CN118032605A - Mine pavement dust detection method and detection system - Google Patents
Mine pavement dust detection method and detection system Download PDFInfo
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- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
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- G01S17/06—Systems determining position data of a target
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- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/483—Details of pulse systems
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Abstract
The invention relates to the technical field of data processing, and discloses a mining area pavement dust detection method and a detection system, wherein the detection method comprises the following steps: calibrating a dust detection area based on a laser radar installed at a rear portion of a moving vehicle, wherein the moving vehicle is in a dust-free environment and in a stationary state when calibration is performed; driving the mobile vehicle to run on the mine pavement, and collecting point cloud data of the dust emission detection area through the laser radar on the mobile vehicle, wherein the point cloud data comprises three-dimensional coordinate information and reflection intensity information; and performing dust concentration analysis based on the point cloud data located in the dust detection area in the acquired single-frame point cloud data to determine a dust concentration index. According to the mining area pavement dust detection method and system, dust carried by the vehicle can be observed in real time, so that accurate real-time data support is provided for a scheduling plan of sprinkling operation.
Description
Technical Field
The application relates to the technical field of data processing, in particular to a mine pavement dust detection method and a mine pavement dust detection system.
Background
The heavy self-unloading vehicles are basically adopted for carrying out earth and stone or mineral transportation in the mining area scene, and the transportation roads are basically non-constructed and hardened earth roads. Heavy-duty vehicles crush earth and dust when traveling on such roads, and the wheels carry a large amount of dust, which not only pollute the surrounding environment and the vehicles and damage the respiratory system health of on-site operators, but also can negatively affect the service life of the vehicles. More importantly, the flying dust can influence the sight of a vehicle driver and the perception data of the vehicle-mounted laser radar, so that the driving safety is endangered, the transportation vehicle has to run at a reduced speed or even stop under the condition of low visibility, and the production efficiency is greatly reduced.
Because the adverse effect of raise dust has all arranged professional vehicles such as watering lorry on the mining area road surface and has been carried out watering dust removal treatment to the road surface, the road surface viscosity after the wetting increases, and the raise dust is showing and is reducing. However, in areas with low precipitation and lack of water resources, the cost of sprinkling water is relatively high. Excessive water can cause the problems of softening, wetting and slipping of the road surface of the soil road, tire slipping, pit sinking and the like. Therefore, the sprinkling operation needs to consider the environmental climate factors according to the local conditions, and a reasonable plan implementation is made, such as suspending the sprinkling operation in overcast and rainy weather, increasing the frequency of the sprinkling operation in dry and high-temperature weather, and the like, so that the road condition can be perfectly combined safely and efficiently only in a reasonable range of the dryness of the transportation road surface.
At present, most of water sprinkling operations in mine environments are planned and scheduled by means of manpower, and information collection, transmission and feedback of actual conditions of the pavement are very low. Some road side measuring equipment is tried to be installed in fixed points in a part of mining areas to monitor dust emission, but the mining areas are complex and changeable in environment, the road side equipment can only detect the condition of a small part of fixed areas where the road side equipment is located, the overall condition of the road is very limited to master, the transportation road can be adjusted along with the stripping progress of the earthwork and the mineral deposit at any time, and the road side equipment needs to be moved frequently and additional cost such as disassembly, migration, reloading and the like is required. More importantly, the detection result of the road side equipment is interfered by various external environmental noises such as natural wind power, passing traffic density and the like, so that the actual condition of the road surface cannot be accurately detected.
Disclosure of Invention
The present disclosure is directed to solving at least one of the above-mentioned problems and disadvantages of the prior art.
According to one aspect of the present disclosure, there is provided a mining area road dust detection method, including:
S10: calibrating a dust detection area based on a laser radar installed at a rear portion of a moving vehicle, wherein the moving vehicle is in a dust-free environment and in a stationary state when calibration is performed;
S20: driving the mobile vehicle to run on the mine pavement, and collecting point cloud data of the dust emission detection area through the laser radar on the mobile vehicle, wherein the point cloud data comprises three-dimensional coordinate information and reflection intensity information;
S40: and carrying out dust concentration analysis based on the point cloud data in the dust detection area in the collected single-frame point cloud data so as to determine a dust concentration index.
According to an exemplary embodiment of the present disclosure, the mining area pavement dust detection method further includes step S50: comparing the determined dust concentration index with a dust concentration index threshold to determine whether the mining area pavement needs to be subjected to a sprinkling operation.
