94 results about "Traffic risk" patented technology

The invention discloses a highway intelligent risk monitoring method and system based on multi-modal information, and the method comprises the steps: collecting multi-modal traffic data, constructing a road network holographic portrait through a road network holographic portrait risk prediction model, positioning a potential risk point through a dual time-space mask anomaly detection model, and carrying out the detection of the potential risk point. Lane-level risk assessment is completed by means of a lane-level risk dynamic assessment algorithm, and risk information is output after multi-dimensional fusion analysis is carried out through a traffic risk intelligent research and judgment platform. All the models and algorithms are operated cooperatively, data integration, feature mining, anomaly recognition, risk assessment and fusion research and judgment are achieved step by step, a whole-process monitoring system from data collection to result output is constructed, refined and dynamic risk monitoring from the global road network to the local lane is achieved, the risk recognition accuracy and monitoring comprehensiveness are effectively improved, and the risk monitoring efficiency is improved. And technical support is provided for safe operation of the expressway.

The invention provides an urban low-lying road section emergency supervision Internet of Things large model system and method and a storage medium, and belongs to the field of road monitoring. The emergency supervision management platform is configured to obtain driving data of a low-lying area through the emergency supervision object platform; determining ponding data based on the driving data; determining a traffic risk based on the ponding data; in response to the traffic risk greater than a first threshold, determining a drainage parameter; determining scheduling parameters based on the drainage parameters; and sending a scheduling packet containing the drainage parameters to the unmanned vehicle cluster through the heterogeneous communication network, controlling the unmanned vehicle carrying the drainage device to run to the low-lying area, and controlling the water pumping power of the drainage device to be adjusted to the water pumping power of the drainage parameters. The method can effectively reduce the traffic obstruction and accident risk caused by water accumulation, and improves the emergency response efficiency of urban low-lying road sections during heavy rainfall.

The invention discloses a meteorological early warning information fusion method and system based on a road network, and the method comprises the steps: obtaining the topology and meteorological characteristics of the road network, carrying out the clustering to form an aggregation unit, and setting a sharing threshold value; obtaining multi-source early warning data according to units and standardizing the data; a comprehensive result is obtained through duplicate removal, sorting and conflict resolution of a scheduling mechanism; a weather system movement parameter is extracted to calculate a differentiated early warning advance; determining a risk level and a confidence interval by combining real-time traffic state fuzzy reasoning; inquiring emergency resources and linking the traffic control system to push early warning; historical data closed-loop feedback optimization model parameters are collected; and applying the optimization model to realize road network meteorological early warning. According to the invention, the space-time accuracy and response efficiency of meteorological early warning are significantly improved, and the road network traffic risk in severe weather is reduced.

The invention relates to a traffic risk deduction method and system based on a dynamic knowledge graph. The method comprises the following steps: establishing a communication interface and collecting heterogeneous data streams; unifying all data in the heterogeneous data stream to the same space-time reference and fusing the data; based on the fused data, constructing a dynamic knowledge graph, and performing incremental updating on the dynamic knowledge graph by using continuously inflowing new data; inputting the dynamic knowledge graph into a trained time sequence diagram neural network model and a pre-established causal rule base to respectively obtain a prediction result and a reasoning result; carrying out fusion, verification and deduction on the prediction result and the reasoning result to obtain a deduction result; and based on the deduction result, generating structured early warning information, and directionally sending the structured early warning information to the screened target. Compared with the prior art, the method has the advantage of high prediction precision.

The invention, which relates to the technical field of logistics supply management, discloses a production-marketing docking and cold-chain logistics integrated intelligent scheduling method comprising the following steps: S10, constructing a unified scheduling data model, and fusing multi-temperature-zone attributes, cargo dynamic energy consumption, a vehicle real-time state, an order temperature-sensitive characteristic and a delivery emergency degree to form a standardized decision data set; s20, executing multi-objective collaborative optimization, coupling the road surface type energy consumption coefficient with the order urgency score, and realizing accurate trade-off among the transportation cost, the energy consumption and the timeliness; s30, integrating meteorological and geological data, dynamically predicting path traffic risks caused by rainfall and soft soil texture, and correcting traffic parameters in real time; and S40, through cooperative sensing and image recognition of a vehicle-mounted sensor, the vehicle body pollution state is automatically judged, delayed use is executed on the limited vehicle, and standby transport capacity is started. According to the invention, through data fusion, dynamic optimization, risk early warning and closed-loop management and control, the problems of data islands, rigid path planning, slow environment response and out-of-control vehicle state are solved.

