223results about "Traffic control supervision" patented technology

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

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 discloses a highway hazardous chemical substance liquid leakage accident monitoring method and a highway hazardous chemical substance liquid leakage accident monitoring system based on computer vision. The method comprises the following steps: arranging and installing an identifier configured for hazardous chemical substance vehicle identification on a vehicle for loading and transporting hazardous chemical substances; the identifier comprises an optical identifier configured to be used for improving the visual identification degree of the dangerous goods vehicle and a digital identifier configured to realize encrypted storage and quick reading of vehicle information; on the basis of an existing road monitoring system, a lightweight identification model configured for detecting the states of an optical identifier and a digital identifier in real time is deployed and loaded in the existing road monitoring system; performing visual positioning on the road surface projection pattern through a camera, and calculating the real-time position of the vehicle by combining map coordinates so as to supplement the positioning blind area of the GPS in the tunnel mountain area; accident probability calculation is carried out based on the identification state to determine whether the vehicle is suspected to have an accident; and when the accident is judged to be suspected, triggering an accident response process. And the accident response efficiency is obviously improved.

The invention relates to the technical field of traffic safety, and discloses a traffic risk real-time alarm method and system. The traffic risk real-time alarm method comprises the following steps: determining a traffic event alarm area and a first traffic participant detection frame according to traffic perception information sent by a multi-source perception device in real time; determining a target traffic participant detection frame according to the first traffic participant detection frame; based on the state information of the target traffic participant detection frame at the current time, predicting the position information of the target traffic participant detection frame at the future time; and carrying out traffic risk identification and alarm according to the position information of the target traffic participant detection frame at the current time, the position information of the target traffic participant detection frame at the future time and the traffic event alarm area. Through the scheme of the invention, missed identification and wrong identification of traffic risk events are avoided, the reliability and accuracy of traffic risk identification and alarm are improved, the alarm time delay is reduced, and invalid alarm is avoided.

The embodiment of the invention discloses a traffic instruction conflict detection method and system and a computer storage medium. The traffic instruction conflict detection method comprises the steps that instruction core information is extracted from a received traffic instruction; querying an instruction conflict rule in an instruction conflict database according to the instruction core information to obtain a query result; performing instruction conflict hierarchical detection based on the query result to obtain an instruction conflict type corresponding to the traffic instruction; according to the instruction conflict type, performing conflict processing on the traffic instruction to obtain a conflict processing result; and feeding back the conflict processing result to the instruction conflict database, and updating an instruction conflict rule.

The invention relates to the technical field of intelligent traffic equipment management, in particular to a signal equipment health state monitoring method and system, and the method comprises the steps: receiving operation, external field sensing and communication link data of signal equipment through a protocol conversion interface, converting the data into a unified format, and storing the data in a data medium; after abnormal value filtering and missing value completion, operation characteristic parameters are extracted, a layered health degree evaluation model is constructed, equipment-level, intersection-level and trunk-level health degrees are calculated in sequence, and early warning is triggered if the health degrees are lower than a preset threshold value; constructing a device-link-scene association map, positioning a fault type and a fault source in combination with a fault feature matching algorithm, and analyzing a fault influence range; and according to the fault influence range, the device priority and the operation and maintenance resource real-time state, generating an operation and maintenance work order and a resource scheduling suggestion, and pushing early warning information, a fault positioning result and a health degree report to an operation and maintenance terminal. Full-level health perception and operation and maintenance resource optimization can be realized, the fault handling period is shortened, and the traffic passing efficiency is guaranteed.

The embodiment of the invention relates to the technical field of driving safety management, and particularly discloses a driving safety management method and system based on driving track monitoring. According to the embodiment of the invention, the driving state data of the target engineering vehicle is directly or cooperatively collected; generating smooth trajectory data, and performing anomaly evaluation on the smooth trajectory data; an optimal driving path is planned, and dynamic driving guidance is carried out; performing dynamic risk analysis and early warning prompt on the target engineering vehicle; and constructing a driving risk portrait, and performing periodic assessment on a target driver of the target engineering vehicle. Trajectory cleaning and reconstruction can be carried out, smooth trajectory data can be generated, abnormity assessment can be carried out, dynamic risk analysis and early warning prompt can be carried out on a target engineering vehicle, a driving risk portrait can be constructed, periodic assessment can be carried out, personalized management of engineering transport vehicle driving safety can be realized, management precision can be improved, and the engineering transport vehicle driving safety management efficiency can be improved. And the driving data can be effectively utilized, and a basic support is provided for management decision guidance and strategy optimization.