According to an exemplary embodiment of the present disclosure, the mining area pavement dust detection method further includes S30 before S40: and screening the point cloud data acquired by the laser radar based on the running speed and the front wheel steering angle of the mobile vehicle.
According to an exemplary embodiment of the present disclosure, screening the point cloud data collected by the lidar based on the traveling speed and the front wheel steering angle of the moving vehicle includes:
Filtering out point cloud data acquired by the laser radar when the running speed of the mobile vehicle is less than 10 km/h; and
And filtering out point cloud data acquired by the laser radar when the front wheel rotation angle of the mobile vehicle is larger than 12 degrees.
According to an exemplary embodiment of the present disclosure, calibrating the dust detection area includes: the flying dust detection area is preset by taking a point, which is a distance N from the laser radar along the central axis of the laser radar, as the center of the flying dust detection area, wherein the flying dust detection area is in a cuboid shape, the length L of the flying dust detection area is 4 times of the diameter of a rear wheel tire of the mobile vehicle, the height H is 1.4 times of the ground clearance height of the laser radar, and the width is the width of a vehicle body of the mobile vehicle.
According to an exemplary embodiment of the present disclosure, the value of the distance N should simultaneously satisfy the following conditions:
，
，
，
wherein Bmin is the minimum detection distance of the laser radar;
Φa is the rear tire diameter of the mobile vehicle;
HL is the ground clearance of the laser radar;
θ is the maximum gradient angle of the mine road surface;
Bmax is the maximum sensing distance of the lidar to a 10% reflectivity object.
According to an exemplary embodiment of the present disclosure, in step S10, further comprising performing a verification of the dust detection area, the verification comprising:
s11: acquiring single-frame point cloud data by the laser radar when the mobile vehicle is in a dust-free environment and in a static state;
S12: determining whether all points in the single-frame point cloud data are not located in the current dust detection area, if points located in the current dust detection area exist, indicating that background points exist in the current dust detection area, reducing the length, the width and the height of the current dust detection area, and then repeating the step S12 until no background point exists in the reduced dust detection area.
According to an exemplary embodiment of the present disclosure, in step S40, performing dust concentration analysis based on point cloud data located in the dust detection area among the collected single frame of point cloud data to determine a dust concentration index includes determining a dust concentration index R based on the following formula:
R = i1 + i2 + … + im，
wherein i1, i2, …, im are respectively the collected reflection intensities of the point clouds in the dust detection area.
According to an exemplary embodiment of the present disclosure, multi-frame point cloud data is collected by the lidar on the moving vehicle traveling on the mine road surface in step S20, and then steps S30 and S40 are performed to determine dust concentration indexes based on each frame of point cloud data screened out of the multi-frame point cloud data, respectively, and to average the dust concentration indexes of each frame of point cloud data screened out to obtain the dust concentration index of the mine road surface.
According to an exemplary embodiment of the present disclosure, the mean statistics include averaging a plurality of dust concentration indexes determined during traveling a predetermined distance after accumulating the traveling distance for the moving vehicle; or averaging a plurality of dust concentration indexes determined when the mobile vehicle travels in a pre-divided section in a map.
According to another aspect of the present disclosure, there is also provided a mining area road surface dust detection system for detecting dust on a mining area road surface using the mining area road surface dust detection method as described above, including:
A lidar mounted on a rear portion of a moving vehicle and configured to collect point cloud data in the dust detection area behind the vehicle, wherein the point cloud data includes three-dimensional coordinate information and reflection intensity information;
The dust concentration determining module is configured to perform dust concentration analysis based on point cloud data located in the dust detection area in the collected single-frame point cloud data so as to determine a dust concentration index.
According to an exemplary embodiment of the present disclosure, the mining area road dust detection system further includes a screening module configured to screen the point cloud data collected by the lidar based on a traveling speed and a front wheel steering angle of the moving vehicle.
According to an exemplary embodiment of the present disclosure, the mining area road dust detection system further comprises a dust detection area calibration module configured to calibrate a dust detection area based on a laser radar installed at a rear of the mobile vehicle, wherein the mobile vehicle is in a dust-free environment and in a stationary state when calibrating.
According to an exemplary embodiment of the present disclosure, the dust detection region calibration module is further configured to verify the dust detection region.
According to an exemplary embodiment of the present disclosure, the dust concentration determination module is further configured to compare the determined dust concentration index with a dust concentration index threshold to determine whether the mining area road surface requires a watering operation.
