CN108961790B - Bad weather early warning management system and method based on four-dimensional live-action traffic simulation - Google Patents

Bad weather early warning management system and method based on four-dimensional live-action traffic simulation Download PDF

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CN108961790B
CN108961790B CN201810818810.6A CN201810818810A CN108961790B CN 108961790 B CN108961790 B CN 108961790B CN 201810818810 A CN201810818810 A CN 201810818810A CN 108961790 B CN108961790 B CN 108961790B
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vehicle
data
information
dimensional
simulation
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CN108961790A (en
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冯保国
付增辉
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Hebei Deguroon Electronic Technology Co ltd
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Hebei Deguroon Electronic Technology Co ltd
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/091—Traffic information broadcasting
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/048—Detecting movement of traffic to be counted or controlled with provision for compensation of environmental or other condition, e.g. snow, vehicle stopped at detector
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0968—Systems involving transmission of navigation instructions to the vehicle
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast

Abstract

The invention provides a severe weather early warning management system based on four-dimensional live-action traffic simulation; the system comprises a multi-element omnibearing tracking detection radar sensor, a data acquisition and analysis processor, a cloud processing server and a four-dimensional simulation management workstation main device; the radar tracks vehicles and pedestrians on the road and acquires position information of the vehicles and the pedestrians, synchronously triggers the license plate snapshot camera to snapshot the vehicles in real time, the system acquires node weather information of the current road section, carries out four-dimensional simulation of traffic road condition environment and starts early warning rules, judges the road section where the vehicles and the pedestrians are about to pass through by acquiring the position information of the vehicles and the pedestrians, and sends warning information to the vehicles and the pedestrians about to pass through the road section in a point-to-point or broadcast mode to carry out warning prompt. According to the four-dimensional live-action traffic simulation, a road manager can formulate an emergency plan, manage and control roads and rescue accident vehicles and pedestrians, push information to avoid planning paths, and reduce the risk of high-speed travel in severe weather.

Description

Bad weather early warning management system and method based on four-dimensional live-action traffic simulation
Technical Field
The invention relates to the technical fields of vehicle pedestrian tracking and positioning technology, license plate snapshot technology, vehicle pedestrian three-dimensional simulation technology, road video monitoring technology, road environment monitoring technology, virtual camera arrangement monitoring technology, traffic condition simulation technology, network communication technology, vehicle road cooperation technology, weather simulation technology, environment simulation technology, computer graphics technology, video rendering technology, three-dimensional high-precision GIS map technology, virtual reality fusion technology, vehicle road cooperation technology, navigation technology, early warning technology and self-learning technology, in particular to a four-dimensional live-action traffic road condition perception early warning monitoring and managing system based on radar tracking and positioning.
Background
With the rapid increase of the mileage of highway traffic and the increase of traffic flow in China, the highway transportation has higher and higher status in national economy. Under the existing severe weather, a driver cannot clearly see road surface information due to various environmental interferences and light interference. The high-speed driving speed is high, a driver can only observe the weather of the current environment visually, the weather of a destination to be approached cannot be predicted in advance, the weather changes rapidly, the weather is often rainy in the east and west or the local weather is severe, and the weather of other adjacent areas is gentle, if the front road section is severe, for example, the weather of heavy fog, hail, rainstorm, heavy snow and the like appears on the front road section; and the present highway section of traveling just in time can walk around bad weather highway section, but current traffic early warning equipment does not possess the function of propelling movement near the highway section weather, and the driver can not in time know the weather condition of the highway section that is about to the way, has increased the risk of driving from this, and the driver can not in time carry out preferred route conversion through the weather condition of knowing in time, leads to because weather reason shutoff when high-speed, causes the traffic number to the pressure increase.
Disclosure of Invention
The object of the present invention is to solve at least one of the technical drawbacks mentioned.
In order to achieve the above object, an embodiment of an aspect of the present invention provides a severe weather early warning management system based on four-dimensional live-action traffic simulation, which includes a vehicle snapshot camera, a data acquisition analysis processor, a multi-element omni-directional tracking detection radar, a cloud processing server, a four-dimensional live-action traffic simulation monitoring management station, and other main devices; the multi-element omnibearing tracking detection radar sensor equipment carries out real-time tracking and positioning on all moving vehicles or pedestrians in a radar area and acquires the real-time position of each target and the original data information of the radar in a 360-degree omnibearing scanning mode; the all-round tracking detection radar sensor is the core component of this scheme anterior segment core data acquisition, and this radar adopts and is become 77GHz high frequency transmitting unit, signal receiving unit, data processing unit and communication unit etc. group by dominant frequency, and its core data processing unit adopts multithreading high speed processor can trail the location simultaneously and be no less than 1000 target object. The radar detector can track and position at least 1000 target objects in a whole area with the radius of 500 meters by taking a radar as a center in a 360-degree high-speed scanning mode, the target tracking and positioning accuracy error is less than 17.5 cm, the moving speed range of a target detected by the radar is 0-250Km/h, the positioning requirement of the vehicle for realizing full-speed intelligent driving is completely met by data interaction with the tracked target for 800 times per second, and in addition, the radar can also provide important information such as the instant speed, the moving direction, the longitude and latitude, the target size, the ID number, the direction angle and the like of each vehicle within one kilometer. The multi-element sensor that multi-element omnidirectional tracking detected radar sensor self was taken and synthesize meteorological data and comprehensive gaseous data that other sensors gathered include: wind speed, wind direction, rainfall, air pressure, temperature, humidity, illumination, light intensity, sulfur dioxide, carbon monoxide, methane and volatile matters; the radar sensor adopts an integrated design, and the whole equipment adopts IP67 safety protection level for ensuring the service life of the radar. All parts of the radar are selected and adopt low-power-consumption designs and devices. The radar adopts a 100M network port to carry out data communication with the outside.
The license plate snapshot camera is mainly used for acquiring and snapshotting license plate information and picture information of vehicles running on a road, and characteristic information of each vehicle running on the road is acquired through a graphic analysis technology, a trigger signal of the license plate snapshot camera is synchronously triggered and snapshotted by a multi-element omnibearing tracking detection radar sensor through a data acquisition analysis processor, the data acquisition analysis processor simultaneously acquires radar data information (tracking and positioning information) of the vehicle, which is snapshotted by the radar trigger license plate snapshot camera, and vehicle picture information and vehicle license plate information which are grabbed by the license plate snapshot camera, and performs data summarization and packaging to be transmitted to the cloud processing server, and the cloud processing server performs analysis processing and data reduction on the data;
the data acquisition and analysis processor is used for acquiring original radar scanning data sent by the multi-element omnibearing tracking and detecting radar sensor, then carrying out interference filtering, forming tracking, summarizing, analyzing and judging on the original data to give real-time tracking and positioning information of each vehicle or pedestrian, sending the real-time tracking and positioning information to the cloud processing server through the transmission communication equipment to process the data, carrying out data transmission on the data of the multi-element omnibearing tracking and detecting radar sensor with overlapped N areas through the data acquisition and analysis processor arranged on the roadside or the cloud processing server, realizing the whole-course tracking real-time uninterrupted tracking and positioning of the tracked vehicle or pedestrian in such a way until the tracked vehicle or pedestrian leaves a detection area covered by N continuous radars, wherein when the same vehicle runs on a road through the multi-element omnibearing tracking and detecting radar sensor with the overlapped N detection areas, the system can distribute unique identification information for the vehicle, all vehicles running on the road can be endowed with unique identification codes to keep the uniqueness and sustainability of the information of the vehicles in the four-dimensional real-scene traffic road condition perception early warning monitoring management system, and the data acquisition analysis processor also needs to complete the following contents:
1) setting detection area and detection lane for radar
2) Calling a mathematical operation model to obtain important information such as real-time movement speed, direction, distance, target included angle, track, driving time, area and the like of each target object
3) And sending the acquired data information into a preset alarm judgment rule model and an alarm type judgment rule model for verification, and screening out targets with composite conditions for calibration and outputting alarms.
