447results about "Traffic control supervision" patented technology

The invention discloses a traffic supervision system applied to an intelligent street lamp and an intelligent supervision method thereof, relates to the technical field of intelligent traffic, and solves the problems that an existing intelligent street lamp system lacks a physical-digital mapping relation, edge computing resource allocation is low in efficiency and cloud computing delay is high. According to the scheme, on the basis of multi-sensor data fusion, space-time reference unification is carried out by adopting an atomic clock and a GNSS, and a dynamic causal graph is constructed through a graph neural network, so that abnormal event detection is optimized; an improved Jaccard space-time similarity algorithm is adopted to optimize calculation task allocation, an edge calculation cluster is constructed based on 5G-V2X, and high-risk region identification and traffic flow prediction are carried out; a LiFi or 5G-UWB communication medium is adaptively selected through a multi-modal fusion reinforcement learning algorithm, and efficient early warning information synchronization is realized; according to the method, the multi-source data fusion value and the early warning precision are remarkably improved, the computing power resource utilization rate is optimized, and the instruction real-time performance and the system self-adaptive capability in a complex environment are enhanced.

The invention relates to a traffic signal lamp fault diagnosis method and system based on artificial intelligence, and the method comprises the steps: obtaining hardware sensor data, visual monitoring data and communication data of traffic signal lamps, carrying out the integrated label processing, and obtaining a label multi-source data set; performing feature extraction on the label multi-source data set to obtain a hardware state feature set, a display anomaly feature set and a communication fault feature set; performing multi-modal fusion analysis on the hardware state feature set, the display anomaly feature set and the communication fault feature set according to a preset signal fault diagnosis model to obtain a fault diagnosis result and a confidence score; and performing strategy matching on the confidence score and a preset strategy library to obtain an initial control strategy, and performing instruction optimization on the initial control strategy according to a fault diagnosis result to obtain a fault processing control strategy. According to the invention, comprehensive analysis of multi-source data can be realized, and comprehensiveness and accuracy of fault diagnosis are improved.

The invention discloses an application method for parking project calculation, and relates to the technical field of parking lot control. The application method of parking project measurement and calculation comprises the following steps: S1, data acquisition; s2, evaluating a network state; s3, acquiring an environment compensation coefficient; and S4, evaluating the operation state of the camera. According to the invention, the network abnormity evaluation value is obtained through the obtained network performance data, whether network optimization is carried out or not is judged based on the network abnormity evaluation value and the preset network abnormity threshold value, and then the operation state data of the main camera and the environment data obtained after optimization are analyzed to obtain the environment compensation coefficient. And according to the operation state data of the main camera, the environment compensation coefficient and the network abnormity evaluation value, obtaining an operation abnormity evaluation value, and based on the operation abnormity evaluation value and a preset operation abnormity threshold value, determining whether to adjust the main camera, thereby improving parking space guiding accuracy. The problem of low parking space guiding accuracy in parking space guiding application of parking project measurement and calculation in the prior art is solved.

The invention relates to the technical field of traffic management, in particular to a road traffic dispersion and emergency command system construction method based on multi-source data fusion, and the method comprises the steps: collecting traffic flow sensor data, video monitoring data, meteorological data, social media user feedback data and the like through a multi-source data collection module; a data fusion and processing module is used, and data-level, information-level and decision-level fusion algorithms are adopted to carry out standardization processing, key information extraction and comprehensive evaluation and prediction on multi-source data. And constructing a traffic flow prediction model based on machine learning, such as a long and short-term memory network model, and accurately predicting the traffic flow. And according to the prediction result and the real-time traffic condition, a traffic dispersion strategy making module is used for adjusting signal lamp timing, issuing traffic guidance information and allocating traffic police resources. In the aspect of emergency command, multi-source data are integrated to evaluate event severity, deploy rescue resources and determine an optimal rescue route, and multi-department information sharing and cooperative work are achieved.