The invention relates to the technical field of road early warning, and discloses an intelligent traffic risk early warning device and method.The intelligent traffic risk early warning device comprises a vehicle type recognition module, a vehicle meeting conflict judgment module and a conflict isolation protection module, the vehicle type recognition module recognizes and collects vehicle images through a YOLOv7 method based on fusion CBAM enhancement, outputs various kinds of information of vehicles passing through a curve, and sends the information to the collision isolation protection module; the collision isolation and protection module is used for receiving a collision signal and outputting the collision signal to the meeting collision judgment module, establishing a summarized data set of meeting actual conditions and executing a meeting safety control method, and the collision isolation and protection module is used for controlling the lifting of a plurality of flexible warning columns arranged at the entrances of the curves on the two sides based on the meeting collision judgment module. According to the device, the minimum vehicle turning radius is dynamically calculated in combination with the vehicle meeting conflict judgment module and compared with the same type of a vehicle meeting critical model, and signal early warning dynamic isolation dual protection is matched, so that the problems that a traditional convex mirror depends on subjective judgment of a driver, and an existing early warning device is manually operated and poor in environmental adaptability are solved; and the safety of vehicle meeting at the mountain road curve is improved.

The application provides a vehicle control method and a vehicle, and belongs to the technical field of vehicle control. The vehicle control method comprises the following steps: obtaining a traffic risk level of a road section in front of the vehicle and current state information of the vehicle, and the vehicle is provided with an active suspension; determining a target response strategy based on the traffic risk level and the current state information; and controlling the vehicle to execute the target response strategy, wherein the target response strategy comprises a height adjustment strategy of the active suspension. Thus, different traffic risk levels are divided according to the road conditions of the road section in front of the vehicle, and different target response strategies are provided in combination with the current state information of the vehicle, for example, direct traffic, traffic after height adjustment, or detouring in the case that there is still a risk of scratching after adjustment. Therefore, the vehicle intelligently and objectively controls the corresponding target response strategy based on different traffic risk levels and the current state of the vehicle, timely and effectively prevents scratching during vehicle driving, and improves vehicle safety.

The invention discloses a highway logistics transportation road icing disaster risk early warning method based on multi-source data fusion, and belongs to the technical field of traffic risk early warning, and the method comprises the steps: S10, obtaining meteorological data along a highway, the meteorological data comprising air temperature, humidity, wind speed, wind direction, rainfall type and rainfall amount; step S20, analyzing the regular change of the meteorological data, including analyzing the regular change of air temperature and humidity, and distinguishing the road according to the analyzed regular change, including dividing the road into a road section with an icing risk and a road section without the icing risk; and S30, obtaining road section information of the road section with the icing risk, wherein the road section information comprises a bridge, a ramp and a tunnel. The road icing risk is comprehensively evaluated by integrating multi-source data such as meteorological data, road section information, historical disaster data and vehicle traffic data, and the early warning accuracy and reliability are improved.

This invention belongs to the field of intelligent transportation and road safety management technology, and discloses a method for assessing traffic risk in mountainous expressway networks considering multiple factors. The method includes the following steps: S1. Acquiring multi-source data of the target mountainous expressway network and performing standardization processing; S2. Constructing a three-layer risk assessment indicator system of "basic-dynamic-additional"; S3. Based on the indicator system constructed in step S2, performing tiered indicator quantification calculations; S4. Based on the calculation results of step S3, calculating the overall traffic risk value of the road network through a comprehensive assessment model, classifying the risk levels, and outputting the risk levels and corresponding control recommendations. This invention can sensitively reflect subtle changes in the risk status of the road network, providing quantitative decision-making basis for operation and management units to carry out precise monitoring, engineering rectification, traffic organization optimization, and emergency resource pre-positioning, significantly improving the refinement and scientific level of safety management of mountainous expressway networks.

