55 results about "Traffic optimization" patented technology

The invention belongs to the technical field of traffic optimization, and particularly relates to a traffic optimization method based on a three-layer Stackelberg game, and the method comprises the steps: obtaining mixed traffic data; according to the mixed traffic data, constructing a three-layer Stackelberg game model; an equilibrium solution of the three-layer game model is solved through a deep reinforcement learning algorithm, an SAC algorithm is introduced into an upper layer to generate a global constraint strategy, and a DDPG algorithm is adopted in a lower layer to optimize cooperative driving and safe obstacle avoidance driving behaviors of the intelligent vehicle; according to the global constraint strategy and the intelligent vehicle driving behavior optimization result, the traffic efficiency, safety and energy consumption and carbon emission indexes of the mixed traffic flow are optimized, the traffic state is continuously updated through behavior feedback data of a middle layer and a lower layer, and strategy optimization is carried out in combination with SAC and DDPG models. Therefore, the problems of insufficient dynamic adaptability, low efficiency of main body interaction processing, limitation of a game model and the like in the prior art are solved.

The invention discloses a computer network management system based on a cognitive digital twinning technology. The computer network management system comprises an intelligent sensing layer, a multi-dimensional fusion layer and an enhanced visualization layer, wherein the intelligent sensing layer is used for outputting a state portrait to the multi-dimensional fusion layer and the enhanced visualization layer; the multi-dimensional fusion layer is used for outputting a knowledge graph to the cognitive decision-making layer and the enhanced visualization layer; the cognitive decision-making layer is used for performing second-level traceability on the fault propagation path, verifying the diagnosis hypothesis in parallel and then feeding back verification results of the fault propagation path and the diagnosis hypothesis to the enhanced visualization layer; carrying out intelligent decision making, and outputting a decision making strategy to a collaborative autonomous layer; the collaborative autonomous layer is responsible for automatically executing configuration change and flow optimization operations and feeding back the operations to the intelligent sensing layer; and the enhanced visualization layer is used for forming a dynamic visual network topology, establishing a visual map and simulating an attack path, when an administrator triggers rapid drilling analysis through an interaction instruction, a visualization verification result is fed back to the cognitive decision-making layer, and the data acquisition priority of the intelligent perception layer is adjusted.

The invention discloses a traffic signal global collaborative prediction method based on a quantum entanglement state, and the method comprises the steps: building a quantization model of a dynamic evolution path of a target traffic network in a time window in the future, and constructing a space-time diagram model; mapping the signal phase state of the intersection into a time-space integrated quantum state by adopting a layered quantum coding scheme; on the basis of a preset traffic optimization target, constructing Hamiltonian including spatial coupling, time evolution and a cost item, solving a ground state of the Hamiltonian through a mixed quantum-classical calculation method, and determining an optimal dynamic evolution path; coding the actual state of the current traffic network into an initial state, carrying out sequential measurement through an optimal evolution operator, decoding a cooperative signal control strategy of each time point in the future, and executing the cooperative signal control strategy through a rolling time domain control framework; according to the invention, global optimization is carried out by using quantum parallelism, the optimal cooperation strategy in the whole space-time range can be obtained at one time, and the overall operation efficiency, predictability and robustness of the traffic network are improved.

The invention discloses a regional traffic dynamic optimization control method based on vehicle-road cloud coordination, relates to the technical field of traffic control, and designs a self-adaptive space-time dynamic compensation algorithm and a three-level communication fault-tolerant architecture aiming at space-time asynchronization and communication interruption risks existing in multi-source data of a vehicle end, a road side and a cloud end. According to the method, accurate alignment and stable transmission of multi-dimensional heterogeneous data are realized, an attention mechanism enhanced multi-agent deep reinforcement learning architecture is adopted, online element reinforcement learning is combined, global and local optimization control of cloud-side double-loop cooperation is realized, communication is performed by adopting a block chain-based security consensus protocol, the system security and stability are effectively guaranteed, and the system reliability is improved. According to the method, the operation efficiency, the emergency response capability and the environmental friendliness of urban smart traffic are remarkably improved, and the method is suitable for a smart city global traffic optimization scene.