The invention provides a road traffic space-time service platform and method based on multi-source heterogeneous data fusion. The platform comprises a data acquisition module; a data fusion management module; the base map making module is used for generating a road traffic space-time service base map taking a road intersection as a key node; the traffic situation analysis module is used for forming a traffic situation prediction map; and the decision support and personalized service module is used for providing real-time and visual traffic situation display, traffic guidance strategies and personalized travel services for traffic managers and traffic participating users based on the traffic situation prediction map. According to the invention, real-time and visual decision support is provided for traffic managers, and personalized travel schemes can be customized for traffic participating users, so that comprehensive perception and accurate regulation and control of road traffic operation states are realized, and the intelligent level of traffic operation management is greatly improved.

The invention, which relates to the technical field of traffic safety dispersion, discloses an anti-vehicle-blocking traffic safety dispersion method for reconstruction and expansion of an expressway, and the method comprises the following components: S1, constructing a multi-dimensional information collection system, S2, establishing an information intelligent transmission network, S3, carrying out a traffic jam prediction algorithm, and S4, carrying out precise early warning and intelligent guidance. According to the invention, rapid and efficient transmission of information is realized through the converged communication technology, the communication technology not only improves the speed and stability of data transmission, but also can realize intelligent switching in different signal environments, thereby providing timely and reliable traffic information for a traffic management center, enabling the traffic management center to make a response rapidly, and improving the traffic management efficiency. Accurate early warning and intelligent guidance are implemented, and traffic jam and safety accidents are effectively avoided.

The invention discloses an expressway in-transit vehicle dynamic behavior analysis method and device and a medium, and belongs to the field of electric digital data processing. The method comprises the following steps: preprocessing multivariate data collected in real time, and fusing the preprocessed multivariate data to form original path information of a vehicle; on the basis of a road network topological structure and a shortest path algorithm, original path information is fitted, and a continuous and complete vehicle driving track is generated; based on the vehicle driving track, comparing the multivariate data in real time, determining whether the vehicle has an abnormal behavior, if yes, continuing to judge an abnormal type corresponding to the vehicle, and generating early warning information corresponding to the abnormal type; and according to the multivariate data of the to-be-predicted road section in the expressway, predicting the traffic flow of the to-be-predicted road section in real time, and displaying the traffic flow and early warning information of the to-be-predicted road section through a user interaction interface, thereby realizing dynamic behavior analysis of the in-transit vehicles on the expressway.

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.

The invention discloses a traffic transportation emergency command and dispatch system based on big data resources, which is constructed based on the big data resources and mainly comprises a data acquisition module, a big data processing and analysis module, an event early warning and prediction module, an emergency command and dispatch module, an information display and interaction module and the like. Various devices (a sensor, a camera, a GPS and the like) are utilized to comprehensively collect data such as traffic flow, road conditions, weather, events and the like. Acquisition has the characteristics of high frequency and high precision, supports a plurality of data transmission modes and protocols, and can integrate and format the acquired data to guarantee data quality and convenience of subsequent processing. According to the system, effective utilization of traffic data is realized through collaborative operation of all the modules, and the traffic transportation emergency management capability is improved from multiple aspects, including improvement of management efficiency, enhancement of emergency response capability, improvement of decision support, enhancement of information transparency, optimization of resource allocation, reduction of economic loss and improvement of public safety.