The invention discloses a vehicle end multi-source traffic risk identification method based on a feature matrix, and relates to the technical field of vehicle end risk identification. The method comprises the following steps: constructing a multi-source risk data set; carrying out condition generation by adopting a condition table generative adversarial network CTGAN, and constructing a class-balanced multi-source risk data set; performing feature crossing to generate an enhanced risk data set; performing feature screening on the enhanced risk data set based on an information theory method, and constructing a main risk feature set; constructing a feature relation graph for nodes according to the main risk feature set, and learning and optimizing through a graph neural network GCN, and outputting a feature representation matrix; and inputting the feature representation matrix into a pre-constructed MHSA-ResNet risk identification model, detecting the traffic risk of the vehicle, and outputting a risk identification result. According to the method, the multi-source heterogeneous traffic data is fused, the high-discrimination and explainable risk feature set is screened and constructed in combination with the feature crossing and information theory, and the risk identification accuracy is improved.

The application discloses a kind of meteorological warning information fusion method and system based on road network, comprising: obtaining road network topology and meteorological characteristics, clustering forms aggregation unit and sets shared threshold;According to unit, obtain multi-source warning data and standardization;By scheduling mechanism, deduplicate, sequencing and conflict resolution get comprehensive result;Extract weather system movement parameter and calculate differentiated early warning advance;Determine risk level and confidence interval in combination with real-time traffic state fuzzy reasoning;Query emergency resource linkage traffic control system and push warning;Collect historical data closed-loop feedback optimization model parameters;Application optimization model realizes road network meteorological warning.The application significantly improves the spatiotemporal accuracy and response efficiency of meteorological warning, reduces the road network traffic risk under adverse weather.

The invention relates to the technical field of vehicle engineering and intelligent traffic, and discloses a road gradient calculating and grading method based on multi-source vehicle data, which comprises the following steps: introducing a vehicle model adaptation mechanism to carry out multi-source data acquisition and preprocessing to obtain multi-source key data; based on the multi-source key data, obtaining an original time sequence slope angle through gravity component extraction, scene adaptive double-model slope angle calculation and noise suppression multi-layer optimization based on slope continuity characteristics; performing continuous gradient validity verification on the original time sequence gradient angle to obtain a target time sequence gradient angle; based on the target time sequence slope angle, performing primary grading by adopting a slope grading system pre-constructed by combining the road type and the traffic risk; and correcting the initial grading based on a time sequence grading comparison strategy to obtain a final grading result. According to the method, multi-source data are fused, a physical model is constructed to achieve slope accurate calculation, a dynamic grading system is constructed based on the road engineering standard to achieve scientific grading, and the precision and practicability problems are solved from the principle level.

The application provides a solution method, device and equipment for ECU abnormal reset and a readable storage medium. The method comprises the following steps: when the electric control unit (ECU) is reset and fails to recover, the number of continuous resets of the ECU, the maximum interval duration between all adjacent two resets during the continuous multiple resets of the ECU, the battery temperature and the current vehicle speed are obtained; when the number of continuous resets of the ECU, the maximum interval duration, the battery temperature and the current vehicle speed meet preset conditions, the battery motor relay switch is enabled through a hardware circuit. Through the application, when the ECU of a vehicle running at a high speed suddenly resets and fails to recover, the vehicle loses power, and the problem of great traffic risk is solved.

This invention discloses a method, medium, and system for dynamic testing of water traffic risks. The method includes: establishing an internal information set of the water traffic system by extracting information from ship equipment, maritime departments, and captain consultations; this information set includes navigation environment information, human-computer interaction information, and ship-to-shore communication information; extracting and measuring the non-uniform characteristics of information transmission within the water traffic system using information flow theory; and using wavelet transform and spectral analysis to extract and analytically model the dynamic characteristics of water traffic risks; then constructing a dynamic testing model of water traffic risks using a data-driven approach; finally, inputting the abnormal characteristic values ​​of the water traffic system information in the test area into the dynamic risk testing model to obtain the calculated risk value for that area. This invention can quickly achieve dynamic testing of water traffic risks, improving the efficiency and targeting of water traffic safety supervision.

