1001 results about "Service flow" patented technology

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 discloses a mobile communication load collaborative scheduling system and method based on energy efficiency perception, and belongs to the technical field of mobile communication, energy consumption data of each functional module in a base station and external network environment parameters are collected in real time through a multi-source sensor network in the base station, and a dynamic energy efficiency map is generated; based on historical energy efficiency data and real-time load characteristics, constructing an energy efficiency construction prediction model, and predicting an energy efficiency hotspot area; based on the historical movement track of the user and the spatial and temporal distribution characteristics of the service flow, the movement trend of the user and the change trend of the service flow are analyzed, and in combination with an energy efficiency prediction model, a potential overload base station is identified, and overload early warning is triggered; after receiving the early warning information, screening candidate target base stations according to a preset radius by taking the overload base station as a core; and generating a load idle list in a descending order according to the real-time energy efficiency ratio of the candidate base station, and performing energy efficiency sensitivity priority grading on the to-be-migrated service, thereby generating a collaborative scheduling strategy and executing load migration.

The invention discloses a P4 switch processor core-oriented multi-service traffic packet scheduling method, which mainly comprises the following steps of: proposing a feature extraction method based on a statistical interval and Sketch, integrating data packet time correlation and traffic dynamic change, optimizing a Bloom filter conflict detection mechanism, and improving the adaptability and accuracy of an algorithm in a limited resource environment; a decision tree model is combined with scheduling, a decision tree is reconstructed to adapt to an operation mode and a data processing requirement of a P4 switch, and high-precision service flow classification under linear speed operation is ensured; a packet scheduling method AD-PIFO is designed based on a variable priority, the forwarding priority of a data packet is dynamically adjusted, the QoS requirements of different services are met, and the overall performance and efficiency of the switch are improved. The method is outstanding in high-priority traffic throughput and low-delay performance, is superior to similar algorithms in medium-low priority traffic throughput, is stable and effective under different load conditions, is reasonable in extra time overhead of data packet transmission, and is widely applied to SDN network optimization data packet scheduling scenes.

The invention relates to the technical field of resource allocation, in particular to a unified resource pooling management method for multiple computing power sources of an unmanned aerial vehicle platform, which comprises the following steps of: analyzing an unmanned aerial vehicle service flow to obtain subtask nodes and dependency relationship edges, and measuring and calculating the execution time of each subtask on different heterogeneous computing power units. According to the method, subtask nodes and dependency edges are obtained by analyzing the unmanned aerial vehicle service flow, the refined execution time of each subtask on a heterogeneous computing power unit is measured and calculated on the basis of task execution priority ranking, the actual consumed time of data transmission between tasks is clearly calculated, a complete heterogeneous computing power pool scheduling basic view is formed, and the scheduling efficiency of the heterogeneous computing power pool is improved. Therefore, the task execution efficiency and the resource scheduling accuracy are improved; meanwhile, in the resource allocation process, the real-time operation temperature and the calculation error rate of each computing power unit are continuously monitored, the dynamic health state is intelligently analyzed and calculated, the potential fault risk is recognized in time, and a pre-degradation control signal is actively triggered.

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 provides a multi-level service priority dynamic scheduling simulation method and device, a medium and a product. The method comprises the following steps: constructing a priority label system; establishing a dynamic binding relationship between the service flow and the priority label; when a service layer generates a data packet, embedding a priority label for the data packet based on the dynamic binding relationship; extracting a priority label carried by the data packet on a protocol stack MAC layer, and ensuring that the priority label is unvarnished to a lower layer protocol in a lossless manner; and based on the priority labels carried by the extracted data packets, classifying the data packets into logic queues with corresponding priorities, and executing a differentiated scheduling strategy. The method can adapt to diversified requirements of data services of different types and priorities for network resources, effectively improves the simulation model accuracy, improves the simulation experiment efficiency and simulation real-time performance and reliability in a multi-service priority 5G communication network simulation scene, more accurately reflects the performance of a real network, and improves the simulation experience of the 5G communication network. And meanwhile, the simulation platform has higher expansibility and flexibility.