The invention belongs to the technical field of data processing, and particularly relates to a vehicle brake reminding method and system based on big data, and the method comprises the steps: collecting environment data around a vehicle through a sensor in the vehicle, and collecting historical driving data, including driving habits of a driver, a vehicle driving track and a road congestion condition; formats of data from different sources are unified, subsequent processing and analysis are facilitated, and data from different sensors and external data sources are fused; based on the driving state of the current vehicle and the surrounding environment data, the road congestion condition and the accident occurrence probability are analyzed in real time, a machine learning or deep learning model is utilized, historical data and real-time data are combined, and the necessity and the emergency degree of braking under the current road condition are evaluated; according to the real-time analysis result and the brake risk assessment, whether the driver needs to be reminded to brake is determined, and the effects of predicting possible brake risks in the future, reminding the driver to pay attention to the front road condition and taking necessary precautionary measures are 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 method and a system for identifying traffic conflicts in a road interleaving area, particularly relates to the field of traffic management, is used for solving the problems of low identification precision and early warning delay of the traffic conflicts in the interleaving area, and is used for accurately capturing risk events and filtering non-intentional behavior interference based on a composite trigger mechanism of turn light activation and transverse displacement rate; a behavior feature vector is matched with a conflict type template library to realize self-adaptive interception of an asymmetric time window, and risk evolution cycles of scenes such as sharp cut-in and slow lane change are completely covered; introducing a dynamic window expansion mechanism driven by a conflict energy gradient to ensure the whole-process capture of a risk accumulation stage; multi-scale spatio-temporal features are extracted in combination with a convolutional network to generate a thermodynamic diagram, and refined positioning of risk spatio-temporal distribution is achieved; an early warning signal grading mechanism and a closed loop feedback optimization system are established, the system adaptability is continuously improved, real-time and accurate decision support is provided for an intelligent traffic management system, the secondary accident risk is effectively restrained, and the road network traffic efficiency is optimized.

The invention relates to a method for situational hazard assessment (24) of an operating situation (16) of a vehicle (12), comprising environmental sensing (28) of a vehicle environment (30) that at least partially includes one road user (14) during the operating situation (16) of the vehicle (12) and calculation (32) of a hazard probability (34) in the operating situation (16) depending on at least one operating parameter (36) of the vehicle (12), wherein the hazard probability (34) depends on the at least one road user (14), the controllability (22) of the road user (14) in connection with the operating situation (16) is estimated, and the hazard probability (34) is calculated depending on the estimated controllability (22). Furthermore, the invention relates to a method for vehicle operation (10).

The invention relates to the technical field of intelligent traffic, and discloses an intelligent traffic-oriented 5G + V2X module and risk early warning terminal and an application method thereof, and the intelligent traffic-oriented 5G + V2X module and risk early warning terminal comprises an equipment end and a cloud platform. The equipment end is mounted on a vehicle and comprises a real-time data acquisition module, a front-end anomaly detection model and a risk alarm; the real-time data acquisition module is responsible for acquiring data related to a vehicle and real-time data of the surrounding environment of the vehicle; the front-end anomaly detection model is deployed at an equipment end; the risk alarm triggers an alarm device when the front-end anomaly detection model detects a potential risk or an abnormal condition; the cloud platform comprises a data extraction module, a data center module and a real-time calculation module; wherein the data extraction is responsible for receiving, extracting and storing data acquired by a vehicle sensor and a camera from an equipment end; the data center stores and manages a large amount of vehicle data; accurate collection of the vehicle body motion state is improved, the position information of the vehicle is more accurate, and the tracking capacity of the system on the vehicle position and the driving track is improved.

The invention provides a blind area early warning method and device based on a look-around system, a vehicle and a storage medium, and the method comprises the steps: obtaining a current look-around image, and recognizing an object in the current look-around image and the type of the object; obtaining a plurality of continuous historical look-around images, and obtaining the relative movement speed of each object according to the current look-around image and the plurality of continuous historical look-around images; according to the relative movement speed, the object type and the object number, obtaining an object weighted characteristic value, and according to the current operation state information and the current driving environment information of the vehicle, obtaining an operation state weighted characteristic value and a driving environment weighted characteristic value respectively; according to the object weighted feature value, the running state weighted feature value and the driving environment weighted feature value, a trained early warning strategy classification model is adopted, an early warning strategy is obtained, and blind area early warning is completed. Based on multi-dimensional data analysis, it is ensured that a complex traffic environment can be accurately responded in real time, and the adaptability of blind area early warning is improved.

Facility marking modules (FMM) and methods and systems of using the same. Sets of FMMs are configured and deployed in a given facility, such as a facility configured with very narrow aisles (VNA) to enable alerts between vehicles in danger of colliding in the same aisle of the facility while preventing nuisance alerts between vehicles that are in close proximity but in different aisles such that the danger of colliding is minimal.