The invention relates to the technical field of traffic control systems, and discloses an unmanned aerial vehicle highway inspection method, which comprises the following steps: monitoring link quality parameters through a basic communication link between a dormant unmanned aerial vehicle and a roadside unit, calculating a communication entropy value of the link quality parameters, and triggering the unmanned aerial vehicle to accurately wake up to execute inspection when a phase change early warning condition is met; according to the method, traditional active inspection is converted into an event-driven passive response mechanism by utilizing internal association of traffic flow phase change and communication entropy, so that unmanned aerial vehicle resources are naturally focused at a traffic state critical point, and meanwhile, stable operation and efficient energy utilization of the system under complex working conditions are realized through environment adaptive compensation and responsibility area overlapping driving.

The invention belongs to the technical field of conflict detection, and particularly relates to a traffic permission and traffic prohibition signal conflict detection circuit system and a traffic permission and traffic prohibition signal conflict detection control method. The Gate end voltage of a power MOS tube in the power supply starting control circuit is pulled down when the passing permission signal conflicts with the passing forbidding signal, so that the power supply starting control circuit is kept in an off state, and the quick starting circuit is prevented from being restarted due to the change of the output current; the voltage conflict detection circuit detects whether a conflict exists between the passing permission signal and the passing prohibition signal, and generates a conflict detection signal when the conflict is detected; and the current output control circuit is used for receiving the conflict detection signal of the voltage conflict detection circuit, lowering the input detection when the passing permission signal conflicts with the passing prohibition signal, so that the whole circuit system mistakenly considers that the input voltage is kept below the minimum starting voltage, and meanwhile, the current output control circuit is matched with the power supply starting control circuit to limit the circuit output and reduce the circuit loss.

The invention discloses a highway emergency rescue early warning system, which relates to the technical field of intelligent early warning, comprises a division module, a selection module and an early warning module, and is used for dividing severity levels, obtaining resource data, carrying out first selection, checking the resource data, carrying out secondary distribution and carrying out early warning. The accident severity level is quantified in a second level, once the level is locked, a rescue spectrogram gives fixed vehicle and personnel configuration, zero manual intervention of data, level and resources is realized, delay caused by traditional telephone layer-by-layer reporting is avoided, dishes are directly ordered according to orders no matter which road section the accident occurs, the problems of wrong vehicle type adjustment and less equipment are solved, and the service life of the system is prolonged. And the arrival time of the rescue station and the arrival time of the toll station are calculated at the same time, the first selection is made by using the same algorithm, secondary allocation is automatically triggered during missing, supplementary adjustment is carried out from adjacent stations or an emergency linkage library, and level and resource alignment is ensured.

The invention discloses a highway edge calculation flow regulation and control method based on OpenHarmony, and belongs to the technical field of intelligent traffic control systems. The method comprises the following steps: traffic flow detection adopts a lightweight improved YOLOv8 model, a backbone network is replaced by an OfficientViT, a neck network is optimized by phantom convolution, a feature fusion module is enhanced by SCConv, and a dynamic non-monotonic focusing loss function is introduced; according to traffic flow prediction, a spatio-temporal feature fusion Transform model is constructed, a multi-head convolution low-rank decomposition attention mechanism is adopted to capture time dependence, spatial topological features are extracted in combination with attention graph convolution, spatio-temporal information is fused through a gating unit, and a prediction error is controlled within 1.5-3.0 times of historical standard deviation; a triple lightweight LSTM-AutoEncoder is adopted for abnormal early warning, so that the false alarm rate is reduced; the OpenHarmony distributed task scheduling is cooperated with the above modules, the variable information sign and the dynamic speed limit sign are driven to execute regulation and control, the response delay is reduced, the problem of real-time accurate regulation and control under the condition that the computing power of the edge device is limited is solved, and the traffic efficiency is improved.

The invention relates to the field of intelligent traffic and vehicle-road cooperation, in particular to a traffic operation double-control intelligent system. Comprising a multi-mode roadside sensing module used for obtaining traffic operation state data and environment state data of a road section; the double-control cooperation unit is used for jointly generating variable speed limit setting and signal control adjustment quantity based on the traffic operation state data and the environment state data; the signal control module is used for carrying out on-line adjustment on intersection phase timing or trunk line phase difference according to the signal control adjustment amount; the road information broadcasting module is used for broadcasting road information to the vehicle; the consistency verification module is used for carrying out consistency comparison on the road information, the actual signal lamp state and the phase remaining time; and the safety domain management module is used for applying double-hysteresis safety domain constraint to the variable speed limit setting and the signal control adjustment amount. According to the invention, the cooperation of two links is realized, the vibration suppression steady state is consistent with the release, and the driving behavior is stably and verifiably guided.