The invention discloses a TSN scheduling optimization method and device based on flow sensing autonomous learning, equipment and a medium. The method comprises the following steps: deploying a lightweight flow detection module in a switch or a router, and after a controller receives a data request, automatically identifying a newly arrived unknown service flow by using the lightweight flow detection module, and judging whether the newly arrived unknown service flow is a periodic TT flow or an unpredictable burst flow; the controller performs classification management on the identified service flow types, collects topological information and flow requirements of the whole network and issues the topological information and the flow requirements to the terminal nodes through a network management interface; the controller constructs an intelligent queue scheduling task based on the collected network topology information and traffic demand and converts the task into a Markov decision process MDP, network resources, queue states and priorities are used as a state space, a scheduling strategy is used as an action space, a reward function is designed in combination with throughput and delay indexes, and an intelligent queue scheduling task is obtained. Driving a dynamic environment through real-time data and training a DRL model; an enhanced queue scheduling mechanism Pro-CQF is adopted, different priority labels are configured according to classified flow types, and then mixed flow scheduling is carried out; the controller periodically collects time delay, packet loss rate and end-to-end transmission delay indexes and feeds back the indexes to the DRL model, and a scheduling strategy is updated online. According to the method, the traffic sensing and scheduling efficiency is greatly improved in a network environment in which multiple service flows coexist and end-side equipment functions are different, and the reliability and the expandability of the TSN in industrial Internet of Things, edge computing and other high-real-time application scenes are remarkably enhanced.

The invention provides an intelligent computing fusion network routing system and a routing method thereof. The system comprises an entrance gateway, a routing forwarding node, a computing node and a computing network controller, the computing network controller is used for carrying out path selection and computing resource allocation in combination with a real-time network state and load information of the computing node, dynamically generating and updating a computing network routing table through a computing network fusion routing algorithm, and issuing the computing network routing table to the entry gateway and the routing forwarding node; and the entry gateway receives and stores the computing network routing table issued by the computing network controller as a data stream access point, receives a computing power data stream which is input from the outside and carries a computing power service request, executes computing power routing table matching according to the computing power service request, and forwards the computing power data stream to a proper routing forwarding node according to a matching result. According to the invention, by optimizing the joint scheduling of the computing power service paths and the computing nodes, a plurality of paths of the anycast computing power routing and the weights of the paths are generated, the service flow level load balancing is realized, and the end-to-end time delay is reduced.

The invention relates to the technical field of data processing, in particular to a load awareness and data dynamic layout method and system based on deep learning, and the method comprises the steps: collecting hardware load indexes of all nodes in a distributed cluster in real time, and carrying out the preprocessing of the hardware load indexes; based on the preprocessed hardware load index, calculating a comprehensive load score of each node through a weighted fuzzy logic algorithm; dividing load intervals according to the comprehensive load score and matching a predefined strategy combination; and dynamically adjusting a compression algorithm, a compression ratio and a fragment merging parameter based on the current service flow peak-valley state of the target data block and a matched predefined strategy combination, the service flow peak-valley state being determined through a request processing rate and response time. The objective of the invention is to realize dynamic and fine-grained load sensing and data layout.

The invention discloses a predictive topology-based satellite internet deterministic routing data plane migration method, which mainly solves the problem of data packet and service interruption during satellite switching in the prior art. According to the implementation scheme, service flow terminal equipment firstly sends an access request to an entrance edge node; the entry edge node calculates a tunnel path and resource reservation according to the request, performs service identification, encapsulates various identifiers into deterministic specific segment identifiers, and encapsulates the original data packet into an SRv6 data packet based on the tunnel path and the specific segment identifiers; when the service flow is transmitted in the satellite network, if a satellite is switched, service flow tunnel migration is carried out, and when a queue data packet exists in the switched satellite, queue cache migration is carried out. According to the invention, the forwarding of the service flow in the satellite switching process is not influenced, the flow interruption or transmission error caused by lack of information pre-synchronization in the traditional satellite switching is avoided, the priority transmission of the high-priority data packet is ensured, and the method can be used for data transmission between a ground terminal and a deterministic satellite network.

