253 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.

The invention discloses a data processing method and device, equipment and a medium. The method comprises the steps that a detection threshold value is determined based on a first access traffic log; determining an access behavior for the service domain name based on the number of domain name requests determined by the access traffic indicated by the second access traffic log and a detection threshold; when the access behavior aiming at the service domain name belongs to the abnormal access behavior, taking a behavior determination time point corresponding to the abnormal access behavior as a critical time point, and taking a third access traffic log obtained based on the critical time point as an abnormal access traffic log; when a normal traffic feature determined based on the first access traffic log is obtained, determining an abnormal traffic feature of the service domain name based on the abnormal access traffic log, and performing feature comparison on the abnormal traffic feature and the normal traffic feature to obtain an attack feature of the service domain name; a protection policy for the service domain name is determined based on the attack feature. According to the invention, the security and stability of the business server can be maintained.

A method includes receiving image data captured by a traffic feature detection system. The image data is representative of a traffic feature within an environment. The method includes identifying a type of the traffic feature. The method includes isolating a portion of the image data containing the traffic feature. Based on the portion of the image data containing the traffic feature, the method includes determining a pixel color value for the traffic feature. Based on the type of the traffic feature, the method includes determining an expected pixel color value for the traffic feature. Based on a comparison of the expected pixel color value and the determined pixel color value for the traffic feature, the method includes determining a health status for the traffic feature.

The invention relates to the technical field of network security detection, and discloses a malicious network traffic detection and analysis method based on artificial intelligence. The method comprises the following steps: acquiring network flow data through preset equipment, and constructing a network flow characteristic representation containing a time sequence statistical characteristic and a protocol attribute characteristic; determining a multi-level associated entity of each traffic fragment in combination with the network security knowledge graph, and aggregating the features to generate aggregated network traffic features; performing malicious probability evaluation on the aggregation features, and determining target malicious traffic by means of the maximum response value of the thermodynamic map; determining a similar malicious traffic mode based on the aggregation feature similarity; generating a detection prompt text in combination with the target malicious traffic and the similar mode, and inputting a preset model to output a detection result; and adjusting model noise covariance matrix parameter optimization detection according to the flow dynamic index. The method can comprehensively capture traffic characteristics, mine associated information, improve the accuracy and adaptability of malicious traffic detection, and effectively cope with malicious attacks in a complex network environment.

The invention provides an ultra-large city comprehensive traffic data and intelligent research and strategy system, which establishes a multi-dimensional traffic research and strategy task through a traffic research and strategy task module, and processes the traffic research and strategy task through a traffic characteristic diagnosis module to obtain a traffic characteristic index vector rank and a candidate traffic root cause index set. The traffic traceability analysis module performs further processing to obtain main traffic root cause indexes and measure indexes, and the traffic strategy generation module outputs traffic characteristic indexes according to the main traffic root cause indexes and the measure indexes to optimize a pre-generation strategy; the traffic simulation deduction module optimizes a simulation task item distribution model generated by a pre-generation strategy according to traffic characteristic indexes to obtain a pre-selection result, and the traffic strategy evaluation module and the traffic strategy release module finally perform evaluation and release, thereby solving a problem that a single factor outputs a single execution scheme with relatively high precision. Therefore, when single execution schemes are overlapped and fused into a set of optimal executable solutions for the comprehensive traffic of the super-large city, scheme execution conflicts are easy to occur.

The invention provides an urban traffic management intelligent evaluation system and method based on a large language model, and belongs to the technical field of intelligent traffic management. The system comprises a data processing module, an MECA traffic flow analysis module, a feature engineering module, a driving behavior evaluation module, a clustering analysis module, an LLM intelligent decision module, a visualization generation module and a visualization module. The system analyzes the road traffic data collected by the unmanned aerial vehicle, adopts the MECA technology to automatically identify the road type and the traffic environment, intelligently judges the congestion level, evaluates the driving behavior, and combines a big language model to generate a targeted traffic management optimization suggestion. According to the method, the adaptive context sensing technology is innovatively combined with multi-criterion learning, adaptive congestion judgment of different road types is realized, different traffic characteristics of urban expressways, common urban roads and expressways can be accurately recognized, and differentiated management strategies are provided accordingly. The system supports real-time processing of large-scale traffic data, and provides scientific and accurate decision support for urban traffic management departments.