According to the method for evaluating the urban trunk traffic signal control effect based on the single-vehicle theory provided by the invention, the obtained gate data is used for deducing to obtain a time theoretical value sequence for all vehicles on a certain lane to arrive at a downstream intersection within a certain time period needing phase positioning; and further obtaining a green light phase arrival proportion and an upstream intersection signal control phase evaluation index. According to the evaluation method provided by the invention, the Platon Ratio index calculation model which is more suitable for the actual situation and is based on the bicycle identification data is provided; the theoretical arrival time of the vehicle arriving at the parking line at the intersection is deduced by using the vehicle matching characteristics of the single vehicle identification data, and the new model has practical guiding significance; the TAD trunk line coordination evaluation index based on the single vehicle identification data is provided, the PCD index for evaluating the trunk line coordination control effect provided in the prior art and a calculation method are improved, the limitation of the PCD index in domestic application is improved, and the method has more practical guiding significance in China.

The invention is suitable for the technical field of traffic management, and provides an urban traffic dynamic command method and system based on digital twinning. According to the method, the high-quality data set is generated through direct multi-source data acquisition, restoration and integration; hierarchical dynamic storage is carried out, and block chain security management is carried out; carrying out stored multi-source data acquisition and processing, and constructing a real-time dynamic traffic twin model; based on the real-time dynamic traffic twinborn model, decision optimization of traffic control is carried out, evaluation feedback is carried out through edge calculation, and model parameters of the real-time dynamic traffic twinborn model are corrected; a three-level data storage system is adopted, a data management center is constructed, and data life cycle management is carried out. According to the method, the real-time dynamic traffic twin model can be constructed by fusing multi-source data, traffic flow simulation, congestion prediction and dynamic optimization are realized, a scientific basis is provided for smart city traffic management, traffic congestion is effectively relieved, and the urban traffic efficiency is 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.

A traffic information providing method and a system thereof are provided. The traffic information providing method according to an embodiment of the present disclosure is executed by a computing system, and may comprise the steps of: inputting a plurality of traffic mode data and real-time traffic information, which are generated in advance, into a traffic prediction model; outputting an error of each piece of traffic mode data in the plurality of pieces of traffic mode data at the first time point as an input result; and generating traffic information from a departure place to a destination using first traffic mode data with the smallest error among the plurality of traffic mode data, where the traffic mode data is a combination of mode speeds for each road segment, which is a smallest unit of a road.

This invention provides a highway traffic spatiotemporal service platform and method based on multi-source heterogeneous data fusion. The platform includes: a data acquisition module; a data fusion management module; a base map creation module for generating a highway traffic spatiotemporal service base map with highway intersections as key nodes; a traffic situation analysis module for generating a traffic situation prediction map; and a decision support and personalized service module for providing real-time, visualized traffic situation displays, traffic guidance strategies, and personalized travel services to traffic managers and traffic participants based on the traffic situation prediction map. This invention not only provides real-time, visualized decision support for traffic managers but also allows for customized personalized travel plans for traffic participants, thereby achieving comprehensive perception and precise control of highway traffic operation status and greatly improving the level of intelligent traffic operation management.

The invention relates to a signal control problem diagnosis method for an urban trunk road, and the method comprises the steps: taking the average vehicle control delay of the urban trunk road as an evaluation index, quantifying the benefits of different signal control schemes, and obtaining a benefit matrix; based on the scene to be diagnosed, defining a cost function, and obtaining a cost matrix; based on the benefit matrix and the cost matrix, generating a recommendation system special value matrix, and performing complementation and deviation correction on the recommendation system special value matrix; quantifying the similarity between the scene to be diagnosed and each typical traffic demand scene in the recommendation system characteristic value matrix, and calculating the reference value weight of the typical traffic demand scene to the scene to be diagnosed according to the similarity proportion; and based on the reference value weight, calculating unreasonable setting probabilities of various signal control parameters in the scene needing to be diagnosed, and realizing signal control problem diagnosis according to the unreasonable setting probabilities. Compared with the prior art, the urban trunk signal control problem can be diagnosed only by depending on small sample historical data.

