority nodes are allocated by using a binary tree block internal equal division method. In the dynamic time slot allocation period, the manta ray algorithm is used to allocate time slots to other nodes. The application is relatively simple to implement, guarantees absolute transmission of the nodes, considers the priorities of the nodes, meets burstiness and long message transmission of the nodes, improves the convergence speed of the algorithm, and improves the overall performance of the network.

The invention relates to the field of electric power business scheduling, and particularly discloses an electric power system business collaborative scheduling method based on a 5G technology, which constructs a high-low layer collaborative double-layer intelligent scheduling architecture. In a high level, situation prediction is carried out by fusing power grid operation data and historical traffic, and the macroscopic resource ratio of network slices is adjusted prospectively to cope with the sudden demand of power business, so that the problem of insufficient resources of static resource allocation in sudden scenes such as faults is solved. And at the lower layer, service semantic analysis is carried out deeply in the data packet, so that the real-time emergency level of key services such as differential protection and the like is accurately identified, and resources are further refined into a micro resource pool special for the services in combination with slice configuration issued by the higher layer, so that real-time scheduling and preemption based on semantics are realized. Through the combination of macroscopic predictive reservation and microscopic semantic scheduling, it is ensured that high-priority power services can obtain communication guarantee under any network load.

The invention discloses a short message sending method and device, equipment, a storage medium and a program product, and the method comprises the steps: segmenting short message service flow according to preset time slice configuration, and obtaining short message data under a plurality of time slices; performing rate segmentation on the plurality of channel resources according to a preset rate arrangement strategy and a thread concurrency number to obtain rate configuration of a plurality of threads of each channel resource; wherein the rate arrangement strategy is used for indicating rate configuration of each channel resource; binding the short message data with a plurality of threads so as to enable the threads to execute sending on the bound short message data in each time slice according to own rate configuration; according to time arrangement and careful division of the channel rate, the sending efficiency of a multi-short-message service large-batch sending scene can be improved, sudden surge short message data can be effectively dealt with, multi-short-message service channel blockage caused by short message backlog is avoided, and the success rate of short message service sending loss is improved.

The application provides a mobile edge computing dynamic service deployment method based on deep reinforcement learning. In the learning process, a migration conflict resolution mechanism is introduced to maintain the service performance of the user. Specifically, not only the network state is predicted, but also the user connected to each edge node is calculated according to the user trajectory, and it is determined which node is the crowded node according to the user request and the service on the edge node, the resource reservation is carried out for the crowded node, and whether the conflict will be produced is detected. In order to achieve this goal, first, the network architecture of edge computing is determined, then the system model and the quality of service (QoS) model are established, and the problem to be solved is formulated. The application studies the joint optimization problem of delay and cost under the constraints of physical resources and operation cost, taking the minimum total cost of users as the target, and considering guaranteeing that multiple service requests can be responded in time under the sudden aggregation.

The invention discloses a non-cellular intelligent beam management-oriented generative learning-assisted twinning enhancement pre-training method, which comprises the following steps of: firstly, constructing a long short-term memory network fusion diffusion model generative model as a channel prediction digital twinborn body, and predicting a future channel state in a probability mode; secondly, constructing an autoregressive comprehensive moving average model and a variational auto-encoder mixed model to carry out generative flow prediction, and accurately capturing the periodicity and the burstiness of the flow; and finally, jointly applying channel and flow prediction results to a monotonic Q value function decomposition beam selection algorithm after state enhancement. According to the invention, through prior knowledge provided by generative learning, the problems of difficult data sample acquisition, high training cost, slow convergence and poor dynamic traffic adaptability faced by training a beam selection algorithm in a real environment are solved. Compared with a traditional method, the training efficiency, the convergence speed and the final performance in a complex dynamic scene of a beam selection algorithm can be remarkably improved.