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 discloses an unmanned aerial vehicle autonomous inspection system, and the system comprises a far-end monitoring device which receives and stores information related to the traffic condition of each road section from at least one big data platform, generates a task instruction enabling an unmanned aerial vehicle to execute autonomous cruise, and transmits the task instruction to a cloud server; the cloud server receives and analyzes the task instruction, generates a control instruction for enabling the unmanned aerial vehicle to perform autonomous cruise, and sends the control instruction to the unmanned aerial vehicle control device according to the information of the position of the road section; the unmanned aerial vehicle control device receives the control instruction from the cloud server, selects an unmanned aerial vehicle according to a preset rule, and sends the control instruction to the selected unmanned aerial vehicle; and the unmanned aerial vehicle performs autonomous cruise based on the control instruction received from the unmanned aerial vehicle control device. According to the unmanned aerial vehicle autonomous inspection system, the accuracy and efficiency of the unmanned aerial vehicle autonomous inspection system for road network monitoring are improved by counting traffic road condition information from each channel, and the unmanned aerial vehicle autonomous inspection system is applied to construction of a Qinghai-Tibet Plateau bulk mineral database.

The invention discloses a safety supervision system for road operation, and belongs to the technical field of road operation supervision, and the system specifically comprises an instruction collection module which receives a digital permission instruction set comprising an operation area polygon vertex coordinate set and an effective construction period set; the instruction analysis module generates a road self-adaptive dynamic supervision boundary, divides operation stages and configures state identifiers according to the road self-adaptive dynamic supervision boundary; the space-time detection module acquires geographic coordinates and operation timestamps of construction machinery in real time; the space-time judgment module verifies space compliance and time validity, and triggers road network traffic monitoring when border crossing or aging failure occurs; the traffic reverse control module generates a permission correction instruction based on the vehicle passing mode change and feeds back the permission correction instruction to the approval system to reconstruct a permission instruction set; according to the invention, all the modules cooperate to form a whole-process closed loop from instruction receiving, analysis, detection, determination to dynamic correction, so that the accuracy and efficiency of highway operation supervision are effectively improved.

The invention provides a traffic risk intelligent monitoring and early warning method, system and device and a medium, and belongs to the technical field of intelligent traffic. The method comprises the following steps: collecting real-time traffic data, environmental meteorological data and accident report data as source data; standardizing the source data, and storing the standardized source data into a database according to different data structures in a classified manner; classifying the source data according to information recorded by the source data by utilizing a target monitoring model and an object comparison technology; the event type of the source data comprises a weather event, a vehicle event, a facility event and an environment event; based on the source data of different event types, respectively calculating the score of each event; calculating a real-time comprehensive risk index based on the score of each event; and determining a risk level based on the real-time comprehensive risk index, adding a risk identifier at an early warning position in a monitoring platform map according to the risk level, and dividing a road section management area for the early warning position.

The invention relates to the technical field of traffic control systems, and particularly discloses an ETC-based in-transit vehicle digital monitoring method and system, and the method comprises the steps: data acquisition, abnormal parking evaluation, road section congestion degree judgment, and road section dredging plan optimization. The method comprises the following steps: determining a vehicle abnormal parking evaluation coefficient, counting the number of abnormal vehicles, timely finding potential safety hazards, combining abnormal vehicle driving data and portal road section data, determining a road section congestion degree index, matching and implementing a dredging plan, obtaining a dredged road section and vehicle driving information, calculating a second dredging degree value, and matching a threshold value. And comparing with the first index, and comprehensively judging whether to optimize the plan or not, thereby realizing the digital monitoring of the total in-transit vehicles in the highway network, improving the overall monitoring efficiency of the in-transit vehicles, and ensuring the smoothness of the highway network.