The invention belongs to the technical field of traffic safety risks, and discloses a traffic safety risk accurate triggering and closed-loop treatment method based on multi-modal sensing, which comprises the following steps: acquiring traffic environment data in real time through multi-modal sensing equipment such as a camera, a laser radar, an ultrasonic sensor, a meteorological sensor and an unmanned aerial vehicle; a traffic environment model is generated by adopting a data fusion algorithm, and risks are identified and early warning is dynamically triggered based on deep learning and reinforcement learning algorithms; the system automatically executes signal lamp regulation and control, path guidance, emergency scheduling, driver warning and other response measures according to the risk level; and the feedback module continuously evaluates the treatment effect and adaptively optimizes the strategy, so that accurate identification, intelligent decision and closed-loop treatment of traffic risks are realized, and the road safety and traffic efficiency are effectively improved.

This invention belongs to the field of bridge traffic risk assessment technology, and specifically relates to a method for assessing traffic risk on downhill bridge sections based on reliability analysis. The method comprises the following steps: using driving speed as a random variable, constructing a vehicle braking traffic risk function for the downhill bridge section based on the sight distance required for vehicles going downhill and the emergency stopping distance provided by the bridge deck; analyzing reliability indices and failure probabilities to determine traffic risk level classification standards; determining reliability indices based on the longitudinal force coefficient detection value of the bridge deck, thereby determining the traffic risk level of the section and assessing the risk of the downhill bridge section. This invention quantifies the impact of decreased bridge deck anti-skid performance on driving safety, predicts potential accidents from a probabilistic perspective, and thus achieves the purpose of timely risk warning and timely hazard elimination.

1. Name of the product in this design: Branch Sentinel (A3). 2. Purpose of this design: A warning device for real-time monitoring and early warning of traffic risks at road intersections, reducing the risk of traffic accidents. 3. The key design feature of this product is its shape. 4. The image or photograph that best illustrates the design's key points: a 3D model.

This invention discloses a highway fog visibility monitoring and fog clearance notification control system, belonging to the field of highway fog monitoring technology. The invention includes a highway fog prediction module, a highway fog monitoring generation module, a highway fog monitoring module, a highway fog clearance control module, and a database. By predicting the fog level of a corresponding fog day on the highway, it extracts the parameter set of the corresponding fog day video monitoring, analyzes the factors affecting the jumps in the corresponding fog day video monitoring, and obtains the set of adjustment viewpoint directions for the corresponding fog day video monitoring, thereby supplementing the corresponding highway monitoring facilities to improve facility monitoring. It also analyzes the visibility of the corresponding fog day on the highway, and implements the corresponding fog day clearance control to ensure highway traffic safety and reduce the traffic risks caused by poor visibility in foggy weather.

The present application belongs to the technical field of vehicle trajectory analysis, and provides a kind of spatiotemporal conflict risk composite risk assessment method and system, respectively based on collision time and rear intrusion time to construct two kinds of basic conflict risk metrics of rear-end and lane change;Introduce Copula theory to depict the nonlinear statistical dependence relationship between collision time and rear intrusion time, construct a composite conflict risk that can quantify the synergistic gain effect of rear-end and lane change;Construct a three-dimensional orthogonal verification system composed of extreme deceleration events, high-energy conflict events and high-speed fluctuation events;Two-stage Copula selection is carried out, and the weight is determined by multi-objective weight optimization method;Based on two kinds of basic conflict risk, composite conflict risk, optimal Copula function and determined weight, the final comprehensive spatiotemporal conflict index is calculated, and composite risk assessment is carried out according to the comprehensive spatiotemporal conflict index.The present application improves the precision of traffic risk assessment.

The application discloses a kind of vehicle traffic risk avoidance systems and methods based on road constraint perception, it is related to network freight security supervision technical field, including: acquisition tire-pavement contact vibration signal, frequency spectrum characteristic vector is obtained based on short-time fourier transform, and current road friction coefficient identification value is output by preset feature mapping;Operable width is constructed at each longitudinal section in the foresight distance in front of the vehicle driving direction;Road surface state is converted into operable width;With current speed, the road surface state is converted into operable width from spatial domain to time domain, and time series operable width curve is obtained;The upper limit of lateral maneuvering speed at each time section is calculated, and the safe operation time window is obtained;According to the relationship with preset first time threshold value and second time threshold value, determine the current traffic risk level.The application solves the problem of safety operation space estimation deviation caused by uneven road adhesion under the same meteorological condition.

