CN113899667B - Walking type particulate matter detection system and method - Google Patents
Walking type particulate matter detection system and method Download PDFInfo
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- CN113899667B CN113899667B CN202111125065.5A CN202111125065A CN113899667B CN 113899667 B CN113899667 B CN 113899667B CN 202111125065 A CN202111125065 A CN 202111125065A CN 113899667 B CN113899667 B CN 113899667B
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- 239000013618 particulate matter Substances 0.000 title claims abstract description 72
- 238000001514 detection method Methods 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 34
- 238000012937 correction Methods 0.000 claims abstract description 98
- 239000012528 membrane Substances 0.000 claims abstract description 77
- 238000005070 sampling Methods 0.000 claims abstract description 70
- 238000003384 imaging method Methods 0.000 claims abstract description 54
- 239000002245 particle Substances 0.000 claims abstract description 46
- 238000004458 analytical method Methods 0.000 claims abstract description 40
- 230000005250 beta ray Effects 0.000 claims abstract description 27
- 238000004364 calculation method Methods 0.000 claims abstract description 20
- 230000003287 optical effect Effects 0.000 claims abstract description 6
- 238000013507 mapping Methods 0.000 claims description 17
- 238000004220 aggregation Methods 0.000 claims description 10
- 230000002776 aggregation Effects 0.000 claims description 10
- 238000010586 diagram Methods 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 description 13
- 238000000149 argon plasma sintering Methods 0.000 description 10
- 238000009825 accumulation Methods 0.000 description 3
- 230000007547 defect Effects 0.000 description 3
- 230000007613 environmental effect Effects 0.000 description 3
- 230000006870 function Effects 0.000 description 3
- 238000005259 measurement Methods 0.000 description 2
- UGFAIRIUMAVXCW-UHFFFAOYSA-N Carbon monoxide Chemical compound [O+]#[C-] UGFAIRIUMAVXCW-UHFFFAOYSA-N 0.000 description 1
- 230000002159 abnormal effect Effects 0.000 description 1
- 230000002238 attenuated effect Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 239000012895 dilution Substances 0.000 description 1
- 238000010790 dilution Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 239000003546 flue gas Substances 0.000 description 1
- 239000007789 gas Substances 0.000 description 1
- 239000003112 inhibitor Substances 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
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- 239000000243 solution Substances 0.000 description 1
- 238000001228 spectrum Methods 0.000 description 1
- 230000009466 transformation Effects 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/06—Investigating concentration of particle suspensions
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- G01N15/075—
Abstract
The invention provides a walking type particulate matter detection system and a walking type particulate matter detection method, wherein the walking type particulate matter detection system comprises a walking vehicle, a sampling pipe and a particulate matter collecting unit, and the sampling pipe penetrates through the top wall of the walking vehicle; further comprises: the optical particulate matter detection device comprises a first imaging unit, a second imaging unit, an analysis module, a correction module and a memory, wherein the analysis module obtains the calculated concentration C of particulate matters on a filter membrane according to imaging 1 The method comprises the steps of carrying out a first treatment on the surface of the The correction module calculates the concentration C according to the calculation 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient; the memory stores a correction coefficient k; the beta-ray particle detection device obtains the content C of particles in a correction area on the filter membrane during correction β The method comprises the steps of carrying out a first treatment on the surface of the The calculation module calculates the concentration and the content C of the particulate matters β Obtaining new correction coefficientAnd sending the new correction coefficient to a memory; the control module replaces the correction coefficients in the memory with new correction coefficients. The invention has the advantages of accurate detection and the like.
Description
Technical Field
The invention relates to atmospheric particulate detection, in particular to a walking type particulate detection system and method.
Background
The sources of the environmental air particles are wide, and the environmental air particles are easily influenced by environmental parameters such as wind speed, wind direction and the like, so that different time-space distribution differences are larger. Currently, particulate matter monitoring in air is mainly classified into off-line monitoring (such as a weighing method), on-line monitoring (such as a beta-ray method, an oscillating balance method and a light scattering method). The off-line monitoring accuracy is high, but the off-line monitoring cannot be performed on site, and the operation is complex, time-consuming and labor-consuming. The online monitoring can make up for the defect that the offline monitoring cannot realize on-site counting, but cannot meet the requirement of large-scale real-time counting, and a special detector is required to be equipped, so that the instrument cost is high, and the expertise of operators is high.