4) Starting a tracking camera and a monitoring camera to track and view a target object in real time
5) Acquiring and tracking video images of the camera and sending the images to a monitoring center for managers to check videos in real time
6) Various data acquired by the local multi-element sensor are acquired, real-time analysis and judgment are carried out through the data analysis alarm model, and when the preset judgment mechanism is met
7) Detecting the event type includes: important events such as vehicle stop, traffic accident, vehicle jam, vehicle queue, vehicle reverse running, vehicle slow running, throwing, pedestrian, illegal lane change and illegal invasion of specific area, abnormal weather and the like
The cloud processing server acquires node weather information, comprehensive gas data information, vehicle image characteristic information and vehicle radar data information of a current road section which is secondarily processed, packaged and sent by the front-end data acquisition analysis processor, carries out four-dimensional simulation on a traffic state and a road condition environment, starts an early warning rule, judges a road section where vehicles and pedestrians are about to pass through by acquiring position information of the vehicles and pedestrians, and sends warning information to the vehicles and pedestrians about to pass through the road section in a point-to-point or broadcast mode to give a warning prompt. According to the four-dimensional live-action traffic simulation, a road manager can make an emergency plan, manage and control roads and rescue accident vehicles and pedestrians, push information to avoid planned paths, and reduce the risk of high-speed travel in severe weather
The four-dimensional real scene management workstation calls and generates a three-dimensional high-precision simulation GIS map according to the data sent by the cloud processing server, the three-dimensional high-precision simulation GIS map is formed by taking a 3DGIS graph rendering engine as a bottom layer, acquiring road data through unmanned aerial vehicle shooting and laser scanning and combining with corresponding three-dimensional simulation software,
the cloud processing server is mainly communicated with a data acquisition and analysis processor in front, acquires data information, alarm information, video information and the like sent by the data acquisition and analysis processor, sends a control instruction, a parameter setting instruction, a linkage instruction and the like to the data acquisition and analysis processor, starts a database to store all data in the system, carries out data communication in man-machine interaction work, returns the alarm information, receives and completes all information and control instructions sent by a workstation, and completes the configuration information of all data acquisition and analysis processors in the system, builds a system architecture, builds a network data link, and carries out important work such as local or remote maintenance, data viewing, screening and exporting. All potential or possible accident-forming key information is processed, corresponding decision rules are started for processing, information interaction is carried out with a monitoring data server, an SDK secondary development kit or an API data interface can be provided for a road monitoring system or a third party, and linkage, sound alarm and the like with the monitoring system (including a video control platform) are achieved. The equipment mainly completes the following work: the method comprises the following steps of data exchange, storage function, system communication, maintenance, centralized alarm, plan generation, linkage plan execution, loading of a GIS electronic map, graphic acceleration engine, detection and alarm of abnormal accidents of vehicles and pedestrians, image storage, data storage, alarm event video storage, report production, third-party linkage instruction sending, equipment system parameter setting, preset plan sending and daily operation human-computer interface centralized alarm.
The vehicle license plate snapshot camera sends the captured vehicle photos and the vehicle license plate information to the data acquisition and analysis processor for image analysis, the obtained vehicle characteristic information is sent to the cloud processing server for image analysis and data analysis, various three-dimensional models identical to the actual vehicle and the vehicle characteristic data information corresponding to the three-dimensional models are stored in a database of the cloud processing server, the server obtains the vehicle photos and the vehicle characteristic information of each vehicle sent by the vehicle license plate snapshot camera and then performs one-to-one comparison and matching with the three-dimensional vehicle model data in the local database, and the vehicle three-dimensional models which are identical to or close to the data are extracted. Secondly, the multi-element omnibearing tracking detection radar sensor further fuses and matches the vehicle tracking positioning data information acquired by the real-time radar of the vehicle with the three-dimensional model of the vehicle, and simultaneously displays the three-dimensional model of the vehicle on a three-dimensional high-definition simulation GIS map, three-dimensional vehicle simulation animation which is the same as the actual road in motion direction, motion speed, real-time position, vehicle color and vehicle appearance can be obtained after the three-dimensional vehicle simulation animation is processed by combining data compensation technology and a graphic rendering acceleration engine, the one-to-one monitoring of the actual moving vehicle on the road can be realized by monitoring the motion track and the traffic state of each vehicle in the three-dimensional simulation animation, and then the multi-element weather data information, the air quality data, the illumination data and the clock information acquired by the multi-element omnibearing tracking detection radar sensor are sent to the system for omnibearing environment simulation so as to more vividly simulate the traffic road conditions, The traffic state, the running vehicles and the environment are simulated in an all-around way, and high-quality four-dimensional graphic pictures can be displayed for a user manager.
The cloud processing server receives and stores all matched vehicle characteristic information, vehicle tracking and positioning information, meteorological data information, comprehensive gas data information and acquired third-party data; the method comprises the steps that a four-dimensional live-action simulation module is used for matching characteristic information with all prestored three-dimensional vehicle models according to received vehicle characteristic information and vehicle tracking and positioning information, extracting the three-dimensional vehicle models which are the same as vehicles running on an actual road, carrying out multi-data fusion simulation on real-time tracking and positioning information scanned by a radar of each vehicle, the three-dimensional vehicle models and a three-dimensional high-precision GIS map prestored in a cloud processing server, forming a four-dimensional live-action simulation display, viewing and monitoring scheme, and detecting the weather environment of the vehicles running on the actual road in real time by monitoring the weather environment of the vehicles in the four-dimensional live-action simulation; the cloud processing server performs big data comprehensive analysis according to an artificial intelligence learning method by using preset weather information early warning rules of the current road section, preset weather early warning rules of a road section to be driven into and preset weather early warning rules of a plurality of time periods in the future according to the received alarm information, generates an early warning processing plan and sends the early warning plan to a four-dimensional simulation monitoring management station;
the four-dimensional simulation monitoring management station receives three-dimensional vehicle simulation fusion data and four-dimensional simulation driving paths sent by the cloud processing server and carries out simulation animation display in the weather environment; and checking the alarm event according to the received alarm event and generating an alarm event report.
Preferably, the multi-element sensor carried by the multi-element omnibearing tracking detection radar sensor and the meteorological data and the comprehensive gas data collected by integrating other sensors comprise: wind speed, wind direction, rainfall, air pressure, temperature, humidity, illumination, light intensity, sulfur dioxide, carbon monoxide, methane and volatile matters; the vehicle information includes: vehicle license plate information, vehicle size, vehicle body color, vehicle model, vehicle type, vehicle series and vehicle logo; the radar scan data includes: instantaneous speed, moving direction, longitude and latitude, ID number and direction angle, XYZ three-dimensional coordinate.
Preferably, the method further comprises the steps that the cloud processing server carries out prejudgment on a road section which the vehicle is going to enter, and first priority prejudgment is carried out according to whether the road section in front of the driving direction of the vehicle is a cross road section and a high-speed passing road section; and carrying out second priority prejudgment according to the daily average traffic flow of the road section in front of the driving direction of the vehicle.
Preferably, the cloud processing server further includes path planning, four-dimensional real scene simulation, road condition and environment simulation of road state, early warning output, and three-party warning prompting device, including: and data communication is carried out by automobile navigation, mobile phone navigation, vehicle-road cooperative equipment, a road information board, fog zone induction and the like. Performing four-dimensional real-scene traffic weather road condition simulation in a numerical value increasing mode by using historical data or current traffic state data, road condition data and other data acquired by the system, and judging whether severe weather occurs on the road section according to a result after simulation; when the system judges severe weather and sends out an alarm prompt, the system starts a path planning function module or starts an artificial path planning function module to plan a safe path, adopts traffic control and pushes alarm information, road condition information, traffic state information, safety prompt information, traffic control information, fault vehicle avoidance information and the like to drivers and conductors in the road section or the area to carry out safety alarm prompt by adopting a point-to-point mode or a broadcast mode through third parties (mobile, Unicom, telecom, Gaode and Baidu) or road side-mounted vehicle and road cooperation equipment, road condition information board equipment and the like.
Preferably, the storing, in the cloud processing server database, vehicle type three-dimensional vehicle models of various series of all brands of vehicles and data information corresponding to the three-dimensional models includes: the cloud processing server carries out image processing according to a vehicle picture shot by the vehicle snapshot camera, and obtains vehicle type characteristic information, vehicle body color information, vehicle type information, vehicle appearance information, vehicle size information and vehicle logo brand information after carrying out image analysis according to the vehicle picture obtained by the license plate snapshot camera; calling a vehicle three-dimensional model which is most consistent with a shot vehicle picture through an information matching technology, displaying the model in a three-dimensional high-precision GIS map which is acquired in advance in a cloud processing server, acquiring radar tracking data of each vehicle which is scanned by a radar and sent by a data acquisition and analysis processor according to the cloud processing server, fusing real-time tracking data of the radar vehicle with a corresponding three-dimensional vehicle model displayed in the three-dimensional high-precision GIS map, and starting a graphic rendering engine to perform graphic acceleration processing by adopting an inertial vehicle data compensation technology and a graphic processing technology; then, the acquiring of the multi-element environment data sent by the front-end data acquisition and analysis processor by the cloud service processor comprises the following steps: temperature and humidity data, comprehensive gas (sulfur dioxide, carbon monoxide, methane and volatile matters) data, brightness light intensity data, rainfall data, wind speed and wind direction data are superposed and called for environment simulation, road condition simulation and traffic simulation, so that a severe weather early warning management system based on four-dimensional live-action traffic simulation is formed, a corresponding early warning condition judgment module is started, and when the condition is met, warning is sent out outwards to realize multidirectional linkage processing, so that man-machine interaction and real-time display are carried out on a four-dimensional simulation monitoring management workstation.