The invention relates to the technical field of communication network security, in particular to a communication network security auditing method and system under multi-source heterogeneous data association, and the method comprises the steps: collecting network flow data and log data in a communication network in real time; distinguishing non-service flow data and signaling data (also called service flow data) from the collected network flow data; performing deep analysis and classification processing on non-service flow data, performing unified analysis and association analysis on log data, and performing protocol decoding and content analysis on signaling data; constructing a multi-source heterogeneous data association relationship analysis model, integrating non-service flow data, signaling data and log data, mining space-time association and causal relationship among the data by applying a machine learning algorithm, and constructing a network security situation map; and based on a multi-dimensional analysis result, automatically generating a security audit strategy matched with the multi-dimensional analysis result. According to the invention, accurate detection, real-time blocking and risk early warning of communication network security threats are realized, and the network security protection capability and the operation and maintenance efficiency are improved.

The invention provides a substation secondary system communication network adaptive topology reconstruction method, which comprises the following steps: modeling by adopting multi-scale time delay feature fusion, and accurately predicting protocol conversion time delay; designing a hierarchical fusion protocol conversion time delay control framework, wherein the framework comprises two-stage time delay control of feed-forward compensation and feedback adjustment based on a resource-time delay sensitivity matrix; proposing a protocol semantic dynamic cutting technology, and realizing time delay demand-based differential semantic processing through a performance-semantic tradeoff function; and constructing a topology reconstruction decision-making mechanism of end-to-end time delay perception, and comprehensively evaluating the influence of the candidate topology scheme on the key service flow time delay. According to the method, the protocol conversion time delay of key services in the multi-protocol environment of the transformer substation is reduced, and millisecond-level time delay guarantee and self-adaptive optimization of network topology are realized.

The invention provides a communication method and device, and belongs to the technical field of communication, in the method, a network element at a network side, such as a first network element, can obtain network congestion information of a service flow so as to determine the possibility of congestion of a current network and execute supervision. For example, after the network congestion degree is increased, if the possibility of network congestion has reached that an application side needs to adjust the transmission of the service flow to reduce the possibility, if the first network element determines the application side by monitoring the transmission of the service flow and adjusts the transmission of the service flow, the first network element determines the application side by monitoring the transmission of the service flow. If not, the transmission can be managed and / or controlled, so that network congestion is avoided, fairness between services is guaranteed, and the use experience of a user is guaranteed.

A routing method for supporting differential service requirements in a satellite internet comprises the following steps: step 1, constructing multi-dimensional network state perception as a base layer which is responsible for collecting and quantifying physical states and logic states of a satellite network in real time, and obtaining objective network states and subjective QoS (Quality of Service) requirements of services; 2, generating a nonlinear adaptive weight driven by a service as a strategy layer, mapping an objective network state and a subjective QoS (Quality of Service) demand of the service, dynamically adjusting the value orientation of routing judgment, and generating a dynamic weight; and step 3, taking the dynamic cost routing decision as an execution layer, calculating the comprehensive cost based on the dynamic weight, and executing next hop selection and path maintenance, and through cooperative work of the dynamic cost routing decision, the comprehensive cost and the next hop selection and path maintenance, realizing intelligent path selection and high-reliability transmission for different service flows in a high-dynamic and resource-limited environment of the low-orbit satellite. According to the invention, while the routing computation overhead is effectively reduced, multiple requirements of differentiated services on low time delay and high reliability can be considered.

The invention discloses a communication network bandwidth resource visualized dynamic allocation method and system based on an SDN, and relates to the technical field of communication networks, and the method comprises the steps: obtaining the channel interference intensity, the spectrum occupancy rate and the service flow priority data of each node in a wireless network based on a southbound interface of an SDN controller, and obtaining the channel interference intensity, the spectrum occupancy rate and the service flow priority data of each node through a channel resource dynamic allocation table; generating a multi-dimensional resource view; according to the multi-dimensional resource view, multi-level coverage domains of the wireless network are divided, the service load level of each coverage domain is identified, and a pre-allocation scheme of cross-domain bandwidth resources is generated; and executing a pre-allocation scheme of cross-domain bandwidth resources by using a northbound interface of the SDN controller, and adjusting bandwidth allocation parameters of each coverage domain. According to the invention, visual monitoring, accurate division and flexible scheduling of the network resource state are realized, and the utilization efficiency of network bandwidth resources is remarkably improved.