At present, a monitoring method combining light scattering and beta rays is presented, and main units comprise a light scattering unit, a beta ray detection unit and a control unit. Wherein the light scattering unit and the beta ray unit are two paths of parallel units. The light scattering unit comprises a light scattering unit and a jet pump, and is used for detecting the flue gas and the dilution gas entering the light scattering unit in proportion. The beta ray unit comprises a flowmeter, a sampling pump, a beta ray calibration film, a beta ray source and a detector. The beta rays emitted by the radioactive source pass through the paper tape and then are attenuated, and the detector above the paper tape detects the intensity of the beta rays. And correcting the light scattering concentration according to the measurement result of the whole point of the beta-ray detector. The method has certain quick response characteristics, but still has the defects such as:
1. the time resolution is low;
the minimum time resolution is 1s of the light scattering detector, and the moving speed of 5m/s is calculated by the navigation monitoring, so that the requirement cannot be met. If the air is sampled at the site A, the result is obtained when the vehicle reaches the site B, and the work such as rechecking can not be carried out on the detection result of the site A.
2. The volume structure of the light scattering detection unit is bigger, and the portable requirement of the navigation mode is not met.
3. The production cost and maintenance cost of the product are also higher, and instrument operators need to have certain professional operation literacy.
Disclosure of Invention
In order to solve the defects in the prior art, the invention provides a walking type particulate matter detection system.
The invention aims at realizing the following technical scheme:
the system comprises a walking vehicle, a sampling tube and a particle collection unit, wherein the sampling tube penetrates through the top wall of the walking vehicle, the particle collection unit comprises a filter membrane, a driving module, a first wheel and a second wheel, one end of the filter membrane is wound on the first wheel, the other end of the filter membrane is wound on the second wheel, and the driving module drives the first wheel to rotate forwards; the walk-behind particulate matter detection system further includes:
the optical particle detection device comprises a first imaging unit, a second imaging unit, an analysis module, a correction module and a memory, wherein the first imaging unit and the second imaging unit are respectively arranged at the side part of the sampling tube and used for imaging the filter membrane before and after sampling; the analysis module is used for obtaining the calculated concentration C of the particulate matters on the filter membrane according to the imaging 1 The method comprises the steps of carrying out a first treatment on the surface of the The correction module is used for calculating concentration C according to the calculation 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient; the memory is used for storing a correction coefficient k;
beta-ray particle detection device for obtaining the content C of particles in the correction area on the filter membrane during correction β ；
The calculation module is used for obtaining a new correction coefficient according to the output of the analysis module and the beta-ray particulate matter detection deviceAnd sending the new correction coefficients to the memory;
and the control module is used for replacing the correction coefficient in the memory with the new correction coefficient.
The invention also aims at providing a walking type particulate matter detection method, which is realized by the following technical scheme:
the method for detecting the walking type particulate matters comprises the following steps:
(A1) In the course of the navigation vehicle, outside air passes through the sampling tube and enters the navigation vehicle;
(A2) A first imaging unit positioned at one side of the sampling tube acquires an image of a first area of a blank filter membrane, and two ends of the filter membrane are respectively wound on a first wheel and a second wheel;
(A3) A first wheel rotates positively, the filter membrane moves positively, and the first area is positioned at the lower side of the sampling tube and collects particles;
(A4) The first wheel rotates positively, the filter membrane moves positively, a first area of the filter membrane moves out of the lower side of the sampling tube, and the second imaging unit obtains an image of the first area;
(A5) The analysis module obtains the calculated concentration C of the particulate matters according to the images of the first area before and after sampling 1 ；
(A6) The correction module calculates the concentration C according to the calculation 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient.
