67 results about "Traffic model" patented technology

The invention discloses a vehicle state sensing method and system in a vehicle-road cooperation environment, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting the real-time traffic data of a target intersection; establishing a temporary V2X communication link; receiving real-time traffic data and K pieces of real-time vehicle state information of K running vehicles; if the one-dimensional risk detection results of the roadside edge computing device on the real-time traffic data and the K pieces of real-time vehicle state information are set to be 0, compensating the K pieces of real-time vehicle state information by adopting the real-time traffic data to obtain K pieces of compensated vehicle state information, constructing a global dynamic traffic model, and performing track conflict prediction and potential collision risk positioning; and according to the risk avoidance priority, broadcasting the potential collision risk to K running vehicles through a V2X communication link. The technical problems of inaccurate traffic modeling and untimely collision risk identification caused by incomplete vehicle-mounted sensing information in the prior art are solved, and the technical effects of improving the vehicle state sensing accuracy and the collision risk early warning timeliness are achieved.

The invention relates to the field of intelligent traffic systems, in particular to an intelligent intersection multi-vehicle cooperative control method. The method comprises the following steps: S1, sensing a vehicle state and sharing vehicle node information by adopting a dual-mode communication mechanism; s2, fusing state data at edge computing nodes; establishing a dynamic traffic model according to the fused state data; s3, acquiring node data of the edge calculation node, calculating a vehicle passing priority index according to the node data, and performing vehicle priority decision according to the passing priority index; s4, performing grading processing on the vehicles with the overlapped tracks; s5, the RSU device broadcasts the control instruction, and if the vehicle-mounted unit receives the control instruction within the set time, an execution report is transmitted back to the RSU device; and if the vehicle-mounted unit does not receive the control instruction within the set time, a redundancy execution mechanism is started to detect and control the running state of the vehicle. According to the invention, millisecond-level synchronous transmission and high-precision perception of information are realized, and the real-time performance and accuracy of traffic environment modeling are improved.

The invention provides a traffic model construction method and system based on fluid mechanics, and relates to the technical field of traffic model construction.Traffic flow data are collected in real time, the characteristic speed and congestion wave propagation speed in the fluid mechanics are combined, dynamic and fine division of traffic light road sections is achieved, and the traffic flow data are acquired in real time. The spatial mutation of the traffic state is effectively captured, and the accuracy of traffic flow state recognition is improved; based on the divided road section intervals, a multi-model strategy is adopted to match different traffic states, vehicle flow characteristics under smooth, slight congestion and serious congestion are comprehensively reflected, and the adaptability and physical rationality of the model are enhanced; through combination of the vehicle dynamics safety constraint and the signal lamp control period, collaborative optimization of the optimal vehicle speed and the optimal vehicle distance is realized, coordination of vehicle safe passing and signal lamps is guaranteed, and the road passing efficiency and the driving safety are improved.

The application provides a power deterministic network routing and packet scheduling method and system, the method comprising: determining the feasible path set from the source to the destination node for each deterministic flow according to the network and traffic model; constructing the three-dimensional scheduling variable consisting of the path, time offset and CSQF period specification set for each flow; jointly solving the three-dimensional variable of each flow to obtain the routing and packet scheduling scheme satisfying the resource, sending, delay and period constraint with the minimum objective function, the objective function being used to realize the double load balancing of the link and period window; and calculating the network remaining resource based on the obtained scheme and allocating the transmission resource for the non-deterministic flow according to the network remaining resource. Through the three-dimensional joint scheduling of the routing, time and period and the double load balancing of the link and window, the application can significantly reduce the jitter under the strict delay constraint, effectively improve the network resource utilization and the reliability of the deterministic service.