The invention provides a road signal holographic optimization control system based on an SCATS system, and relates to the technical field of traffic control, the control system comprises a roadside sensing module, the roadside sensing module is arranged at one side of a road intersection, and the roadside sensing module is configured to obtain traffic data of a current road; the model construction module is configured to construct a traffic optimization model according to the traffic data; and the scheme generation and issuing module is configured to import real-time traffic data of a current road into the traffic optimization model so as to generate a road optimization scheme, and the road optimization scheme is used for controlling a corresponding traffic signal controller based on the SCATS system. Through the control system, the problem that an SCATS system at the present stage cannot singly overcome traffic state control at the present stage is solved.

The invention relates to a large-scale network traffic optimization method and system, and the method comprises the steps: employing a hierarchical traffic engineering strategy, carrying out the precise optimization of an object flow through linear programming, carrying out the rapid distribution of a mouse flow through a heuristic algorithm, and improving the calculation efficiency; a lightweight routing update strategy is adopted, L1 norm constraint is introduced into a linear programming optimization model, the range of routing configuration change is limited, and routing table update overhead is reduced; the robust traffic engineering strategy adopts a fault recovery mechanism for neighborhoods, realizes quick response to link faults, ensures the stability of network performance, and realizes millisecond-level recovery only by local re-optimization in the n-hop neighborhoods of the robust traffic engineering strategy; according to the method, the efficiency, the stability and the robustness of a large-scale network are remarkably improved while the optimal flow distribution quality is kept, the routing table updating amount is greatly reduced, and the response speed to link faults is improved; the method can be widely applied to flow optimization and management in a large-scale network, and is particularly suitable for scenes such as a data center network and an operator wide area network.

The invention relates to the technical field of resource management of mobile data communication service, and discloses a network flow optimization service method based on artificial intelligence, which comprises the following steps: collecting a hybrid automatic repeat request sequence of a target wireless link in a preset time window, counting the flipping frequency between an acknowledgement state and a non-acknowledgement state, and extracting polarity flipping characteristics; monitoring a physical resource block adjustment step length of the scheduling authorization instruction in real time; when the step length exceeds a preset disturbance threshold value, determining a blanking weight coefficient inversely proportional to the step length; correcting the polarity flipping feature by using the coefficient to obtain a purified feature vector; inputting the vector into linear discrimination logic to determine a traffic burst bias item; according to the method, closed-loop suppression of scheduling decision action and feedback disturbance is realized, and resource allocation oscillation generated by control feedback self-excitation is eliminated.

The invention discloses a vehicle network interaction flow optimization method based on information and physical fusion, and belongs to the technical field of intelligent traffic and energy internet crossing. Comprising the steps that a vehicle network is divided into a plurality of grid areas, and each grid area comprises physical layer operation data and information layer operation data; constructing a vehicle network diagram structure, and calculating the schedulable potential of the EV in the stay duration of the charging station; according to the schedulable potential and the information layer operation data of the EV in the stay time of the charging station, the method aims at minimizing the peak-valley difference, maximizing the renewable energy consumption, the vehicle network interaction income and the operation income. Constructing an objective function of the multi-objective flow optimization model, an EV charging and discharging power constraint, an EV battery charge state constraint, a charging pile capacity constraint and a power grid safety constraint; and solving the multi-target traffic optimization model to obtain a vehicle network interaction traffic optimization result. According to the method, the prediction error of the EV arrival time is reduced, and the V2G flow regulation and control are refined.

The present application relates to heavy load AGV cooperative control technical field, especially in a kind of heavy load AGV group's cooperative control method, the motion state parameter of each heavy load AGV is collected to construct motion state vector, local traffic congestion parameter is predicted, and then local congestion index is calculated and dynamic coordination trigger threshold is determined;Distributed speed coordination is triggered based on local congestion index and dynamic coordination trigger threshold, to determine the reference coordination speed of each heavy load AGV;The reference coordination speed is passed to relevant heavy load AGV, to generate transmission coordination speed;Based on ground state parameter, the motion state vector of each heavy load AGV and transmission coordination speed, the traffic optimization sorting of each heavy load AGV is determined, to modify transmission coordination speed and determine modified coordination speed;Based on the modified coordination speed of each heavy load AGV, the timing and number of subsequent local traffic congestion are predicted, to determine the effective duration of coordination control.The present application improves the running efficiency and safety of heavy load AGV group in complex traffic environment.

