2410 results about "Traffic volume" patented technology

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 non-stationary traffic prediction method based on wave flow decomposition and time delay perception. The method mainly comprises the following steps: constructing a traffic network diagram and inputting historical traffic flow data; constructing a decoupling flow layer, and decoupling the original flow into a wave component and a flow component; constructing a time gating convolution module, and capturing short-time dependency; constructing a space-time causal chain of time-delay perception directed graph attention and processing wave components; constructing an adaptive graph convolutional network, and processing global steady-state features of flow components; constructing a time gating convolution module, and capturing long-term time dependence; constructing an adaptive event fusion module; constructing a full connection layer; and outputting the predicted traffic flow data. The method overcomes the limitation of a traditional method in the aspects of prediction accuracy and coping with a complex traffic network, and effectively solves the problems that a traditional traffic flow prediction method is insufficient in prediction accuracy in an intelligent traffic system, cannot reflect the influence of the traffic network and the like.

The invention provides a low-orbit satellite resource allocation method and device based on beam and frequency domain collaborative optimization, and the method comprises the steps: dividing the overall bandwidth of a user region into a central sub-band and an edge sub-band through employing a dynamic soft frequency reuse scheme; all ground users are subjected to spatial clustering according to geographic positions and traffic demands, coverage cells are dynamically divided according to clustering results, and balanced service is achieved; on the basis of dynamic cell division, user clusters with high service volume are selected according to priorities, a beam activation set is generated, and a hopping beam matrix is updated; establishing a channel model between a user and a beam and a switching matrix between satellites; constructing a system utility target, and constructing a multi-target optimization problem model by taking the system utility target as a main part and integrating interference, switching overhead and delay penalty; and the multi-dimensional resource collaborative optimization framework based on deep reinforcement learning reconstructs a multi-objective optimization problem model, and outputs a solution of the multi-objective optimization problem model, including an optimal bandwidth and a power allocation result. According to the invention, resource allocation can be carried out on low-orbit satellites.

Disclosed embodiments generally relate to edge-based multi-Radio Access Technology (RAT) traffic management (TM) solutions to support delay-sensitive traffic over heterogeneous networks. Embodiments include delay-aware TM implementations that split and / or steer network traffic across different RATs for the edge network control plane. Embodiments also include utilization threshold-based implementations to achieve delay-aware multi-path TM at the network's edge. The multi-path TM includes multi-RAT, multi-access, or multi-connectivity traffic routes. Embodiments include strategies to sort users (or devices) for making multi-RAT traffic distribution decisions and to determinate the utilization thresholds. Embodiments also include message exchange mechanisms or learning utilization thresholds and other useful system properties. Other embodiments may be described and / or claimed.

The invention provides a sequential network traffic prediction method and system based on swarm intelligence parameter optimization, and relates to the technical field of network traffic prediction. The method comprises the following steps: acquiring indexes such as throughput packet loss rate and round-trip delay of a target link by using a network probe, and performing deletion filling normalization and multi-scale decomposition to obtain a standardized traffic sequence; calculating information entropy, constructing a traffic complexity feature vector, and dividing a training set and a verification set; constructing a hybrid depth prediction model composed of a one-dimensional convolutional network and a gating cycle unit, and establishing a hyper-parameter search space; using particle swarm optimization and entropy-driven inertia weight adjustment and mutation probability mapping to reconstruct a speed and position updating strategy, and iteratively outputting a global optimal hyper-parameter; and generating a benchmark prediction result according to full-amount training, extracting a residual error, training a nonlinear residual error compensation model to carry out superposition correction and reverse normalization, obtaining a final flow prediction result, and improving prediction precision and generalization ability.

The invention relates to the technical field of communication base station energy-saving control, and discloses an energy-saving optimization control method and system for a communication base station. The method comprises the following steps: collecting and preprocessing traffic energy consumption data of a communication base station to obtain standardized characteristics; inputting the features into a double-flow space-time convolutional network, and carrying out parallel calculation through time and space branches to predict a load; performing hierarchical quantitative pruning on the network, and distributing bit width according to the influence degree to obtain a compression structure; and processing the real-time data based on the compression network, and generating an energy-saving control strategy for radio frequency resource configuration and power regulation. Based on accurate load prediction and differentiated resource scheduling strategies, the optimal balance between energy saving and service quality is realized, the problems of passive response and coarse-grained control in a traditional method are effectively solved, and the energy saving effect is maximized on the premise of ensuring communication quality.

