A road dust monitoring and early warning system based on big data and its use method

Abstract

The present invention discloses a road dust monitoring and early warning system based on big data and a method for using the system, including an environment perception system, the environment perception system is used to obtain meteorological data, dust particle spectrum data, road dust data and image data on the highway; a data processing system, the data processing system is used to process the data obtained by the environment perception system; an intelligent early warning system, the intelligent early warning system warns of the road dust condition according to the data obtained by the environment perception system and the data results processed by the data processing system; a control system, the control system is respectively connected to the environment perception system, the data processing system and the intelligent early warning system. The system has the advantages of realizing intelligent and reliable monitoring and early warning of dust on the highway.

Description

Technical Field

[0001] The present invention relates to the technical field of road dust monitoring and early warning, and in particular to a road dust monitoring and early warning system based on big data and a method for using the system. Background Art

[0002] At present, with the continuous expansion of highway mileage, traffic safety issues are becoming increasingly prominent, especially under severe weather conditions such as blowing sand and sandstorms. Vehicle driving faces huge risks, visibility is reduced, and wind erosion, accumulation and sand burial are prone to occur along highways affected by wind and sand, which directly affect the traffic safety level of highways. In addition, sand and dust are like frosted sand, which will cause wear and tear on vehicle glass and body at high speeds.

[0003] In the prior art, a single monitoring device is used to monitor the real-time air quality status of particulate matter concentration, and no monitoring and early warning is performed for dust accumulation on the highway pavement.

[0004] In summary, there is at least one of the following technical problems:

[0005] A single monitoring device is used to monitor the real-time air quality status of particulate matter concentration, and no monitoring and graded warning are carried out for sand and dust accumulation on highway surfaces.

[0006] It is difficult to reliably monitor the thickness of dust accumulation on highways and ensure the reliability of detection.

[0007] It is difficult to accurately obtain the visibility and safety of the highway corresponding to the actual scene, and it is difficult to issue corresponding early warnings corresponding to the real-time scene of the highway. Summary of the invention

[0008] The main purpose of the present invention is to provide a road dust monitoring and early warning system based on big data and a method of use, so as to solve at least one of the technical problems that the prior art uses a single monitoring device to monitor the real-time monitoring of the air quality of particulate matter concentration, and does not monitor and grade early warning for dust accumulation on the road surface of highways. It is difficult to reliably monitor the thickness of dust accumulation on highways to ensure the reliability of detection. It is difficult to accurately obtain the visibility and safety of the highway corresponding to the actual scene, and it is difficult to give corresponding early warnings for the real-time scenes of highways.

[0009] In order to achieve the above object, according to one aspect of the present invention, a road dust monitoring and early warning system based on big data is provided, comprising:

[0010] An environmental perception system, which is used to obtain meteorological data, dust particle spectrum data, road dust data and image data on the highway;

[0011] A data processing system, wherein the data processing system is used to process data acquired by the environment perception system;

[0012] An intelligent early warning system, which warns of road dust based on data acquired by the environmental perception system and data processed by the data processing system;

[0013] A control system, wherein the control system is connected to the environment perception system, the data processing system and the intelligent early warning system respectively.

[0014] Preferably, the environment perception system includes:

[0015] A vertical pole, wherein the vertical pole is arranged at a position of a central isolation zone of a highway;

[0016] A solar panel, wherein the solar panel is arranged on the top of the pole;

[0017] A camera, wherein the camera is arranged on the vertical pole;

[0018] An automatic meteorological particle size spectrometer, wherein the automatic meteorological particle size spectrometer is arranged on a vertical pole;

[0019] A road dust monitoring device, wherein the road dust monitoring device is arranged at the lower end of the pole;

[0020] A detection head is arranged on the road dust monitoring device.

[0021] Preferably, the control system includes a controller, which includes a central processing unit, a camera control module, an automatic meteorological particle size spectrometer control module, a road dust accumulation monitoring equipment control module, a detection head control module, a solar panel control module, a data processing module and an intelligent prediction module, and the camera control module, the automatic meteorological particle size spectrometer control module, the road dust accumulation monitoring equipment control module, the detection head control module, the solar panel control module, the data processing module and the intelligent prediction module are respectively connected to the central processing unit.

[0022] Preferably, the camera control module is connected to the camera, the automatic meteorological particle size spectrometer control module is connected to the automatic meteorological particle size spectrometer, the road dust monitoring equipment control module is connected to the road dust monitoring equipment, the detection head control module is connected to the detection head, the solar panel control module is connected to the solar panel, and the data processing module is connected to the data processor.