The invention provides a network traffic intelligent scheduling method and related equipment thereof. The method comprises the following steps: acquiring traffic data of a target terminal; carrying out classification identification processing on the traffic data, and determining a classification result; constructing a traffic optimization model based on the classification result; performing optimization processing on the traffic data through the traffic optimization model, and determining an optimization result; and adjusting and scheduling the network configuration corresponding to the flow data based on the optimization result. Through the steps of the method, identification and modeling of traffic of different service types can be realized, and an optimal scheduling scheme is generated in combination with a multi-objective optimization algorithm, so that dynamic resource allocation and adaptive scheduling are realized in a complex network environment, the network delay and packet loss rate are reduced, and the bandwidth utilization rate and the service quality of services are improved.

The embodiment of the invention provides a regional traffic optimization method and device, equipment and a storage medium, a traffic network is divided into a plurality of regions, the type of each region is identified, and the intersection and conflict type is that at least one trunk line group exists in the region, and the overlapping intersection of each trunk line in the trunk line group and other trunk lines is greater than or equal to 2; deleting at least one trunk line from the trunk line group in each optimization period for the cross and conflict type region, and reconstructing the region into a cross but non-conflict type; and for the region of the crossed but non-conflict type, when the region does not conflict with the adjacent region, the absolute phase of any intersection of the region is taken as a reference, and the absolute phases of other intersections are optimized according to the relative phase difference of the adjacent intersections in the region. According to the technical scheme provided by the embodiment of the invention, the cross and conflict area is reconstructed into the cross but non-conflict area, so that the coordination optimization of different time and different space competition of the traffic network is realized.

The invention discloses a tourist personalized travel path optimization method based on multi-mode transport capacity collaborative configuration, and belongs to the technical field of smart tourism and traffic optimization, and the method comprises the following steps: S1, obtaining multi-source basic data and personalized parameters of tourists; s2, constructing a multimode traffic space-time network model comprising spatial positions corresponding to different time points; s3, constructing a multi-target mixed integer programming model for simultaneously optimizing tourist experience and traffic enterprise operation efficiency; s4, obtaining a Pareto optimal solution meeting all constraint conditions by adopting an adaptive genetic algorithm so as to obtain an optimal path solution adaptive to the personalized requirements of tourists; and S5, obtaining a personalized travel itinerary through visual processing operation of converting the path solution into full-process details in a timeline form. By adopting the method, personalized travel route optimization of multi-mode transport capacity collaboration under the support of multi-source data is realized, and meanwhile, the use experience of tourists is improved through the visual travel itinerary.

The invention relates to the technical field of traffic planning, and discloses an urban traffic optimization and planning method and system based on artificial intelligence data processing, and the method comprises the steps: collecting a time sequence perception data flow of a target intersection, and carrying out the extraction of an occupancy state tensor based on a time window, and obtaining the occupancy state tensor of the target intersection in the time window; using the occupancy mode analysis model to output occupancy mode probability distribution of the target intersection in the time window; and outputting the lane traffic capacity of the target intersection by using a lane traffic capacity calculation function which is corrected by introducing dynamic factors of a temporary parking type occupation reduction factor and a queuing type occupation delay factor, and dynamically adjusting a short-time traffic control strategy of the target intersection. According to the invention, through time sequence perception and occupation mode analysis, the temporary parking and queuing occupation modes are distinguished, and the traffic capacity of the lane is dynamically quantified and corrected, so that the short-time traffic control strategy is adjusted in real time, and refined and adaptive traffic control and optimization under complex disturbance conditions in a city are realized.

The invention discloses a port ship traffic organization optimization and regulation and control method, which belongs to the technical field of waterway traffic optimization and intelligent management and control, and comprises the following steps: collecting port data, constructing an information physical system framework, and dynamically managing a port by using the port data through the information physical system framework. The method comprises the following steps of: extracting and quantizing, performing uniform feature fusion, obtaining a judgment result by utilizing an event triggering mechanism, updating a system state, predicting the updated system state by utilizing a model prediction control algorithm to obtain a system step length, predicting the system step length in real time by utilizing a self-adaptive vision field mechanism to obtain a real-time step length, and calculating the real-time step length according to the real-time step length. And solving by utilizing a multiplier alternating direction method according to the judgment result and the real-time step length to obtain a port ship traffic scheduling control strategy. According to the invention, high-efficiency ship traffic scheduling and resource optimization can be realized in a complex and changeable port environment, the port transportation efficiency is obviously improved, traffic congestion is reduced, and the self-adaptive capability of the system is enhanced.