According to an exemplary embodiment of the present disclosure, the dust concentration determination module is further configured to average a plurality of dust concentration indexes determined during traveling of the predetermined distance after accumulating traveling of the moving vehicle for the predetermined distance; or averaging a plurality of dust concentration indexes determined when the mobile vehicle travels in a pre-divided section in a map.
According to the mine pavement dust detection method and system disclosed by the embodiment of the disclosure, the dust condition of the pavement can be accurately controlled, so that the water sprinkling operation can be reasonably scheduled, the production efficiency is improved, and the safety production is ensured.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the disclosure, nor is it intended to be used to limit the scope of the disclosure. Other features of the present disclosure will become apparent from the following specification.
Drawings
The drawings are for a better understanding of the present solution and are not to be construed as limiting the present disclosure.
Fig. 1 is a flowchart of a mine pavement dust detection method according to an exemplary embodiment of the present disclosure.
Fig. 2 is a schematic diagram of a mobile vehicle, lidar and dust detection region according to an example embodiment of the present disclosure.
Fig. 3 is a schematic diagram of a lidar and a cradle according to an example embodiment of the present disclosure.
Detailed Description
For a clearer description of the objects, technical solutions and advantages of the present disclosure, embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. It is to be understood that the following description of the embodiments is intended to illustrate and explain the general concepts of the disclosure and should not be taken as limiting the disclosure. In the description and drawings, the same or similar reference numerals refer to the same or similar parts or components. For purposes of clarity, the drawings are not necessarily drawn to scale and some well-known components and structures may be omitted from the drawings.
Unless defined otherwise, technical or scientific terms used in this disclosure should be given the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The terms "first," "second," and the like, as used in this disclosure, do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. The terms "a" or "an" do not exclude a plurality. The word "comprising" or "comprises", and the like, means that elements or items preceding the word are included in the element or item listed after the word and equivalents thereof, but does not exclude other elements or items. The terms "connected" or "connected," and the like, are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", "top" or "bottom" and the like are used only to indicate a relative positional relationship, which may be changed accordingly when the absolute position of the object to be described is changed. When an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, it can be "directly on" or "under" the other element or intervening elements may be present.
When the dust concentration is higher, the number of dust point clouds which can be detected by the laser radar is more, and the reflection intensity is higher: the light beam emitted by the laser radar can directly penetrate through the scattered dust block because the dust particles have too large gaps to form effective reflection if the light beam is hit on the scattered dust block, and only a small amount of low-reflection echoes are sensed by the laser radar at scattered aggregation positions. When the dust particle density is increased, the blocking and reflecting effects on the laser radar beam are synchronously enhanced, the effect reflected on the radar echo is that the effective imaging point is increased, and the reflecting intensity (echo intensity) of a single point is obviously increased. When the dust concentration is extremely high, the laser radar beam penetration path is shortened sharply, so that more energy is reflected back to the radar sensing element, and the point cloud imaging effect is very close to that of a solid sediment wall.
As shown in fig. 1 and 2, according to the present disclosure, there is provided a mining area pavement dust detection method, including:
S10: calibrating a dust detection area 3 based on a laser radar 2 installed at the rear of a moving vehicle 1, wherein the moving vehicle 1 is in a dust-free environment and in a stationary state when calibration is performed;
s20: driving the mobile vehicle 1 to run on a mining area pavement, and collecting point cloud data of a dust detection area 3 through a laser radar 2 on the mobile vehicle 1, wherein the point cloud data comprises three-dimensional coordinate information and reflection intensity information;
S40: and carrying out dust concentration analysis based on the point cloud data positioned in the dust detection area 3 in the collected single-frame point cloud data so as to determine a dust concentration index.
After the dust detection area 3 is calibrated, driving the mobile vehicle to run on the mine pavement, collecting at least one frame of point cloud data aiming at the dust detection area 3 in real time through the laser radar 2 in the running process of the mobile vehicle, and analyzing the dust concentration based on single frame of point cloud data in the at least one frame of point cloud data. If no point is found in the dust detection area 3, it may be directly determined that there is no dust, and if a point exists in the dust detection area 3, dust concentration analysis is performed on the point cloud data located in the dust detection area 3 to determine a dust concentration index. According to the invention, the laser radar 2 is adopted as vehicle-mounted sensing hardware, and the flying dust carried by the vehicle is observed in real time by combining an optical reflection principle and the active light sensing characteristic of the laser radar 2, so that the data result of the flying dust on the quantification of the shielding degree of the driver and the laser radar 2 can be analyzed, and accurate real-time data support is provided for the scheduling plan of the sprinkling operation.