[0023] Preferably, dust particle spectrum data is collected by an automatic meteorological particle size spectrometer, and six data of PM1, PM2.5, PM10, TSP, central peak data and particle spectrum change time in the dust particle spectrum data are statistically analyzed to form eight indicators, which are recorded as ZB1-ZB8; different weights are set for each indicator, and the dust confidence is calculated and judged. When the confidence Y is greater than 80%, the first level warning is issued and the camera is triggered to take pictures intelligently. The confidence calculation formula is as follows:

[0024] Y=ZB1*10%+ZB2*10%+ZB3*5%+ZB4*5%+ZB5*40%

[0025] +ZB6*10%+ZB7*10%+ZB8*10%

[0026] Among them: ZB1 indicates whether the peak mass concentration of inhalable particulate matter PM10 reaches threshold A. If the index is met, ZB1 is recorded as 1, and if the index is not met, ZB1 is recorded as 0; ZB2 indicates whether the peak mass concentration of total suspended particulate matter TSP reaches threshold B. If the index is met, ZB2 is recorded as 1, and if the index is not met, ZB2 is recorded as 0; ZB3 indicates whether the PM10 / TSP ratio is less than threshold C. If the index is met, ZB3 is recorded as 1, and if the index is not met, ZB3 is recorded as 0; ZB4 indicates whether the PM2.5 / PM10 ratio is less than threshold D. If the index is met, ZB4 is recorded as 1, and if it is not met, ZB4 is recorded as 0. If the index is met, ZB4 is recorded as 0; ZB5 indicates whether the central peak of the particle size spectrum corresponds to the particle size interval E. If the index is met, ZB5 is recorded as 1, and if the index is not met, ZB5 is recorded as 0; ZB6 indicates the threshold value F% of the decrease in the number concentration density of particles less than 1μm. If the index is met, ZB6 is recorded as 1, and if the index is not met, ZB6 is recorded as 0; ZB7 indicates the threshold value G% of the increase in the number concentration density of particles greater than 10μm. If the index is met, ZB7 is recorded as 1, and if the index is not met, ZB7 is recorded as 0; ZB8 indicates the change time t, which is 1 for minutes and 0.7 for hours.

[0027] Preferably, the camera of the environmental perception system takes photos of the surroundings at fixed points and distances, and reviews the photos through machine training. When visibility is greater than a first threshold, the camera will continue to take photos and monitor, and will not issue an early warning notification; when visibility is less than the first threshold, the system will issue a second-level early warning notification, and issue a second-level early warning signal to passing vehicles through map navigation or high-speed display, and trigger the road dust monitoring equipment to monitor the road dust; when the road dust monitoring equipment detects that the sand surface coverage rate reaches the second threshold, the system will issue a third-level early warning notification, issue a no-travel prompt to vehicles, and issue a cleaning instruction to personnel through the system, APP or text message.

[0028] Preferably, it also includes a first calibration point and a second calibration point, the first calibration point is set on one side of the highway, and the second calibration point is set on the other side of the highway, the first calibration point includes several calibration positions, the second calibration point includes several calibration positions, each calibration position is provided with a calibration rod and a calibration ball, a warning light is provided in the calibration ball, the cameras are arranged in pairs, one camera takes photos and calibrates several calibration positions of the first calibration point, and the other camera takes photos and calibrates several calibration positions of the second calibration point, wherein each calibration position is set in steps according to the correspondence between the vehicle's safe driving distance and the straight-line distance of the camera, a threshold is set according to the visible number of calibration balls at different calibration positions in the photos taken, and the visibility monitoring results of the corresponding positions are mutually verified by the two cameras to improve the monitoring reliability.

[0029] Preferably, the road dust monitoring equipment includes a turntable, which is arranged on a vertical pole, and a first lateral moving platform and a second lateral moving platform are provided on the turntable, the first lateral moving platform and the second lateral moving platform are symmetrically arranged relative to the vertical pole, a plurality of first longitudinal moving platforms are provided on the first lateral moving platform, a plurality of second longitudinal moving platforms are provided on the second lateral moving platform, probes are provided at the lower ends of the first longitudinal moving platform and the second longitudinal moving platform, a plurality of probe layers are provided in the probe, each probe layer is provided with a photosensitive sensor, and a conical safety zone is provided at the lower end of the probe.

[0030] Preferably, the road dust monitoring device also includes a dust plate, on which a plurality of collection domains are provided. The collection domains are arranged in a ring, and the collection domains correspond one-to-one with the probes. The probes dynamically collect dust in the corresponding collection domains, and the dust thickness on the highway is determined by processing the collection results and comparing them with a threshold.

[0031] According to another aspect of the present invention, a method for using a road dust monitoring and early warning system based on big data is provided, comprising:

[0032] Step 1: Control the environmental perception system through the control system to obtain meteorological data, dust particle spectrum data, road dust data and image data on the highway in real time;

[0033] Step 2: The control system controls the data processing system to process the data obtained by the environmental perception system and calculate the dust confidence. When the confidence is greater than the set value, the camera system is triggered to take photos and issue a first-level warning. When the visibility is less than the set threshold, the ground dust monitoring device is triggered to detect the dust thickness and issue a second-level warning.

[0034] Step 3: When the dust thickness reaches the threshold, the control system controls the intelligent early warning system to issue a third-level early warning.

[0035] The application of the technical solution of the present invention has the following technical effects:

[0036] By setting up the environmental perception system through the control system, it is possible to accurately and reliably obtain meteorological data, dust particle spectrum data, road dust data and image data on the highway, and process the data through the data processing system. Then, by setting up a graded warning mode, reliable warning of dust levels is given to improve traffic safety.