The application discloses a port ship traffic organization optimization and regulation method, and belongs to the technical field of waterway traffic optimization and intelligent management and control. The method comprises the following steps: collecting port data, constructing an information physical system framework, using the information physical system framework to realize dynamic management of the port, uniformly fusing features after extraction and quantization, using an event triggering mechanism to obtain a judgment result, updating a system state, using a model prediction control algorithm to predict the system step length, using an adaptive horizon mechanism to obtain a real-time step length, using a multiplier alternating direction method to solve according to the judgment result and the real-time step length, and obtaining a port ship traffic scheduling control strategy. The application can realize efficient ship traffic scheduling and resource optimization in a complex and changeable port environment, significantly improve port transportation efficiency, reduce traffic congestion, and enhance the adaptive ability of the system.

The application discloses a kind of urban traffic signal optimization management system and method based on multi-mode satellite navigation, it is related to traffic signal optimization technical field, system includes initial determination module, for determining target area;Distance group processing module, determine the distance between target area and the nearest traffic signal lamp in fixed direction, form distance group;Area influence radius analysis module, based on the number of distance group and target area lane, determine the area influence radius of target area;Multi-mode satellite monitoring module, determine the multi-mode satellite monitoring area of target area;Traffic prediction module, determine the traffic prediction data of target area;Traffic signal optimization module, determine traffic signal optimization instruction.The application integrates a variety of satellite navigation systems, can monitor the real-time state of each traffic area in real time, from the level of social practice, solve the early warning defects of existing traffic optimization algorithm.

The invention discloses a communication network flow optimization method and system based on artificial intelligence, and relates to the field of flow optimization, and the method comprises the steps: collecting first-level network parameters and unmanned plane state information in real time; when the average value of the first-level signal intensity in the first-level network parameters is smaller than a preset intensity average value threshold value, a second-level communication network is constructed, and second-level network parameters are monitored in real time; stability evaluation of the communication network is completed, and first-level communication stability and second-level communication stability are obtained; when the second-level communication stability is greater than the first-level communication stability, determining disaster area topographic features of a second-level communication network coverage area; and self-adaptive link switching of all secondary communication links is completed, and flow optimization of the secondary communication network is completed. The problem that stable operation of disaster area communication links is difficult to realize in the prior art can be effectively solved.

The invention discloses a 5G electric power virtual private network service flow simulation evaluation method, relates to the technical field of 5G electric power, and solves the technical problems that collected data lacks an effective anomaly filtering and compensation mechanism, abnormal values and missing values in the data can seriously influence the accuracy of simulation evaluation, and simulation results generate deviation. According to the invention, through three-dimensional hierarchical modeling of the power business, the power business is divided into three levels according to reliability requirements, a differentiated flow simulation reference is established, and preliminary matching of business requirements and network resources in simulation evaluation is effectively realized in combination with three-layer framework data acquisition and preprocessing and dynamic load perception. The accuracy of data is ensured by an exception filtering and data compensation mechanism, the dynamic weight calculation can adjust the bandwidth and the energy consumption optimization weight in real time according to the network load, the simulation deviation caused by data exception or sudden load change is reduced, and reliable basic data support is provided for subsequent flow optimization.

The invention relates to the technical field of traffic control, in particular to an intersection traffic control method and system based on vulnerable group passage optimization. When the traffic light is in a green light state, vehicles pass, pedestrians wait at the two sides of the pedestrian path, the identification module obtains human body characteristics and modes of the pedestrians at the two sides of the path at the moment to identify whether vulnerable pedestrians exist or not, and if the vulnerable pedestrians exist, the identification module marks the vulnerable pedestrians as first vulnerable pedestrians and determines vulnerability labels and levels of the vulnerable pedestrians; and the control module determines a delay coefficient according to the delay coefficient, and prolongs the duration of the traffic light in the red light state next time according to the delay coefficient, so that the first vulnerable pedestrian has more passing duration. Besides, in order to avoid corresponding traffic flow congestion caused by red light delay, cloud and dynamic adjustment can be carried out on flow dredging of the original traffic flow based on flow dredging compensation delay increment of the downstream intersection, a more humanized traffic environment can be constructed in an aging structure, and the method has remarkable social benefits.

