143 results about "Traffic characteristic" patented technology

Provided are a method and an apparatus for constructing a road congestion prediction model, a device, a medium, and a product. A road traffic network is defined as a directed weighted graph. Historical dynamic traffic features of each road segment in the road traffic network are obtained as sample data, including recent dynamic traffic features and periodic dynamic traffic features. The sample data is input into a mixture of adaptive graph learners (MAGL) model for learning, and a probability prediction vector is output. The sample data is input into a trend expert model, and a trend distribution vector of a predicted probability of future traffic conditions is output. The periodic dynamic traffic features are fused to determine a periodicity prediction vector. An aggregated logit vector is obtained. An objective function is determined based on the aggregated logit vector. Congestion prediction training is performed to obtain a road congestion prediction model.

A transportation system for generating transportation recommendations may (1) receive a transportation request associated with a user; (2) identify, using the transportation request, a first location and a second location associated with the transportation request; (3) determine, using the first location and the second location, routes between the first location and the second location; (4) receive travel data associated with each of the plurality of the routes, the travel data including information indicating a current or predicted future travel condition along each of the routes; (5) generate, using historic transportation characteristics associated with the user and the travel data, a safety or other score for each route, the score indicating an estimated level of safety of traveling along the route; and / or (6) generate a user interface providing indicators associated with the scores of the routes.

The invention discloses a traffic shaping scheduling method and device, electronic equipment and a storage medium. The method comprises the following steps: determining a target service type of a first data stream at a first time, and obtaining a second data stream at a second time corresponding to the target service type; the second time is before the first time and comprises flow data of a plurality of time periods; determining flow characteristic data of a third data stream at a third time according to the second data stream; the flow characteristic data is used for describing the flow rate and the burst degree of the third data flow; and determining a control instruction of a token bucket shaper of the flow queue based on the flow characteristic data, and adding a token to the token bucket shaper based on the control instruction to meet the flow transmission of the third data flow. According to the invention, through real-time adjustment of the token bucket parameters, accurate control of network congestion is realized.

The invention discloses a traffic jam prediction method and device based on comparative learning and knowledge distillation. The method belongs to the technical field of traffic jam prediction. Comprising the following steps: S1, preprocessing an original traffic data set to obtain traffic characteristic time series data; s2, constructing a traffic jam prediction model; s3, constructing a multi-task loss function; s4, training a traffic jam prediction model by using the traffic characteristic time series data, and optimizing model parameters by minimizing a multi-task loss function; and S5, inputting a to-be-predicted traffic data set into the trained traffic congestion prediction model, and outputting a predicted congestion level of each node. According to the invention, the comparison learning module is adopted to maximize the hidden space difference between the samples so as to enhance the robustness of the features, the problem that the generation mode of a traditional variational auto-encoder is single is relieved, and the prediction precision in a complex scene is improved.

The invention discloses a traffic video multi-dimensional semantic understanding method and system based on a multi-modal large model, and relates to the field related to intelligent traffic, and the method comprises the steps: obtaining multi-source collection data in real time; preprocessing the multi-source collected data to obtain a traffic data sequence; performing multi-modal feature extraction and cross-modal feature alignment on the traffic data sequence, and establishing a traffic feature alignment vector; inputting the traffic feature alignment vector into a multi-modal large model to obtain a multi-dimensional preliminary semantic understanding result; performing multi-dimensional semantic analysis and optimization on the preliminary semantic understanding result to obtain a structured semantic report; and according to a feedback data set of the structured semantic report, carrying out periodic increment updating optimization on the multi-modal large model and the traffic rule knowledge base. The technical problems of single semantic understanding dimension and insufficient recognition precision in a complex scene in the existing traffic video semantic understanding are solved, and the technical effects of enriching the semantic coverage dimension and improving the recognition precision of the complex scene are achieved.

A system for optimizing network traffic distribution including a control plane connected to a data plane. The control plane includes one or more processors and memory storing instructions that, when executed by the one or more processors, cause the control plane to: implement a reinforcement learning module configured to: model a network state space comprising interface characteristics and traffic characteristics; define an action space for allocating specific flows to specific interfaces; establish a reward function that encourages efficient utilization of bandwidth while penalizing packet loss, latency, and jitter; learn optimal flow-to-interface allocations based on the modeled state space, action space, and reward function; and implement a flow allocation module configured to generate flow allocation policies based on the learned optimal flow-to-interface allocations. The data plane including one or more network devices configured to route network traffic flows based on the implemented flow allocation policies.