[0037] According to the monitoring accuracy requirements, the monitoring radius of the dust accumulation plate is set, and the turntable is driven to rotate by the power device, and the first horizontal moving platform and the second horizontal moving platform are driven to rotate synchronously by the rotation of the turntable, and the corresponding first vertical moving platform and the second vertical moving platform are driven to rotate respectively by the first horizontal moving platform and the second horizontal moving platform, wherein the first vertical moving platform and the second vertical moving platform are respectively driven to move downward and inserted into the dust accumulation when the rotation reaches the corresponding collection domain, wherein the first horizontal moving platform and the second horizontal moving platform are used to adjust the monitoring radius, and the first vertical moving platform and the second vertical moving platform are used to realize step monitoring as the radius increases. The measurement and symmetrical monitoring are realized by rotating the rotating disk to realize circumferential segmented monitoring: when the dust thickness data detected by the probes on the first lateral moving platform and the second lateral moving platform are similar and the data error range is within the threshold, at the same time, the turntable is rotated to monitor in all directions around the circumference. When the dust thickness data error detected by several probes on the first lateral moving platform is within the threshold, it indicates that the dust accumulation on the dust plate monitored here is uniform, and the dust thickness data monitored by the probe can be used as early warning data; when the threshold is exceeded, the first lateral moving platform and the second lateral moving platform are controlled by the control system to extend laterally, expand the monitoring radius, and compare again until it is within the threshold range. Among them, the probe monitoring is realized by setting several detection layers in the probe, and each detection layer corresponds to the early warning type: high, medium, and low. The dust covers the detection layer, and the photosensitive sensor of the first detection layer senses it, and then corresponds to the corresponding early warning type: high, medium, and low. The lowest end is a conical structure, which is easy to insert, and there is no early warning state below the safety zone. It has the technical effect of reliably monitoring the thickness of dust on the highway and ensuring the reliability of detection.

[0038] According to the safe distance corresponding to the highway driving, several first calibration points and second calibration points are set in the first calibration direction and the second calibration direction. The control system controls the camera angle to shoot several calibration points in sequence. The threshold is established by the number of calibration points. Different calibration points correspond to different safety distances. The cameras are set in pairs to symmetrically collect calibration point images. The collected results are verified by the symmetrically set images. When the consistency is within the threshold, it is used as visibility warning data for warning. By setting the above combined results in sections on the highway, accurate visibility monitoring and warning are carried out to adapt to the scene. When traveling together is prohibited and the highway needs to be driven off as soon as possible, the warning light in the calibration ball can be controlled to light up, and a high warning is given to drive off the highway as soon as possible, thereby improving the safety warning of the road section scene. Accurately obtain the visibility and safety of the highway corresponding to the actual scene, and make corresponding warnings for the real-time scene of the highway. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments and descriptions of the present invention are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0040] Figure 1 A schematic diagram of the structure of a road dust monitoring and early warning system based on big data according to the present invention is shown;

[0041] Figure 2 Shows Figure 1 A method flow chart of a road dust monitoring and early warning system based on big data;

[0042] Figure 3 Shows Figure 1 The layout view of the environmental perception system of the road dust monitoring and early warning system based on big data;

[0043] Figure 4 Shows Figure 1 The structural view of the road dust monitoring equipment in the road dust monitoring and early warning system based on big data;

[0044] Figure 5 Shows Figure 1 The dust collection domain structure view of the road dust monitoring and early warning system based on big data;

[0045] Figure 6 Shows Figure 1 Probe structure view of the road dust monitoring and early warning system based on big data.

[0046] The above drawings include the following reference numerals:

[0047] Expressway 1; pole 2; camera 3; automatic meteorological particle size spectrometer 4; road dust monitoring equipment 5; detection head 6; solar panel 7; first collection unit 8; second collection unit 9; first calibration point 10; second calibration point 11; middle isolation zone 12; first calibration direction 13; second calibration direction 14; probe 15; detection layer 16; dust accumulation plate 17; turntable 18; first lateral moving platform 19; first longitudinal moving platform 20; second lateral moving platform 21; second longitudinal moving platform 22; safety domain 23; collection domain 24. DETAILED DESCRIPTION

[0048] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0049] like Figures 1 to 6 As shown, an embodiment of the present invention provides a road dust monitoring and early warning system based on big data, including: an environmental perception system, the environmental perception system is used to obtain meteorological data, dust particle spectrum data, road dust accumulation data and image data on the expressway 1; a data processing system, the data processing system is used to process the data obtained by the environmental perception system; an intelligent early warning system, the intelligent early warning system warns of road dust according to the data obtained by the environmental perception system and the data results processed by the data processing system; a control system, the control system is respectively connected to the environmental perception system, the data processing system and the intelligent early warning system.