As shown in fig. 2, the lidar 2 may be installed right behind an axle of the mobile vehicle 1 in order to observe dust whipped up during traveling. In addition, in order to ensure the accuracy of the measurement results, certain protective measures may be taken to ensure that the lidar 2 works in an environment where dust splashes for more than, for example, a whole day (the mining operation vehicle performs a check before going out once a day), during which the surface of the lidar 2 can be cleaned once. For example, as shown in fig. 3, the lidar 2 may be embedded in a bracket 4, and a visible window 41 with a horizontal direction of about 160 degrees FOV and a vertical direction of about 120 degrees FOV may be mounted on the bracket 4, and the lidar 2 may scan the environment in the mining area through the visible window 41. Below the support 4 is a square groove 43 with a baffle 42, the baffle 42 combines the functions of cable protection and mud guard to prevent dust from directly splashing and adhering to the visible window 41 of the support 4, thereby ensuring accuracy of sensing data. The bracket 4 is arranged at the rear part of the frame of the mobile vehicle 1 and is positioned below the hopper, the whole design occupies a small volume, so that collision interference can not be generated between the bracket and the hopper discharge lifting action, dust carried by the rear wheels of the mobile vehicle 1 in the mining area road surface driving process is in the detection view field of the laser radar 2, and noise with larger randomness such as environmental wind power and dust generated by other vehicles is largely blocked by the vehicle body of the own vehicle, so that the caused error disturbance is extremely small.
According to an exemplary embodiment of the present disclosure, the mining area pavement dust detection method further includes: s30: and screening the point cloud data acquired by the laser radar 2. When the vehicle speed is in a low position, the acting force of the wheels on the road surface is low, the dust raising amount brought up is insufficient to reasonably judge the dust raising condition of the road section, and the detection area is possibly invaded by other vehicles and personnel to cause false detection, so that when the point cloud data collected by the laser radar 2 are screened, the point cloud data collected by the laser radar 2 when the running speed of the moving vehicle 1 is smaller than the preset speed are filtered. In this embodiment, the predetermined speed may be, for example, 10km/h, and in other embodiments, the predetermined speed may also be another value, for example, 8km/h, 15km/h, etc., and the specific value may be specifically set according to the specific situation. In addition, when the wheels turn to a large extent, the dust stirring-up area and the dust detection area 3 are offset, at this time, the quantitative result of the dust detection is inconsistent with the actual situation, and it is possible to detect the interference objects such as the lane retaining wall near the narrow bend, so when the point cloud data collected by the laser radar 2 is screened, the point cloud data collected by the laser radar 2 when the front wheel rotation angle of the moving vehicle 1 is larger than the predetermined angle should be filtered. In this embodiment, the predetermined angle is selected to be 12 °. However, in other embodiments, the predetermined angle may also take other values, such as 10 °, and the like, and the specific value thereof may be specifically set according to the specific situation. That is, only the point cloud data acquired when the running speed is equal to or higher than the predetermined speed and the front wheel rotation angle is equal to or lower than the predetermined angle is selected, and the data which do not meet the two conditions can be directly filtered out and no subsequent processing is performed.
According to an exemplary embodiment of the present disclosure, the mining area pavement dust detection method further includes step S50: comparing the determined dust concentration index with a dust concentration index threshold to determine whether the mining area pavement needs to be subjected to a sprinkling operation. When the determined dust concentration index is greater than the dust concentration index threshold, the water spraying operation is required, otherwise, the water spraying operation is not required. The dust concentration index threshold value can be determined directly by data statistics and empirical induction through pavement actual measurement, and can be a numerical value or a numerical range. For example, the measurement results of a plurality of groups of vehicle speed S and dust R are substituted into a quaternary cubic simulation equation for least square fitting through repeatedly measuring the same vehicle at a speed interval of 10km/h-40km/h on the same road section which is in critical operation requiring sprinkling, so as to obtain an approximate solution of a fitting coefficient, and further obtain a functional relation of the vehicle speed and the dust index, so as to determine a dust concentration index threshold value at a certain vehicle speed.