The invention discloses a traffic anomaly monitoring method and system based on big data and a storage medium, and belongs to the field of network security. The method comprises the following steps: determining a monitoring target and a monitoring range, and defining a network range in which traffic monitoring needs to be carried out; data collection and arrangement: using a traffic monitoring tool to collect network traffic data, and performing data cleaning and preprocessing; analyzing traffic characteristics, including statistical analysis, protocol and port analysis and traffic mode identification; setting a baseline threshold, setting the baseline threshold based on the flow characteristic analysis result and the service demand, and establishing an adaptive threshold adjustment mechanism; and continuous verification and optimization: monitoring whether the network flow exceeds a baseline threshold in real time, triggering an anomaly detection mechanism, generating early warning information, executing a response strategy, and optimizing the baseline threshold according to feedback. By establishing the network flow base line and monitoring the deviation condition in real time, network abnormity can be found in time, the network security protection capability is improved, and the method is suitable for security monitoring of various network environments.

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 an urban garbage treatment station site selection method and device, electronic equipment and a storage medium, and relates to the technical field of site selection. The method comprises the following steps: dividing a target city area into a plurality of grid areas; acquiring signaling data of each grid region, and estimating population distribution characteristic data; acquiring historical garbage clearance data and commercial interest point data, and processing to obtain garbage prediction data; obtaining road data, establishing a topological road network model, and performing traffic characteristic evaluation on the topological road network model to obtain traffic characteristic data; acquiring ecological data, and acquiring service coverage data of each garbage disposal station; based on the obtained data, obtaining a multi-dimensional evaluation result, and determining a plurality of preliminary site selection of the garbage disposal station; and determining a preset number of addresses from the plurality of preliminary addresses as a final address of the garbage disposal station of the target city. By implementing the technical scheme provided by the invention, the accuracy of site selection of the urban garbage treatment station can be improved.

The invention relates to the technical field of data communication and artificial intelligence, and particularly discloses an intelligent data optimization system and method based on artificial intelligence, and the system comprises a characteristic curve acquisition module, a period determination module, a node grouping module, a priority calculation module, a resource distribution module and a data scheduling module. The method comprises the following steps: firstly, acquiring a flow characteristic curve of each data flow, and determining a flow fluctuation period; then, K-means clustering is combined with genetic algorithm optimization, and nodes are grouped; then, integrating hardware performance parameters and flow stability, and calculating a transmission priority weight; bandwidth resources are allocated according to the weights, basic requirements of low-priority nodes are ensured, and dynamic adjustment is carried out to adapt to network load changes; and finally, constructing a multi-objective optimization model through a deep reinforcement learning algorithm DQN, dynamically scheduling a data flow according to a bandwidth allocation result, and realizing efficient data transmission by taking delay, a packet loss rate and a bandwidth utilization rate as optimization objectives, thereby effectively improving the network transmission efficiency and performance.

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.

Apparatuses, systems, and methods for conditional handover (CHO) and extended reality (XR) capacity enhancements. A base station may receive XR traffic characteristics from a user equipment (UE) and / or from a core network. The XR traffic characteristics may be UE specific and / or flow specific. The base station may provide, to one or more target base stations, the XR traffic characteristics as part of a conditional handover request. The XR traffic characteristics may be provided in an information element over an Xn interface. In addition, the base station may receive, from the one or more target base stations, handover request acknowledgments. The handover request acknowledgments may be based, at least in part, on admission control procedures performed based on the XR traffic characteristics. Further, the base station may generate a list of candidate target base stations based on the handover request acknowledgments and provide the list of candidate target base stations to the UE.