The invention also provides a severe weather traffic early warning method based on the four-dimensional real-scene simulation road condition perception early warning monitoring management system, which is used for the severe weather traffic early warning system and comprises the following steps:
step S1, acquiring radar scanning, tracking and positioning data of each vehicle running on the current expressway by using a multi-element omnibearing tracking and detecting radar sensor, acquiring meteorological data and comprehensive gas data of the road section in real time according to preset frequency by using the multi-element omnibearing tracking and detecting radar sensor, synchronously triggering a license plate snapshot camera by a radar to acquire image information of the tracked vehicle, and respectively sending the radar scanning and tracking data of the vehicle, the image data of the vehicle, the meteorological data and the comprehensive gas data of the road section node to a data acquisition and analysis processor for preliminary data processing;
step S2, the data acquisition analysis processor starts an image recognition technology to extract vehicle characteristic information according to the received vehicle image data, and extracts vehicle tracking and positioning information according to the received radar scanning data; packaging the vehicle characteristic information, the vehicle tracking and positioning information, the meteorological data and the comprehensive gas data and then sending the packaged data to a cloud processing server to carry out secondary processing on the data again; the data acquisition analysis processor starts a target object tracking algorithm, a clutter map algorithm, an inertial motion data compensation algorithm and a dynamic data compensation algorithm according to the primary received vehicle radar scanning original data, screens, filters and analyzes the original data according to various parameters of abnormal event accident vehicle judgment conditions, detection areas, detection lanes and the like sent by the server to finally form effective data, and distributes target radar tracking and positioning identification information for each tracking target including vehicles and pedestrians. The data acquisition analysis processor simultaneously acquires vehicle image information captured by a license plate snapshot camera synchronously triggered and installed above a lane and radar vehicle tracking and positioning information for triggering the license plate snapshot camera (the information forms a pulse trigger signal after data shaping to trigger the license plate snapshot camera to snapshot), so that the data received by the server are the synchronous image information and radar tracking and positioning data information of the vehicle.
Step S3, the cloud processing server receives and stores all matched vehicle characteristic information, vehicle tracking and positioning information and weather parameters; the method comprises the steps that a three-dimensional simulation module is used for matching characteristic information with all pre-stored three-dimensional vehicle models according to received vehicle characteristic information and vehicle tracking and positioning information, the three-dimensional vehicle models which are the same as vehicles running on an actual road are extracted, real-time tracking and positioning information scanned by a radar of each vehicle, the three-dimensional vehicle models and a three-dimensional high-precision GIS map pre-stored in a cloud processing server are subjected to multi-data fusion simulation, a four-dimensional live-action simulation display, viewing and monitoring scheme is formed, and the weather environment of the vehicles running on the actual road is detected in real time by monitoring the weather environment of the vehicles in the four-dimensional live-action simulation; the cloud processing server performs big data comprehensive analysis according to an artificial intelligence learning method by using preset weather information early warning rules of the current road section, preset weather early warning rules of a road section to be driven into and preset weather early warning rules of a plurality of time periods in the future according to the received alarm information, generates an early warning processing plan and sends the early warning plan to a four-dimensional simulation monitoring management station;
step S4; the four-dimensional simulation monitoring management station receives the three-dimensional vehicle simulation fusion data and the four-dimensional simulation driving path sent by the cloud processing server and carries out simulation animation display in the weather environment; and checking the alarm event according to the received alarm event and generating an alarm event report.
Preferably, in step S1, the severe weather warning management system based on the four-dimensional live-action traffic simulation is characterized in that, in step S1, the radar real-time data of the vehicle includes: important information such as tracking and positioning information, instant speed information, movement direction information, longitude and latitude information, target size information, ID number information, type information, movement direction angle, XYZ three-dimensional coordinates and the like of the vehicle; the characteristic data of the vehicle includes: color, vehicle family, appearance, model, brand, model, age, vehicle license plate information, etc.; the multi-element environmental data includes: temperature and humidity data, comprehensive gas (sulfur dioxide, carbon monoxide, methane and volatile matters) data, brightness light intensity data, rainfall data and wind speed and direction data.
Preferably, in step S3, the method further includes the steps of pre-judging, by the cloud processing server, a road segment to which the vehicle is about to drive, and performing first priority pre-judgment according to whether the road segment ahead of the driving direction of the vehicle is a crossing road segment and a high-speed passing road segment; and carrying out second priority prejudgment according to the daily average traffic flow of the road section in front of the driving direction of the vehicle.
Preferably, the method further includes step S4, where the cloud processing server further includes path planning, four-dimensional live-action simulation, road condition and environment simulation of road state, early warning output, and three-party warning prompting devices, including: and data communication is carried out by automobile navigation, mobile phone navigation, vehicle-road cooperative equipment, a road information board, fog zone induction and the like. Performing four-dimensional real-scene traffic weather road condition simulation in a numerical value increasing mode by using historical data or current traffic state data, road condition data and other data acquired by the system, and judging whether severe weather occurs on the road section according to a result after simulation; when the system judges severe weather and sends out an alarm prompt, the system starts a path planning function module or starts an artificial path planning function module to plan a safe path, adopts traffic control and pushes alarm information, road condition information, traffic state information, safety prompt information, traffic control information, fault vehicle avoidance information and the like to drivers and conductors in the road section or the area to carry out safety alarm prompt by adopting a point-to-point mode or a broadcast mode through third parties (mobile, Unicom, telecom, Gaode and Baidu) or road side-mounted vehicle and road cooperation equipment, road condition information board equipment and the like.
Preferably, in step S2, storing, in the cloud processing server database, vehicle type three-dimensional vehicle models of all branded vehicles in various series and data information corresponding to the three-dimensional models includes: the cloud processing server carries out image processing according to a vehicle picture shot by the vehicle snapshot camera, and obtains vehicle type characteristic information, vehicle body color information, vehicle type information, vehicle appearance information, vehicle size information and vehicle logo brand information after carrying out image analysis according to the vehicle picture obtained by the license plate snapshot camera; calling a vehicle three-dimensional model which is most consistent with a shot vehicle picture through an information matching technology, displaying the model in a three-dimensional high-precision GIS map which is acquired in advance in a cloud processing server, acquiring radar tracking data of each vehicle which is scanned by a radar and sent by a data acquisition and analysis processor according to the cloud processing server, fusing real-time tracking data of the radar vehicle with a corresponding three-dimensional vehicle model displayed in the three-dimensional high-precision GIS map, and starting a graphic rendering engine to perform graphic acceleration processing by adopting an inertial vehicle data compensation technology and a graphic processing technology; then, the acquiring of the multi-element environment data sent by the front-end data acquisition and analysis processor by the cloud service processor comprises the following steps: temperature and humidity data, comprehensive gas (sulfur dioxide, carbon monoxide, methane and volatile matters) data, brightness light intensity data, rainfall data, wind speed and wind direction data are superposed and called for environment simulation, road condition simulation and traffic simulation, so that a severe weather early warning management system based on four-dimensional live-action traffic simulation is formed, a corresponding early warning condition judgment module is started, and when the condition is met, warning is sent out outwards to realize multidirectional linkage processing, so that man-machine interaction and real-time display are carried out on a four-dimensional simulation monitoring management workstation.
Compared with the existing high-speed detection device, the severe weather early warning management system based on the four-dimensional live-action traffic simulation provided by the embodiment of the invention at least has the following advantages:
1. a large number of weather parameters collected by a multi-element sensor in the multi-element all-directional radar sensor are detected by the multi-element all-directional tracking densely distributed along the road, and the weather data acquired through the Internet are combined, so that the actual traffic condition of each ID number running vehicle on the current road section can be simulated more truly.
2. The weather early warning module carries out pre-judgment on the road sections where the vehicles pass according to the extracted current speed parameters and current position parameters, calls weather parameters uploaded by a current multi-element weather sensor of the pre-judged road sections where the vehicles pass according to the pre-judged road sections where the vehicles pass, and pushes weather information of the pre-judged road sections where the vehicles pass one by one according to the sequence of the vehicles from the near to the far of the pre-judged road sections where the vehicles pass; the method and the device realize the early monitoring of the weather of the front road section, are favorable for a driver to select a proper road section in time and avoid the road section with severe weather.