The application discloses a time slot allocation method and device, equipment and medium, and relates to the field of time slot allocation. The method comprises the following steps: an unmanned system generates a backbone network based on network topology information of a plurality of communication nodes in a previous time frame through a center node, so as to dynamically determine backbone nodes of a current time frame by using the backbone network; the center node dynamically predicts time slot segments allocated to the center node and the backbone nodes in the current time frame based on traffic demands of the plurality of communication nodes in the previous time frame; and each node in the center node and the backbone nodes dynamically predicts time slots allocated to each node and management sub-nodes of each node in a time slot segment corresponding to each node based on traffic demands of each node and the management sub-nodes of each node in the previous time frame, so as to meet the networking communication demand of the unmanned system with high reliability and low delay, better adapt to the non-stationary environment with strong business burstiness and frequent topology changes of the unmanned system, and realize efficient and reliable time slot allocation for large-scale unmanned system networking.

The application discloses a kind of dynamic protocol identification method and system based on multi-modal data, belong to the technical field of data protocol identification, identification method includes: S1: calculate data packet arrival interval time standard deviation, judge communication mode is periodic or burst communication mode;S2: data packet is divided according to length, calculate and count the probability distribution of each interval, and calculate the Shannon entropy value of data packet;S3: according to data packet arrival interval time standard deviation, the Shannon entropy value of data packet and the probability distribution of each interval, generate protocol fingerprint;S4: using edge AI classification engine classifies data packet.The application can improve the accuracy of protocol identification, improve network security protection capability.

The invention provides a PCIe (Peripheral Component Interconnect Express) dynamic bandwidth allocation and low-delay communication method and system based on an FPGA (Field Programmable Gate Array), and the method comprises the steps: carrying out the real-time statistics of the data transmission quantity of a plurality of virtual channels, and obtaining the flow data of the plurality of virtual channels; dynamically allocating bandwidth weights of the plurality of virtual channels according to the traffic data of the plurality of virtual channels to obtain credit values of the plurality of virtual channels; according to the credit values of the plurality of virtual channels, controlling the data transmission burst amount of the plurality of virtual channels, and performing hardware unloading processing on a transaction layer data packet received by the PCIe interface through a transaction layer packet analysis operation to obtain target address information and a data load; according to the target address information, the data load is directly written into an internal storage area of the FPGA through memory mapping operation, and transmission completion data is obtained. The method and the device can adapt to sudden data transmission requirements, and the bandwidth utilization rate is improved.

The present application relates to a kind of value burst flow detection method based on P4 and Sketch.Break through the detection scheme of traditional based on successive burst, propose value burst flow definition, observe the burst of traffic on the flow level, process the input data packet under the limited memory overhead by the data structure design based on Sketch, detect burst data packet according to the time interval between data packet, and then detect value burst flow. When data packet arrives, each data packet is mapped to a bucket set, but at most to one bucket is operated, under appropriate conditions, can vote according to the voting mechanism based on burst; when each measurement period ends, traverse all buckets, obtain the value burst flow detected by query operation and feedback to control plane. The present application reduces bandwidth overhead and user burden in burst detection process, while only limited memory occupation and low time complexity are generated.

The application discloses a multi-channel satellite frequency point file processing method and system based on threshold optimization, relates to the technical field of satellite communication data processing, and comprises the following steps: calculating the burst position and burst length of a main channel file; counting the burst quantity in a historical main channel file, and calculating a threshold according to the burst quantity in the historical main channel file; if the burst quantity of the main channel file is smaller than the threshold, a low-burst processing mode is selected, otherwise, a high-burst processing mode is selected; and according to the selected mode, the data corresponding to the burst is read from a main auxiliary channel file and processed. The dynamic threshold is estimated, and the reading mode is selected according to the dynamic threshold, which is suitable for efficiently processing the burst data in the multi-channel satellite frequency point file, and solves the problems of burstiness, low-delay requirement of data access and accurate reading according to position in the satellite positioning scene.