[0050] In this embodiment, the environmental perception system is used to obtain meteorological data, dust particle spectrum data, road dust accumulation data and image data on the highway 1; the environmental perception system includes: a pole 2 that plays a supporting role, and the pole 2 is arranged at the position 12 of the middle isolation zone of the highway 1; a solar panel 7 that converts solar energy into electrical energy, and the solar panel 7 is arranged at the top of the pole 2; a camera 3 that is used to collect image information, and the camera 3 is arranged on the pole 2; an automatic meteorological particle size spectrometer 4 that is used to obtain dust particle spectrum data, and the automatic meteorological particle size spectrometer 4 is arranged on the pole 2; a road dust accumulation monitoring device 5 that is used to monitor the dust accumulation thickness of the road surface, and the road dust accumulation monitoring device 5 is arranged at the lower end of the pole 2; a detection head 6 that is used to detect road dust accumulation, and the detection head 6 is arranged on the road dust accumulation monitoring device 5. The dust particle spectrum data is collected by the automatic meteorological particle size spectrometer 4, and the six data of PM1, PM2.5, PM10, TSP, central peak data and particle spectrum change time in the dust particle spectrum data are statistically analyzed to form eight indicators, which are recorded as ZB1-ZB8; different weights are set for each indicator, and the dust confidence is calculated and judged. When the confidence Y is greater than 80%, the first level warning is issued and the camera 3 is triggered to take smart photos. The confidence calculation formula is as follows:

[0051] <h2 style=";text-align:left;direction:ltr">Y=ZB1*10%+ZB2*10%+ZB3*5%+ZB4*5%+ZB5*40%<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0052] <h2 style=";text-align:left;direction:ltr"> +ZB6*10%+ZB7*10%+ZB8*10%<h2 style=";text-align:left;direction:ltr"> <h2 style=";text-align:left;direction:ltr">

[0053] Among them: ZB1 indicates whether the peak mass concentration of inhalable particulate matter PM10 reaches threshold A. If the index is met, ZB1 is recorded as 1, and if the index is not met, ZB1 is recorded as 0; ZB2 indicates whether the peak mass concentration of total suspended particulate matter TSP reaches threshold B. If the index is met, ZB2 is recorded as 1, and if the index is not met, ZB2 is recorded as 0; ZB3 indicates whether the PM10 / TSP ratio is less than threshold C. If the index is met, ZB3 is recorded as 1, and if the index is not met, ZB3 is recorded as 0; ZB4 indicates whether the PM2.5 / PM10 ratio is less than threshold D. If the index is met, ZB4 is recorded as 1, and if it is not met, ZB4 is recorded as 0. If the index is met, ZB4 is recorded as 0; ZB5 indicates whether the central peak of the particle size spectrum corresponds to the particle size interval E. If the index is met, ZB5 is recorded as 1, and if the index is not met, ZB5 is recorded as 0; ZB6 indicates the threshold value F% of the decrease in the number concentration density of particles less than 1μm. If the index is met, ZB6 is recorded as 1, and if the index is not met, ZB6 is recorded as 0; ZB7 indicates the threshold value G% of the increase in the number concentration density of particles greater than 10μm. If the index is met, ZB7 is recorded as 1, and if the index is not met, ZB7 is recorded as 0; ZB8 indicates the change time t, which is 1 for minutes and 0.7 for hours. The camera 3 of the environmental perception system takes photos of the surroundings at fixed points and distances, and reviews the photos through machine training. When the visibility is greater than the first threshold, the camera 3 will continue to take photos and monitor, and will not issue a second-level warning notification; when the visibility is less than the first threshold, the system will issue a second-level warning notification, and issue a second-level warning signal to passing vehicles through map navigation or high-speed display, and trigger the road dust monitoring device 5 to monitor the road dust; when the road dust monitoring device 5 detects that the sand surface coverage rate reaches the second threshold, the system will issue a third-level warning notification, issue a no-passing prompt to vehicles, and issue a cleaning instruction to personnel through the system, APP or SMS. The road dust monitoring device 5 includes a rotating disk 18, which is arranged on the upright pole 2. The rotating disk 18 is provided with a first transverse moving platform 19 and a second transverse moving platform 21. The first transverse moving platform 19 and the second transverse moving platform 21 are symmetrically arranged relative to the upright pole 2. The first transverse moving platform 19 is provided with a plurality of first longitudinal moving platforms 20, and the second transverse moving platform 21 is provided with a plurality of second longitudinal moving platforms 22. The lower ends of the first longitudinal moving platform 20 and the second longitudinal moving platform 22 are provided with a probe 15, and the probe 15 is provided with a plurality of layers of probes 16, each layer of the probe 16 is provided with a photosensitive sensor, and the lower end of the probe 15 is provided with a conical safety area 23. The road dust monitoring device 5 also includes a dust plate 17, and the dust plate 17 is provided with a plurality of collection areas 24, and the collection areas 24 are arranged in an annular manner. The collection areas 24 correspond to the probe 15 in a one-to-one manner. The probe 15 dynamically collects the dust in the corresponding collection area 24, and determines the dust thickness of the highway 1 by processing the collection results and comparing them with the threshold.