The invention discloses a digital twinborn traffic flow intelligent monitoring method and system, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining basic configuration information, dividing a road network into a plurality of local regions, building a local digital twinborn body at an edge node, and building a global digital twinborn body at a cloud end; the edge node receives the multi-source traffic perception data, carries out time-space alignment fusion, extracts traffic features, updates local digital twins, carries out short-time traffic state deduction and generates a local control strategy; and the edge nodes upload model updating information to the cloud, the cloud aggregates and updates the global digital twins and issues model parameters, meanwhile, medium and long-term traffic state deduction is carried out, and a global optimization control strategy is generated, decomposed and issued. According to the invention, real-time monitoring and multi-time scale prediction of the traffic flow are realized through a cloud-side collaborative double-layer digital twinborn architecture and a bidirectional feedback mechanism, and the monitoring precision and the response speed are improved.

This invention discloses a traffic prediction method and system based on a spatiotemporal hierarchical network. The method includes: acquiring traffic data and preprocessing the data to construct a hierarchical regional augmentation network and a traffic feature matrix; using the hierarchical regional augmentation network and the traffic feature matrix as input to a prediction model, learning spatial and temporal correlations, and outputting prediction results; the prediction model includes a region-aware spatial correlation model and a region-aware temporal correlation model. The system includes a preprocessing module and a prediction module. By using this invention, the spatiotemporal correlations in traffic data are effectively captured, improving the accuracy of traffic flow prediction. This invention, as a traffic prediction method and system based on a spatiotemporal hierarchical network, can be widely applied in the field of traffic prediction.

This application provides a method and system for rapid response to traffic incidents based on multi-source data fusion, relating to the field of traffic incident response technology. The method includes: extracting traffic feature vectors from multi-source traffic data for multi-source fusion calculation; determining whether abnormal traffic incidents exist in the fused situational data; when abnormal traffic incidents exist, determining the type and locating the information; generating an initial response plan based on the determined type and location information; pushing the initial response plan to the handling department's terminal and receiving execution status information from the handling department; recording the timestamps and operation logs of the entire process, and evaluating the effectiveness of this response process. This application addresses the technical problem of lacking multi-source traffic data fusion analysis in existing technologies, enabling rapid response to traffic incidents based on multi-source traffic data fusion, and achieving the technical effect of improving the accuracy of traffic incident identification.

The invention discloses a traffic flow prediction method based on a Prompt-Tuning strategy, and relates to the technical field of traffic flow prediction. According to the traffic flow prediction method based on the Prompt-Tuning strategy, for traffic long-tail data, through scenarized Prompt and a multi-expert model, in combination with long-tail sample oversampling, abnormal scene prediction precision is improved, complex space-time dependence is accurately captured by depending on Transform backbone and space-time attention gating, flow characteristic differences of different road sections and time periods are adapted, Prompt-Tuning efficient fine tuning is adopted, and the traffic flow prediction precision is improved. According to the method, generalization ability and reasoning efficiency can be taken into consideration, the comprehensive prediction precision is improved, meanwhile, prediction confidence and key influence factors are output, accurate decision support is provided for traffic scheduling, and intelligent traffic management efficiency is improved.

The invention discloses a method, device and equipment for switching VLAN isolation levels based on terminal behaviors and a storage medium, and relates to the technical field of communication, and the method comprises the steps: obtaining traffic data when a new terminal accesses a network; redirecting the flow data to a network bridge of a common isolation level according to a preset VLAN distribution strategy; collecting traffic characteristics of the terminal in real time based on a hook program deployed in a kernel network protocol stack; judging whether an abnormal behavior is met or not according to the traffic feature and a traffic feature threshold value; and if the abnormal behavior is satisfied, modifying a flow control rule, and switching the flow data of the terminal from the network bridge of the common isolation level to a network bridge of a high isolation level. According to the method, the accuracy of switching the isolation level of the VLAN can be improved.

The present application relates to a kind of layered reinforcement learning driven car-road cooperation automatic driving longitudinal following control method and system, belong to the field of automatic driving vehicle control.For the problem that the generalization ability of existing following control method is insufficient, strategy update is easy to lose stability, easy to fall into local optimum, adopt layered architecture, upper layer adapts intersection, high-speed straight road and ramp multiple typical traffic scenes, based on real-time car-road cooperation communication information dynamic planning follow target, after normalizing processing to multi-source heterogeneous traffic characteristics, generate environment state vector;Lower layer uses proximal policy optimization algorithm, outputs continuous accelerator or brake command through actor-critic double network, and updates network parameters by combining strategy entropy with pruning loss with Jacobian correction.The method improves the cross-scene generalization ability and perception robustness, effectively guarantees the stability and monotonicity of strategy update, avoids instruction step mutation, breaks the exploration deadlock, and balances driving safety, traffic efficiency and driving comfort.

