87 results about "Traffic model" patented technology

The invention relates to the technical field of flow forwarding, in particular to a controllable flow forwarding method. The method comprises the following steps: S1, demand analysis, S2, traffic model construction, S3, equipment initial configuration, S4, traffic monitoring and evaluation, S5, traffic classification and priority setting, S6, bandwidth limitation and rate control, S7, load balancing, S8, resource reservation, S9, security encryption and verification of a data packet, S10, traffic caching and data loss prevention, and S11, dynamic adjustment of a forwarding strategy. According to the method, accurate control and management of network traffic are realized through traffic model construction, traffic monitoring and evaluation, traffic classification and priority setting, bandwidth limitation and rate control, load balancing and resource reservation, the security of data in a forwarding process is improved through security encryption and verification of a data packet, log recording and performance monitoring, and the data forwarding efficiency is improved. And data loss is avoided through flow caching, data loss prevention and dynamic adjustment of a forwarding strategy.

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

The invention relates to the field of urban public transport deployment, and provides a public transport network matching method and system based on an urban space structure, which divides urban areas by constructing a traffic model, identifies areas with dense demands on the basis of power consumption density, and provides a public transport network matching method and system based on the urban space structure. And the coverage condition of the existing public transport network on the areas with dense demands is evaluated, and uncovered areas are identified. For the uncovered areas, the information of stores and companies in the map data is used to subdivide the uncovered areas into a plurality of types of areas. A bus rapid transit system is additionally arranged in a commercial district to improve transportation efficiency, a peak period special line is additionally arranged in an office building area to meet commuting requirements, a customized bus special line is provided in an industrial district to meet requirements of specific periods and routes, and a conventional bus route is additionally arranged in a residential district to improve daily travel. Accurate matching between the urban space structure and the public transport demand is realized, and the coverage rate and efficiency of public transport service are improved.

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 discloses an urban multi-layer traffic network modeling and toughness analysis method under the influence of rainstorm, and relates to the technical field of traffic control, a rainstorm ponding model is built based on historical rainfall information, and a runoff depth and a ponding depth are simulated based on the rainstorm ponding model; building a multi-mode traffic model based on SUMO software; a rainstorm ponding model and a multi-mode traffic model are integrated to construct a coupling model, ponding information is matched to a corresponding road through a road ID, failure mechanisms of different traffic modes under rainstorm ponding are obtained, ponding influences are considered for road traffic and rail traffic respectively, and corresponding influence parameters are set. Adjusting the road speed limit in real time and updating the route in real time; the SUMO simulation output result is utilized, the toughness index change in different scenes is calculated, the comprehensive traffic network toughness in different rainstorm scenes is analyzed from the four dimensions of topology, convenience, service and safety, the comprehensive traffic network toughness is evaluated, the dynamic capture of traffic characteristics is realized, and the analysis depth and breadth are improved.

The invention discloses a high-concurrency scene-oriented government affair system automation performance test method and system, and the method comprises the steps: deploying test nodes in a government affair system, and constructing a distributed pressure test cluster; constructing a system flow model of a mixed load mode containing an instantaneous peak value and continuous pressure; collecting a resource index set and an application performance index set of the tested system in real time; according to the dynamic capacity expansion and contraction test, comparing the real-time monitoring index data with the system flow model data in the test process, and automatically triggering a horizontal expansion test scene; injecting three types of abnormal events into the system, and recording the self-healing time and the abnormal propagation path of the system; and constructing an intelligent analysis model, identifying a performance inflection point based on an anomaly detection algorithm of a time sequence, and generating a test result. The method has the advantages that the hybrid load model is constructed through the historical business data, the fault injection and intelligent analysis model is combined, real-time monitoring and dynamic capacity expansion and contraction testing are carried out, and the performance of the government affair system in a high-concurrency scene is comprehensively evaluated.