The invention discloses an intelligent traffic flow dynamic regulation and control method and system based on multi-source data fusion, and relates to the technical field of data processing. The method comprises the following steps: executing multi-source data acquisition through a data acquisition interface to obtain a multi-source traffic data set; performing spatial fusion on the multi-source traffic data set to obtain a flow marking map; performing current traffic state analysis based on the flow marking map, identifying local and global congestion, and constructing a traffic situation map; and performing traffic flow state prediction in a preset future time zone based on the traffic situation map, performing regulation and control decision based on a prediction result, and generating a traffic flow regulation and control strategy. The technical problem that traffic flow prediction and regulation are not accurate enough in the prior art is solved, and the technical effects that intelligent traffic flow dynamic regulation is achieved through multi-source data fusion and space-time analysis, and the traffic management precision and the response speed are improved are achieved.

The invention belongs to the technical field of air traffic management, and relates to an air flight flow prediction method based on a lightweight adaptive network, which comprises the following steps of: firstly, acquiring data and fusing the data to generate a flow time sequence matrix indexed by airport identification and timestamp; secondly, constructing a self-adaptive dynamic graph; performing mixed graph convolution on the output dynamic graph and node features to form a spatial feature tensor, and performing frequency domain enhancement on the spatial feature tensor to obtain an event enhancement sequence; then processing the event strengthening sequence through an hour-level large convolution kernel and a minute-level expansion small convolution kernel, amplifying a congestion peak based on trend-fluctuation gating after time alignment, and outputting a fusion feature sequence; and finally, carrying out recursive decoding and prediction to obtain the predicted air flight flow. According to the method provided by the invention, under the conditions of dynamically changing airspace topology and strong noise and multi-scale coupled time sequence data, a set of lightweight spatial-temporal model is constructed and updated online in a self-adaptive manner, so that high-precision multi-step prediction of the multi-element flight flow can be realized.

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

The invention discloses a traffic flow prediction method based on a dynamic graph convolution circulation network, and belongs to the technical field of traffic information. The method comprises the following steps: acquiring historical traffic flow data, preprocessing the historical traffic flow data, and constructing a data set; an AGCRN model is improved, firstly, a dynamic filter of a self-attention mechanism is added to a graph generation part of an AGCRN to capture dynamic spatial features, and then a dynamic graph generation module is added to a gating recursive unit part to capture periodic time dependence. And finally, adding a residual error correction module into the AGCRN to carry out error feature extraction. Training the improved AGCRN model until a predetermined performance index is reached, and obtaining a traffic flow prediction model; and predicting the traffic flow of the traffic network by using the traffic flow prediction model. According to the method, the graph convolutional network is improved, so that the prediction accuracy of the traffic network flow is improved.

The invention discloses a network traffic abnormal behavior detection method and device. The method comprises the steps of performing normalization processing on traffic feature data, operation log data and running state data to obtain a multi-dimensional feature sequence; adjusting a first transmission rate peak value corresponding to the network flow data to obtain a second transmission rate peak value; under the condition that the average transmission rate of the network flow data in the target time window is greater than a second transmission rate peak value, performing similarity matching on the multi-dimensional feature sequence and a standard feature sequence to obtain a matching distance; and under the condition that the matching distance is greater than a first preset threshold value, if the plurality of preset features do not meet the constraint condition, determining that the network flow data has an abnormal behavior. According to the method and the device, the technical problem that normal work of a service system is affected due to the fact that misjudgment possibly exists in a related network flow abnormal behavior detection method is solved.