Disclosed in the present application are a service flow scheduling method, apparatus and device based on deep reinforcement learning, and a medium. The method comprises: acquiring target data, wherein the target data comprises a current target topology state, current target service flow information and current target link information of a time-sensitive network; and inputting the target data into a deep reinforcement learning model to obtain a scheduling scheme, so as to implement scheduling of service flows on the basis of the scheduling scheme, wherein the deep reinforcement learning model comprises an agent and an environment, the agent is configured to determine gate control orchestration of the scheduling scheme, and the environment is configured to determine slot orchestration of the scheduling scheme on the basis of the gate control orchestration.

The invention discloses a wireless ad hoc network routing and resource cross-layer joint optimization method based on a genetic algorithm. The method comprises the following steps: step 1, aiming at a plurality of end-to-end communication services in a wireless ad hoc network, carrying out mathematical modeling on a normalized difference sum of each service flow path hop count and a theoretical minimum value thereof and a constraint condition; performing joint optimization on link layer resource allocation and network layer routing decision; and step 2, solving the mathematical model based on a genetic algorithm to realize the targets of reasonable distribution of network resources and optimization of service quality. According to the method, link layer resource allocation and network layer routing decision are jointly optimized in a global mode under the condition that network communication resources are limited, it is ensured that a service flow is close to shortest path transmission as far as possible, end-to-end delay and network loads are reduced, and effective allocation of resources and flexible construction of routing are achieved.

The invention relates to the field of low-altitude communication network planning methods, in particular to a 5G three-dimensional coverage and capacity planning design method for low-altitude economy, which comprises the following steps: performing airspace range limitation and application scene division on a target airspace, establishing a mobile model of a low-altitude terminal, and providing dynamic input for simulation; the method comprises the following steps: establishing a dual-frequency heterogeneous network, and determining base station addresses covering targets at different heights and meeting different signal station spacing; performing three-dimensional coverage and quality simulation on the established dual-frequency heterogeneous network, performing network KPI evaluation on the simulation until the standard is reached, and outputting a network deployment planning scheme; deploying according to a network deployment planning scheme, predicting low-altitude service flow, and adjusting a beam direction and a PRB reservation ratio to realize on-demand allocation of resources; and carrying out actual measurement on the deployed network, collecting an air measurement index, comparing a comparison deviation between the air measurement index and a simulation result, and carrying out fine adjustment on the network according to the comparison deviation. According to the invention, diversified communication requirements are met, and the success rate of terminal access is improved.

The invention relates to the technical field of optical communication, in particular to an algorithm service system and method for an optical transport network, and the system comprises an optical network sensing module which is used for collecting optical layer performance parameters, service flow data and node equipment operation states of the optical transport network; the deep algorithm decision center predicts service traffic based on a space-time diagram convolutional network, and optimizes routing wavelength allocation through reinforcement learning; the strategy adaptation module is used for dynamically generating an operation and maintenance strategy and an energy-saving scheduling instruction according to the prediction result and the optimization scheme; the risk assessment unit is used for assessing a link fault risk, a service interruption risk and a network congestion risk in real time, quantifying a risk weight through an analytic hierarchy process, and optimizing an operation and maintenance strategy in combination with historical fault data; and the intelligent resource reconstruction unit is used for performing optical channel reconstruction according to the optimized operation and maintenance strategy, dynamically allocating optical layer resources or switching redundant transmission paths. Therefore, the problems of poor strategy adaptability, low resource scheduling efficiency, insufficient fault-tolerant capability and the like in the prior art are solved.