3. When the four-dimensional simulation monitoring management station judges that the road section is a severe weather road section according to the weather parameters, parameters such as the speed parameter of a vehicle and the distance between adjacent vehicles are further extracted, when the speed of the vehicle is too low and the distance between adjacent vehicles is too small, the current weather condition is judged to be not suitable for high-speed driving, and the path planning module generates a driving avoiding planned path according to the weather parameters of the road section in front and pushes the driving avoiding planned path to drivers and conductors; the evacuation of vehicles in advance is realized, and the traffic pressure caused by closing the highway is favorably relieved.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
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The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a connection structure diagram of a severe weather early warning management system based on four-dimensional live-action traffic simulation according to an embodiment of the present invention;
fig. 2 is a flowchart of a method of a severe weather early warning management system based on four-dimensional live-action traffic simulation according to an embodiment of the present invention;
FIG. 3 is a schematic structural diagram of a multi-element weather sensor in a severe weather early warning management system based on four-dimensional live-action traffic simulation according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of a process from data acquisition by front-end equipment to central data fusion to four-dimensional live-action traffic simulation to early warning information transmission in the severe weather early warning management system based on four-dimensional live-action traffic simulation according to the embodiment of the present invention;
fig. 5 is a flow conversion diagram of artificial intelligence learning of a cloud processing server in the severe weather early warning management system based on four-dimensional live-action traffic simulation according to the embodiment of the present invention;
fig. 6 is a schematic diagram of path planning in bad weather early warning in a bad weather early warning management system based on four-dimensional live-action traffic simulation according to an embodiment of the present invention;
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
As shown in fig. 1, the severe weather early warning management system based on four-dimensional live-action traffic simulation provided by the embodiment of the invention includes a multi-element omni-directional tracking detection radar sensor, a vehicle snapshot camera, a data acquisition and analysis processor, a cloud processing server, a four-dimensional simulation monitoring management station, and other auxiliary devices and information receiving, sending and displaying devices;
the multi-element omnibearing tracking detection radar sensor acquires radar scanning tracking positioning data of each vehicle running on the current expressway, simultaneously acquires the road section meteorological data and comprehensive gas data in real time according to preset frequency, synchronously triggers the license plate snapshot camera to acquire the image information of the tracked vehicle, and respectively sends the vehicle radar scanning tracking data, the vehicle image data, the meteorological data and the comprehensive gas data of the road section node to the data acquisition analysis processor for data primary processing
As shown in fig. 4, the multi-element sensor and the integrated gas sensor of the multi-element omni-directional tracking detection radar sensor are used for acquiring meteorological data and integrated gas data of a highway around the multi-element omni-directional tracking detection radar sensor distributed by a plurality of nodes, and sending acquired meteorological parameters of the road section to the data acquisition and analysis processor according to a preset frequency;
as shown in fig. 1, the multi-element sensor carried by the multi-element omni-directional tracking and detecting radar sensor and the meteorological data and the comprehensive gas data collected by other sensors include: wind speed, wind direction, rainfall, air pressure, temperature, humidity, illumination, light intensity, sulfur dioxide, carbon monoxide, methane and volatile matters; the vehicle information includes: vehicle license plate information, vehicle size, vehicle body color, vehicle model, vehicle type, vehicle series and vehicle logo; the radar scan data includes: instantaneous speed, moving direction, longitude and latitude, ID number and direction angle, XYZ three-dimensional coordinate.
The data acquisition analysis processor receives the vehicle images and the radar scanning data at the same time, receives meteorological parameters of all road sections in real time, extracts vehicle characteristic information according to the vehicle images, and extracts vehicle tracking and positioning information according to the radar scanning data; the vehicle characteristic information, the vehicle tracking and positioning information and the meteorological parameters are sent to a cloud processing server; the data acquisition analysis processor allocates an ID number of a unique identifier to each vehicle according to the primarily received radar scanning data of the vehicle; extracting radar scanning data of the ID numbered vehicle to obtain vehicle tracking and positioning information, extracting vehicle characteristic information according to the received vehicle image, and matching the vehicle tracking and positioning information of the ID numbered vehicle with the vehicle characteristic information; the matched vehicle characteristic information and the matched vehicle tracking and positioning information are sent to a cloud processing server; the data acquisition analysis processor acquires a weather early warning rule set in the cloud processing server, extracts the current driving speed and the current position information of the vehicle according to the vehicle tracking and positioning information according to the weather early warning rule, performs weather information early warning on the current road section, weather early warning on the road section to be driven into and weather early warning in a plurality of time periods in the future, and sends early warning information to the cloud processing server.
As shown in fig. 4, a great deal of climate change data collected by a multi-element sensor in a multi-element omnidirectional radar sensor or weather data and sunshine (brightness intensity) data obtained through the internet are detected by the multi-element omnidirectional tracking distributed densely along the road, and the data are sent to a cloud processing server in combination with the change of time nodes (such as 24-hour time change, seasonal change and the like), so that the system can simulate the actual traffic condition more truly, the influence importance degree of the future weather condition on the traffic can be simulated by continuously deducing according to the existing traffic environment, and a more detailed and comprehensive travel scheme can be made by an end user.
It should be noted that the weather early warning rules pre-stored in the cloud processing server include real-time weather early warning and weather early warning at a future time, wherein, during the weather early warning at the future time, the cloud processing server acquires historical data and real-time weather satellite cloud map information on the internet, displays the weather forecast information for 24 hours, 48 hours or even 72 hours in the future according to the weather cloud map information and combines the recorded historical weather data, and performs the weather early warning according to the time selected by the user. For example, when an automobile is traveling on a highway in which storms are likely to occur, such as a coastal sea area or a desert, it is necessary to perform weather warning based on historical weather information at that time, which is recorded in combination with the speed and direction of movement of a typhoon center, a sandstorm center, or the like, displayed on a meteorological satellite cloud.
On one hand, the real-time weather early warning comprises the steps that the cloud processing server carries out real-time weather pushing on vehicles running on the road section according to real-time received meteorological parameters of the current road section, and real-time weather early warning of the current road section is achieved; and on the other hand, the method comprises the steps that the cloud processing service requires real-time provision of meteorological information of the road section for the vehicle about to enter the road section according to real-time received meteorological parameters of the road section to which the vehicle is about to enter, and in the process, the road section to which the vehicle is about to enter is pre-judged.
In one embodiment of the invention, when the cloud processing server carries out the judgment of the road section passed by the vehicle, the first priority judgment is carried out according to whether the road section in front of the driving direction of the vehicle is a cross road section and a high-speed passing road section; and carrying out second priority prejudgment according to the daily average traffic flow of the road section in front of the driving direction of the vehicle.
In the embodiment, when a vehicle runs on a high-speed road section, according to the running speed of the vehicle, if the running speed of the vehicle is maintained at 100KM/h, a four-dimensional simulation monitoring management station acquires the weather conditions of the road section at the position 20KM ahead in real time and sends the weather conditions to a driver, if the position within the position 20KM ahead and 10KM away from the vehicle exists as a cross road section and a high-speed passing road section, the weather conditions of the cross road section and the high-speed passing road section are preferentially pushed, and the weather of the road section with a large traffic flow is pushed in the high-speed passing road section in advance according to historical statistical data. Taking the example that the vehicle runs at the great high speed of G45, when the vehicle runs at the great high speed of G45 and runs to a male security road section, the front 20KM is a Kbazhou road section, the front 10KM is an intersection of the great high speed of G45 and the great high speed of G18, the weather condition of the intersection is preferentially pushed, further pushing is carried out according to the traffic flow information of the statistical intersection, historical statistical data analysis shows that the traffic flow of the road section going to the great high speed of G45 and the intersection of G18 is higher than that of the road section going to the great high speed of G45 and Beijing through the G18, and therefore when the weather information is pushed, the weather of the great high speed of G45 and the road section going to Beijing is preferentially pushed until the vehicle runs through the intersection, and the running road section is selected, the weather of the road section at the front 20KM is pushed.
As shown in fig. 4, the cloud processing server receives and stores all the matched vehicle feature information, vehicle tracking and positioning information and weather parameters; the method comprises the steps that a three-dimensional simulation module is used for matching characteristic information with all pre-stored three-dimensional vehicle models according to received vehicle characteristic information and vehicle tracking and positioning information, the three-dimensional vehicle models which are the same as vehicles running on an actual road are extracted, real-time tracking and positioning information scanned by a radar of each vehicle, the three-dimensional vehicle models and a three-dimensional high-precision GIS map pre-stored in a cloud processing server are subjected to multi-data fusion simulation, a four-dimensional live-action simulation display, viewing and monitoring scheme is formed, and the weather environment of the vehicles running on the actual road is detected in real time by monitoring the weather environment of the vehicles in the four-dimensional live-action simulation; the cloud processing server performs big data comprehensive analysis according to an artificial intelligence learning method by using preset weather information early warning rules of the current road section, preset weather early warning rules of a road section to be driven into and preset weather early warning rules of a plurality of time periods in the future according to the received alarm information, generates an early warning processing plan and sends the early warning plan to a four-dimensional simulation monitoring management station; it should be noted that, as shown in fig. 5, an artificial intelligence learning method is adopted to generate an alarm plan for meteorological parameters, and a cloud processing server obtains real-time meteorological information and historical weather information on the internet, performs simulation according to the information, and generates a long-term strategy according to the information; the real-time simulation comprises the perception of the future traffic state and the weather perception of the future meteorological disaster; and generating a traffic dispersion plan through the acquired traffic state and the acquired meteorological disaster degree, wherein the traffic dispersion plan comprises the prevention of the traffic state and the optimization of a traffic path. Preferably, when the cloud processing server matches the vehicle information of each vehicle with the radar scanning data of the vehicle, vehicle models of all series of vehicle types under all brand vehicles are preset in the cloud processing server, the cloud processing server performs image processing according to vehicle pictures shot by the vehicle snapshot camera, and according to vehicle type features, vehicle body colors, vehicle sizes and vehicle logo brands in the shot pictures; and calling a vehicle model which is most consistent with the shot vehicle picture, and generating real-time road condition simulation driving animation of the vehicle model according to the acquired radar scanning data and the weather parameters of the current road section of the vehicle for displaying in a four-dimensional simulation monitoring management station.