The invention discloses a data traffic scheduling method and a communication system based on traffic characteristics and path selection, and the method comprises the steps: presetting a large flow path and a small flow path according to the historical state information of each path, and enabling the path blocking risk and the path stability of the large flow path to meet a preset first requirement; the path blockage risk and the time delay of the small flow path meet preset second requirements, and the path blockage risk, the path stability and the time delay are determined according to historical state information; predicting whether the data stream sent by the sending end is a large stream or a small stream according to the traffic characteristics of the data stream historically sent by the sending end, and if the data stream is the large stream, determining that the sending end is a large stream sending end and distributing the data stream to a large stream path; and if the small stream is predicted, determining that the sending end is a small stream sending end and distributing the data stream sent by the small stream sending end to a small stream path. The bandwidth utilization rate and the response speed of a communication system can be effectively improved, and congestion and delay are reduced.

The invention discloses a segment scanning type DDoS attack detection method and device, electronic equipment and a storage medium. The detection method comprises the following steps: for each IP address in a plurality of IP addresses, acquiring a first traffic feature set comprising traffic features of N dimensions of a first traffic set taking the IP address as a destination IP address in a first time period; calculating weights respectively corresponding to the traffic characteristics of the N dimensions based on a historical traffic set; corresponding weights are given to the traffic features of the N dimensions of the IP address, and a second traffic feature set corresponding to the IP address is obtained; clustering the plurality of IP addresses based on a plurality of second traffic feature sets corresponding to the plurality of IP addresses to obtain a clustering result; and determining the IP address network segments subjected to the segment scanning type DDoS attack in the IP address network segments where the plurality of IP addresses are located based on the clustering result. According to the method and the device provided by the embodiment of the invention, the segment scanning type DDoS attack can be effectively detected, the false alarm and the missing alarm are reduced, and the network security is improved.

The invention belongs to the technical field of intelligent traffic information, and discloses a traffic control subarea dynamic division method based on congestion evolution, which comprises the following steps of: firstly, based on three types of similarity measurement of a space structure, traffic characteristics and functional attributes, adopting an improved Chameleon algorithm and combining with an optimal subarea quantity determination strategy, clustering road sections of a whole network, and determining the optimal subarea quantity; generating a plurality of initial homogeneous proton regions; thirdly, inputting the feature sequence of each initial subarea and the adjacent matrix into a space-time prediction framework, outputting a future congestion probability through model training, and constructing a prediction association graph according to the future congestion probability to provide space-time dependence information for subsequent dynamic division; and finally, the improved Chameleon algorithm is applied again on the basis of the prediction association graph, reconstruction and feedback iteration are carried out on the sub-regions, the homogeneity in the regions, the difference between adjacent regions and the boundary continuity are optimized, a sub-region division result and a corresponding macroscopic fundamental graph are finally output, and decision support can be provided for refined traffic control.

The invention discloses an abnormal traffic monitoring method and system based on a security gateway, and the method comprises the steps: obtaining a TLS handshake feature in a traffic request in response to the traffic request of a target user, and carrying out the Hash calculation of the TLS handshake feature, and obtaining a JA3 Hash fingerprint; performing three-level cache matching on the JA3 hash fingerprints in sequence, and extracting fingerprint tags of the JA3 hash fingerprints which are not matched in three-level cache; performing feature extraction on the traffic data corresponding to the fingerprint tag to obtain traffic features; and inputting the traffic characteristics into a pre-trained abnormal traffic identification model based on Transform to obtain an abnormal traffic identification result. According to the method provided by the invention, the detection precision of the abnormal traffic 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.

The invention relates to the technical field of road traffic management, and particularly discloses a signal lamp fault monitoring and early warning method based on Internet data, which comprises the following steps: establishing a historical database, acquiring historical traffic characteristic data of an intersection, establishing a historical traffic characteristic database, and setting an early warning threshold value for each traffic characteristic data; performing data judgment and early warning, comparing the real-time traffic characteristic data of the intersection in the same time section with historical traffic characteristic data, and when any one of the real-time traffic characteristic data reaches an early warning threshold value, sending early warning information to a traffic management department; and fault processing: after the traffic management department receives the early warning information, the traffic management department sends a checker to check the fault, and if the fault check is true, the traffic management department arranges a maintainer to carry out fault maintenance processing. According to the signal lamp fault monitoring and early warning method based on the signal lamp data, the problems that signal lamp fault operation and maintenance are not timely and incomplete, cost is high and the like are effectively solved, and potential traffic safety hazards caused by signal lamp faults are reduced.