This invention discloses a real-time traffic safety risk early warning method and system based on vehicle-road-cloud integration, belonging to the field of intelligent transportation technology. The method includes: acquiring vehicle-side perception data, roadside perception data, cloud-based historical traffic data, and high-precision map data; performing spatiotemporal alignment and heterogeneous fusion of vehicle-side and roadside perception data, combining high-precision map data and cloud-based historical traffic data to generate a dynamic traffic environment digital twin, inputting it into a spatiotemporal trajectory prediction model, and outputting a future trajectory probability distribution; calculating collision risk indicators based on the future trajectory probability distribution and generating a global risk field map; generating tiered early warning information based on the global risk field map and distributing it to target vehicles to complete real-time early warning. This invention solves the technical problem that existing technologies cannot predict and quantitatively assess the future collision risks of traffic participants in advance, achieving the technical effect of forward-looking prediction and accurate early warning of traffic risks.

The invention provides a tunnel construction vehicle scheduling system and method, and relates to the technical field of tunnel construction, and the scheduling system comprises a data obtaining unit which is used for obtaining construction stage data corresponding to each point location of a tunnel construction line; the grade analysis unit is used for performing protection analysis based on the construction stage data to obtain the tunnel protection grade of each point location; the index calculation unit is used for calculating a traffic risk index of the point location based on the tunnel protection level and the historical vehicle traffic data; the restriction generation unit is used for generating vehicle restriction data corresponding to each point location according to the traffic risk indexes; and the scheduling management and control unit is used for performing scheduling management and control on the construction vehicles according to the vehicle limitation data. According to the scheduling system and method provided by the invention, traffic risk monitoring can be carried out on each point location in the tunnel, so that the traffic risk is kept in a controllable range under scheduling management, and the traffic safety of vehicles in the tunnel is improved.

1. Name of the product in this design: Branch Sentinel (A5). 2. Purpose of this design: A warning device for real-time monitoring and early warning of traffic risks at road intersections, reducing the risk of traffic accidents. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The invention provides a large transport vehicle traffic capacity dynamic evaluation method based on traffic obstacle information, and relates to the technical field of satellite remote sensing and intelligent traffic decision cross. A candidate road network corridor is used as a geo-fence to call a satellite image to construct a width correction road topology; dynamically calculating a turning radius based on the topological road vector surface, and outputting minimum turning radius data; recognizing a height-limiting obstacle and gradient data by taking topology as a reference and combining with a satellite image; extracting a road center line network, and projecting the three types of data to generate traffic obstacle distribution data; and adopting vehicle traffic capacity constraint to construct a traffic association envelope, traversing obstacle data to execute multi-parameter linkage evaluation, and outputting a traffic risk thermodynamic diagram. The technical problems that road obstacle data updating lags behind, turning space matching is not accurate, and the height limit misjudgment rate is high in the prior art are solved. The technical effects of providing real-time and accurate traffic capacity pre-judgment and path decision support for large transport vehicles and effectively avoiding traffic risks are achieved.

The invention provides a vehicle early warning method, device and equipment, a medium and a program product. According to the method, response duration of each traffic risk event corresponding to a user identifier is acquired according to the acquired user identifier; according to the obtained traffic element data and the vehicle driving data, prediction data of each traffic risk event is determined, and the prediction data comprises a prediction occurrence result and a prediction occurrence duration. And for each traffic risk event whose predicted occurrence result indicates that the traffic risk event will occur, if the predicted occurrence duration of the traffic risk event is less than or equal to a target duration, early warning is performed, and the target duration is the sum of a preset redundant duration and a response duration of the traffic risk event. According to the scheme, the response duration of each traffic risk event corresponding to the user identifier is determined, and the traffic risk events are screened according to the target duration, so that the matching between the event prediction occurrence duration and the response duration of the user to the traffic risk events can be improved, and the early warning accuracy is improved.