The invention relates to a bottleneck road section passing optimization method based on upstream variable speed limit, and is suitable for the field of traffic transportation. The method comprises the steps that the maximum speed limit value is determined based on the traffic safety condition of the current time period, and the traffic safety condition comprises the weather condition and the number of various types of vehicles entering a variable speed limit control road section on the upstream of a bottleneck road section in the current time period; a population is initialized, each individual in the population comprises a speed limit value code of the variable speed limit control road section, and the speed limit value in the individuals is constrained by the maximum speed limit value; simulation is carried out based on the speed limit value of each individual and the number of vehicles entering the variable speed limit control road section in the current time period, the fitness value of each individual is calculated by combining a simulation result with a variable speed limit control target function, and the target function is constructed based on the passing efficiency of the bottleneck road section; and performing iterative optimization based on the fitness value of each individual, and determining the optimal speed limit value of the variable speed limit control road section in the next time period.

This invention discloses a high-efficiency network traffic optimization and control method based on a switch, belonging to the field of network traffic optimization technology. By introducing a micro-time detection window on the output port side and identifying synchronous burst time intervals, the traditional judgment logic based on long-term congestion thresholds is refined to a micro-time scale. This allows the switch to identify the independent phenomenon of short-term concentrated arrivals, rather than relying solely on whether the queue has grown for a long time as a trigger condition. This identification mechanism clearly locates the instantaneous traffic aggregation behavior that was previously difficult to distinguish individually, providing a targeted processing basis for subsequent scheduling actions. By constructing a set of micro-time slices and combining them with the offset budget of the service flow, a micro-time slice offset table is generated in the control plane. This allows multiple synchronously arriving service flows to be actively split into different time slices for dequeueing in terms of transmission timing, changing the traditional method of directly releasing queues in order.

The application discloses a 5G+TSN fusion network asynchronous flow optimization scheduling method with high schedulability, comprising the following steps: S1, in the fusion network of 5G and TSN, first, the overall information of the network is collected by the central controller through the network control protocol, the information includes the channel condition and frequency resource in the 5G network, the network topology and link rate of the TSN network, and the communication demand of the flow; S2, the asynchronous flow optimization scheduling model of the 5G+TSN hybrid network is run through the central controller, the routing decision and transmission scheduling of the flow collected in the above step in the hybrid network are carried out, the scheduling result is obtained, and the consumption of the network resource is minimized while meeting the transmission demand of the hard real-time flow. The application significantly improves the schedulability of the TSN gateway, expands the application range of the asynchronous deterministic access mechanism, and realizes end-to-end flow scheduling.

This invention discloses a global collaborative prediction method for traffic signals based on quantum entanglement states. The method includes: establishing a quantumized model of the dynamic evolution path of the target traffic network within a future time window; constructing a spatiotemporal graph model; mapping the signal phase states of intersections to spatiotemporally integrated quantum states using a hierarchical quantum encoding scheme; constructing a Hamiltonian containing spatial coupling, temporal evolution, and cost terms based on a preset traffic optimization objective; solving for its ground state using a hybrid quantum-classical computation method to determine the optimal dynamic evolution path; encoding the current actual state of the traffic network as an initial state; decoding the collaborative signal control strategies for future time points using the optimal evolution operator and sequential measurements; and executing these strategies through a rolling time-domain control framework. This invention utilizes quantum parallelism for global optimization, enabling the acquisition of the optimal collaborative strategy across the entire spatiotemporal range in a single step, thereby improving the overall operational efficiency, predictability, and robustness of the traffic network.

The present application relates to the technical field of traffic informationization. Specifically relates to a kind of dynamic traffic optimization management method, device, equipment and medium, its method includes using sensing credibility information to the traffic deviation degree information is weighted fusion processing, obtains fusion deviation degree information, obtains candidate signal control scheme set according to fusion deviation degree information and preset phase configuration record information, for each candidate signal control scheme in candidate signal control scheme set, according to traffic state alignment sequence and the timing parameter information corresponding to candidate signal control scheme Queuing Growth Estimation processing is executed, obtain scheme evaluation information, according to scheme evaluation information and preset constraint rule information, determine target signal control scheme from candidate signal control scheme set, according to target signal control scheme to target intersection executes signal timing control, obtains target timing parameter information.The present application has the effect of improving traffic operation state analysis accuracy and control strategy adaptability in traffic management process.