The invention provides an omnibearing intelligent safety monitoring and driving early warning method fusing multiple devices, and the method is characterized in that the method comprises the steps: a multi-source data collection step: collecting motorcycle body data, surrounding environment data, driver state data and vehicle-road cooperation data in real time; a risk assessment step of obtaining a stability risk level and a collision risk level based on the ontology data, the surrounding environment data and the vehicle-road cooperation data, the stability risk level reflecting a vehicle falling risk, and the collision risk level reflecting a collision risk between the vehicle and other traffic participants; and an early warning step of deciding an early warning level and an optimal early warning mode according to the stability risk level and the collision risk level and based on the driver state data.

The invention discloses a traffic light fault real-time identification and alarm method based on improved Yolov5, and relates to the technical field of intelligent traffic, and the method comprises the steps: S1, building a traffic light fault data set in advance, and collecting traffic light images and video data under different scenes, illumination conditions and weathers; step S2, constructing an improved Yolov5 model; and S3, extracting images of a real-time video stream to be detected according to frames, inputting the images into the trained improved Yolov5 model, outputting position information, light color states and fault probabilities of traffic lights, and analyzing and judging fault types in combination with a preset threshold value and a multi-frame time sequence. Real-time video stream processing and multi-frame time sequence analysis are combined, the high-frame-rate real-time detection requirement can be met, time sequence faults can be accurately judged, and the limitation that manual inspection is low in efficiency and poor in real-time performance is broken through; the fault response and handling period is greatly shortened, and powerful technical support is provided for guaranteeing normal operation of the traffic lights and improving urban traffic management efficiency.

The application provides a traffic signal lamp fault emergency management system and method based on an Internet of Things large model, and relates to the field of urban traffic signal lamp supervision. The method is executed by an emergency supervision management platform of the traffic signal lamp fault emergency management system based on the Internet of Things large model, and comprises the following steps: determining a state evaluation result of a signal lamp based on first monitoring data and second monitoring data of the signal lamp; determining a target signal lamp based on the state evaluation result; cutting off the power supply of a fault signal lamp and issuing a detour suggestion to target personnel; determining a fault prediction situation of an abnormal signal lamp based on the first monitoring data, the second monitoring data and the state evaluation result of the abnormal signal lamp; and determining a maintenance parameter based on the fault prediction situation and sending the maintenance parameter to maintenance personnel. The application can accurately diagnose the state of the signal lamp, cut off the power supply and issue a detour suggestion when a fault occurs, predict a fault and determine a maintenance parameter when an abnormality occurs, realize rapid response and active intervention, and improve operation and maintenance efficiency.

The invention relates to a traffic signal lamp emergency control method and system, a terminal and a storage medium, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining lamp bead state data of a preset traffic signal lamp; judging whether the lamp bead state data is consistent with preset lamp bead normal state data or not; if yes, continuing to obtain the lamp bead state data for cyclic judgment; if not, acquiring a lamp bead fault type; and controlling a preset traffic signal lamp adjusting device to adjust the traffic signal lamp according to the lamp bead fault type. The invention has the effect of improving the emergency capability of the traffic signal lamp.

The present invention relates to a method implemented by a processor of a vehicle control unit for managing driving situation anomalies, wherein the vehicle may receive a first driving situation based on data from a first vehicle sensor, receive a second driving situation based on data from another data source, determine a driving situation anomaly based on the first driving situation and the second driving situation, send a request for information from the vehicle to a remote driving situation database, receive the requested information from the driving situation database, and resolve the driving situation anomaly based on the requested information from the driving situation database.

The invention belongs to the field of urban rail transit emergency command, and particularly relates to an urban rail transit emergency command method based on digital twinning, which comprises the following steps: collecting multi-source data and setting an emergency trigger condition; deploying a lightweight self-adaptive adjustment twinborn model; according to the rail transit guidance method, the problem of how to estimate the flow direction of crowds and issue an evacuation strategy under the condition that a fire occurs in a scene of urban rail transit and a camera is shielded by smoke or powered off is solved; according to the invention, the method can achieve the estimation of the flow direction of the crowd under the condition of no sight line, and gives an evacuation command accordingly.

An information processing system (10) comprises: an image acquisition means (110) that acquires a captured image of the interior and / or exterior of a moving body (50); a processing means (130) that executes a plurality of different processes on the basis of the image; and an information acquisition means (120) that acquires moving-body-related information indicating a status pertaining to the moving body. The processing means controls an execution mode pertaining to each of the plurality of processes on the basis of the moving-body-related information. According to such an information processing system, a plurality of processes related to a moving body can be executed appropriately.