The invention relates to the technical field of video and image analysis, in particular to a traffic road law enforcement intelligent management system based on a video and image recognition technology, which comprises a road video monitoring acquisition module, a video image recognition module, an intelligent calculation analysis module, a risk level quantification module and a decision generation module. According to the method, through dynamic comparison of pixel differences of continuous frames in a video sequence and fusion of convolution feature extraction, a vehicle running track and road surface state change can be accurately captured in a changeable traffic environment, and quantitative aggregation is performed on abnormal areas in combination with a probabilistic calculation mode, so that a risk distribution result has stable and continuous spatial features, and the risk distribution accuracy is improved. And further, joint evaluation of multi-dimensional parameters is introduced into risk level division, so that boundaries among different risk levels are clearer and more consistent, the identification precision and classification rationality of complex traffic risks are improved, refined monitoring of road operation states and visual expression of risk results are realized, and a reliable spatial decision basis is provided.

The invention discloses a ship traffic transportation safety supervision method and system based on AI and big data, particularly relates to the field of ship traffic supervision, and is used for solving the problems that hidden ship positions and supervision blind areas are difficult to dynamically identify and the traffic risk early warning capability is insufficient in the existing ship traffic supervision process. AIS ship dynamic data is collected through a base station to generate a real-time traffic state set, a hidden suspected ship is marked after ship AIS signal loss is detected, steering avoidance behaviors of surrounding ships are analyzed, a hidden probability position domain is inferred through reverse cross positioning, space-time correlation verification is performed on a hidden target, a motion parameter range of the hidden target is reversely deduced, and a real-time traffic state is obtained. And meanwhile, a monitoring blind area migration path and a monitoring blind area range are identified based on a continuous monitoring period, a dynamic monitoring risk area is generated in the electronic chart, and traffic monitoring early warning is carried out on ships entering the area, so that dynamic identification and traffic safety monitoring of hidden ship activities are realized.

The invention discloses an intelligent traffic guidance system based on risk grading management and control. A risk grading module performs risk grading on multi-source data by means of PCA, RFE and LSTM-GRU models; the traffic guidance module generates a road section level macro strategy and a lane level micro strategy according to the risk level, and equipment is coordinated by the Petri network; the information issuing module realizes effective information issuing through differentiated tone coding, laser attenuation compensation and information cognition load optimization; the operation guarantee module uses an LSTM-Autoencoder model to predict equipment faults, adopts a mode of combining edge deployment with cloud synchronization, and cooperates with dual-mode anti-interference communication to guarantee stable operation of the system. The method comprises the steps of risk grading, traffic guidance strategy generation, information release and operation guarantee. According to the system and the method, traffic risks can be accurately predicted, multi-mode cooperative guidance is realized, the intelligent level of traffic control in severe weather is improved, stable and reliable operation of the system is guaranteed, the accident rate is effectively reduced, and the system and the method are suitable for intelligent traffic management scenes of expressways.

1. Name of the product in this design: Sidewalk Sentinel with Camera (A6). 2. Purpose of this design: A warning device for real-time monitoring and early warning of traffic risks at road intersections, reducing the risk of traffic accidents. 3. The key design features of this product are the combination of shape and pattern. 4. The image or photograph that best illustrates the design's key points: a 3D model.

The application discloses a highway full-chain risk identification early warning method and system, relates to the technical field of highway risk identification, and has the technical scheme as follows: three-dimensional terrain point cloud data, wind-sand movement trajectory data, traffic flow data, highway section information and road domain structure constraint data of a to-be-monitored highway are acquired; wind-sand traffic risk correlation regions are obtained based on the wind-sand movement trajectory data, the traffic flow data and the highway section information, wherein each wind-sand traffic risk correlation region is correlated with a wind-sand influence time range and a terrain type; risk identification constraint conditions are generated according to the road domain structure constraint data and regional risk weight values; and a risk factor identification early warning scheme containing an early warning facility layout scheme, a monitoring strategy and hierarchical early warning threshold values is generated according to the risk identification constraint conditions, so that the feasibility and pertinence of the early warning scheme are effectively improved.