The invention relates to a wireless mesh network for high capacity backhaul of data traffic, such as telecommunications networks. Reliability is increased by resilience, i.e., data traffic may be transmitted via multiple paths between a client device (e.g., a telecommunication cell) and a fiber point. Depending on the network state, the node may choose to direct traffic via different paths, thereby reducing the opportunity of packet loss. Once resilient is established, network traffic may be optimized in response to a variable nature of traffic density. By doing so, network reliability can be further improved, and throughput can be optimized while minimizing costs.

This invention discloses a smart city traffic optimization method and system based on 5G networks, belonging to the field of smart city traffic management technology. It addresses the issues of information consistency and conflict resolution in urban traffic flow control and shared parking space guidance. The method includes uploading traffic flow and signal phase status, combining parking space occupancy status and vehicle owner location information for path guidance and parking space recommendation, and identifying and managing parking space status conflicts across multiple heterogeneous platforms. It constructs parking space status behavior sequences, heterogeneous contention indices, state disturbance propagation graphs, and conflict score clusters, performing state masking and path correction for high-conflict parking spaces. Simultaneously, it collects vehicle owner and vehicle behavior data to complete parking space usage records and settlements, identify and process illegal parking, and constructs a shared parking space reservation permission management mechanism. This method is applicable to optimization and guidance under dynamic urban traffic control and multi-platform access to parking space resources.

The invention discloses a traffic management method, device and equipment and a storage medium, and relates to the technical field of computer network traffic management and optimization, and the method comprises the steps: obtaining a switch configuration address, a switch configuration path and a to-be-sent data packet; locking a mapping relation between each switch port and corresponding target equipment based on the switch configuration address, and determining a static address table; setting a routing path and a priority corresponding to each data flow based on the switch configuration path, and determining a static routing table; switching a network label corresponding to the to-be-sent data packet based on the static address table and the static routing table, and determining label switching information; and forwarding the to-be-sent data packet along the forwarding path identified by the label switching information to complete routing flow management. According to the invention, the static address table and the static routing table are utilized to accurately control the data flow and select the optimization path, network label switching is carried out to realize rapid and efficient forwarding, and the flow optimization efficiency is improved.

The application provides a traffic optimization analysis method, device, equipment and medium for a collection system, the method comprising: collecting traffic through the collection system and uploading the traffic to a traffic processing server in the form of a traffic package; splitting and deduplicating the traffic package according to a set strategy at a fixed time to obtain processed traffic data; setting a trigger to divide the traffic data into risk data and non-risk data and import the data into a database; extracting the risk data and the non-risk data according to a set strategy and performing desensitization to obtain desensitized data; forwarding the risk data to a first setting personnel and forwarding the desensitized risk data to a second setting personnel; and solving the problem of low and unstable analysis and processing efficiency of massive data in a traditional method.

The invention belongs to the field of network management, and discloses a network traffic optimization method based on big data analysis, which comprises the following steps: step 1, acquiring original data, constructing a three-dimensional tensor based on the original data, and acquiring a core tensor based on the three-dimensional tensor; step 2, obtaining a candidate path set, and respectively calculating a path stability score of each path in the candidate path set; step 3, constructing a path feature vector based on the path stability score; step 4, acquiring an action set based on the path feature vector; and step 5, performing path scheduling based on the action set. According to the method, whether scheduling is conducted or not is improved to be a primary optimization object, the scheduling behavior and the network state structure are tightly coupled, the flow optimization capacity of millisecond-level perception and second-level convergence is achieved, and the scheduling precision, stability and resource utilization rate in the complex network environment are remarkably improved.

The invention provides an encrypted traffic classification method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: based on acquired first encrypted traffic data, obtaining a first statistical feature corresponding to the first encrypted traffic data; performing gradient classification on the first statistical feature by using a pre-trained gradient boosting tree model to obtain a first classification result and second encrypted traffic data; the second encrypted traffic data is the first encrypted traffic data of which the classification result is not determined; extracting gradient features from the second encrypted traffic data, and dividing the second encrypted traffic data into a plurality of classification clusters; and using a traffic secondary classification discrimination model based on semi-supervised fingerprints and association clustering, and combining the classification cluster to re-classify the second encrypted traffic data to obtain a second classification result. According to the embodiment of the invention, technical support is provided for network security and traffic optimization through hierarchical gradient classification in combination with feature extraction, statistical analysis, gradient boosting tree training and complex traffic deep processing.