The invention discloses a burst degree control and delay guarantee parameter configuration method for a time-sensitive network, which comprises the following steps of: directly constraining an accumulated upper limit of a credit value in a CBS mechanism by introducing a credit upper limit parameter CLP which is independent of a credit growth rate and can be dynamically adjusted, and further limiting excessive occupation of burst flow on channel resources from the source; meanwhile, a worst-case delay analysis model of an integrated credit upper bound CLP is constructed based on a network calculation theory, end-to-end delay constraints of traffic can still be strictly met while traffic burst control is implemented, and burst control and delay guarantee under a dynamic load network are realized. The method has the advantages that through cooperative regulation and control of a single additional parameter CLP, unification of burst control and deterministic time delay guarantee is achieved on the premise that existing CBS standardized parameter configuration is not changed, and the performance and deployment flexibility of the TSN in a dynamic load environment are remarkably improved.

The application provides a base station traffic prediction method and device, comprising: obtaining a traffic space-time sequence and a base station traffic prediction model, the traffic space-time sequence comprising first and second traffic sequences; a first branch in the model extracts the spatial correlation between adjacent base station traffic based on the first traffic sequence and the time correlation of a single base station traffic sequence, and uses a self-attention mechanism in the time dimension to model the burst change of the same base station traffic over time, and uses a multi-head attention mechanism in the space dimension to model the mutation distribution law of the traffic between different base stations in the same period to obtain a first feature tensor; a second branch extracts the time correlation of the second traffic sequence to obtain a second feature tensor; and the first and second feature tensors are fused based on an output layer to output the traffic prediction value of each base station to be predicted period. The variation law of the base station traffic can be more comprehensively captured, the complex relationship in the space-time sequence data can be more effectively processed, and the prediction accuracy is improved.

The invention discloses a thread pool computing power collaborative redistribution method and device, equipment and a medium, and relates to the technical field of computers, and the method comprises the steps: generating a time sequence vector covering an object distribution rate, an Eden region growth slope, an old memory occupancy rate and a Survivor region object promotion rate through a monitoring layer, the probability of occurrence of a garbage collection event in a future set time period is pre-judged through a neural network model of a prediction layer, when the probability reaches a set threshold value, a decision layer generates an adjustment instruction containing a thread pool vertical shrinkage proportion and a computing power resource horizontal migration proportion, and then an execution layer calls a thread pool regulator and a k8s regulator to implement adjustment. Therefore, the problem that the computing power is suddenly caused by the garbage collection event is fundamentally solved, and the prospective redistribution of the computing power resources is realized; violent fluctuation of calculation power can be effectively restrained in the garbage recycling pause period, and abrupt reduction and recovery lag of the calculation power are avoided; meanwhile, system delay is reduced and resource utilization rate is improved.

The application belongs to the technical field of network operation and maintenance, and specifically discloses a switch flow adaptive control method and system, which samples data frames of a switch, strictly distinguishes the priority of data transmitted by the switch, and performs flow rate determination and rate overrun analysis based on the priority distinguishing result, suspends the reception or discards low-priority flow when the rate is overrun, avoids excessive consumption of the transmission bandwidth of the switch by the low-priority flow, and ensures the continuity of high-priority flow, so as to solve the problem that the performance of key services cannot be guaranteed and network resources are easily exhausted by abnormal flow when the flow of the switch is congested in the prior art; the rate limitation and smooth output control of high-priority flow can eliminate the burst problem, completely block the impact of abnormal flow on the switch, stabilize the output flow, and reduce the pressure on downstream equipment; and all determination processes are based on simple rate conversion, are suitable for hardware high-speed implementation, do not require complex queue management states, and are convenient to deploy and apply.

The present invention discloses a network switch control method and system, which specifically relate to the field of network traffic management technology, and are used to solve the problem of sudden loss or rearrangement of low-frequency traffic when high-frequency traffic is dynamically compressed in the prior art. By monitoring all traffic entering the network switch, its frequency characteristics are obtained, and the traffic is classified into high-frequency traffic and low-frequency traffic according to the frequency characteristics; a dynamic traffic compression algorithm is applied to compress the high-frequency traffic; for the low-frequency traffic, a sparse representation iterative reweighted least squares method is used to analyze the topological links and identify the link interference strength; the logical coherence of the low-frequency traffic data packets is evaluated by a variational autoencoder; the priority scheduling strategy of the low-frequency traffic is determined based on the link interference strength and logical coherence; finally, resources are allocated and scheduled for the compressed high-frequency traffic and the prioritized low-frequency traffic.