Preferably, the cloud processing server acquires the weather parameters of the road section in real time and judges whether the road section has severe weather or not; when severe weather is judged according to the weather parameters, the cloud processing server extracts the speed parameters from the received radar scanning data of the vehicles with the ID numbers, when the running speed parameters of all the vehicles are lower than 60km/h, the vehicles are judged that the current weather conditions are not suitable for high-speed running, and the path planning module generates a driving avoiding planning path according to the weather parameters of the front road section and pushes the driving avoiding planning path to drivers and conductors by the data acquisition analysis processor.
The four-dimensional simulation monitoring management station receives the three-dimensional vehicle simulation fusion data and the four-dimensional simulation driving path sent by the cloud processing server to perform simulation animation display; and checking the alarm event according to the received alarm event and generating an alarm event report.
As shown in fig. 5, in another embodiment of the present invention, when the cloud processing server analyzes and obtains severe weather such as heavy fog, heavy rain, heavy snow, etc. on the road section ahead through the weather parameters collected in real time and the parameters acquired from the internet, the four-dimensional simulation monitoring management station pushes the weather information in time and plans the driving-avoiding path of the vehicle according to the synchronously acquired vehicle driving position information; the route avoiding path comprises a toll station which is used for pushing the vehicle at a current high speed in time according to the position of the vehicle, and acquiring road congestion conditions of national roads and provincial roads closest to the toll station in time from the internet to generate a most preferable line changing path; the driver is reminded to change the lane through the national lane or the provincial lane, and the vehicle is prevented from being detained at a high-speed intersection or a service area due to high-speed plugging.
In rainy and snowy days, sand and dust weather, haze weather and night driving, the monitoring manager is very lost, and the common monitoring camera is mainly blinded under the condition, so that the monitoring and controlling functions cannot be continuously played, and the vehicle cannot be induced. Although the vehicle guidance system is installed on the existing expressway, the expressway is passive, vehicle guidance is not really achieved, and only the vehicle driving warning effect is achieved. Many remaining problems of the vehicle are not solved, for example, whether a traffic accident occurs in the vehicle or not, whether a road is congested or other abnormal conditions are not judged. However, the radar detection equipment is not affected by the climate and light, the cameras buried on the road can be completely reproduced by the integration of the three-dimensional GIS map and the four-dimensional simulation system, great help is provided for a traffic manager to realize active real-time induction and rapid processing and rapid query of abnormal events, actual traffic road condition information and vehicle position information on the road can be sent to an induction system and a mobile phone navigation or automobile navigation system which are installed on the road side by a communication port of the radar or vehicle-road cooperative communication equipment, and the intelligent traffic management system in the true sense can be effectively and perfectly realized.
When special conditions are met and temporary road closure or traffic control is needed, the aim is to reduce the road traffic pressure and reduce unnecessary unpredictable loss. More detailed and definite traffic control and dispersion schemes can be made in advance through the forms of traffic passage and road condition simulation. The system can adopt real and accurate historical data and combine the existing traffic conditions to perform real-time road control simulation and lane-closing influence traffic condition simulation, the simulation basic principle is that real-time data information and historical information sent back by a multi-element omnibearing tracking detection radar sensor, a license plate snapshot camera and other data acquisition equipment which are arranged on two sides of a road are collected and analyzed, a small cloud server starts an artificial intelligent deep learning module, a three-dimensional simulation module and an automatic plan generation module to evaluate the short-term travel demand of the future road network and the road traffic state after traffic control in real time, the travel demand is distributed to a simulation road network through a path distribution algorithm, the road network traffic running state is simulated and reproduced, and corresponding coping strategies and schemes are given.
As shown in fig. 2, the invention provides a severe weather traffic early warning method based on a four-dimensional live-action simulation road condition perception early warning monitoring management system, which is applied to the severe weather traffic early warning system and comprises the following steps:
and step S1, a multi-element omnibearing tracking detection radar is used for simultaneously acquiring the vehicle image of each vehicle running on the current corresponding highway section and the radar scanning data of each vehicle, the multi-element weather sensor acquires the road section meteorological parameters in real time according to the preset frequency, and the vehicle image, the radar scanning data and the weather parameters are sent to the data acquisition analysis processor.
Preferably, in step S1, the meteorological parameters collected by the multi-element meteorological sensor include wind speed, wind direction, liquid precipitation, atmospheric pressure, temperature and relative humidity; the vehicle information comprises license plate numbers, vehicle sizes, vehicle body colors, vehicle models and vehicle logos; the radar scanning data comprises driving speed, moving direction, longitude and latitude, ID number and direction angle.
Step S2, the data acquisition analysis processor receives the vehicle image and the radar scanning data at the same time and receives the meteorological parameters of all road sections in real time, the vehicle characteristic information is extracted according to the vehicle image, and the vehicle tracking and positioning information is extracted according to the radar scanning data; the vehicle characteristic information, the vehicle tracking and positioning information and the meteorological parameters are sent to a cloud processing server; the data acquisition analysis processor allocates an ID number of a unique identifier to each vehicle according to the primarily received radar scanning data of the vehicle; extracting radar scanning data of the ID numbered vehicle to obtain vehicle tracking and positioning information, extracting vehicle characteristic information according to the received vehicle image, and matching the vehicle tracking and positioning information of the ID numbered vehicle with the vehicle characteristic information; the matched vehicle characteristic information and the matched vehicle tracking and positioning information are sent to a cloud processing server; the data acquisition analysis processor acquires a weather early warning rule set in the cloud processing server, extracts the current driving speed and the current position information of the vehicle according to the vehicle tracking and positioning information according to the weather early warning rule, performs weather information early warning on the current road section, weather early warning on the road section to be driven into and weather early warning in a plurality of time periods in the future, and sends early warning information to the cloud processing server.
Step S3, the cloud processing server receives and stores all matched vehicle characteristic information, vehicle tracking and positioning information and weather parameters; the method comprises the steps that a three-dimensional simulation module is used for matching characteristic information with all pre-stored three-dimensional vehicle models according to received vehicle characteristic information and vehicle tracking and positioning information, the three-dimensional vehicle models which are the same as vehicles running on an actual road are extracted, real-time tracking and positioning information scanned by a radar of each vehicle, the three-dimensional vehicle models and a three-dimensional high-precision GIS map pre-stored in a cloud processing server are subjected to multi-data fusion simulation, a four-dimensional live-action simulation display, viewing and monitoring scheme is formed, and the weather environment of the vehicles running on the actual road is detected in real time by monitoring the weather environment of the vehicles in the four-dimensional live-action simulation; and the cloud processing server performs big data comprehensive analysis according to an artificial intelligence learning method by using preset weather information early warning rules of the current road section, preset weather early warning rules of the road section to be driven into and preset weather early warning rules of a plurality of time periods in the future according to the received alarm information, generates an early warning processing plan and sends the early warning plan to the four-dimensional simulation monitoring management station.
Step S4; the four-dimensional simulation monitoring management station receives the three-dimensional vehicle simulation fusion data and the four-dimensional simulation driving path sent by the cloud processing server to perform simulation animation display; and checking the alarm event according to the received alarm event and generating an alarm event report.
In the embodiment, a great amount of climate change data acquired by a multi-element sensor in the multi-element omnidirectional radar sensor or weather data acquired through the internet, sunshine (brightness intensity) data and the like are detected by the multi-element omnidirectional tracking distributed densely along the road, and the data are sent to the cloud processing server in combination with the change of time nodes (such as 24-hour time change, seasonal change and the like), so that the system can simulate the actual traffic condition more truly, the influence importance degree of the future weather condition on the traffic can be simulated by continuously deducing according to the existing traffic environment, and a more detailed and comprehensive travel scheme can be made by an end user.
In step 2, the weather early warning rules prestored in the cloud processing server include real-time weather early warning and weather early warning at a future moment, wherein when the weather early warning is performed at the future moment, the cloud processing server acquires historical data and real-time weather satellite cloud map information on the internet, automatically generates weather forecast information for 24 hours, 48 hours and even 72 hours in the future according to weather cloud map information display and by combining recorded historical weather data, and performs weather early warning according to the moment selected by the user. For example, when an automobile is traveling on a highway in which storms are likely to occur, such as a coastal sea area or a desert, it is necessary to perform weather warning based on historical weather information at that time, which is recorded in combination with the speed and direction of movement of a typhoon center, a sandstorm center, or the like, displayed on a meteorological satellite cloud.