A traffic prediction method based on distributed machine learning is provided for predicting road traffic. In this case, the traffic prediction method based on distributed machine learning comprises the following steps: a learning server distributes a global multi-task traffic model to a learning agent to locally train the traffic model; the learning agent uploads the locally trained traffic model to a learning server; updating, by the learning server, the global multi-task traffic model using locally trained traffic model parameters acquired from the learning agent; the learning server generates a time-dependent global traffic map by using the trained global multi-task traffic model; distributing the time-dependent global traffic map to vehicles running on the road; and calculating, by the vehicle, an optimal travel route with minimal travel time based on the driving plan usage time dependent global traffic map.

The invention discloses a network risk identification method and device and a storage medium, and relates to the technical field of network security. The network risk identification method comprises the following steps: extracting historical traffic characteristics based on collected historical traffic, and constructing a normal traffic model; extracting actual network traffic characteristics based on the collected actual network traffic; obtaining the feature similarity of each network device according to the historical traffic features and the actual network traffic features; setting a feature similarity threshold of each network device, and identifying a specific risk device and a risk type according to the feature similarity of each network device; and fusing the actual network traffic characteristics, which are identified as normal, of the equipment with the normal traffic model, and iteratively identifying the network security risk. According to the method, extraction of hidden features and identification and positioning of unknown attacks can be realized, and dynamic adjustment can be carried out according to newly added normal traffic so as to adapt to dynamic changes of network services.

The present application discloses a traffic scene recognition and analysis method based on a large traffic model, which relates to the field of traffic control technology. The method obtains the operating parameters and traffic signal control parameters of a traffic intersection within a first time period; the first time period includes at least one time period; based on the vehicle flow in the operating parameters, the time period is divided into a first time period and a second time period; based on the operating parameters of multiple entrance sections and multiple exit sections included in the traffic intersection within the first time period, the congestion type and congestion period of the traffic intersection within the first time period are determined; the second time period is divided into multiple sub-time periods, and based on the operating parameters and traffic signal control parameters of at least one target turn of the traffic intersection in each sub-time period, the problem type and problem period of the traffic intersection within the second time period are determined, thereby solving the problem in the prior art that the corresponding problem intersection type under various scenarios cannot be given, thereby reducing the workload of manual analysis.

A system receives GPS data and on-board sensor data from several connected vehicles indicating locations of nearby vehicles and objects. The system processes the data to create a shared-world model that includes locations and velocities of the connected vehicles and nearby vehicles and objects, and the system determines whether driving hazards exist, such as potential collisions. The system may transmit an alert to at least one of the connected vehicles, to cause the connected vehicle or a mobile device to present a warning message to a driver, such as a visual, audio, or haptic message, or to cause the connected vehicle to implement an action to avoid the driving hazard, such as activating emergency braking or altering course. The system may create, and transmit to a connected vehicle or mobile device, a lane-level traffic model indicating traffic density, traffic speed, and traffic throughput.

The present invention discloses a method for intelligent traffic event recognition based on a large traffic model using cross-modal retrieval. The method comprises the following steps: inputting an image of a traffic event to be recognized and its text description into a traffic event recognition network model; the traffic event recognition network model comprises: a visual feature extraction module for extracting visual features from the image; a text feature extraction module for extracting text features from the text description; a visual selective filtering module for selectively filtering irrelevant visual extraction features to obtain visual filtering features; a text selective filtering module for selectively filtering unnecessary text extraction features to obtain text filtering features; a cross-modal selective alignment module for cross-modally aligning visual and text extraction features to obtain visual alignment features and text alignment features; and a calculation module for fusing the visual filtering features and visual alignment features, as well as the text filtering features and text alignment features, to determine a recognition result. The present invention improves the accuracy and efficiency of retrieval and recognition.