The invention relates to the technical field of network traffic detection, in particular to a network traffic anomaly detection system based on deep learning. According to the entropy change-based time window adaptive adjustment method, the window length is dynamically adjusted according to the entropy of traffic characteristics in a window, and the flexibility of time segmentation and the sensitivity to abnormal traffic abrupt change are improved; meanwhile, importance weighting, irrelevant dimensionality control and key dimensionality enhancement of each dimensionality feature are achieved on an original input feature vector by combining multi-category feature gating, a model is helped to more effectively pay attention to abnormal related signal features, and deep analysis of network flow complex time sequence context is achieved by combining a self-attention mechanism; mahalanobis distance secondary detection of a normal detection template is introduced, statistical distribution is taken as a basis, the discrimination capability of a boundary sample is enhanced, the false alarm and missing report rate is reduced, and an anomaly detection system which accurately senses complex behavior fluctuation, adaptively adjusts analysis granularity, fuses multiple types of features and has robust discrimination capability is constructed.

The invention relates to the technical field of traffic control optimization, and discloses a traffic jam control optimization algorithm based on a regional population scale and an economic structure. The algorithm comprises the following steps: firstly, obtaining population distribution data, economic industry data and real-time traffic flow original data of a target area, and generating an area traffic characteristic representative data set through multi-dimensional clustering processing; generating a traffic regulation and control allocation instruction set based on the data set; then constructing a dynamic traffic state hierarchical optimization model containing a top layer road network stability model and a bottom layer resource cost model; and finally, determining a cooperative constraint condition of the two models, inputting an instruction set into the models, performing cross-layer iteration updating solution according to the constraint condition, and outputting an optimal traffic control strategy configuration scheme. According to the algorithm, multi-dimensional data are fused, collaborative optimization of road network stability and resource cost is realized, the accuracy and adaptability of traffic jam treatment are improved, and regional traffic jam is effectively relieved.

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.

The application provides a per-port flow control method, medium and equipment suitable for BCube topology, which comprises a per-port queue allocation strategy, a dynamic flow control mechanism and a queue grouping strategy. In the design of the switch port queue, each outgoing port of the switch has an independent queue corresponding to the downstream switch port, thereby reducing the congestion problem caused by burst traffic. In the design of the host port queue, the host is allocated a special queue according to the traffic characteristics, thereby avoiding the head-of-line blocking when different types of traffic contend for resources. The dynamic flow control mechanism dynamically adjusts the sending rate of the upstream node queue according to the buffer state of the downstream switch, thereby preventing the buffer overflow; the queue type of the congested port is monitored in real time, a pause or resume signal is generated, different types of traffic are managed separately, and the transmission efficiency is improved. In the queue grouping strategy, the switch port queue is divided into different priorities, the propagation of the congestion signal is reduced, the circular dependency is avoided, and the network is prevented from stalling.

The application relates to a traffic anomaly detection method based on a graph convolutional neural network self-encoder, and a one-dimensional convolution and context coding network combining traffic anomaly and deep learning is designed, the network mainly comprises a mirror time domain convolution module and two graph convolution gate cycle modules which are sequentially cascaded, traffic features such as speed and flow are extracted to predict traffic conditions and possible sent anomalies, an adaptive method is used before the mirror time domain convolution module to adapt to different road sections, more features are transmitted into the time convolution module through mirroring, more information is obtained through the time convolution module, the network continuously learns the traffic network, a Gaussian kernel function module is used in the graph convolution gate cycle module, distribution is more concentrated in a high-dimensional space, hidden spatial correlation is captured by using the characteristics of the graph convolution network architecture, possible abnormal point occurrence is captured by combining the graph convolutional neural network, the method is more accurate, and the reliability of the predicted anomaly is greatly improved.

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.