[0054] In this embodiment, the data processing system is used to process the data obtained by the environmental perception system; the intelligent early warning system warns of road dust according to the data obtained by the environmental perception system and the data results processed by the data processing system; the control system is respectively connected to the environmental perception system, the data processing system and the intelligent early warning system. The control system includes a controller, the controller includes a central processing unit, a camera 3 control module, an automatic meteorological particle size spectrometer 4 control module, a road dust monitoring device 5 control module, a detection head 6 control module, a solar panel control module, a data processing module and an intelligent prediction module, and the camera 3 control module, the automatic meteorological particle size spectrometer 4 control module, the road dust monitoring device 5 control module, the detection head 6 control module, the solar panel control module, the data processing module and the intelligent prediction module are respectively connected to the central processing unit. The camera 3 control module is connected to the camera 3, the automatic meteorological particle size spectrometer 4 control module is connected to the automatic meteorological particle size spectrometer 4, the road dust monitoring device 5 control module is connected to the road dust monitoring device 5, the detection head 6 control module is connected to the detection head 6, the solar panel control module is connected to the solar panel, and the data processing module is connected to the data processor.

[0055] In this embodiment, it also includes a first calibration point 10 and a second calibration point 11, the first calibration point 10 is set on one side of the highway, and the second calibration point 11 is set on the other side of the highway 1, the first calibration point 10 includes several calibration positions, the second calibration point 11 includes several calibration positions, each calibration position is provided with a calibration rod and a calibration ball, and a warning light is set in the calibration ball, the cameras 3 are arranged in pairs, one of the cameras 3 takes pictures and calibrates several calibration positions of the first calibration point 10, and the other camera 3 takes pictures and calibrates several calibration positions of the second calibration point 11, wherein each calibration position is set in steps according to the correspondence between the vehicle driving safety distance and the straight-line distance of the camera 3, and the threshold is set according to the visible number of calibration balls at different calibration positions of the photographed photos, and the two cameras 3, respectively the first acquisition unit 8 and the second acquisition unit 9, compare and verify the corresponding distance visibility monitoring results with each other to improve the monitoring reliability. This patent can realize real-time reliable monitoring and early warning, effectively monitor and warn the sand burial problem of the highway 1, and improve the traffic safety level of the highway 1.

[0056] In order to monitor and graded early warning of dust accumulation on the road surface of Highway 1, the control system sets up an environmental perception system to accurately and reliably obtain meteorological data, dust particle spectrum data, road dust accumulation data and image data on Highway 1, and processes the data through the data processing system, and then sets a graded early warning mode to provide reliable early warning of dust levels and improve traffic safety.

[0057] In order to reliably monitor the thickness of dust accumulation on the expressway 1 and ensure the reliability of detection, a dust accumulation plate 17 is set to monitor the radius according to the monitoring accuracy requirements, and the turntable 18 is driven to rotate by a power device, and the first lateral moving platform 19 and the second lateral moving platform 21 are driven to rotate synchronously by the rotation of the turntable 18, and the corresponding first vertical moving platform and the second vertical moving platform are driven to rotate by the first lateral moving platform 19 and the second lateral moving platform 21 respectively, wherein the first vertical moving platform and the second vertical moving platform are driven to move downward to be inserted in the dust accumulation, wherein the first lateral moving platform 19 and the second lateral moving platform 21 are used to adjust the monitoring radius, and the first vertical moving platform and the second vertical moving platform are used to realize step monitoring and symmetrical monitoring as the radius increases, and the rotating disk rotates to realize circumferential segmented monitoring:

[0058] When the dust thickness data detected by the probes 15 on the first lateral moving platform 19 and the second lateral moving platform 21 are similar and the data error range is within the threshold, at the same time, the dust thickness data error detected by the several probes 15 on the first lateral moving platform 19 is within the threshold through the circumferential all-round monitoring by the turntable 18, indicating that the dust accumulation on the dust plate 17 monitored here is uniform, and the dust thickness data monitored by the probes 15 can be used as early warning data; when the threshold is exceeded, the first lateral moving platform 19 and the second lateral moving platform 21 are controlled by the control system to extend horizontally, expand the monitoring radius, and compare again until it is within the threshold range. Among them, the probe 15 monitors by setting several detection layers 16 in the probe 15, each detection layer 16 corresponds to the early warning type: high, medium, low, dust covers the detection layer 16, the first detection layer 16 photosensitive sensor senses, and then corresponds to the corresponding early warning type: high, medium, low, wherein the bottom end is a conical structure, which is convenient for insertion, and at the same time, it is below the safety zone 23 for no early warning state.

[0059] In order to accurately obtain the visibility and safety of the actual scene corresponding to Highway 1, corresponding early warnings are issued for the real-time scene of Highway 1.

[0060] By setting a number of first calibration points 10 and a second calibration point 11 in a first calibration direction 13 and a second calibration direction 14 according to the safety distance corresponding to the driving on the highway, the control system controls the angle of the camera 3 to shoot a number of calibration points in sequence, and a threshold is established by the number of calibration points. Different calibration points correspond to different safety distances. The cameras 3 are set in pairs to symmetrically collect calibration point images, and the collection results are verified by the symmetrically set images. When the consistency is within the threshold, it is used as visibility warning data for warning. By setting the above-mentioned combined results in sections on the highway 1, accurate visibility monitoring and warning are performed to adapt to the scene. When traveling together is prohibited and the highway needs to be driven off as soon as possible, the warning light in the calibration ball can be controlled to light up, and a high warning is given to drive off the highway 1 as soon as possible, thereby improving the safety warning adapted to the road section scene.