The invention provides a method for constructing a dynamic traffic model of a signal trunk road. The method comprises the following steps: S1, constructing a simplified microscopic model of vehicle movement; s2, according to the ordered movement of the traffic flow queue along the main road, marking the signal period of each intersection along the way by using an incremental sequence, the same mark of the signal period reflecting the time-space association of the vehicle fleet of the adjacent road section; s3, the dynamic traffic demand of each intersection is indexed by using the mark of the signal period, and the traffic flow from the upstream main road and the upstream side road is emphasized and distinguished; and S4, for each intersection, defining the traffic retention volume of the previous signal period as a traffic state, and constructing a traffic dynamic equation. According to the method, the switching signal moment is directly used as a decision variable, the problem that other models do not directly simulate the influence of signal change on traffic conditions is effectively solved, and the method is suitable for main road real-time signal cooperative control; with the road section and the signal period as units, the model scale is minimum; model input can be directly measured, and calculation is simple.

Imitation learning, or artificial intelligence-based learning from demonstration, aims to acquire an agent policy by observing and mimicking the behavior demonstrated in expert demonstrations. Imitation learning can be used to generate reliable and robust learned policies in a variety of tasks involving sequential decision-making, such as autonomous driving and robotics tasks. However, existing methods that use next-token-prediction (NTP) models, where the policy reduces to a classifier over a discrete set of trajectory tokens, suffer from covariate shift due to their open-loop training a closed-loop execution. The present disclosure provides closed-loop fine tuning of autonomous agent policies in a manner that can mitigate covariate shift.

An intrusion detection method for smart substations based on Hidden Markov Models includes the following steps: S1: Segmenting network traffic according to time scale, extracting packet identifiers, packet measurement data, and packet throughput to construct an intrusion detection dataset; S2: Calculating the difference between state variables and measurement values ​​based on the unscented Kalman filter method to establish a state model capable of calculating the attack detection index; S3: Establishing an ARIMA model, selecting the optimal traffic model, determining the detection confidence interval, and establishing a traffic throughput detection model; S4: Establishing multiple protocol compliance detection rules and, based on the Hamming distance calculation method, establishing a standardized detection model; S5: Real-time detection of network traffic in the smart substation, calculating detection vectors based on the models established in S2, S3, and S4 respectively, and using them as input variables for the Hidden Markov Model, comprehensively analyzing system anomaly characteristics, and achieving final discrimination.

The invention discloses a highway charging load simulation method and system based on multi-period traffic balance. The method comprises the following steps: acquiring highway network topological parameters, charging station distribution parameters, user travel origin and destination demand data and electric vehicle battery parameters; the method comprises the following steps of: aiming at the characteristics of long road section distance and large driving time span of an expressway, establishing a multi-period space-time network model of the expressway, and constructing a space-time topological structure comprising a space-time driving arc and a virtual staying arc; based on a traffic balance principle, constructing a highway multi-period traffic distribution model comprising the main problem and the sub-problems; and solving the highway multi-period traffic distribution model in the step 3 by adopting an iterative algorithm, and mapping the converged space-time path flow into a charging demand so as to obtain space-time load distribution of each charging station of the highway. According to the method, the defect that a traditional single-period traffic model cannot accurately describe the long-distance cross-period driving characteristics and the queuing congestion effect of the expressway can be overcome.

The application discloses a traffic generation device. A traffic model is encapsulated into a first data packet by a software processor, and a routing network table is encapsulated into a second data packet, which are respectively stored into corresponding first and second storage modules by a packet distribution circuit in a hardware processor. A traffic generation circuit parses the traffic model in the first data packet from the first storage module, and independently generates an initial message and corresponding index information. A routing processing circuit quickly obtains corresponding routing entries from the second storage module according to the index information and returns in real time, so that the routing access operation and the data packet generation operation are decoupled and executed in parallel at the hardware level. The routing processing circuit and the traffic generation circuit realize accurate cooperation through the index information, so that the real-time query process of the routing network table no longer blocks the message generation pipeline, thereby effectively solving the problems of message loss and traffic distortion caused by external storage access delay.