On one hand, the real-time weather early warning comprises the steps that the cloud processing server carries out real-time weather pushing on vehicles running on the road section according to real-time received meteorological parameters of the current road section, and real-time weather early warning of the current road section is achieved; and on the other hand, the method comprises the steps that the cloud processing service requires real-time provision of meteorological information of the road section for the vehicle about to enter the road section according to real-time received meteorological parameters of the road section to which the vehicle is about to enter, and in the process, the road section to which the vehicle is about to enter is pre-judged.
In one embodiment of the invention, when the cloud processing server carries out the judgment of the road section passed by the vehicle, the first priority judgment is carried out according to whether the road section in front of the driving direction of the vehicle is a cross road section and a high-speed passing road section; and carrying out second priority prejudgment according to the daily average traffic flow of the road section in front of the driving direction of the vehicle.
When the vehicle runs on a high-speed road section, according to the running speed of the vehicle, if the running speed of the vehicle is maintained at 100KM/h, the cloud processing server acquires the weather conditions of the road section at the position 20KM ahead in real time and sends the weather conditions to a driver, if the position within 20KM ahead and 10KM away from the vehicle exists as a cross road section and a high-speed passing road section, the weather conditions of the cross road section and the high-speed passing road section are preferentially pushed, and the weather of the road section with large traffic flow is pushed in advance in the high-speed passing road section according to historical statistical data. Taking the example that the vehicle runs at the great high speed of G45, when the vehicle runs at the great high speed of G45 and runs to a male security road section, the front 20KM is a Kbazhou road section, the front 10KM is an intersection of the great high speed of G45 and the great high speed of G18, the weather condition of the intersection is preferentially pushed, further pushing is carried out according to the traffic flow information of the statistical intersection, historical statistical data analysis shows that the traffic flow of the road section going to the great high speed of G45 and the intersection of G18 is higher than that of the road section going to the great high speed of G45 and Beijing through the G18, and therefore when the weather information is pushed, the weather of the great high speed of G45 and the road section going to Beijing is preferentially pushed until the vehicle runs through the intersection, and the running road section is selected, the weather of the road section at the front 20KM is pushed.
As shown in fig. 4, the cloud processing server generates an alarm plan for the meteorological parameters by using an artificial intelligence learning method, acquires real-time meteorological information and historical weather information on the internet, performs simulation according to the information, and generates a long-term strategy according to the information; the real-time simulation comprises the perception of the future traffic state and the weather perception of the future meteorological disaster; and generating a traffic dispersion plan through the acquired traffic state and the acquired meteorological disaster degree, wherein the traffic dispersion plan comprises the prevention of the traffic state and the optimization of a traffic path. Preferably, when the cloud processing server matches the vehicle information of each vehicle with the radar scanning data of the vehicle, vehicle models of all series of vehicle types under all brand vehicles are preset in the cloud processing server, the cloud processing server performs image processing according to vehicle pictures shot by the vehicle snapshot camera, and according to vehicle type features, vehicle body colors, vehicle sizes and vehicle logo brands in the shot pictures; and calling a vehicle model which is most consistent with the shot vehicle picture, and generating real-time road condition simulation driving animation of the vehicle model according to the acquired radar scanning data and the weather parameters of the current road section of the vehicle for displaying in a four-dimensional simulation monitoring management station.
Preferably, the method further includes step S5, the four-dimensional simulation monitoring management station obtains the weather parameter of the road section in real time, and determines whether the road section has severe weather; when severe weather is judged according to the weather parameters, the four-dimensional simulation monitoring management station extracts the speed parameters from the received radar scanning data of the vehicle with each ID number, when the running speed parameters of all vehicles are lower than 60km/h, the vehicles are judged to be unsuitable for high-speed running under the current weather condition, and the path planning module generates a route avoiding planning path according to the weather parameters of the road section in front and pushes the route avoiding planning path to drivers and conductors. In another embodiment of the invention, when the four-dimensional simulation monitoring management station analyzes and obtains severe weather such as heavy fog, heavy rain, heavy snow and the like in the front road section through the weather parameters acquired in real time and the parameters acquired from the internet, the four-dimensional simulation monitoring management station pushes weather information in time and plans the driving-avoiding path of the vehicle according to the synchronously acquired vehicle driving position information; the route avoiding path comprises a toll station which is used for pushing the vehicle at a current high speed in time according to the position of the vehicle, and acquiring road congestion conditions of national roads and provincial roads closest to the toll station in time from the internet to generate a most preferable line changing path; the driver is reminded to change the lane through the national lane or the provincial lane, and the vehicle is prevented from being detained at a high-speed intersection or a service area due to high-speed plugging.
As shown in fig. 6, in another embodiment of the present invention, when the cloud processing server analyzes and obtains severe weather such as heavy fog, heavy rain, heavy snow, etc. on the road section ahead through the weather parameters collected in real time and the parameters acquired from the internet, the four-dimensional simulation monitoring management station pushes the weather information in time and plans the driving-avoiding path of the vehicle according to the synchronously acquired vehicle driving position information; the route avoiding path comprises a toll station which is used for pushing the vehicle at a current high speed in time according to the position of the vehicle, and acquiring road congestion conditions of national roads and provincial roads closest to the toll station in time from the internet to generate a most preferable line changing path; the driver is reminded to change the lane through the national lane or the provincial lane, and the vehicle is prevented from being detained at a high-speed intersection or a service area due to high-speed plugging.
In rainy and snowy days, sand and dust weather, haze weather and night driving, the monitoring manager is very lost, and the common monitoring camera is mainly blinded under the condition, so that the monitoring and controlling functions cannot be continuously played, and the vehicle cannot be induced. Although the vehicle guidance system is installed on the existing expressway, the expressway is passive, vehicle guidance is not really achieved, and only the vehicle driving warning effect is achieved. Many remaining problems of the vehicle are not solved, for example, whether a traffic accident occurs in the vehicle or not, whether a road is congested or other abnormal conditions are not judged. However, the radar detection equipment is not affected by the climate and light, the cameras buried on the road can be completely reproduced by the integration of the three-dimensional GIS map and the four-dimensional simulation system, great help is provided for a traffic manager to realize active real-time induction and rapid processing and rapid query of abnormal events, actual traffic road condition information and vehicle position information on the road can be sent to an induction system and a mobile phone navigation or automobile navigation system which are installed on the road side by a communication port of the radar or vehicle-road cooperative communication equipment, and the intelligent traffic management system in the true sense can be effectively and perfectly realized.
When special conditions are met and temporary road closure or traffic control is needed, the aim is to reduce the road traffic pressure and reduce unnecessary unpredictable loss. More detailed and definite traffic control and dispersion schemes can be made in advance through the forms of traffic passage and road condition simulation. The system can adopt real and accurate historical data and combine the existing traffic conditions to perform real-time road control simulation and lane-closing influence traffic condition simulation, the simulation basic principle is that real-time data information and historical information sent back by a multi-element omnibearing tracking detection radar sensor, a license plate snapshot camera and other data acquisition equipment which are arranged on two sides of a road are collected and analyzed, a small cloud server starts an artificial intelligent deep learning module, a three-dimensional simulation module and an automatic plan generation module to evaluate the short-term travel demand of the future road network and the road traffic state after traffic control in real time, the travel demand is distributed to a simulation road network through a path distribution algorithm, the road network traffic running state is simulated and reproduced, and corresponding coping strategies and schemes are given.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made in the above embodiments by those of ordinary skill in the art without departing from the principle and spirit of the present invention. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (10)

1. A bad weather early warning management system based on four-dimensional live-action traffic simulation is characterized by comprising a multi-element omnibearing tracking detection radar sensor, a data acquisition and analysis processor, a cloud processing server and four-dimensional simulation management workstation main equipment;
the multi-element omnibearing tracking detection radar sensor equipment carries out real-time tracking and positioning on all moving vehicles or pedestrians in a radar area and acquires the real-time position of each target and the original data information of the radar in a 360-degree omnibearing scanning mode; the method comprises the following steps that a license plate snapshot camera is mainly used for obtaining and snapshotting license plate information and picture information of vehicles running on a road, and then a graph analysis technology is used for obtaining characteristic information of each vehicle running on the road, a trigger signal of the license plate snapshot camera is synchronously triggered and snapshot by a multi-element omnibearing tracking detection radar sensor through a data acquisition analysis processor, the data acquisition analysis processor simultaneously obtains radar data information of the vehicle, which is snapshot by the radar trigger license plate snapshot camera at the time, and vehicle picture information and vehicle license plate information, which are captured by the license plate snapshot camera, and performs data summarization and packaging to be transmitted to a cloud processing server, and the cloud processing server performs analysis processing and data reduction on the data;