The invention relates to the field of big data services, in particular to a dynamic resource scheduling and optimizing method for big data services, which comprises the following steps: constructing a time sequence feature set, outputting a resource demand predicted value in a future preset advance time window, calculating a reservation proportion of an elastic buffer pool, and executing container preheating operation before a load peak value arrives; wherein the reservation proportion is dynamically adjusted according to the confidence coefficient of the prediction result, and when the prediction confidence coefficient is higher than a preset threshold value, the scale of the preheating container is automatically expanded to cover the predicted load increment. According to the method, the time sliding window is constructed, the active prediction model is utilized to analyze the periodicity and the burstiness of the load, the container preheating is executed before the peak value arrives based on the pre-reaction principle, and the advanced time window is introduced, so that the cold start time of the traditional passive expansion is eliminated, the resource is used as required, and the resource utilization rate is improved. And the response speed of the system to the tidal flow is obviously improved.

The invention discloses a frequency sequence estimation method based on a FARIMA-LSTM model, and the method comprises the steps: collecting a frequency band signal in a target environment, carrying out the preprocessing of the frequency band signal, mapping the preprocessed frequency band signal to a (0, 1) interval, obtaining a frequency sequence, inputting training set data into a constructed FARIMA model, estimating model parameters, generating a residual sequence according to the model parameters, and carrying out the estimation of the frequency sequence through the residual sequence. And training the constructed LSTM model by using the residual sequence, combining the FARIMA model with the trained LSTM model, inputting test set data into the FARIMA-LSTM model for single-step prediction, evaluating model performance and adjusting parameters, generating a frequency sequence from a signal to be estimated, inputting the frequency sequence into the trained FARIMA-LSTM model for single-step or multi-step frequency sequence prediction, and finally, performing frequency sequence prediction on the signal to be estimated. And outputting a single-step or multi-step estimation result. Fractional difference processing is carried out on a frequency time sequence through an FARIMA model, interference of periodic components on trend prediction is eliminated, meanwhile, deep mining is carried out on randomness, burstiness and nonlinear dynamic characteristics in FARIMA residual errors through an LSTM network, and step-by-step estimation of the frequency sequence is achieved.

The application discloses an online operation monitoring system and method based on a cable branch box, and relates to the technical field of power monitoring.The system performs online operation monitoring on multiple parameters of the cable branch box, accurately identifies whether the cable branch box has dominant operation abnormalities, effectively avoids sudden failures, and after eliminating the dominant abnormalities, relies on a digital twin body constructed by a cable branch box simulation monitoring model to perform long-term trend simulation on progressive and implicit defects such as busbar deformation and conductor creep, performs dynamic similarity analysis on historical data and simulation data by tracing the monitoring unified value system, amplifies early weak abnormal signals and quantifies them into identifiable risk indexes, relies on the digital twin model to simulate the long-term trend of the progressive and implicit defects, combines the dynamic similarity analysis to quantify the risk indexes, realizes the leap from "passive maintenance" to "active prevention", and improves the reliability and operation and maintenance efficiency of the distribution network.

A message processing method includes: after obtaining a message to be processed from a data stream, determining the maximum transmission unit (MTU) of the data stream based on the data stream identifier in configuration information; if the message length of the message to be processed is greater than the MTU of the data stream, obtaining a target message identifier, and then dividing the message to be processed into multiple message fragments with target message identifiers according to the MTU of the data stream; shaping the multiple message fragments according to the output interval of the data stream; and sending the shaped message fragments. This method can shape data streams including large data messages, reducing the burstiness of the data stream output and the possibility of data loss. This application also provides a network device capable of implementing the above message processing method.