Imitation learning, or AI-based demonstration-based learning, aims to derive a policy for an agent by observing and imitating the behavior shown in expert demonstrations. Imitation learning can be used to generate reliable and robust learned policies for a variety of tasks requiring sequential decision-making, such as autonomous driving and robotics. However, existing methods that use next-token prediction (NTP) models, where the policy is reduced to a classifier over a discrete set of trajectory tokens, suffer from covariate shifting due to their open-loop training and closed-loop execution.The present disclosure provides a fine-tuning of the policies of autonomous closed-loop agents in a manner that can mitigate covariate shifting.

The invention discloses an automatic OpenStack stability test method and system, and relates to the technical field of cloud platform stability test.The method comprises the steps that test configuration information including test scene configuration parameters, flow model parameters, expected monitoring indexes and test execution strategies is acquired; initializing a tested system environment and test scene deployment based on the test scene configuration parameters; generating and sending test traffic to the tested system based on the traffic model parameters; in the testing process, actual monitoring index data of the tested system are collected, and when a specified abnormal alarm is triggered, testing is stopped, and a field testing environment state is reserved; comparing the actual monitoring index data with the expected monitoring index, and recording an abnormal log entry according to a comparison result; and generating a test report based on the test process data and the exception log entry. According to the method, the stability evaluation capability and the operation and maintenance response level of the cloud platform in a complex scene are enhanced while the test efficiency and the result credibility are improved.

The invention relates to the technical field of stacking, in particular to a stacking scene unknown unicast flooding suppression method and device and a program product, and the method comprises the steps: determining the exit type of a locally forwarded target message and the type of an effective far-end port; the type of the outlet and the type of the effective far-end port comprise an aggregation port and a non-aggregation port; when it is determined that an outlet of a locally forwarded target message is an aggregation port, whether the flow of the target message exceeds a threshold value is judged, and if yes, a flow model is established based on the flow exceeding the threshold value; if not, deleting the existing traffic model; copying the target message according to the type of the effective far-end port; adding identification information to the copied message based on the traffic model; and sending the message containing the identification information to an effective far-end device, so that the effective far-end device forwards the message according to the identification information. According to the technical scheme provided by the invention, the forwarding performance of the equipment can be improved.

The invention discloses a flow generation device. A flow model is packaged into a first data packet through a software processor, a routing netlist is packaged into a second data packet, and the first data packet and the second data packet are respectively stored to a corresponding first storage module and a corresponding second storage module through a packet distribution circuit in a hardware processor. The traffic generation circuit analyzes the traffic model in the first data packet from the first storage module, and independently generates an initial message and corresponding index information. And the route processing circuit quickly acquires the corresponding route entry from the second storage module according to the index information and returns the route entry in real time, so that decoupling and parallel execution of the route access operation and the data packet generation operation on the hardware level are realized, and the route processing circuit and the flow generation circuit realize accurate collaboration through the index information. The message generation assembly line is not blocked in the real-time query process of the routing netlist, so that the problems of message loss and traffic distortion caused by external storage access delay are effectively solved.

The present application relates to a kind of 5G / B5G power communication network traffic analysis method based on multi-time series data mining, specific method steps include: 1) 5G / B5G power communication network traffic characteristic analysis 2) establish 5G / B5G power communication network traffic model, select multi-time series data mining method to establish traffic model, describe the real situation of 5G / B5G communication network service based on power service.Different from the traditional sense of power communication traffic analysis, new division is carried out to 5G / B5G power communication traffic level, using multi-time series data mining algorithm, 5G / B5G power communication traffic is reasonably statistically described, multiple traffic characteristic time series are analyzed as a whole, produce effective abnormal network traffic characteristic association rules, accurately describe the security situation of entire 5G / B5G power communication network, power communication fault prediction, network design, traffic control, resource management and network design have very important application value.