The application provides a traffic signal intelligent control method and system, an electronic device and a storage medium, and belongs to the technical field of traffic signal control. The method collects intersection traffic state data through different types of detectors to obtain monitoring data of each control parameter, extracts traffic characteristic values after standardizing the monitoring data, performs correlation analysis on the traffic states to obtain the correlation degree between the parameters, evaluates the data quality by using a pre-trained signal control evaluation model, determines the control reliability in combination with the correlation degree, and generates a signal control scheme by optimizing the data according to the correlation degree when the reliability is lower than a threshold. The control parameters include vehicle flow, queue length and the like. The system comprises acquisition, processing and execution modules. The electronic device and the storage medium implement the execution of the method. The scheme improves the data reliability, optimizes the control scheme, improves the intersection passing efficiency and is suitable for the intelligent traffic signal control scene through multi-dimensional data acquisition, correlation analysis and quality evaluation.

The application discloses a kind of orthogonal test methods, equipment and storage medium based on user account, comprising: obtaining real-time account state information and real-time service traffic data;From real-time account state information, target test account is allocated, and the mapping relationship of target test account and test case is established;Real-time service traffic data is analyzed for scene characteristics, and the parameter structure of orthogonal test table is generated parameterized test case combination based on characteristic dimension and value distribution dynamic adjustment;Parameterized test case combination and target test account are injected into test environment to execute traffic playback, and according to the difference feedback of playback response data and expected result, adjust the test parameter value weight and case execution priority in orthogonal test table.The application forms closed loop feedback by account state dynamic identification and orthogonal table adjustment driven by traffic characteristics, realizes the matching of test account and case and test parameter adaptive optimization, to improve test scene coverage and execution efficiency.

The application provides a data processing method, device and equipment for driving time prediction and a medium. Traffic feature maps corresponding to driving areas and road network data maps are obtained. The traffic feature maps and the road network data maps are respectively divided and clustered to obtain multi-class traffic feature target maps and road network data target maps. Source domains and target domains are determined from the traffic feature target maps and the road network data target maps. A graph convolution network is trained according to data of the source domains to obtain an initial training model. The initial training model is trained according to data of the target domains to obtain a target training model. The application realizes accurate prediction of the driving time of a mine car by training a convolution network on historical driving trajectories and road network data, thereby solving the technical problem that the prior art cannot predict the driving time of a mine car in an open-pit mine area that has not obtained driving features and road network data.

The application relates to an APP traffic anomaly detection method and system based on a multi-scale time sequence and belongs to the field of mobile internet traffic monitoring.The system comprises a traffic data acquisition module, a traffic data preprocessing module, a multi-scale time sequence decomposition module and an abnormal traffic detection module.The network traffic of a target APP is packet captured through the traffic data acquisition module, classified according to API calling, resource loading and advertisement request types, and the APP running state is recorded as context information; the traffic data acquisition module groups the collected traffic data according to scenes and extracts statistical features based on a set time window; the multi-scale time sequence decomposition module performs wavelet transformation and empirical mode decomposition on the feature vector sequence, extracts high-frequency components and low-frequency components of the traffic respectively, and captures short-term bursts and long-term trend changes.The application designs an abnormal detection method according to the APP traffic characteristics, and can more accurately detect APP traffic anomalies.

This application provides a traffic prediction method and related apparatus, relating to the field of software technology. The method includes: acquiring the temporal traffic characteristics and interface spatial characteristics of a target interface; retrieving a trained traffic prediction model, wherein the traffic prediction model is obtained by iteratively training a basic model using the temporal traffic characteristics and interface spatial characteristics of a calibrated interface as training samples, with the goal of the predicted traffic of the training samples approximating its actual traffic; and inputting the temporal traffic characteristics and interface spatial characteristics of the target interface into the traffic prediction model to obtain the predicted traffic of the target interface. This application achieves high-precision prediction and reliability assessment of interface traffic in complex business environments, providing data support for resource scheduling and risk prevention.