According to an exemplary embodiment of the present disclosure, calibrating the dust detection area 3 includes: the dust detection area 3 is preset by taking a point, which is a distance N from the laser radar 2 along the central axis of the laser radar 2, as the center of the dust detection area 3, wherein the dust detection area 3 is in a cuboid shape, as shown in fig. 2, the length L of the dust detection area 3 is 4 times the diameter Φa of the rear wheel tire of the mobile vehicle 1, the height H is 1.4 times the ground clearance HL of the laser radar 2, and the width W is the width of the vehicle body of the mobile vehicle 1.
According to an exemplary embodiment of the present disclosure, the value of the distance N should simultaneously satisfy the following conditions:
，
，
，
wherein Bmin is the minimum detection distance of the lidar 2;
Φa is the rear tire diameter of the mobile vehicle 1;
HL is the ground clearance of the laser radar 2;
θ is the maximum gradient angle of the mine road surface;
Bmax is the maximum sensing distance of the lidar 2 to a 10% reflectivity object.
According to an exemplary embodiment of the present disclosure, in step S10, further comprising performing a verification of the dust detection region 3, the verification comprising:
S11: acquiring single-frame point cloud data by the lidar 2 on the mobile vehicle 1 while the mobile vehicle 1 is in a dust-free environment and in a stationary state;
S12: determining whether all points P in the single-frame point cloud data are not located in the current dust detection area 3, if points located in the current dust detection area 3 exist, indicating that background points exist in the current dust detection area 3, reducing the length, width and height of the current dust detection area 3, and repeating the step S12 until no background point exists in the reduced dust detection area 3.
At the time of reduction, for example, the length, width, and height of the dust detection area 3 may be reduced by the same or different distances, for example, 10cm, or 5cm, or the like, respectively, and a specific reduction size may be based on the number and distribution of points within the single-frame point cloud data, for example, when the number of points is large and the distribution is relatively disordered, the reduction distance may be set to a larger value, and when the number of points is small and the distribution is relatively concentrated, the reduction distance may be set to a smaller value. The length, width and height of the dust detection area 3 may also be reduced in a reduced distance decreasing manner, for example by a first reduction of 10cm, a second reduction of 8cm and a third reduction of 5cm.
According to an exemplary embodiment of the present disclosure, in step S40, performing dust concentration analysis based on the collected single frame point cloud data to determine a dust concentration index includes determining a dust concentration index R based on the following formula:
R = i1 + i2 + ... + im，
Where i1, i2, …, im are the detected reflected intensities at each point in the dust detection area 3, respectively.
When the dust shielding area is larger, the point value m is higher, the dust particle density is higher, the laser reflection intensity value i is larger, and the finally calculated R value is also higher.
It should be noted that, the dust concentration index R calculated based on the single-frame point cloud data represents the result observed by the current data frame, however, the uncertainty of the single-frame data still exists, and in order to improve the accuracy of the detection result, the dust concentration index of the mining area pavement can be obtained by a multi-frame data result average value calculation method. Specifically, multi-frame point cloud data is acquired by the lidar 2 on the moving vehicle 1 traveling on the mine road surface in step S20, and then steps S30 and S40 are performed to determine dust concentration indexes based on each frame of point cloud data screened out from the multi-frame point cloud data, respectively, and to average the dust concentration indexes of each frame of the screened out point cloud data to obtain a dust concentration index average value of the mine road surface as a final dust concentration index of the mine road surface.
According to an exemplary embodiment of the present disclosure, the average statistics include averaging a plurality of dust concentration indexes determined during the travel of the predetermined distance after the travel of the moving vehicle 1 is accumulated for a predetermined distance, for example, averaging a plurality of dust concentration indexes determined during the travel of 10m after the accumulated travel exceeds 10 m; or the plurality of dust concentration indexes determined when the moving vehicle 1 travels in the previously divided sections in the map are averaged, for example, the plurality of dust concentration indexes determined by the coordinates of the moving vehicle 1 in the respective divided sections in the map are averaged. The specific implementation form is designed according to the current mining area condition according to local conditions.
According to another aspect of the present disclosure, as shown in fig. 3, there is also provided a mining area pavement dust detection system using the above mining area pavement dust detection method, including:
A lidar 2 mounted on the rear of the moving vehicle 1 and configured to collect point cloud data in a dust detection area 3 behind the vehicle, wherein the point cloud data includes three-dimensional coordinate information and laser reflection intensity information;
and a dust concentration determining module configured to perform dust concentration analysis based on point cloud data located in the dust detection area 3 among the collected single frame point cloud data to determine a dust concentration index.