A communication method includes a first device that receives first indication information and a service flow identifier of a protocol data unit (PDU) session from a session management network element, where the first indication information indicates the first device to determine a transmission link for a service flow of the PDU session. The first device selects the transmission link for the service flow of the PDU session based on the service flow identifier and the first indication information.

The invention relates to the technical field of power internet of things, in particular to a broadband / narrowband self-adaptive switching power internet of things communication system and a control method, which intelligently identify the priority and quality of service (QoS) requirements of different services by collecting network states, terminal states and service flow data in real time. And an optimal communication mode switching strategy is dynamically generated based on a pre-trained machine learning model, seamless switching of broadband and narrowband communication links is realized, and the decision accuracy is continuously improved through a feedback optimization mechanism. According to the method, the problems that a traditional electric power Internet of Things communication mode is fixed and single and cannot intelligently adapt to services and network states are effectively solved, the system communication reliability is remarkably improved, the transmission time delay and energy consumption are reduced, and high-adaptability and high-reliability intelligent communication guarantee in a complex electric power scene is achieved.

The invention discloses a hydroelectric power station state monitoring communication method, system and device and a medium, and the method comprises the steps: carrying out the channel attenuation prediction through employing an integrated learning algorithm, and obtaining a channel attenuation prediction result; based on a channel attenuation prediction result, communication parameter optimization is carried out through a control optimization algorithm, and the optimal transmitting power and coding modulation scheme are obtained; analyzing through a deep learning framework to obtain a communication traffic prediction result; dynamically adjusting resource allocation of each service flow through a bandwidth allocation strategy based on a communication flow prediction result to obtain optimized bandwidth resource allocation; through the configuration instruction, the communication parameter setting is dynamically adjusted, and the fine management of the communication system is realized. The satellite communication system of the hydropower station is more reliable and efficient, and better support is provided for operation management of the hydropower station.

The invention relates to the technical field of industrial networks, in particular to a time-sensitive service flow resource adaptation method for large-bandwidth multicast flow awareness, which comprises the following steps: S1, a controller collects registered service flow information and real-time network state information; s2, after service flow priorities are divided according to the information, a mapping relation between service flows and multiple queues is established, and a forwarding rule is established according to the mapping relation; s3, establishing an optimization target and a resource constraint according to the mapping relation and the forwarding rule; and S4, designing a Markov decision according to the optimization target and the resource constraint, training the neural network model according to the Markov decision and the double-delay depth deterministic strategy gradient so as to output a scheduling decision, and obtaining a multi-dimensional resource adaptation result by a solver according to the scheduling decision. According to the invention, a basic queue model support is provided for end-to-end deterministic transmission of the unicast stream and the multicast stream through the mapping relation and the forwarding rule, the utilization rate of network resources and the number of service streams capable of being subjected to incremental scheduling are improved, and the worst end-to-end delay of the large-bandwidth multicast stream is reduced.

The invention relates to a service awareness method of a user terminal, communication equipment, the user terminal and a storage medium. The service awareness method is applied to a user plane function network element UPF in a core network. The method comprises the following steps: monitoring a plurality of service flow events forwarded by a local machine under the condition of receiving a service flow subscription message sent by a policy and charging function network element PCF; the service flow subscription information comprises a user terminal ID and an application program ID; if a target service flow event indicated by the user terminal ID and the application program ID is monitored, a service flow event occurrence notification message is sent to the PCF, and the service flow event occurrence notification message comprises the user terminal ID, the application program ID and an occurrence identifier of the target service flow event. By adopting the method, the perception of the user terminal at the user level and the service level can be realized.

The invention discloses a cross-domain time-sensitive satellite network traffic scheduling method based on a TSN and MF-TDMA, and the method comprises the steps: a cross-domain controller collects network topology information, builds a satellite cluster global network model, carries out the collection of data and task demands, and a user controller collects an intra-domain sending end traffic access request, the traffic demand is reported to the cross-domain controller and the in-satellite controller; the cross-domain controller performs flow characteristic analysis and establishes a flow model; the in-satellite controller evaluates the network load, constructs a dynamic network resource state information base, and determines the traffic scheduling granularity according to the long-time stable service; according to the positions of the source node and the destination node of the service flow, the cross-domain controller and the in-satellite controller respectively calculate the scheduling paths of the cross-domain flow and the single-domain flow; and according to the routing result of each flow, based on the scheduling decision variable, establishing a wireless and wired joint scheduling constraint and optimization target, generating an integer linear equation, and solving an optimal solution to generate a scheduling scheme.