The invention discloses a big data resource capacity management method and device, and belongs to the field of artificial intelligence, and the method comprises the following steps: S1, periodically collecting system resource data, data behavior characteristics and business operation indexes according to a set collection period T, the resource data comprising a CPU usage rate, a memory usage rate, a disk I / O and a network bandwidth usage rate, and the business operation indexes comprising the CPU usage rate, the memory usage rate, the disk I / O and the network bandwidth usage rate; the data behavior characteristics comprise data access frequency, data growth rate and activeness, and the service indexes comprise request frequency, concurrent connection number and SLA level. According to the method, the deep recurrent neural network (LSTM) is introduced to perform learning and trend modeling on historical multi-dimensional resource indexes, periodic and sudden change rules are captured through a sliding window mechanism, and compared with traditional methods such as linear regression and moving average, the method has better prediction precision and robustness in multiple scenes.

The invention discloses a large model reasoning method and system based on active load awareness, and belongs to the technical field of large model reasoning services. Aiming at the problem that reasoning service is difficult to consider resource efficiency and service quality due to burstiness and heterogeneity of mixed loads in the prior art, the invention provides a closed-loop method of cycle planning, online scheduling and self-adaptive adjustment and optimization. In the period planning stage, prospective instance pre-configuration is carried out by utilizing long-term load prediction; in the online scheduling stage, the request is routed and scheduled through response length sensing and a heterogeneous scheduling strategy; and in the self-adaptive tuning stage, fine-grained correction is carried out on the instance scale based on peak working set pre-judgment. According to the method, resource supply and request scheduling are coordinated, the SLO is guaranteed, the delay peak is effectively inhibited, the GPU utilization rate is improved, and the method is suitable for an enterprise-level LMaaS platform.

The invention provides a data transmission method and device, a medium, electronic equipment and a program product, and relates to the technical field of data transmission, the data transmission method comprises the following steps: determining an original transmission rate according to feedback information sent by a receiving end; the feedback information is feedback information sent by the receiving end based on a historical video frame received before a target video frame, and the target video frame is a video frame to be transmitted currently; determining the frame type of the target video frame, wherein the frame type represents the size of the target video frame; determining a target transmission rate according to the original transmission rate and the frame type of the target video frame; and transmitting the target video frame to the receiving end based on the target transmission rate. By determining the frame type of the current to-be-transmitted target video frame, the more accurate target transmission rate is obtained, so that the target transmission rate can be more adaptive to the dynamic change of the traffic, the problem of data transmission congestion caused by traffic suddenness and intermittency is avoided, and the quality of a live video is improved.

The application provides a method and system for supporting S-UPF policy control of satellite-ground computing integration, relates to the technical field of satellite-ground communication, and comprises the following steps: constructing a link interruption feature vector sample and training an interrupted link prediction model; analyzing current satellite-ground link multi-dimensional operation real-time parameters; identifying a data control strategy and performing analysis when the interruption risk probability is greater than the expected risk probability; jointly reconstructing a transmission path structure, a transmission cache state and a redundancy protection strategy based on a link interruption data recovery cost model, obtaining an optimized data control strategy, and updating the S-UPF policy. The application solves the technical problem that, in the prior art, satellite-ground link interruption is sudden, data transmission is interrupted passively and needs to be rescheduled, and satellite-ground computing collaboration efficiency is affected. Through the construction of an interruption prediction mechanism and the pre-position optimization of a strategy, the application realizes the rapid recovery of data transmission with minimum scheduling resources, and improves the satellite-ground computing collaboration efficiency.

A communication prediction-based energy saving method and an apparatus, the method including receiving, by a first radio access network device, from a second network device a communication prediction result that includes a communication period, traffic, a periodic communication indication, or a burst communication indication, where the traffic represents a predicted uplink or downlink data traffic volume, where the periodic communication indication represents whether communication predicted to occur is periodic, and when the communication predicted to occur is periodic, a communication interval, or the uplink or downlink data traffic volume of the periodic communication, where the burst communication indication represents whether the communication predicted to occur is bursty, and, when the communication predicted to occur is bursty, a time point, the uplink or downlink data traffic volume of the burst communication, and performing, by the first radio access network device, an energy saving operation based on the communication prediction result.