According to an exemplary embodiment of the present disclosure, the mining area road dust detection system further includes a screening module configured to screen the point cloud data collected by the lidar 2 based on the traveling speed and the front wheel rotation angle of the moving vehicle 1.
According to an exemplary embodiment of the present disclosure, the mining area road dust detection system further comprises a dust detection area calibration module configured to calibrate the dust detection area 3 based on the laser radar 2 installed at the rear of the mobile vehicle 1, wherein the mobile vehicle 1 is in a dust-free environment and in a stationary state when calibration is performed. Preferably, the dust detection zone calibration module is further configured to verify the dust detection zone 3.
According to an exemplary embodiment of the present disclosure, the dust concentration determination module is further configured to compare the determined dust concentration index to a dust concentration index threshold to determine whether the mining area road surface requires a watering operation.
It should be noted that, the screening module, the dust concentration determining module, and the dust detection area calibration module may be a vehicle-mounted controller or an industrial personal computer, or may be a cloud server, which has no specific requirements and is therefore not described in detail.
According to an exemplary embodiment of the present disclosure, the dust concentration determination module is further configured to average a plurality of dust concentration indexes determined during the travel of the predetermined distance after accumulating the travel of the moving vehicle 1 for the predetermined distance; or the dust concentration indexes determined when the mobile vehicle 1 travels in the pre-divided sections in the map are averaged.
According to another aspect of the present disclosure, there is also provided an electronic device including: the device comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory are communicated through the bus when the electronic device is operated, and the machine-readable instructions are executed by the processor to perform the steps of the non-hardened road surface roughness detection method.
According to another aspect of the present disclosure, there is also provided a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of the method for detecting the roughness of a non-hardened road surface as described above.
According to the mining area pavement dust detection method and the mining area pavement dust detection system, quantitative pavement dust index information can be obtained, so that watering operation can be reasonably scheduled, production efficiency is improved, and safe production is guaranteed. Through experiments, the mining area pavement dust detection method can reduce the abnormal alarm times of the sensing distance of the laser radar by 60 percent, the alarm mechanism is statistics of the number of effective imaging points which are 20 meters and 50 meters away from each other in a sensing software inspection data frame, and if more than 3 continuous frames detect that the number of laser radar reflection points is less than 20 out of 50 meters or the number of laser radar reflection points is less than 400 out of 20 meters, the alarm mechanism alarms, and suspects that the vision of the laser radar is shielded. By using the mining area pavement dust detection method disclosed by the invention, the running average speed of the main road is increased from 19.8 km/h to 23.5 km/h, the emergency braking frequency is reduced from 23 times a day to 7 times a day, and the emergency braking is usually caused by the fact that a sensing system finds an obstacle or a laser radar alarms abnormally.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps recited in the present disclosure may be performed in parallel, sequentially, or in a different order, provided that the desired results of the disclosed aspects are achieved, and are not limited herein.
The above detailed description should not be taken as limiting the scope of the present disclosure. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present disclosure are intended to be included within the scope of the present disclosure.
Claims (16)
1. A mining area pavement dust detection method comprises the following steps:
S10: calibrating a dust detection area based on a laser radar installed at a rear portion of a moving vehicle, wherein the moving vehicle is in a dust-free environment and in a stationary state when calibration is performed;
S20: driving the mobile vehicle to run on the mine pavement, and collecting point cloud data of the dust emission detection area through the laser radar on the mobile vehicle, wherein the point cloud data comprises three-dimensional coordinate information and reflection intensity information;
S40: and carrying out dust concentration analysis based on the point cloud data in the dust detection area in the collected single-frame point cloud data so as to determine a dust concentration index.
2. The mining area road surface dust detection method according to claim 1, wherein the mining area road surface dust detection method further comprises step S50: comparing the determined dust concentration index with a dust concentration index threshold to determine whether the mining area pavement needs to be subjected to a sprinkling operation.
3. The mining area road surface dust detection method according to claim 2, wherein the mining area road surface dust detection method further comprises S30 before S40: and screening the point cloud data acquired by the laser radar based on the running speed and the front wheel steering angle of the mobile vehicle.
4. The mining area road surface dust detection method according to claim 3, wherein screening the point cloud data collected by the laser radar based on the traveling speed and the front wheel steering angle of the moving vehicle comprises:
Filtering out point cloud data acquired by the laser radar when the running speed of the mobile vehicle is less than 10 km/h; and
And filtering out point cloud data acquired by the laser radar when the front wheel rotation angle of the mobile vehicle is larger than 12 degrees.