Compared with the prior art, the invention has the following beneficial effects:
through redesigning, the pretreatment part is under the condition of having the inhibitor function, realizes the direct connection with the chromatographic column profile, thereby has reached:
1. the resolution is high;
the high-resolution imaging unit is used for realizing images before and after particulate matter collection, the time resolution of 0.04s is realized by utilizing an algorithm, and the monitoring requirement of large-scale space rapid transformation under a sailing mode is met;
2. the detection is accurate;
matching the pattern spectrum by using a neural network to realize high-precision matching conversion of the gray value and the concentration of the particulate matter image;
the system has the function of updating the beta-ray correction coefficient, automatically updates the coefficient k before measurement, adapts to different monitoring environments and improves the accuracy of optical monitoring;
the system has the function of suspicious concentration self-identification, and the beta rays of abnormal concentration are retested in real time and the correction coefficient is updated, so that the monitoring result is accurate and reliable;
3. the detection efficiency is high;
by utilizing forward and reverse movement of the filter membrane, the detection (sampling) of the second region is realized when the first region is sampled (detected), namely parallel processing is realized, and the detection efficiency is obviously improved;
4. the cost is low;
the forward and reverse movement of the filter membrane is utilized, so that the same region on the filter membrane repeatedly collects particles and detects, and the detection cost is reduced.
Drawings
The present disclosure will become more readily understood with reference to the accompanying drawings. As will be readily appreciated by those skilled in the art: the drawings are only for illustrating the technical scheme of the present invention and are not intended to limit the scope of the present invention. In the figure:
FIG. 1 is a schematic diagram of a walk-behind particulate matter detection system according to an embodiment of the present invention;
fig. 2 is a flow chart of a method for detecting airborne particulate in accordance with an embodiment of the present invention.
Detailed Description
Figures 1-2 and the following description depict alternative embodiments of the invention to teach those skilled in the art how to make and reproduce the invention. For the purpose of explaining the technical solution of the present invention, some conventional aspects have been simplified or omitted. Those skilled in the art will appreciate variations or alternatives derived from these embodiments that fall within the scope of the invention. Those skilled in the art will appreciate that the features described below can be combined in various ways to form multiple variations of the invention. Thus, the invention is not limited to the following alternative embodiments, but only by the claims and their equivalents.
Example 1:
fig. 1 shows a schematic structural diagram of a walking particulate matter detection system according to an embodiment of the present invention, as shown in fig. 1, where the walking particulate matter detection system includes:
the device comprises a carrier, a sampling tube 11 and a particle collection unit, wherein the sampling tube passes through the top wall of the carrier, the particle collection unit comprises a filter membrane 23, a driving module, a first wheel 21 and a second wheel 22, one end of the filter membrane 23 is wound on the first wheel 21, the other end is wound on the second wheel 22, and the driving module drives the first wheel 21 to rotate forwards;
optical particulate detectionThe device comprises a first imaging unit 31, a second imaging unit 32, an analysis module 51, a correction module 61 and a memory 71, wherein the first imaging unit 31 and the second imaging unit 32 are respectively arranged at the side part of the sampling tube 11 and are used for imaging the filter membrane 23 before and after sampling; the analysis module 51 is used for obtaining the calculated concentration C of the particulate matter on the filter membrane 23 according to the imaging 1 The method comprises the steps of carrying out a first treatment on the surface of the The correction module 61 is used for calculating the concentration C according to the calculated concentration 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient; the memory 71 is used for storing a correction coefficient k;
beta-ray particulate matter detection device 41, said beta-ray particulate matter detection device 41 being adapted to obtain, at calibration, the particulate matter content C of the calibration area on said filter membrane 23 β ；
A calculation module 81, wherein the calculation module 81 is used for obtaining a new correction coefficient according to the output of the analysis module 51 and the beta-ray particulate matter detection device 41And sends the new correction coefficient to the memory 71;
a control module 92, wherein the control module 92 is configured to replace the correction coefficient in the memory 71 with the new correction coefficient.
To improve the detection efficiency, further, the driving module drives the second wheel 22 to rotate reversely according to the instruction of the control module.