[0061] Another embodiment of the present invention provides a method for using a road dust monitoring and early warning system based on big data, including:

[0062] Step 1: Control the environment perception system through the control system to obtain meteorological data, dust particle spectrum data, road dust data and image data on the highway 1 in real time;

[0063] Step 2: The control system controls the data processing system to process the data obtained by the environmental perception system and calculate the dust confidence. When the confidence is greater than the set value, the camera system is triggered to take photos and issue a first-level warning. When the visibility is less than the set threshold, the ground dust monitoring device is triggered to detect the dust thickness and issue a second-level warning.

[0064] Step 3: When the dust thickness reaches the threshold, the control system controls the intelligent early warning system to issue a third-level early warning.

[0065] Specifically, by cross-verifying data from the particle database of the automatic meteorological particle spectrometer, the image database of the camera 3, and the road dust database, it is determined whether sandstorms occur, and an early warning or alarm notification is issued according to the data.

[0066] Step 1, determine whether sandstorm has occurred through spectrum analysis of the automatic meteorological particle size spectrometer 4: through the automatic meteorological particle size spectrometer 4 self-data verification and by comparing with the meteorological and particulate matter data of the fixed site, determine that the output data of the automatic meteorological particle size spectrometer 4 is normal.

[0067] Statistical analysis is performed on six important data in the particle size spectrum: PM1, PM2.5, PM10, TSP, central peak data, and particle spectrum change time, to form eight important indicators, which are recorded as ZB1-ZB8;

[0068] Each indicator has a different weight to determine the dust confidence level: Y = ZB1*10%+ZB2*10%+ZB3*5%+ZB4*5%+ZB5*40%+ZB6*10%+ZB7*10%+ZB8*10%;

[0069] When the confidence level Y is greater than 80%, the intelligent photo taking or road dust monitoring device 5 is triggered to perform synchronous pre-monitoring;

[0070] Among them: ZB1 indicates whether the peak mass concentration of inhalable particulate matter PM10 reaches the threshold A. If the index is met, ZB1 is recorded as 1, and if the index is not met, ZB1 is recorded as 0;

[0071] ZB2 indicates whether the peak mass concentration of total suspended particulate matter TSP reaches the threshold value B. If the index is met, ZB2 is recorded as 1, and if the index is not met, ZB2 is recorded as 0;

[0072] ZB3 indicates whether the PM10 / TSP ratio is less than the threshold C. If the index is met, ZB3 is recorded as 1; if the index is not met, ZB3 is recorded as 0;

[0073] ZB4 indicates whether the PM2.5 / PM10 ratio is less than the threshold D. If the indicator is met, ZB4 is recorded as 1; if the indicator is not met, ZB4 is recorded as 0;

[0074] ZB5 indicates whether the central peak of the particle size spectrum corresponds to the particle size interval E. If the index is met, ZB5 is recorded as 1, and if the index is not met, ZB5 is recorded as 0;

[0075] ZB6 represents the threshold value F% of the number concentration density of particles smaller than 1 μm. If the index is met, ZB6 is recorded as 1, and if the index is not met, ZB6 is recorded as 0;

[0076] ZB7 represents the increase threshold value G% of the number concentration density of particles larger than 10 μm. If the index is met, ZB7 is recorded as 1, and if the index is not met, ZB7 is recorded as 0;

[0077] ZB8 represents the change time t, which is recorded as 1 at the minute level and 0.7 at the hour level.

[0078] Step 2: Smart photo verification

[0079] Camera 3 takes photos of the surrounding fixed points and distances, and reviews the photos through machine training. When visibility is greater than the first threshold, camera 3 will continue to take photos and monitor without issuing a warning notification. When visibility is less than the first threshold, the system will issue a warning notification and issue different deceleration and speed limit reminders to passing vehicles through map navigation or highway display screens.

[0080] Step 3: Road dust monitoring verification

[0081] When visibility is less than the first threshold, the ground dust monitoring equipment is triggered to start air intake monitoring. When the road sand coverage rate reaches the second threshold, the system issues a warning notification, issues a no-travel prompt to vehicles, and sends cleaning instructions to personnel through the system, APP or text messages.

[0082] From the above description, it can be seen that the above embodiments of the present invention achieve the following technical effects:

[0083] The control system sets up an environmental perception system to accurately and reliably obtain meteorological data, dust particle spectrum data, road dust accumulation data and image data on Expressway 1, processes the data through a data processing system, and then sets a graded warning mode to provide reliable warning of dust levels, thereby improving traffic safety.