5. A mining area pavement dust detection method according to claim 3, wherein calibrating the dust detection area comprises: the flying dust detection area is preset by taking a point, which is a distance N from the laser radar along the central axis of the laser radar, as the center of the flying dust detection area, wherein the flying dust detection area is in a cuboid shape, the length of the flying dust detection area is 4 times of the diameter of a rear wheel tire of the mobile vehicle, the height is 1.4 times of the ground clearance height of the laser radar, and the width is the width of a vehicle body of the mobile vehicle.
6. The mining area pavement dust detection method according to claim 5, wherein the distance N should simultaneously satisfy the following conditions:
，
，
，
wherein Bmin is the minimum detection distance of the laser radar;
Φa is the rear tire diameter of the mobile vehicle;
HL is the ground clearance of the laser radar;
θ is the maximum gradient angle of the mine road surface;
Bmax is the maximum sensing distance of the lidar to a 10% reflectivity object.
7. The mining area road surface dust detection method according to claim 3, wherein in step S10, further comprising performing a verification of the dust detection area, the verification comprising:
s11: acquiring single-frame point cloud data by the laser radar when the mobile vehicle is in a dust-free environment and in a static state;
s12: determining whether all points in the single-frame point cloud data are not located in the current dust detection area, if points located in the current dust detection area exist, indicating that background points exist in the current dust detection area, reducing the length, the width and the height of the current dust detection area, and then repeating the step S12 until no background point exists in the reduced dust detection area.
8. The mining area road surface dust detection method according to claim 3, wherein in step S40, performing dust concentration analysis based on point cloud data located in the dust detection area among the collected single frame of point cloud data to determine a dust concentration index includes determining dust concentration index R based on the following formula:
R = i1 + i2 + … + im，
wherein i1, i2, …, im are respectively the collected reflection intensities of the point clouds in the dust detection area.
9. The mining area road surface dust detection method according to claim 3, wherein in step S20, a plurality of frames of point cloud data are collected by the lidar on the moving vehicle traveling on the mining area road surface, and then steps S30 and S40 are performed to determine dust concentration indexes based on each frame of point cloud data screened out of the plurality of frames of point cloud data, respectively, and to average the dust concentration indexes of each frame of point cloud data screened out to obtain the dust concentration index of the mining area road surface.
10. The mining area road surface dust detection method according to claim 9, wherein the average statistics include averaging a plurality of dust concentration indexes determined during traveling a predetermined distance after accumulating traveling the predetermined distance for the moving vehicle; or averaging a plurality of dust concentration indexes determined when the mobile vehicle travels in a pre-divided section in a map.
11. A mining area pavement dust detection system for detecting dust on a mining area pavement using the mining area pavement dust detection method of claim 1, comprising:
A lidar mounted on a rear portion of a moving vehicle and configured to collect point cloud data in the dust detection area behind the moving vehicle, wherein the point cloud data includes three-dimensional coordinate information and reflection intensity information;
The dust concentration determining module is configured to perform dust concentration analysis based on point cloud data located in the dust detection area in the collected single-frame point cloud data so as to determine a dust concentration index.
12. The mining area road dust detection system of claim 11, further comprising a screening module configured to screen the point cloud data collected by the lidar based on a travel speed and a front wheel steering angle of the mobile vehicle.
13. The mining area road surface dust detection system of claim 11, further comprising a dust detection zone calibration module configured to calibrate a dust detection zone based on a lidar mounted at a rear of the mobile vehicle, wherein the mobile vehicle is in a dust-free environment and in a stationary state when calibrating.
14. The mining area roadway dust detection system of claim 13, wherein the dust detection zone calibration module is further configured to verify the dust detection zone.
15. The mining area roadway dust detection system of claim 11, wherein the dust concentration determination module is further configured to compare the determined dust concentration index to a dust concentration index threshold to determine whether the mining area roadway requires a watering operation.
16. The mining area roadway dust detection system of any one of claims 11-15, wherein the dust concentration determination module is further configured to average a plurality of dust concentration indices determined during travel of the predetermined distance after accumulating travel of the mobile vehicle for the predetermined distance; or averaging a plurality of dust concentration indexes determined when the mobile vehicle travels in a pre-divided section in a map.
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