In order to update the correction coefficient as required to improve the detection accuracy, the calculation module 81 is further configured to calculate an average value of the correction concentrations for a plurality of times, and obtain a deviation between the correction concentration and the average value; the walk-behind particulate matter detection system further includes:
a judging module 91, wherein the judging module 91 is configured to judge whether the deviation reaches a threshold value, and send a judgment result to the control module 92;
and if the deviation reaches a threshold value, the control module controls the beta-ray particulate matter detection device to detect the particulate matters corresponding to the correction concentration.
In order to obtain the concentration of particulate matter rapidly and accurately, further, the analysis module 51 obtains the calculated concentration C 1 The method comprises the following steps:
the analysis module 51 obtains the gray value H of the particulate matter accumulation area on the filter membrane 23 according to the images of the filter membrane 23 before and after sampling 1 Obtaining the concentration C' of the particulate matters according to the mapping relation between the concentration of the particulate matters and the gray values of the particulate matters;
in a standard pattern gallery of gray values, the gray value H is found 1 A best matching gray value pattern, the gray value H 'of the gray value pattern' 1 And gray value H 1 The deviation between the two parts is minimum;
the analysis module 51 derives a calculated concentration
In order to establish an accurate mapping relationship, further, the mapping relationship is obtained in the following manner:
setting a photographing frame rate and an image resolution of the first imaging unit 31 and the second imaging unit 32;
the analysis module 51 obtains the gray value of the particle aggregation area on the filter membrane 23 according to the images of the filter membrane 23 before and after sampling, and draws a particle gray value pattern diagram under the concentration;
the neural network is introduced to learn the relation between the gray value of the particle aggregation area and the particle concentration, update the gray value pattern library and establish the mapping relation between the particle gray value and the particle concentration.
Fig. 2 schematically shows a flowchart of a method for detecting airborne particulate in an embodiment of the present invention, as shown in fig. 2, where the method for detecting airborne particulate includes the steps of:
(A1) During the course of the course, outside air enters the course through the sampling tube 11;
(A2) The first imaging unit 31 at one side of the sampling tube 11 acquires an image of a first area of the blank filter membrane 23, and two ends of the filter membrane 23 are respectively wound on the first wheel 21 and the second wheel 22;
(A3) The first wheel 21 rotates positively, the filter membrane 23 moves positively, and the first area is positioned on the lower side of the sampling tube 11 and collects particles;
(A4) The first wheel 21 rotates forward, the filter membrane 23 moves forward, the first area of the filter membrane 23 moves out of the lower side of the sampling tube 11, and the second imaging unit 22 obtains an image of the first area;
(A5) The analysis module 51 obtains the calculated concentration C of particulate matter from the images of the first region before and after sampling 1 ；
(A6) The correction module 61 calculates the concentration C 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient.
In order to update the correction coefficient as required to improve the detection accuracy, the method for detecting the walking particulate matters further comprises the following steps:
(A7) The calculation module 81 calculates the average value of the correction concentrations and obtains the correction concentration C 2 And a deviation from the average;
(A8) The judging module 91 judges whether the deviation reaches a threshold value or not, and the judgment result is sent to the control module 92;
if the deviation reaches a threshold value, the beta-ray particulate matter detection device 41 obtains the particulate matter concentration C of the first region under the control of the control module 92 β ；
(A9) The calculation module 81 calculates the corrected concentration C based on the correction concentration C 2 And particle concentration C β Obtaining new correction coefficientAnd sends the new correction coefficients to the memory 71;
(A10) The control module 92 replaces the correction coefficients in the memory 71 with the new correction coefficients.