the data acquisition and analysis processor is used for acquiring original radar scanning data sent by the multi-element omnibearing tracking and detecting radar sensor, then carrying out interference filtering, forming tracking, summarizing, analyzing and judging on the original data to give real-time tracking and positioning information of each vehicle or pedestrian, sending the real-time tracking and positioning information to the cloud processing server through the transmission communication equipment to process the data, carrying out data transmission on the data of the multi-element omnibearing tracking and detecting radar sensor with overlapped N areas through the data acquisition and analysis processor arranged on the roadside or the cloud processing server, realizing the whole-course tracking real-time uninterrupted tracking and positioning of the tracked vehicle or pedestrian in such a way until the tracked vehicle or pedestrian leaves a detection area covered by N continuous radars, wherein when the same vehicle runs on a road through the multi-element omnibearing tracking and detecting radar sensor with the overlapped N detection areas, the system can distribute unique identification information for the vehicle, all vehicles running on the road can be endowed with unique identification codes to keep the uniqueness and sustainability of the information of the vehicles in the four-dimensional real-scene traffic road condition perception early warning monitoring management system, and the data acquisition analysis processor is also used for completing the following contents:
1) setting a detection area and a detection lane for the radar;
2) calling a mathematical operation model to obtain real-time movement speed, direction, distance, target included angle, track, driving time and area of a target object;
3) the acquired data information is sent to a preset alarm judgment rule model and an alarm type judgment rule model for verification, and a target with a composite condition is screened out for calibration and alarm output;
4) starting a tracking camera to track and view a target object in real time by a monitoring camera;
5) acquiring a video image of a tracking camera and sending the video image to a monitoring center for a manager to check a video in real time;
6) various data collected by a local multi-element sensor are obtained, and real-time analysis and judgment are carried out through a data analysis alarm model;
7) detecting the event type includes: stopping vehicles, traffic accidents, vehicle congestion, vehicle queuing, vehicle reversing, vehicle slowing, sprinkles, pedestrians, illegal lane changes and illegal invasion of specific areas and abnormal weather;
the cloud processing server acquires node weather information, comprehensive gas data information, vehicle image characteristic information and vehicle radar data information of a current road section which are secondarily processed and packaged by the front-end data acquisition analysis processor, carries out four-dimensional simulation on a traffic state and a road condition environment, starts an early warning rule, judges a road section where vehicles and pedestrians are going to pass through by acquiring position information of the vehicles and pedestrians, and sends warning information to the vehicles and pedestrians going through the road section in a point-to-point or broadcast mode to give a warning prompt; according to the four-dimensional live-action traffic simulation, a road manager can formulate an emergency plan, manage and control roads and rescue accident vehicles and pedestrians, push information to avoid planning paths, and reduce the risk of high-speed travel in severe weather;
the four-dimensional real scene management workstation calls and generates a three-dimensional high-precision simulation GIS map according to the data sent by the cloud processing server, the three-dimensional high-precision simulation GIS map is based on a 3DGIS graphic rendering engine as a bottom layer, and road data are obtained through unmanned aerial vehicle shooting and laser scanning and combined with corresponding three-dimensional simulation software to be combined;
the cloud processing server is mainly communicated with a data acquisition and analysis processor in front, acquires data information, alarm information and video information sent by the data acquisition and analysis processor, sends a control instruction, a parameter setting instruction and a linkage instruction to the data acquisition and analysis processor, starts a database to store all data in the system, performs data communication in man-machine interaction work, returns the alarm information, receives and completes all information and control instructions sent by a workstation, and completes configuration information of all data acquisition and analysis processors in the system, building a system architecture, building a network data link, performing local or remote maintenance, checking, screening and exporting data; all potential or possible accident-forming key information is processed, corresponding decision rules are started for processing, information interaction is carried out with a monitoring data server, an SDK secondary development kit or an API data interface can be provided for a road monitoring system or a third party, and linkage and sound alarm with the monitoring system are realized; the equipment mainly completes the following work: the method comprises the following steps of data exchange, a storage function, system communication, maintenance, centralized alarm, plan generation, linkage scheme execution, loading of a GIS electronic map, a graphic acceleration engine, detection and alarm of abnormal accidents of vehicles and pedestrians, image storage, data storage, alarm event video storage, report production, third-party linkage instruction sending, equipment system parameter setting, preset scheme sending and daily operation human-computer interface centralized alarm;
the vehicle license plate snapshot camera sends the captured vehicle photos and the vehicle license plate information to the data acquisition and analysis processor for image analysis, so as to obtain vehicle characteristic information, the vehicle characteristic information is sent to the cloud processing server for image analysis and data analysis, various three-dimensional models identical to the actual vehicle and the vehicle characteristic data information corresponding to the three-dimensional models are stored in a database of the cloud processing server, the server obtains the vehicle photos and the vehicle characteristic information of each vehicle sent by the vehicle license plate snapshot camera and then performs one-to-one comparison and matching with the three-dimensional vehicle model data in the local database, and the vehicle three-dimensional models which are identical to or close to the data are extracted; secondly, the multi-element omnibearing tracking detection radar sensor further fuses and matches the vehicle tracking positioning data information acquired by the real-time radar of the vehicle with the three-dimensional model of the vehicle, and simultaneously displays the three-dimensional model of the vehicle on a three-dimensional high-definition simulation GIS map, three-dimensional vehicle simulation animation which is the same as the actual road in motion direction, motion speed, real-time position, vehicle color and vehicle appearance is obtained after the three-dimensional model of the vehicle is processed by combining a data compensation technology and a graphic rendering acceleration engine, the one-to-one monitoring of the actual moving vehicles on the road is realized by monitoring the motion track and the traffic state of each vehicle in the three-dimensional simulation animation, and then the multi-element weather data information, the air quality data, the illumination data and the clock information acquired by the multi-element omnibearing tracking detection radar sensor are sent to the system for omnibearing environment simulation so as to more vividly simulate the traffic road conditions, Carrying out omnibearing simulation on the traffic state, the running vehicles and the environment, and showing the traffic state, the running vehicles and the environment to a use manager by a high-quality four-dimensional graphic picture;
the cloud processing server receives and stores all matched vehicle characteristic information, vehicle tracking and positioning information, meteorological data information, comprehensive gas data information and acquired third-party data; the method comprises the following steps of detecting the weather environment of a vehicle running on an actual road in real time by monitoring the weather environment of the vehicle in four-dimensional live-action simulation; the cloud processing server performs big data comprehensive analysis according to an artificial intelligence learning method by using preset weather information early warning rules of the current road section, preset weather early warning rules of a road section to be driven into and preset weather early warning rules of a plurality of time periods in the future according to the received alarm information, generates an early warning processing plan and sends the early warning plan to a four-dimensional simulation monitoring management station;
the four-dimensional simulation monitoring management station receives three-dimensional vehicle simulation fusion data and four-dimensional simulation driving paths sent by the cloud processing server and carries out simulation animation display in the weather environment; and checking the alarm event according to the received alarm event and generating an alarm event report.
2. The severe weather early warning management system based on four-dimensional live-action traffic simulation as claimed in claim 1, wherein the meteorological data and comprehensive gas data collected by the multi-element sensor carried by the radar sensor and other sensors are comprehensively tracked and detected by the multi-element omnibearing track and detection system comprises: wind speed, wind direction, rainfall, air pressure, temperature, humidity, illumination, light intensity, sulfur dioxide, carbon monoxide, methane and volatile matters; the vehicle information includes: vehicle license plate information, vehicle size, vehicle body color, vehicle model, vehicle type, vehicle series and vehicle logo; the radar scan data includes: instantaneous speed, moving direction, longitude and latitude, ID number and direction angle, XYZ three-dimensional coordinate.
3. The severe weather early warning management system based on the four-dimensional live-action traffic simulation as claimed in claim 2, further comprising a cloud processing server for pre-judging a road section to which the vehicle is about to enter, and performing first priority pre-judgment according to whether the road section ahead of the driving direction of the vehicle is a cross road section and a high-speed changing road section; and carrying out second priority prejudgment according to the daily average traffic flow of the road section in front of the driving direction of the vehicle.
4. The severe weather early warning management system based on four-dimensional live-action traffic simulation of claim 2, wherein the cloud processing server further comprises path planning, four-dimensional live-action simulation, road condition and environment simulation of road state, and early warning output; the cloud processing server carries out data communication with a third party warning prompt device, and the third party warning prompt device comprises: automobile navigation, mobile phone navigation, vehicle road cooperative equipment, a road information board and a fog zone inducer; historical data or current traffic state data and road condition state data acquired by a cloud processing server; performing four-dimensional live-action traffic weather road condition simulation in a numerical value increasing mode, and judging whether severe weather occurs on the road section according to a simulation result; and when the weather is judged to be severe and an alarm prompt is sent, starting the path planning function module or starting the manual path planning function module to plan a safe path, and pushing the alarm information, the road condition information, the traffic state information, the safety prompt information, the traffic control information and the fault vehicle avoidance information to drivers and conductors in the road section or the region to carry out safety alarm prompt by adopting traffic control and in a point-to-point mode or a broadcast mode.