The invention discloses a method for improving channel traffic volume based on an underwater map, and belongs to the technical field of water transportation traffic, and the method comprises the following steps: S1, constructing an underwater high-precision map, deploying an unmanned ship and an underwater robot, and collecting underwater topographic data by using a multi-beam sounding technology to construct a high-precision underwater map; s2, establishing a dynamic channel planning model, collecting hydrological data and ship traffic data of different time and places of a channel by using a big data analysis and machine learning algorithm, and generating a dynamic channel use scheme; and S3, integrating a ship navigation and management system, developing ship navigation terminal equipment, and integrating the dynamic channel planning model with a monitoring system of a channel management department. According to the invention, channel resource utilization is optimized, intelligent dynamic planning is realized, ship passing efficiency, navigation autonomy, navigation safety and system stability and reliability are improved, channel management scientificity is enhanced, and cooperation of ships and management departments is enhanced.

The invention discloses a data processing method and device, equipment and a medium. The method comprises the steps that a detection threshold value is determined based on a first access traffic log; determining an access behavior for the service domain name based on the number of domain name requests determined by the access traffic indicated by the second access traffic log and a detection threshold; when the access behavior aiming at the service domain name belongs to the abnormal access behavior, taking a behavior determination time point corresponding to the abnormal access behavior as a critical time point, and taking a third access traffic log obtained based on the critical time point as an abnormal access traffic log; when a normal traffic feature determined based on the first access traffic log is obtained, determining an abnormal traffic feature of the service domain name based on the abnormal access traffic log, and performing feature comparison on the abnormal traffic feature and the normal traffic feature to obtain an attack feature of the service domain name; a protection policy for the service domain name is determined based on the attack feature. According to the invention, the security and stability of the business server can be maintained.

The invention relates to the technical field of urban intelligent traffic, in particular to a multi-source traffic data quality evaluation alarm method based on a time sequence large model, which comprises the following steps of: acquiring historical multi-source traffic flow of a target road, associating and screening the historical multi-source traffic flow, acquiring a confidence level grade of each pair of associated equipment by adopting a dynamic time warping algorithm, and calculating the confidence level grade of each pair of associated equipment; performing differential retention on the data according to the confidence level grade to obtain a historical trusted traffic data set; training a time sequence large model according to the historical credible flow data set, inputting the multi-source traffic flow in a previous time period at the current moment into the trained time sequence large model, and obtaining multi-source traffic flow prediction data and a double-source difference degree prediction value at the current moment; acquiring a multi-source traffic actual flow and a double-source difference degree value at the current moment, and comparing the multi-source traffic actual flow and the double-source difference degree value with a predicted value at the current moment to generate a multi-dimensional abnormal score; and performing data abnormity alarm according to the multi-dimensional abnormity score. According to the invention, the accuracy and real-time response capability of anomaly detection are remarkably improved.

The present application relates to the technical field of communications, and discloses a path planning method and apparatus, a device, a system, and a storage medium. The method comprises: acquiring traffic prediction information respectively corresponding to at least one time period; and on the basis of the traffic prediction information respectively corresponding to the at least one time period and at least one selectable path respectively corresponding to each service, determining a target forwarding path of each service meeting path planning objectives. The path planning objectives comprise: the service traffic borne by any link in a hierarchical network in any time period is less than or equal to the bandwidth capacity upper limit of any link, load balancing is realized between multiple links connected to a same network node in the hierarchical network, and the transmission quality of the target forwarding path of any service meets a quality requirement of any service. Path planning of a global service is realized, network link congestion can be reduced, and better effects that load balancing between all network links is realized and a service forwarding path meets a service quality requirement are achieved.

The invention belongs to the technical field of intelligent short message service scheduling, and particularly relates to an intelligent short message scheduling system and method based on 5G network slices. According to the method, the short message service processing efficiency in the 5G network slice environment is effectively improved through accurate prediction and intelligent scheduling of the short message service traffic, the change trend of the short message service traffic can be accurately captured through smooth processing and exponentially weighted moving average processing of the traffic change rate time sequence, and the short message service processing efficiency is improved. Reliable data support is provided for subsequent scheduling decisions, meanwhile, through confidence evaluation and resource utilization evaluation, the predicted demand of short message service flow and the resource utilization condition can be comprehensively considered, the scheduling reasonability and effectiveness are ensured, in addition, the intelligent scheduling mechanism can automatically adjust resource allocation according to the scheduling priority, and the scheduling efficiency is improved. The sending requirement of the high-priority short message service is met, and the processing speed of the short message service and the user experience are further improved.