In order to obtain the concentration of particulate matter quasi-continuously, further, in step (A3), the first imaging unit 31 obtains an image of the second region of the blank filter film 23;
in step (A4), the second area is located at the lower side of the sampling tube 11 and collects particles, the second wheel 22 is rotated in the reverse direction, the filter membrane 23 is moved in the reverse direction, the first area is moved to the lower side of the sampling tube 11 and collects particles again, the second area is moved out of the lower side of the sampling tube 11, and the first imaging unit 31 obtains an image of the second area 32;
in step (A5), the analysis module 51 obtains the calculated concentration C of particulate matter from the images of the second region before and after sampling 1 。
In order to obtain the concentration of particulate matter rapidly and accurately, further, the analysis module 51 obtains the calculated concentration C 1 The method comprises the following steps:
the analysis module 51 obtains the gray value H of the particulate matter accumulation area on the filter membrane 23 according to the images of the filter membrane 23 before and after sampling 1 Obtaining the concentration C' of the particulate matters according to the mapping relation between the concentration of the particulate matters and the gray values of the particulate matters;
in a standard pattern gallery of gray values, the gray value H is found 1 A best matching gray value pattern, the gray value H 'of the gray value pattern' 1 And gray value H 1 The deviation between the two parts is minimum;
the analysis module 51 derives a calculated concentration
In order to accurately obtain the mapping relationship, further, the mapping relationship is obtained in the following manner:
setting a photographing frame rate and an image resolution of the first imaging unit 31 and the second imaging unit 32;
the analysis module 51 obtains the gray value of the particle aggregation area on the filter membrane 23 according to the images of the filter membrane before and after sampling, and draws a particle gray value pattern diagram under the concentration;
the neural network is introduced to learn the relation between the gray value of the particle aggregation area and the particle concentration, update the gray value pattern library and establish the mapping relation between the particle gray value and the particle concentration.
Example 2:
application example of the system and method for detecting airborne particulate matters according to embodiment 1 of the present invention in atmospheric particulate matters detection.
In this application example, as shown in fig. 1, the navigation vehicle adopts a Jiangling complete sequence; the sampling tube 11 passes through the top wall of the navigation vehicle; the particle collecting unit comprises a filter membrane 23, a driving module, a first wheel 21 and a second wheel 22, wherein one end of the filter membrane 23 is wound on the first wheel 21, the other end of the filter membrane is wound on the second wheel 22, and the driving module drives the first wheel 21 to rotate in the forward direction and the second wheel 22 to rotate in the reverse direction; when the sampling tube 11 is lowered and the filter membrane 23 is sandwiched between the sampling tube 11 and the pipe 12, the sampling pump 13 is operated, and the outside air passes through the sampling tube 11, the filter membrane 23 and the pipe 12 in order; when the sampling tube 11 moves up, the filter membrane 23 moves forward if the first wheel 21 rotates forward, and the filter membrane 23 moves backward if the second wheel 22 rotates backward;
in the optical particulate matter detection device, a high-definition camera is adopted by the first imaging unit 31 and the second imaging unit 32, and the first imaging unit 31 and the second imaging unit 32 are respectively arranged at two sides of the sampling tube 11 and are used for imaging the filter membrane 23 before and after sampling; the analysis module 51 is used for obtaining the calculated concentration C of the particulate matter on the filter membrane 23 according to the imaging 1 The method comprises the steps of carrying out a first treatment on the surface of the The correction module 61 is used for calculating the concentration C according to the calculated concentration 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient; the memory 71 is used for storing the correction coefficient k;
the beta-ray particulate matter detection device 41 is used for obtaining the particulate matter content C of the correction area on the filter membrane 23 during correction β The method comprises the steps of carrying out a first treatment on the surface of the The specific construction and operation of the beta-ray particulate matter detection device 41 is within the skill of the art;
a calculation module 81 implemented by software, wherein the calculation module 81 is used for obtaining a new correction coefficient according to the output of the analysis module 51 and the beta-ray particulate matter detection device 41And correcting the newPositive coefficients are sent to the memory 71; the calculating module 81 is further configured to calculate an average value of the correction concentrations, and obtain a deviation between the correction concentration and the average value;
a control module 92, wherein the control module 92 is configured to replace the correction coefficient in the memory 71 with the new correction coefficient;
a judging module 91, wherein the judging module 91 is configured to judge whether the deviation reaches a threshold value, and the judgment result is sent to the control module 92;
if the deviation reaches the threshold value, the control module 92 controls the β -ray particulate matter detection device 41 to detect the particulate matter corresponding to the current correction concentration.