A traffic data acquisition unit acquires traffic volume data. A required time acquisition unit acquires an actual value of a required time. A traffic volume calculation unit calculates an inflow traffic volume of vehicles flowing into each using the traffic volume data and the actual value of the required time. A traffic state prediction unit calculates, for each route, a predicted value of the required time in view of the calculated inflow traffic volume and a predicted value of the required time when the inflow traffic volume is changed using a traffic model generated in advance. An amount of change calculation unit calculates, for each route, an amount of change in the predicted value of the required time when the inflow traffic volume is changed.

The application provides a traffic suppression prediction method, an electronic device and a storage medium, and the traffic suppression prediction method comprises the following steps: determining a suppression point traffic value according to a preset network traffic model, the network traffic model representing a mapping relationship between a value of a network parameter of a transmission network and a traffic value, the suppression point traffic value being a traffic threshold of the transmission network under a current operation strategy; determining a suppression reference value of a target network parameter corresponding to the suppression point traffic value; obtaining a parameter prediction value corresponding to the target network parameter, and determining a traffic suppression prediction result according to the parameter prediction value and the suppression reference value. According to the scheme provided in the embodiment of the application, the traffic suppression prediction result corresponding to the target network parameter can be predicted before traffic suppression occurs, data basis is provided for network optimization in advance, and user experience is effectively improved.

The invention discloses an intelligent driving simulation test method based on data, and the method comprises the steps: carrying out the processing of real collection data and simulation data, and obtaining value data; training a full-connection network by using vehicle driving behaviors and traffic vehicle data in the value data to obtain an intelligent agent model; a regulation and control simulation module is used for carrying out regulation and control simulation testing according to the value data and the intelligent agent model, and real-time sensing data and simulation main vehicle state data are output in the simulation process; training a full-connection neural network by using scene fragments in the value data to obtain a generative traffic model; and performing a prediction simulation test according to the value data and the generative traffic model by using a prediction simulation module. According to the intelligent driving simulation test method based on the data, simulation test can be carried out by fully utilizing the data of road acquisition, test and the like, and the utilization rate of the data is improved; based on the data, a highly real surrounding traffic environment can be obtained, and the logic and performance of the algorithm can be tested more accurately.

Disclosed in the present invention is a method for modeling user service traffic at a network layer in a wireless network, which method is applied to the field of wireless networks. With regard to the problem of user service traffic in a wireless network environment, in the present invention, a network traffic model based on a denoising diffusion probabilistic models (DDPM) diffusion network is established, and for data packets of user service traffic which are captured by a network layer, packet sizes and packet arrival intervals of the data packets are modeled, so as to obtain a traffic model of a user service at the network layer; moreover, by means of the service traffic modeling method in the present invention, a network traffic model maintaining the multi-dimensional correlation of original service traffic while conforming to the statistical distribution of the original service traffic can be established.

Disclosed are systems and methods that provide a computerized framework for accurately and efficiently estimating the backhaul bandwidth and core capacity, which can be utilized to automatically and dynamically configure and / or curate network configurations and topologies to handle network loads and traffic. The framework can utilize and / or operate RF system simulators to perform the estimation of each sector's delivered UL / DL throughputs, as well as user inputs related to the traffic model, the dedicated network's topology and product specifications related to the processing limits of BBUs and private cores in order to determine network bottlenecks. Such determinations and / or predictions can be leveraged to ensure the allocation of backhaul bandwidth and core capacity, among other network features and / or characteristics, are optimized to achieve overall high throughput and remove traffic bottlenecks.

Embodiments of the present application relate to the technical field of data processing, in particular to a traffic protection method and device, equipment and storage medium, aiming to improve the traffic attack protection efficiency of a server. The method comprises: generating a corresponding real-time traffic model according to real-time traffic data in the server; obtaining index data corresponding to at least one protection module from the real-time traffic model; adjusting the protection strategy corresponding to at least one protection module through the index data, and executing the corresponding protection measures; generating a historical traffic model on the basis of the real-time traffic model according to historical traffic data in the server; obtaining a corresponding traffic analysis result according to the historical traffic model in the forwarding process of the real-time traffic data; adjusting the protection strategy corresponding to at least one protection module according to the traffic analysis result, and executing the corresponding protection measures.