The invention relates to an FTTR bandwidth management method based on a PON, and the method comprises the steps: dividing a to-be-managed region into a plurality of sub-regions according to the demands, distributing independent IP sub-networks, predicting the service of each IP sub-network, carrying out the pre-distribution, collecting the ONU flow data in real time, carrying out the feature extraction, obtaining the flow change features, dynamically adjusting the bandwidth distribution strategies of different slices, and constructing a double-layer dynamic weight model. Each network slice corresponds to an independent weight adjustment strategy, a dynamic time slot distribution field is inserted in an MPCP protocol, time slot distribution is carried out according to weight values of different network slices, burst traffic is preprocessed, demand prediction values are reported according to network slice classification, burst traffic information and traffic feature data are integrated, and the data are distributed according to the burst traffic information and the traffic feature data. And dynamically allocating bandwidth to each terminal device in the FTTR network. FTTR network bandwidth fine and dynamic management is achieved through multi-dimensional data processing, the network resource utilization rate and the user service quality are effectively improved, and the transmission requirements of diversified services are met.

The invention provides a traffic prediction method and device for a highway, electronic equipment and a storage medium. The method comprises the following steps: acquiring highway multi-source data; inputting the highway multi-source data into a knowledge graph construction module of a traffic prediction model to obtain a traffic prediction knowledge graph of the highway multi-source data; inputting the traffic flow data, the meteorological data and the holiday and festival data into a time feature extraction module to obtain time features of the highway multi-source data; inputting the road portrait data and the flow prediction knowledge graph into a spatial feature extraction module to obtain spatial features of the highway multi-source data; inputting the time features, the spatial features, the event data and the charging policy data into a feature fusion module to obtain fusion features of the highway multi-source data; and inputting the fused features into a prediction module to obtain a traffic prediction result of the expressway. Thus, through the constructed flow prediction model, the multi-source data of the expressway can be fused for flow prediction, and the accuracy of expressway flow prediction is improved.

The invention discloses an AI-based urban traffic flow prediction and dynamic signal optimization method and system, and relates to the technical field of traffic management, and the method comprises the steps: obtaining real-time traffic data, historical flow data and external environment data of a target region, obtaining multi-source data, and constructing a space-time matrix corresponding to the target region according to the multi-source data; learning the space-time matrix by using a preset space-time fusion model, and outputting a prediction result of the traffic flow in a future preset time period through the space-time fusion model obtained by learning; wherein the space-time fusion model is a combined architecture of a graph convolutional network and a Transform network; and dynamically adjusting traffic signal control parameters in the target area through a multi-agent learning algorithm based on a prediction result. According to the method, closed-loop regulation and control of'data perception-prediction modeling-autonomous decision 'are formed, so that the limitation of spatial feature modeling staticization and time correlation analysis fragmentation of a traditional method is broken through, and the problem of regional imbalance caused by single-point optimization is effectively solved.

The invention provides a network load balancing algorithm based on a credential platform and a credential terminal, and relates to the technical field of credential, and the algorithm comprises the following steps: collecting real-time traffic data from a plurality of ports; carrying out preprocessing of normalization, abnormal value elimination and protocol field standardization on the collected data; performing multi-dimensional protocol fingerprint feature extraction on the flow of each port based on a protocol analysis algorithm; modeling historical and real-time traffic of each port by using a time sequence neural network, and outputting a traffic prediction value and abnormal traffic probability assessment in a future short time period; dynamically generating a multi-port distribution weight factor based on the protocol characteristics and the flow change trend of each port; inputting the prediction result and the real-time load state into a reinforcement learning scheduling agent; upon detection of an abnormal traffic trend, feed-forward load distribution adjustments are performed immediately. According to the method, the data processing efficiency can be improved, the load scheduling intelligence is realized, the resource allocation priority of the port of the credential terminal is ensured, and the service continuity is guaranteed.