[0084] According to the monitoring accuracy requirements, a dust accumulation plate 17 is set to monitor the radius, and the turntable 18 is driven to rotate by a power device, and the first horizontal moving platform 19 and the second horizontal moving platform 21 are driven to rotate synchronously by the rotation of the turntable 18, and the corresponding first vertical moving platform and the second vertical moving platform are driven to rotate by the first horizontal moving platform 19 and the second horizontal moving platform 21, respectively, wherein the first vertical moving platform and the second vertical moving platform are driven to rotate to the corresponding collection domain 24, and the first vertical moving platform and the second vertical moving platform are driven to move down and insert in the dust, wherein the first horizontal moving platform 19 and the second horizontal moving platform 21 are used to adjust the monitoring radius, and the first vertical moving platform and the second vertical moving platform are used to realize the step-by-step adjustment as the radius increases. Monitoring and symmetrical monitoring, the rotating disk rotates to realize circumferential segmented monitoring: when the dust thickness data detected by the probes 15 on the first lateral moving platform 19 and the second lateral moving platform 21 are similar, and the data error range is within the threshold, at the same time, and the turntable 18 rotates circumferentially for all-round monitoring, when the dust thickness data error detected by several probes 15 on the first lateral moving platform 19 is within the threshold, it indicates that the dust accumulation on the dust accumulation plate 17 monitored here is uniform, and the dust thickness data monitored by the probe 15 can be used as early warning data; when the threshold is exceeded, the first lateral moving platform 19 and the second lateral moving platform 21 are controlled by the control system to extend laterally, the monitoring radius is expanded, and comparison is performed again until it is within the threshold range. Among them, the probe 15 monitors, by setting a number of detection layers 16 in the probe 15, each detection layer 16 corresponds to the warning type: high, medium, low, the dust covers the detection layer 16, the first detection layer 16 of the photosensitive sensor senses, and then corresponds to the corresponding warning type: high, medium, low, wherein the bottom is a cone structure, which is easy to insert, and at the same time, it is below the safety zone 23 for no warning state. It has the technical effect of reliably monitoring the thickness of dust accumulation on the highway 1 and ensuring the reliability of detection.

[0085] According to the safety distance corresponding to the highway driving, a number of first calibration points 10 and second calibration points 11 in the first calibration direction 13 and the second calibration direction 14 are set, and the control system controls the camera 3 to shoot the calibration points in sequence. The threshold is established by the number of calibration points. Different calibration points correspond to different safety distances. The cameras 3 are set in pairs to symmetrically collect calibration point images. The collection results are verified by the symmetrically set images. When the consistency is within the threshold, it is used as visibility warning data for warning. By setting the above combination results in sections on the highway 1, accurate visibility monitoring and warning are carried out to adapt to the scene. When traveling together is prohibited and the highway needs to be driven off as soon as possible, the warning light in the calibration ball can be controlled to light up, and a high warning is given to drive off the highway 1 as soon as possible, thereby improving the safety warning of the road section scene. Accurately obtain the field of view visibility and safety corresponding to the actual scene of the highway 1, and make corresponding warnings corresponding to the real-time scene of the highway 1.

[0086] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A road dust monitoring and early warning system based on big data, It is characterized in that include: An environmental perception system, which is used to obtain meteorological data, dust particle spectrum data, road dust data and image data on the highway; A data processing system, wherein the data processing system is used to process data acquired by the environment perception system; An intelligent early warning system, which warns of road dust conditions based on data acquired by the environmental perception system and data processed by the data processing system; The dust particle spectrum data is collected by the automatic meteorological particle size spectrometer of the environmental sensing system, and the PM1, PM2.5, PM10, TSP, central peak data and particle spectrum change time in the dust particle spectrum data are statistically analyzed to form eight indicators, which are recorded as ZB 1 -ZB 8 ; Set different weights for each indicator, calculate and judge the confidence level of sandstorms. When the confidence level Y is greater than 80%, the first-level warning is issued and the camera is triggered to take photos intelligently; The camera of the environmental perception system takes photos of the surroundings at fixed points and distances, and reviews the photos through machine training. When the visibility is greater than the first threshold, the camera will continue to take photos and monitor, and will not issue a second-level warning notification; When visibility is less than the first threshold, the system issues a second-level warning notification, issues a second-level warning signal to passing vehicles through map navigation or highway display, and triggers the road dust monitoring equipment to monitor the road dust; when the road dust monitoring equipment detects that the sand surface coverage rate reaches the second threshold, the system issues a third-level warning notification, issues a prohibition notice to vehicles, and issues a cleaning instruction to personnel through the system, APP or SMS; A control system, wherein the control system is connected to the environment perception system, the data processing system and the intelligent early warning system respectively.

2. The road dust monitoring and early warning system based on big data as claimed in claim 1, It is characterized in that The environment perception system comprises: A vertical pole, wherein the vertical pole is arranged at a position of a central isolation zone of a highway; A solar panel, wherein the solar panel is arranged on the top of the pole; A camera, wherein the camera is arranged on the vertical pole; An automatic meteorological particle size spectrometer, wherein the automatic meteorological particle size spectrometer is arranged on a vertical pole; A road dust monitoring device, wherein the road dust monitoring device is arranged at the lower end of the pole; A detection head is arranged on the road dust monitoring device.

3. The road dust monitoring and early warning system based on big data as claimed in claim 1, It is characterized in that The control system includes a controller, which includes a central processing unit, a camera control module, an automatic meteorological particle size spectrometer control module, a road dust accumulation monitoring equipment control module, a detection head control module, a solar panel control module, a data processing module and an intelligent prediction module. The camera control module, the automatic meteorological particle size spectrometer control module, the road dust accumulation monitoring equipment control module, the detection head control module, the solar panel control module, the data processing module and the intelligent prediction module are respectively connected to the central processing unit.