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 relates to the technical field of traffic prediction, in particular to a cross-regional traffic prediction method and device based on artificial intelligence, and the method comprises the steps: collecting target traffic data meeting processing conditions of a plurality of cities, and constructing initial traffic models of the plurality of cities through the target traffic data; and migrating the spatial-temporal characteristics in the target traffic data to an original traffic data set of the target city by using the initial traffic model to obtain a target traffic data set of the target city, thereby optimizing the initial traffic prediction model of the target city, and obtaining a target MDTLGCN model based on the optimized initial traffic prediction model, and performing cross-regional traffic flow prediction on the target city by using the target MDTLGCN model. Therefore, the problems that in the prior art, when cross-city traffic flow prediction is processed, it is difficult to effectively fuse multi-source heterogeneous data, the cross-city space-time dependency relationship cannot be accurately captured, and the accuracy of traffic flow prediction is reduced are solved.

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 invention discloses an overall automatic driving method, device and system for a vehicle, and relates to the technical field of intelligent traffic, and the method comprises the steps: constructing a traffic model according to the driving related information of the vehicle, and taking the starting point, destination, driving purpose, passenger / article and time demand of the vehicle as the input, the traffic model is utilized to generate an overall automatic driving scheme of the traffic tool, the overall automatic driving scheme of the traffic tool is sent to all the automatic driving traffic tools in the traffic model, and each automatic driving traffic tool performs automatic driving according to the overall automatic driving scheme of the traffic tool. Therefore, the problems that information acquisition is not comprehensive and decisions of the vehicles conflict with one another can be effectively avoided, and the driving safety and the driving efficiency of the vehicles are greatly improved.

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 present invention proposes a real-time centralized wireless network scheduling method and system based on deep reinforcement learning. The method comprises: obtaining a wireless network consisting of an access point and multiple interconnected user nodes; at each time slot, the access point obtains the status of each data stream based on the send queue information corresponding to all data streams; the status of all data streams is aggregated to form the environmental state of the current time slot; the access point obtains the traffic model and link quality of all data streams as environmental feature information; inputs the environmental state and environmental feature information into a decision model; the access point executes a scheduling decision corresponding to the output of the decision model; after executing the scheduling decision, the access point receives feedback from the network environment; stores the interaction information, environmental state, and environmental feature information as experience in a sub-region; and extracts experience from an experience replay pool to train and update the current decision model. The present invention does not increase training time rapidly with the number of data streams, and can quickly converge to the optimal real-time throughput.

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.

The invention discloses an intelligent dynamic load balancing method and system based on multi-dimensional monitoring data, and relates to the technical field of load balancing, and the method comprises the steps: building a flow prediction model based on multi-dimensional time sequence data collected in each channel of a front-end processor, converting the flow prediction model into a channel flow model through a migration algorithm, and generating a flow prediction model library; and obtaining selected channel historical data of the current time node, inputting the selected channel historical data to the corresponding channel flow model to obtain a channel flow prediction result, and dynamically adjusting load balance of the front-end processor by using a reinforcement learning algorithm. According to the method, for operation health management of an integrated monitoring system deployed on a front-end processor channel cluster, firstly, indexes including flow, use load, delay, error rate and the like are collected and processed in real time through a multi-dimensional monitoring data system, and an accurate decision basis is provided for load distribution; and multiple different types of recurrent neural network algorithms are combined with an attention mechanism to model, so that the performance and generalization ability of the model are improved.

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.

The invention relates to a road motor vehicle OD and city motor vehicle OD splicing method. The method comprises the steps that a traffic area is divided into an urban community, a toll station community and an external community; calculating a passenger flow volume proportion between the cells based on the mobile phone signaling data; and calculating the traffic flow between the urban communities and the external communities in combination with the entrance and exit directions of the toll stations. The method has the beneficial effects that the motor vehicle flow of the inner and outer regions of the city is accurately split by combining the in-out direction of the toll station and the passenger flow distribution proportion of the mobile phone signaling data, and the data blank between the city and the regional traffic model is filled. Moreover, a bidirectional flow splitting algorithm is adopted, so that the error of a single data source is effectively reduced, and the reliability of a calculation result is improved.

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.

An embodiment of the present application provides a method, apparatus, device, and medium for testing the media plane of a satellite communication core network. The method includes: simulating a satellite network scenario, where the satellite network scenario includes a satellite terminal and a core network; in response to a core network media plane test request from a user, determining the core network to be tested and starting the simulation virtual component corresponding to the core network to be tested; in response to a test case configuration operation from the user, obtaining the test case configuration; generating a traffic model according to the test case configuration; and running the traffic model to implement the media plane test for the core network to be tested.

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.