The invention discloses a middleware management method and device, relates to the technical field of middleware, and mainly aims to realize unified management and configuration of middleware, reduce the management and maintenance difficulty of the middleware and improve the flexibility and convenience of flow management. According to the main technical scheme, each middleware instance is divided into one or more logic clusters according to operation characteristics of each service, wherein the operation characteristics are used for representing characteristics related to high-reliability operation of the services; general configuration setting is carried out on the one or more logic clusters to obtain global configuration information containing all the logic clusters, and the general configuration setting at least comprises setting of physical mapping and routing strategies of middleware instances docked by all the services in the running process; and respectively issuing the global configuration information to each middleware instance contained in each logic cluster so as to run according to the global configuration information when a service butted with the middleware instance contained in each logic cluster is started. The middleware management method and device are used for middleware management.

The invention provides a smart city construction system based on big data, and relates to the technical field of smart cities, and the system comprises a traffic data collection module, a transmission and preprocessing module, a flow prediction and analysis module, an intelligent signal optimization module, a parking lot scheduling and induction module, a decision support module and a user service module. The traffic flow prediction and analysis module predicts a traffic state in a set time length in the future through a space-time diagram convolutional network in combination with a seasonal autoregression model; and the intelligent traffic signal optimization module is used for dynamically adjusting signal lamp timing based on a deep reinforcement learning algorithm and realizing intersection linkage through a regional cooperative control algorithm. According to the invention, the space-time diagram convolutional network, the deep reinforcement learning intelligent algorithm, the block chain and the distributed computing technology are fused to construct a multi-source data-driven traffic whole-flow intelligent management system, so that the whole-chain intelligence from real-time sensing and dynamic optimization to cross-platform service is realized; and the collaboration, the prediction accuracy and the service ecological openness of the urban traffic system are obviously improved.

The invention relates to the technical field of multi-modal behavior anomaly detection scheme design, in particular to a multi-modal behavior anomaly detection method and system under the condition that encrypted traffic is not decrypted. The method comprises the following steps: capturing a network encrypted traffic data packet in real time; on the premise that decryption is not carried out, multi-mode non-decryption features such as flow layer statistics, time sequence interaction, encryption handshake and context association are extracted in parallel; establishing a dynamic normal behavior baseline model of each feature based on historical data; the real-time features are compared with the baseline model, comprehensive judgment is carried out through a multi-mode correlation analysis algorithm, and an anomaly detection result is output; and when the abnormal condition is judged, the network control equipment is automatically linked for blocking. The method thoroughly gets rid of dependence on traffic decryption, realizes high-precision and self-adaptive detection and rapid automatic response to abnormal behaviors in encrypted traffic through multi-dimensional feature fusion and dynamic baseline technologies, and effectively solves the problem of failure of a traditional detection technology in an encrypted environment.

The invention discloses a non-stationary time sequence flow prediction method and device based on delay space-time dependence, and a medium, and relates to the technical field of intelligent traffic. The method comprises the following steps: constructing a prediction problem of non-stationary traffic flow, and decomposing a traffic flow time sequence into a time-varying component and a time-invariant component by using Fourier transform; based on the time-varying component and the time-invariant component, constructing a Moran operator and a spatial-temporal feature fusion framework, and extracting spatial and temporal features of traffic flow; and based on the space and time characteristics of the traffic flow, predicting the traffic flow and dynamically adjusting parameters by using function-to-function regression and a Bayesian Kriging optimization model. The method is superior to a traditional baseline model in a complex traffic flow prediction scene, and new technical support is provided for non-stationary traffic flow modeling and prediction optimization.

A client driven approach for L4S support and optimization in WLANs, e.g. WiFi WLANs, is described. A client station (STA) generates and sends a SCS w / L4S Request frame including a L4S descriptor element to an access point (AP). The L4S descriptor element includes a L4S classifier mask, ECN threshold and a set of client device specified desired QoS level information to be applied with regard to the transmission of identified L4S traffic. Upon acceptance of the request, the AP selects a queue for future L4S traffic and creates an L4S filter. The created L4S filter includes classification criteria, ECN congestion threshold, and QoS information. The AP implements the created L4S filter, e.g., identifying and directing identified L4S traffic to the designated L4S queue, performing ECN marking with regard to the congestion at the L4S queue, and transmitting L4S data in accordance with the received STA specified desired QoS levels.