4. The road dust monitoring and early warning system based on big data as claimed in claim 3, It is characterized in that The camera control module is connected to the camera, the automatic meteorological particle size spectrometer control module is connected to the automatic meteorological particle size spectrometer, the road dust monitoring equipment control module is connected to the road dust monitoring equipment, the detection head control module is connected to the detection head, the solar panel control module is connected to the solar panel, and the data processing module is connected to the data processor.

5. The road dust monitoring and early warning system based on big data as claimed in claim 1, It is characterized in that The confidence calculation formula is as follows: Y=ZB 1 *10%+ZB 2 *10%+ZB 3 *5%+ZB 4 *5%+ZB 5 *40%+ZB 6 *10%+ZB 7 *10%+ZB 8 *10% Among them: ZB 1 Indicates whether the peak mass concentration of inhalable particulate matter PM10 reaches the threshold A, meets the index, ZB 1 Recorded as 1, the indicator is not met, ZB 1 Recorded as 0; ZB 2 Indicates whether the peak mass concentration of total suspended particulate matter TSP reaches the threshold B and meets the index, ZB 2 Recorded as 1, the indicator is not met, ZB 2 Recorded as 0; ZB 3 Indicates whether the PM10 / TSP ratio is less than the threshold C, meeting the indicator, ZB 3 Recorded as 1, the indicator is not met, ZB 3 Recorded as 0; ZB 4 Indicates whether the PM2.5 / PM10 ratio is less than the threshold D, meeting the indicator, ZB 4 Recorded as 1, the indicator is not met, ZB 4 Recorded as 0; ZB 5 Indicates whether the central peak of the particle size spectrum corresponds to the particle size interval E and meets the index, ZB 5 is recorded as 1, the indicator is not met, and ZB5 is recorded as 0; ZB 6 Indicates that the number concentration density of particles smaller than 1μm decreases by a threshold value of F%, meeting the index, ZB 6 Recorded as 1, the indicator is not met, ZB 6 Recorded as 0; ZB 7 Indicates that the number concentration density of particles larger than 10μm increases by a threshold value of G%, meeting the index, ZB 7 Recorded as 1, the indicator is not met, ZB 7 Recorded as 0; ZB 8 It represents the change time t, where the change time t is recorded as 1 at the minute level and as 0.7 at the hour level.

6. The road dust monitoring and early warning system based on big data as claimed in claim 1, It is characterized in that It also includes a first calibration point and a second calibration point, wherein the first calibration point is set on one side of the highway, and the second calibration point is set on the other side of the highway, the first calibration point includes several calibration positions, and the second calibration point includes several calibration positions, wherein a calibration rod and a calibration ball are provided at each calibration position, and a warning light is provided in the calibration ball, and the cameras are arranged in pairs, wherein one camera takes photos and calibrates several calibration positions of the first calibration point, and the other camera takes photos and calibrates several calibration positions of the second calibration point, wherein each calibration position is set in steps according to the correspondence between the vehicle's safe driving distance and the camera's straight-line distance, and a threshold is set according to the visible number of calibration balls at different calibration positions in the photos taken, and the visibility monitoring results of the corresponding positions are mutually verified by the two cameras to improve the monitoring reliability.

7. The road dust monitoring and early warning system based on big data as claimed in claim 2, It is characterized in that The road dust monitoring equipment includes a turntable, which is arranged on a vertical pole. A first lateral moving platform and a second lateral moving platform are provided on the turntable. The first lateral moving platform and the second lateral moving platform are symmetrically arranged relative to the vertical pole. A plurality of first longitudinal moving platforms are provided on the first lateral moving platform, and a plurality of second longitudinal moving platforms are provided on the second lateral moving platform. Probes are provided at the lower ends of the first longitudinal moving platform and the second longitudinal moving platform, and a plurality of probe layers are provided in the probes. A photosensitive sensor is provided in each probe layer, and a conical safety zone is provided at the lower end of the probe.

8. The road dust monitoring and early warning system based on big data as claimed in claim 2, It is characterized in that The road dust monitoring device also includes a dust plate, on which a plurality of collection domains are arranged in a ring shape, and the collection domains correspond one to one with the probes. The probes dynamically collect dust in the corresponding collection domains, and determine the dust thickness of the highway by processing the collection results and comparing them with a threshold.

9. A method for using a road dust monitoring and early warning system based on big data, based on the road dust monitoring and early warning system based on big data according to any one of claims 1 to 8, It is characterized in that include: Step 1: Control the environmental perception system through the control system to obtain meteorological data, dust particle spectrum data, road dust data and image data on the highway in real time; Step 2: The control system controls the data processing system to process the data obtained by the environmental perception system and calculate the dust confidence. When the confidence is greater than the set value, the camera system is triggered to take photos and issue a first-level warning. When the visibility is less than the set threshold, the ground dust monitoring device is triggered to detect the dust thickness and issue a second-level warning. Step 3: When the dust thickness reaches the threshold, the control system controls the intelligent early warning system to issue a third-level early warning.

Images