The invention provides a clustering-based QoS mapping method and device for a 5G-TSN, and relates to the technical field of 5G time-sensitive network converged communication. The method comprises the following steps: a TSN flow QoS acquisition module sends an acquired TSN flow QoS index to a TSN flow aggregation module; the TSN flow aggregation module performs flow aggregation processing on the TSN flow QoS index through an enhanced density space clustering algorithm to obtain an aggregated flow; the TSN flow aggregation module sends the aggregation flow to a 5QI mapping module; and the 5QI mapping module performs dynamic QoS mapping processing on the aggregation flow through a fuzzy logic algorithm to obtain a mapping result. According to the invention, the TSN service flow can be accurately matched with the 5G QoS parameter, and the problem that the traditional mapping method is rigid and poor in dynamic adaptability can be solved.

The invention provides a service flow processing method and device, electronic equipment and a storage medium, and the method comprises the steps: determining a plurality of network feature dimensions associated with a service target according to the service target corresponding to the service type of the service flow; extracting a plurality of pieces of network data information associated with message header data from the service flow according to the plurality of network feature dimensions; wherein the plurality of pieces of network data information correspond to the plurality of network feature dimensions; and predicting field change characteristics of a plurality of header fields in the message header data according to the plurality of pieces of network data information, and generating a compression strategy matched with the field change characteristics. According to the method, different compression strategies can be dynamically adapted according to the service types of the service flows, so that the pertinence and effectiveness of the compression strategies are improved.

The invention relates to the technical field of power system network and information security, and discloses a power system transverse isolation and secure data transmission method and system.The method comprises the steps that a production control security domain and a management information security domain are determined, and cross-domain service flow information is configured; when transmission between a production control security domain and a management information security domain is needed, cross-domain service flow information to which service data belongs is collected and packaged into a security self-description data unit; on the transverse isolation path, acquiring header information to generate a session token; when the secure self-description data unit is forwarded, feature data are obtained and compared with the associated session token, and forwarding is carried out when limiting conditions are met; and maintaining a sliding time window, carrying out statistics on the forwarded security self-description data unit, and identifying an abnormal behavior. According to the invention, semantic-level access control and dynamic protection of cross-domain services between a production control security domain and a management information security domain are realized.

A service flow adjustment method and a communication apparatus are provided. Slot adjustment information is carried in a client signal flow sent between nodes, so that times at which all nodes on a network switch slot configuration can be unified, to avoid service damage in a process of adjusting a network transmission bandwidth. The method includes: A first node receives a first client signal flow from a second node by using N slots of a first bundling group; and after first duration starting from a time at which the first node detects slot adjustment information, the first node receives the first client signal flow from the second node by using M slots of the first bundling group.

The invention discloses a customer service information generation method and system based on multi-modal intention recognition, and the method comprises the steps: obtaining customer service session data, and carrying out the heterogeneous data classification operation of the session data; performing feature extraction operation on each piece of parting modal information; establishing a semantic bridging matrix among the modal features, performing hierarchical attention routing, and outputting an intention label; a knowledge graph is injected, the knowledge graph comprises a static knowledge base, a real-time service flow and a user historical portrait, and three-source dynamic knowledge is output; constructing a decision tree based on the intention label, the three-source dynamic knowledge and the space-time identifier; and executing a corresponding text generation operation, a visual mark generation operation or a voice synthesis operation based on a response action corresponding to the leaf node of the decision tree, and generating customer service information. According to the method and the system, the buyer intention recognition accuracy of the multi-modal session data can be greatly improved, and the response delay time of generating the customer service information is shortened.