The invention provides a system abnormity intelligent diagnosis and recovery method and system fusing time sequence logs, and relates to the field of distributed system operation and maintaining.The method comprises the steps that operation logs are collected from a plurality of service nodes of a distributed system, time sequence alignment is conducted, and calling relation and performance measurement data are extracted; time sequence change features are calculated in the sliding time window, dynamic feature vectors are generated through fusion coding, and a cross-service time sequence association graph is constructed; identifying an abnormal service node and matching the abnormal service node with historical fault data to obtain a historical propagation path and a feature vector; calculating a propagation convergence coefficient and a characteristic deviation degree based on a historical propagation path to obtain a causal intensity score; and identifying a fault influence degree according to the score, and adaptively adjusting a resource isolation and flow scheduling strategy. According to the invention, accurate diagnosis and efficient recovery of distributed system faults are realized.

The invention discloses an inter-body area network and intra-network joint optimization interference mitigation method based on channel prediction, which comprises the following steps of: firstly, predicting channel quality between each node and a coordinator in a body area network under different frequencies by using an autoregression model, classifying the body area network according to neighbor information among the body area networks, and dividing the channel quality of each node in the body area network according to neighbor information; secondly, distributing channels with different frequencies to various body area networks according to the principle of maximizing the average signal receiving strength of the nodes so as to avoid inter-network interference; in each body area network, the number of required time slots is calculated according to the service volume of each node, and when the number of time slots is not enough, the number of time slots of the nodes is reduced according to the principle that the total utility of the nodes is maximized; and then, according to the predicted channel quality condition of the nodes in the network under the allocated frequency, allocating time slots to the nodes by taking the minimum average packet loss probability as a principle. The method provided by the invention can be used for the scene of coexistence interference of the semi-dynamic body area network, and inter-network and intra-network interference can be avoided.

The invention discloses a global dynamic perception space-time traffic flow prediction method based on graph packet representation learning, and belongs to the technical field of traffic flow prediction, and the method comprises the following steps: S1, traffic data input, S2, traffic graph packet construction, S3, graph packet initial feature extraction, S4, time sequence feature extraction, S5, spatial feature extraction, and S6, traffic flow prediction and output. Through a space-time modeling technology of graph packet representation learning and global dynamic perception, space-time characteristic elements of a traffic road network can be comprehensively covered, traditional traffic indexes such as flow and speed are concerned, elements such as road network topological association and cross-regional multi-hop association are also included, a dynamic dependency relationship between a time sequence and a spatial dimension is deeply mined, and a real-time dynamic perception effect is achieved. Therefore, the prediction result can reflect the real evolution law of the traffic flow more accurately, and a more scientific basis is provided for traffic management and decision making.

The invention discloses a traffic message service type identification method, device and equipment, a medium and a product. A gateway receives a traffic message; analyzing the flow message to obtain a flow characteristic; and identifying the service type of the traffic message according to the traffic feature. According to the scheme of the invention, the traffic service type is quickly analyzed by grasping the traffic characteristics, the service type can be identified when the service traffic is forwarded, and the service function limitation is reduced.

The invention discloses a financial intelligent backbone network flow scheduling method and system based on SRv6, and relates to the technical field of network scheduling, and the method comprises the following steps: S1, carrying out the recognition of the ingress service flow, and dividing the ingress service flow into predefined service types; s2, matching a corresponding SRv6 traffic tunnel model for each service type; step S3, distributing differentiated QoS strategies for different types of service flows, and executing corresponding queue scheduling; s4, aiming at the SLA requirements of different service types, constructing a corresponding optimization objective function to carry out path calculation; s5, when it is detected that the link utilization rate exceeds a preset threshold value, an intelligent tuning process is automatically triggered; and S6, issuing the optimal SRv6 strategy obtained by calculation to entrance equipment for execution. According to the invention, an intelligent and efficient financial backbone network architecture is constructed, and a refined traffic scheduling strategy is realized, so that the utilization rate of network resources and the operation and maintenance efficiency are improved.