DEPCT68 What is provided in this request are the methods and tools for structuring workloads for CDN nodes. And electronic devices and intermediaries, including the assembly method: obtaining traffic characteristics. Regarding the target CP device, which serves as a server for the CDN node; Implementing IO usage of the target CP device that does not exceed the upper limit of IO throughput. As a limitation and based on the traffic characteristics of the target CP device, the decision... Determine the maximum traffic volume of services that can be achieved by the target CP device: The decision to schedule traffic for reference services of the target CP equipment is based on... From the maximum service traffic that can be achieved by the CP target and status. The hardware workload of the target CP device; and the allocation of the CDN node workload to the device. CP target based on traffic of reference service for CP target equipment;

The present disclosure relates to a 5G or 6G communication system for supporting higher data transmission rates. A method performed by a PCF entity in a wireless communication system comprises the steps of: receiving, from an AF entity, first information for indicating a change in a traffic characteristic; identifying a change in a service flow on the basis of the first information; and transmitting the first information to a UPF entity through a SMF entity.

The invention discloses a traffic flow prediction method and device based on multi-modal data collaboration and bidirectional space-time diffusion convolution, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining multi-modal traffic data, carrying out the time alignment and space alignment, and determining the aligned traffic data of each modal; performing feature extraction on each aligned traffic data to generate corresponding traffic features; performing gating dynamic fusion by adopting each traffic feature, and constructing a traffic fusion feature; carrying out bidirectional space-time diffusion convolution feature extraction based on the traffic fusion features, and determining space-time cooperation features; performing prediction according to the space-time cooperation features, and outputting a traffic flow prediction result; based on the above scheme, space-time alignment and gating dynamic fusion are adopted to realize multi-modal data collaboration, heterogeneous data fusion expression ability is enhanced, bidirectional propagation feature capture of time and space dimensions is performed through bidirectional space-time diffusion convolution feature extraction, comprehensive capture of complex space-time dependency relationships is assisted, and data fusion efficiency is improved. And the traffic flow prediction precision is improved on the whole.

This application provides a flow control method, an artificial intelligence distributed training method, an electronic device, a storage medium, and a program product. The method includes: acquiring traffic characteristic information of a data stream to be transmitted sent by an application layer program; generating corresponding flow table rules based on the traffic characteristic information; pre-configuring the flow table rules to a network device and controlling the flow table rules to be in an inactive dormant state; upon receiving a trigger signal sent by the application layer program, sending a control command to the network device to switch the flow table rules to an active state, so that the data stream to be transmitted is transmitted via a target path based on the active flow table rules; the trigger signal is used to instruct the data stream to be transmitted to start transmission. This avoids the problem of repeated PFC oscillations caused by switch buffer congestion due to sudden large traffic flows in traditional RoCE networks, enabling large data streams to be transmitted at a stable rate close to line speed.

A traffic congestion prediction method and device based on contrast learning and knowledge distillation belong to the technical field of traffic congestion prediction. The method comprises the following steps: step S1, preprocessing the original traffic data set to obtain traffic feature time series data; step S2, constructing a traffic congestion prediction model; step S3, constructing a multi-task loss function; step S4, training the traffic congestion prediction model using the traffic feature time series data, and optimizing the model parameters by minimizing the multi-task loss function; and step S5, inputting a to-be-predicted traffic data set into the trained traffic congestion prediction model to output the predicted congestion level of each node. The contrast learning module is used to maximize the hidden space difference between samples to enhance the robustness of the features, alleviate the problem of single generation mode of the traditional variational autoencoder, and improve the prediction accuracy in complex scenes.

The application discloses an inner-outer network security service passing method and system based on edge computing and belongs to the technical field of network security, which comprises the following steps: obtaining multi-dimensional attribute information of an edge node, generating a security policy basic data set, simultaneously generating a traffic characteristic fingerprint and an adaptive environment adaptation fingerprint and calculating a fusion matching degree, combining a business scene adaptive threshold clustering to generate an edge security entity node, and further constructing a security policy meta-model with a security policy blueprint as the core; analyzing the security policy meta-model through a multi-modal semantic analysis engine, generating a dynamic security enhancement model and mapping to a hierarchical security control model; and based on the hierarchical model, outputting an edge node inner-outer network security passing configuration and a corresponding security passing account through a scene configuration generation algorithm. The application realizes adaptive generation, dynamic optimization and precise execution of a security policy, and effectively improves the security, automation level and operation and maintenance efficiency of inner-outer network passing in an edge computing environment.

This invention discloses a Yangtze River waterway classification method based on ship feature clustering, comprising: dividing the Yangtze River waterway into multiple segments using an angle bisector method; assigning geographical semantic knowledge such as ports, prefecture-level cities, and provincial-level administrative regions to each segment using overlay analysis; calculating the navigation time of each type of ship within a square grid by dividing the grid, and calculating static traffic characteristics using a summation-based waterway mapping method; calculating relatively discrete traffic characteristics such as speed, acceleration, and turning rate using the mean method, and mapping them to waterway segments using the maximum value method; calculating flow characteristics using a summation method; selecting traffic characteristics with appropriate similarity based on hierarchical clustering analysis; and classifying the Yangtze River waterway using a combination of K-means clustering and sliding window methods. This invention can automatically classify waterway segments in the Yangtze River waterway based on ship traffic characteristics.