This application relates to a method, apparatus, computer device, and storage medium for detecting equipment anomalies. The method includes: acquiring historical traffic data of the device under test (DUT), and extracting traffic characteristic data of the DUT based on the historical traffic data; determining a dynamic traffic model based on the traffic characteristic data; wherein the dynamic traffic model includes multiple traffic patterns, each corresponding to a test model; dynamically adjusting the test model corresponding to the traffic data of the DUT during the test period based on the dynamic traffic model; and testing the traffic data of the DUT during the test period using multiple test models to obtain an anomaly test report for the DUT. This method can accurately detect equipment in complex operating scenarios and improve the accuracy of equipment anomaly detection.

The application discloses a kind of road network topology updating method based on GPS data, comprising: S1. the GPS trajectory data of vehicle is collected;S2. all road sections of GPS trajectory data are traversed, and each road section in all road sections is processed as follows: the road section in GPS trajectory data is regarded as target road section ID_re, from GPS trajectory data, the road section sequence (ID_1, ID_re, ID_2) including target road section ID_re is extracted;The topology relationship of the road section sequence including target road section ID_re is judged, and the update of road network topology relationship is carried out according to the judgment result;S3. repeat step S2 until road network topology relationship is no longer updated.The application can accurately update road network topology information in real time, and provides technical support for road traffic statistics, road network simulation, the construction of comprehensive traffic model.

The present application provides a traffic prediction system, a traffic prediction method and a computer readable medium, which efficiently predict the change in required time for a route in a case where the traffic volume of the route changes along with a change in action. A traffic volume data acquisition unit acquires traffic volume data. A required time acquisition unit acquires an actual value of required time. A traffic volume calculation unit calculates the inflow traffic volume of each route using the traffic volume data and the actual value of required time. A traffic state prediction unit calculates, for each route, a predicted value of required time for the calculated inflow traffic volume and a predicted value of required time in a case where the inflow traffic volume changes, using a traffic model generated in advance. A change amount calculation unit calculates, for each route, a change amount of the predicted value of required time in a case where the inflow traffic volume changes.

The application relates to the technical field of network security, and discloses a training method and a detection method of an encrypted traffic detection model and an electronic device. The training method of the encrypted traffic detection model comprises the following steps: training a first plaintext traffic model by using plaintext traffic feature data; training a backup plaintext traffic model by using at least part of encrypted traffic feature data to obtain a first encrypted traffic model; cooperatively training the first plaintext traffic model and the first encrypted traffic model by using the encrypted traffic feature data to obtain a second encrypted traffic model; training an adversarial generative network by using the encrypted traffic feature data; distilling training a teacher model and a student model by using the encrypted traffic feature data, and taking the trained student model as the encrypted traffic detection model. The encrypted traffic detection model trained by the application is lightweight and easy to deploy, and can realize high-precision detection of encrypted traffic.

The present application relates to a kind of network energy-saving control method, device, equipment and medium, the present application obtains the grid level network measurement data in target area, traffic model data;Grid level network measurement data, traffic model data are carried out spatial clustering analysis, according to signal strength and service load, from the screening potential redundant grid in spatial clustering analysis result, and the potentially redundant grid that is geographically continuous is aggregated into potential redundant area atlas;According to the reserved constraint of pre-set basic coverage layer, from potential redundant area atlas, candidate cell is screened out and is shut down, and initial energy-saving shutdown strategy is generated based on candidate cell being shut down;Initial energy-saving shutdown strategy is submitted to digital twin network, to carry out energy-saving simulation iteration optimization and eliminate coverage risk, obtain target energy-saving shutdown strategy;Target energy-saving shutdown strategy is issued to network equipment, to make network equipment execute target energy-saving shutdown strategy, realize the energy-saving control of network.