5. The severe weather early warning management system based on the four-dimensional live-action traffic simulation as claimed in claim 2, wherein three-dimensional models of various vehicles are prestored in the cloud processing server; when the cloud processing server carries out four-dimensional live-action traffic simulation, the vehicle characteristics of each vehicle with the ID number are obtained and compared with the parameters of the three-dimensional vehicle model in the local database, the three-dimensional vehicle model with the consistent or close parameters is extracted, the multi-element all-dimensional tracking detection multi-element all-dimensional radar sensor fuses tracking information, position information, longitude and latitude information, speed information, motion direction information and direction angle information of the vehicle with the three-dimensional model of the vehicle, and the three-dimensional model of the vehicle is displayed on a three-dimensional high-definition GIS map at the same time, so that the four-dimensional multi-angle preview vehicle running animation is formed.
6. A bad weather traffic early warning method based on a four-dimensional live-action simulation road condition perception early warning monitoring management system is characterized in that the bad weather traffic early warning method is applied to the bad weather traffic early warning system of any one of the claims 1-5, and comprises the following steps:
step S1, acquiring radar scanning, tracking and positioning data of each vehicle running on the current expressway by using a multi-element omnibearing tracking and detecting radar sensor, acquiring meteorological data and comprehensive gas data of the road section in real time according to preset frequency by using the multi-element omnibearing tracking and detecting radar sensor, synchronously triggering a license plate snapshot camera by a radar to acquire image information of the tracked vehicle, and respectively sending the radar scanning and tracking data of the vehicle, the image data of the vehicle, the meteorological data and the comprehensive gas data of the road section node to a data acquisition and analysis processor for preliminary data processing;
step S2, the data acquisition analysis processor starts an image recognition technology to extract vehicle characteristic information according to the received vehicle image data, and extracts vehicle tracking and positioning information according to the received radar scanning data; packaging the vehicle characteristic information, the vehicle tracking and positioning information, the meteorological data and the comprehensive gas data and then sending the packaged data to a cloud processing server to carry out secondary processing on the data again; the data acquisition analysis processor starts a target object tracking algorithm, a clutter map algorithm, an inertial motion data compensation algorithm and a dynamic data compensation algorithm according to the primary received vehicle radar scanning original data, screens and filters the original data according to various abnormal event accident vehicle judgment conditions, detection areas and detection lanes sent by the server to finally form effective data, and distributes target radar tracking and positioning identification information for each tracking target including vehicles and pedestrians; the data acquisition analysis processor simultaneously acquires vehicle image information captured by a license plate snapshot camera synchronously triggered and installed above a lane and radar vehicle tracking and positioning information for triggering the license plate snapshot camera, so that the data received by the server are synchronous image information and radar tracking and positioning data information of the vehicle;
the data acquisition and analysis processor is also used for completing the following steps:
1) setting a detection area and a detection lane for the radar;
2) calling a mathematical operation model to obtain real-time movement speed, direction, distance, target included angle, track, driving time and area of a target object;
3) the acquired data information is sent to a preset alarm judgment rule model and an alarm type judgment rule model for verification, and a target with a composite condition is screened out for calibration and alarm output;
4) starting a tracking camera to track and view a target object in real time by a monitoring camera;
5) acquiring a video image of a tracking camera and sending the video image to a monitoring center for a manager to check a video in real time;
6) various data collected by a local multi-element sensor are obtained, real-time analysis and judgment are carried out through a data analysis alarm model, and when a preset judgment mechanism is met;
7) detecting the event type includes: stopping vehicles, traffic accidents, vehicle congestion, vehicle queuing, vehicle reversing, vehicle slowing, sprinkles, pedestrians, illegal lane changes and illegal invasion of specific areas and abnormal weather;
step S3, the cloud processing server receives and stores all matched vehicle characteristic information, vehicle tracking and positioning information, meteorological data and comprehensive gas data; the method comprises the steps that a four-dimensional simulation module is used for matching characteristic information with all pre-stored three-dimensional vehicle models according to received vehicle characteristic information and vehicle tracking and positioning information, extracting the three-dimensional vehicle models which are the same as vehicles running on an actual road, carrying out multi-data fusion simulation on real-time tracking and positioning information scanned by a radar of each vehicle, the three-dimensional vehicle models and a three-dimensional high-precision GIS map pre-stored in a cloud processing server, forming a four-dimensional live-action simulation display, viewing and monitoring scheme, and detecting the weather environment of the vehicles running on the actual road in real time by monitoring the weather environment of the vehicles in the four-dimensional live-action simulation; the cloud processing server performs big data comprehensive analysis according to an artificial intelligence learning method by using preset weather information early warning rules of the current road section, preset weather early warning rules of a road section to be driven into and preset weather early warning rules of a plurality of time periods in the future according to the received alarm information, generates an early warning processing plan and sends the early warning plan to a four-dimensional simulation monitoring management station;
step S4; the four-dimensional simulation monitoring management station is a man-machine interaction interface of the system, is mainly used for checking early warning information, road condition information, traffic state information, safety prompt information, traffic control scheme making and failure vehicle avoidance information after four-dimensional live-action simulation, and can perform alarm event storage and generation of an alarm event report according to received alarm events.
7. The bad weather traffic early warning method based on the four-dimensional real-scene simulation road condition perception early warning monitoring management system as claimed in claim 6, wherein in step S1, the radar real-time data of the vehicle comprises: tracking and positioning information, instant speed information, motion direction information, longitude and latitude information, target size information, ID number information, type information, motion direction angle and XYZ three-dimensional coordinates of the vehicle; the characteristic data of the vehicle includes: color, vehicle family, appearance, vehicle type, brand, model, age, vehicle license plate information; the multi-element environmental data includes: temperature and humidity data, comprehensive gas data, brightness light intensity data, rainfall data and wind speed and direction data; the comprehensive gas comprises sulfur dioxide, carbon monoxide, methane and volatile matters.
8. The bad weather traffic early warning method based on the four-dimensional live-action simulation road condition perception early warning monitoring management system as claimed in claim 7, wherein in step S2, the method further comprises the steps of pre-judging the road section to which the vehicle is going to enter by the cloud processing server, and performing first priority pre-judgment according to whether the road section ahead of the vehicle driving direction is a cross road section and a high-speed passing road section; and carrying out second priority prejudgment according to the daily average traffic flow of the road section in front of the driving direction of the vehicle.
9. The bad weather traffic early warning method based on the four-dimensional real-scene simulation road condition perception early warning monitoring management system as claimed in claim 7, further comprising step S4, wherein the cloud processing server further comprises path planning, four-dimensional real-scene simulation, road state and road condition environment simulation, early warning output; the cloud processing server carries out data communication with a third party warning prompt device, and the third party warning prompt device comprises: automobile navigation, mobile phone navigation, vehicle road cooperative equipment, a road information board and a fog zone inducer; historical data or current traffic state data and road condition state data acquired by a cloud processing server; performing four-dimensional live-action traffic weather road condition simulation in a numerical value increasing mode, and judging whether severe weather occurs on the road section according to a simulation result; and when the weather is judged to be severe and an alarm prompt is sent, starting the path planning function module or starting the manual path planning function module to plan a safe path, and pushing the alarm information, the road condition information, the traffic state information, the safety prompt information, the traffic control information and the fault vehicle avoidance information to drivers and conductors in the road section or the region to carry out safety alarm prompt by adopting traffic control and in a point-to-point mode or a broadcast mode.
10. The bad weather traffic early warning method based on the four-dimensional live-action simulation road condition perception early warning monitoring management system as claimed in claim 7, wherein in step S3, storing the model three-dimensional vehicle models of all brands of vehicles in various series and the data information corresponding to the three-dimensional models in the cloud processing server database includes: the cloud processing server carries out image processing according to a vehicle picture shot by the vehicle snapshot camera, and obtains vehicle type characteristic information, vehicle body color information, vehicle type information, vehicle appearance information, vehicle size information and vehicle logo brand information after carrying out image analysis according to the vehicle picture obtained by the license plate snapshot camera; calling a vehicle three-dimensional model which is most consistent with a shot vehicle picture through an information matching technology, displaying the model in a three-dimensional high-precision GIS map which is acquired in advance in a cloud processing server, acquiring radar tracking data of each vehicle which is scanned by a radar and sent by a data acquisition and analysis processor according to the cloud processing server, fusing real-time tracking data of the radar vehicle with a corresponding three-dimensional vehicle model displayed in the three-dimensional high-precision GIS map, and starting a graphic rendering engine to perform graphic acceleration processing by adopting an inertial vehicle data compensation technology and a graphic processing technology; then, the acquiring of the multi-element environment data sent by the front-end data acquisition and analysis processor by the cloud service processor comprises the following steps: temperature and humidity data, comprehensive gas data, brightness light intensity data, rainfall data and wind speed and direction data; the synthesis gas comprises: sulfur dioxide, carbon monoxide, methane, volatiles; the method comprises the steps of superposing multi-element environment data and calling environment simulation, road condition simulation and traffic simulation, so that a severe weather early warning management system based on four-dimensional live-action traffic simulation is formed, starting a corresponding early warning condition judgment module, sending an alarm outwards when the condition is met, realizing multi-directional linkage processing, and carrying out man-machine interaction and real-time display on a four-dimensional simulation monitoring management workstation.
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