The application provides a travel time prediction method fusing uncertainty modeling, and belongs to the field of intelligent traffic and navigation. The method first acquires static road network features, dynamic traffic features and road section external features, constructs a road network directed graph and defines a route R, finds feature values corresponding to road sections in R, constructs initial node embedding and initial edge embedding, and constructs a prediction model; after preprocessing of initial values, each layer of graph convolution network updates node embedding and edge embedding through modeling of interaction between nodes and edges, and finally obtains iterative node embedding and edge embedding; position encoding is performed on the processed route to construct a matrix PE; route embedding is obtained by adding the final node embedding and PE, multi-head attention is performed on the route embedding, and then average pooling is performed to obtain arrival time representation; uncertainty quantization and correction are performed on the arrival time representation to obtain predicted arrival time and upper and lower bounds. The application quantizes the uncertainty of the time prediction result.

The invention relates to the technical field of wireless communication, and discloses a wireless communication transmission service demand generation method and system, a terminal and a storage medium, and the method comprises the steps: obtaining an external scene factor of a user terminal, carrying out the modeling of an environment according to the external scene factor, and obtaining an environment embedded code after full-connection network coding; acquiring a user position of a user terminal, and modeling a target layer behavior state of a user according to the user position to obtain behavior layer behavior probability distribution; and acquiring a historical traffic feature, integrating the environment embedded code, the historical traffic feature, the target layer behavior state and the behavior layer behavior probability distribution into a network input feature, inputting the network input feature and the UE type code into a time sequence generation model, outputting a UE traffic feature, and predicting the UE behavior state. According to the method, continuous UE flow characteristics are dynamically output, user behavior prediction can be updated in real time, and the requirements of a digital twin communication simulation system for reality and dynamics are met.

A method and apparatus for access control parameter adaptation based on traffic characteristic is provided. A wireless device receives multiple sets of access control parameters from a network. Each set of access control parameters is associated with a range of traffic characteristics. The wireless device determines a traffic characteristic, determines a set of access control parameters based on a traffic category and the traffic characteristic, and applies the set of access control parameters for the connection establishment.

The application relates to a traffic flow prediction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring traffic videos collected at at least one traffic detection point; for traffic images of a plurality of historical time points included in each traffic video, determining the number feature of each type of traffic object included in the traffic image, the flow feature of the traffic image and the traffic image feature; for each traffic image, fusing the number feature, the flow feature and the traffic image feature corresponding to the respective traffic image to obtain a traffic feature vector corresponding to the respective traffic image; generating a traffic flow map of a corresponding historical time point based on the traffic feature vectors corresponding to the traffic images of the same historical time point of the at least one traffic detection point; and determining the predicted traffic flow of the at least one traffic detection point according to the traffic flow maps corresponding to the respective historical time points. The method can more accurately determine the predicted traffic flow of the at least one traffic detection point.

The invention provides a network security defense method and device, equipment and a storage medium, and the method comprises the steps: extracting the traffic characteristics of collected traffic data through an analysis module, and enabling the code of the analysis module to be issued through a standard interface; performing identification processing on the flow characteristics through a detection engine, wherein a pre-trained detection model is arranged in the detection engine; and under the condition that the traffic data is identified to be abnormal traffic based on the detection model, processing the abnormal traffic through a security policy issued by a standard interface. Therefore, the abnormal traffic is processed by identifying the abnormal traffic, so that the protection performance of network security is improved.

The invention relates to a system and a method for solving a network NAT (Network Address Translation) session association multilayer fusion algorithm based on traffic characteristics. The system comprises a traffic acquisition module, a protocol identification module, four core association algorithms, an algorithm scheduling engine and a result fusion module. NAT double-end traffic is acquired through traffic acquisition, traffic protocol categories are determined through protocol identification, a scheduling engine adjusts corresponding algorithms according to protocols and priorities, preliminary association is obtained through differentiation feature extraction and matching, standardized NAT session association is integrated through a result fusion module, and accurate association is achieved. Based on double-end flow, four core algorithms are adjusted according to protocol types to obtain preliminary association, and standard output is generated through result fusion. According to the system and the method for solving the network NAT session association multi-layer fusion algorithm based on the traffic characteristics, NAT log dependence can be effectively eliminated, multiple scenes are covered, and the precision and the performance are improved.