The invention discloses a multi-source data fusion and traffic mechanism driven checkpoint equipment closed-loop evaluation method, relates to the technical field of checkpoint equipment evaluation, and solves the technical problem of poor evaluation effect of existing checkpoint equipment. The method comprises the following steps: constructing an upstream equipment mapping table of the bayonet equipment based on road network information and position information of the bayonet equipment; verifying the historical traffic mode of the gate based on the historical traffic data, calculating the real-time accuracy of the gate device based on the floating car trajectory data, and finally comparing the traffic conditions of the to-be-evaluated gate device and the upstream gate device through the upstream device mapping table to judge whether the traffic is conserved or not; determining a mistakenly shot license plate number according to a license plate verification rule; the real license plate number of the mistakenly shot license plate number, the association algorithm and the hardware information are determined by using similar vehicles in the upstream and downstream vehicles, factors which can influence the equipment effect are analyzed, and improvement measures are adopted in a targeted manner; according to the method, environment interference, algorithm output characteristics and hardware state parameters are associated to form a closed-loop feedback mechanism.

The invention relates to the technical field of expressway safety warning, in particular to a segmented flashing warning lamp system triggered at any point and a dynamic indexing method.The system comprises a central monitoring layer, a core control layer, an area control layer and a field equipment layer, and a PLC of the core control layer numbers warning lamps in sequence; after any trigger point i is triggered, the index 300 adjacent warning lamps are turned on, and the specific index number can also be set according to site requirements. The flash is divided into three sections according to the number and is divided into six times per second, four times per second and two times per second, indexes are updated for new trigger points in the same range, warning sections are newly added for new trigger points in different ranges, and resetting can be carried out after faults are removed; according to the segmented flicker warning lamp system triggered at any point and the dynamic indexing method, the first-time warning of an accident can be achieved, a driver can judge the distance from the accident point through the flicker frequency and rapidly brake, major serial traffic accidents are effectively restrained, casualties and economic losses are reduced, and the segmented flicker warning lamp system is suitable for multiple accident scenes and high in reliability.

The invention discloses a highway ice and snow covered pavement traffic safety risk grade prediction method. The method comprises the following steps: firstly, collecting vehicle operation data, meteorological data and road information data; then, dynamic response generated in the braking or accelerating process of the vehicle is analyzed, key parameters such as the speed, the acceleration and the braking force of the vehicle are combined, and the numerical value of the road surface friction coefficient is estimated; afterwards, environmental factors such as ice and snow, temperature and snowfall intensity are comprehensively considered, the road adhesion coefficient obtained through estimation is corrected, a numerical value better fitting the actual road condition is obtained, and road adhesion coefficient data finally used for risk assessment are calculated; and then, in combination with the corrected road adhesion coefficient under the ice and snow weather condition, carrying out detailed analysis on the adhesion coefficients of different areas by applying a risk assessment function. According to the method, low-cost, high-frequency and wide-coverage attachment coefficient estimation is realized, and the comprehensive risk condition of the ice and snow covered pavement can be reflected more comprehensively and stereoscopically.

The invention belongs to the technical field of urban traffic early warning, and provides an urban traffic linkage early warning method and system based on intelligent monitoring, and the method comprises the steps: obtaining multi-source traffic monitoring data, carrying out the multi-mode fusion processing of the multi-source traffic monitoring data, and forming a traffic state data sequence of a target road network; and constructing a traffic change curve based on the traffic state data sequence, carrying out change feature separation on the traffic change curve, classifying change features into rapid change components and trend change components, and carrying out quantitative processing on the separated change features. Through principal component analysis and wavelet transform, a component representing rapid disturbance and a component representing slow change accumulation are separated from the mixed traffic data, and a differentiated clustering model and a data-driven trigger rule are respectively constructed for the two modes with different essence. Therefore, the sudden accident and the slow-moving congestion can be distinguished from the source of the time sequence evolution, and the problems of instantaneous noise false report and trend event missing report are optimized.

In various examples, the systems and methods of this disclosure can train and use machine learning models to determine attributes and, in some cases, classifications related to traffic lights in order to establish traffic rules for the operation of a machine (e.g., an autonomous or semi-autonomous machine or vehicle) in an environment. For example, an image representing a traffic light device can be applied to a machine learning model that includes a variety of component heads. Each of the component heads can be trained to recognize different attributes and / or combinations of attributes associated with the traffic light device. Additionally, in some examples, the machine learning model can include a fusion head trained to classify the traffic light device.For example, the fusion head can classify the traffic light device using the detected attributes and / or using a combined feature vector from multiple feature vectors applied to the multitude of component heads.