A collision detection method, system, medium and program product based on 3D projection modeling, in which the following steps are used: receiving an approach monitoring video; establishing a collision enclosing cuboid model of a vehicle; obtaining video information of other objects in the parking lot, and establishing other collision enclosing cuboid models corresponding to other objects; projecting the collision enclosing cuboid model and other collision enclosing cuboid models onto the ground plane to obtain the vehicle rectangle and other rectangles; projecting the vehicle rectangle and other rectangles onto the length and width to obtain four projection results; when any projection result does not contain an overlap point, the vehicle is determined to be in a safe state; when only three projection results contain an overlap point, the vehicle control computer issues a preset warning message; when all four projection results contain an overlap point, the vehicle is determined to have an accident. This reduces the probability of collision or scratching of cars in small and complex parking lots.

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 discloses an omnibearing blind area monitoring and driving safety auxiliary positioning system, which relates to the field of intelligent traffic systems and specifically comprises a blind area monitoring module, a depth point cloud module, a target tracking module, a target prediction module and an AR display module. High-resolution cameras are installed at all parts of a vehicle, environment image data are collected in real time, feature point detection and matching are carried out, a depth map and left and right environment point cloud data are generated, optical flow analysis and the depth map are utilized, target changes are dynamically detected, real-time monitoring is ensured, a deep learning model is constructed, and the real-time monitoring is realized. Advanced features are extracted, time sequence analysis is carried out, the target motion position is predicted, whether the target enters a monitoring blind area is judged, alarm information is visualized through the augmented reality technology, multiple warning lamps and color codes are used, target motion and the vehicle position and direction are visually indicated, and therefore the driving safety is improved.

The invention relates to the technical field of traffic safety management, in particular to an expressway emergency response blocking device, system and method, which are arranged on a preset road section, and comprises the following steps: a road detection system monitors data of key indexes of vehicles and road conditions in real time; the remote control system receives the data, transmitted by the data transmission system, of the road detection system, judges the road condition according to the data, activates corresponding emergency measures automatically or through manual instructions according to the road condition, and dispatches and guides starting and stopping of the warning system and the physical interception system; the guiding and warning system is used for guiding and warning; when the physical interception system is activated, an entity or virtual barrier is formed to block a road; the road section information feedback system carries out information transmission with an external user system and information transmission between the expressway emergency response blocking devices of different road sections. According to the scheme, the safety and the emergency response efficiency of the expressway are greatly improved, the accident rate is reduced, and secondary accidents are minimized.

The invention relates to the technical field of traffic signal lamps, and discloses a mobile traffic signal lamp with an intelligent network connection function, which comprises a traffic signal lamp module and a control system which are mutually connected, and is characterized in that the traffic signal lamp module comprises a traffic signal lamp body, a camera, a solar charging panel and a storage battery; the camera and the solar charging panel are installed on the traffic signal lamp body, and the storage battery is installed in the traffic signal lamp body. The control system comprises a signal control machine, a channel driver, a cloud platform and a charging controller, the signal control machine is connected with the cloud platform and the channel driver, the channel driver is connected with the traffic signal lamp body, the charging controller is connected with the solar charging panel and the storage battery, and the charging controller is used for controlling the solar charging panel to charge the storage battery. According to the invention, through the intelligent network connection technology, remote monitoring and control of the traffic signal lamp module are realized, and the efficiency and flexibility of traffic management are improved.

The embodiment of the invention provides an abnormal signal lamp data processing method and device, equipment, a medium and a program product, which can be used in the technical field of automatic driving. According to the method, after it is determined that abnormal signal lamp data exists in the vehicle flow direction, the abnormal type is determined according to the signal lamp data of the vehicle flow direction, then the signal lamp data of the vehicle flow direction are processed according to a data processing strategy corresponding to the abnormal type, and target signal lamp data corresponding to the vehicle flow direction are determined. According to the scheme, the traffic flow direction signal lamp data is processed according to the data processing strategy corresponding to the abnormal type, so that the situation that the same traffic flow direction has multiple signal lamp data is processed, and the same traffic flow direction only has one target signal lamp data at one moment; the autonomous vehicle may travel according to the target signal light data.