Fig. 2 schematically shows a flowchart of a method for detecting airborne particulate in an embodiment of the present invention, as shown in fig. 2, where the method for detecting airborne particulate includes the steps of:
(A0) The mapping relation between the gray value of the particulate matters and the concentration of the particulate matters is established by the following specific modes:
setting a photographing frame rate and an image resolution of the first imaging unit 31 and the second imaging unit 32;
the analysis module 51 obtains the gray value of the particle aggregation area on the filter membrane 23 according to the images of the filter membrane 23 before and after sampling, and draws a particle gray value pattern diagram under the concentration;
introducing a neural network for learning the relation between the gray value of the particulate matter aggregation area and the particulate matter concentration, updating a gray value pattern library, establishing a mapping relation between the gray value of the particulate matter and the particulate matter concentration, and storing in a memory 71;
(A1) During the course of the course, outside air enters the course through the sampling tube 11;
(A2) The first imaging unit 31 at one side of the sampling tube 11 acquires an image of a first region of a blank filter membrane 23, both ends of which are wound around the first wheel 21 and the second wheel 22, respectively;
(A3) The first wheel 21 rotates positively, the filter membrane 23 moves positively, and the first area is positioned on the lower side of the sampling tube 11 and collects particles;
at the same time, the first imaging unit 31 obtains an image of the second region of the blank filter membrane 23;
(A4) The first wheel 21 rotates forward, the filter membrane 23 moves forward, the first area of the filter membrane 23 moves out of the lower side of the sampling tube 11, and the second imaging unit 32 obtains an image of the first area;
meanwhile, the second region is located at the lower side of the sampling tube 11 and collects particulate matter;
thereafter, the second wheel 22 is rotated reversely, the filter membrane 23 is moved reversely, the first region is moved to the lower side of the sampling tube 11, the particulate matter is collected again, the second region is moved out of the lower side of the sampling tube 11, and the first imaging unit 31 obtains an image of the second region;
(A5) The analysis module 51 obtains the calculated concentration C of particulate matter from the images of the first region before and after sampling 1 ；
The analysis module 51 obtains the calculated concentration C of particulate matter from the image of the second region before and after sampling 1 ；
The analysis module 51 obtains the calculated concentration C 1 The method comprises the following steps:
the analysis module 51 obtains the gray value H of the particulate matter accumulation area on the filter membrane 23 according to the images of the filter membrane 23 before and after sampling 1 Obtaining the concentration C' of the particulate matters according to the mapping relation between the concentration of the particulate matters and the gray values of the particulate matters;
in a standard pattern gallery of gray values, the gray value H is found 1 A best matching gray value pattern, the gray value H 'of the gray value pattern' 1 And gray value H 1 The deviation between the two parts is minimum;
the analysis module 51 derives a calculated concentration
(A6) The correction module 61 calculates the concentration C 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient;
(A7) The calculation module 81 calculates an average value of the correction concentrations for a plurality of times and derivesConcentration C of this time correction 2 And a deviation from the average;
(A8) The judging module 91 judges whether the deviation reaches a threshold value or not, and the judgment result is sent to the control module 92;
if the deviation reaches a threshold value, the beta-ray particulate matter detection device 41 obtains the particulate matter concentration C of the first region under the control of the control module 92 β ；
(A9) The calculation module 81 calculates the corrected concentration C based on the correction concentration C 2 And particle concentration C β Obtaining new correction coefficientAnd sends the new correction coefficients to the memory 71;
(A10) The control module 92 replaces the correction coefficient in the memory 71 with the new correction coefficient;
in the above process, the repeated sampling of the first region and the second region is realized in the manner of step (A4), and when the sampling number reaches the threshold value, the filter membrane 23 is moved forward, and then the repeated sampling of the third region and the fourth region is realized in the manner of steps (A3) to (A4).
Example 3:
an application example of the system and method for detecting airborne particulate in embodiment 1 of the present invention in detecting atmospheric particulate is different from embodiment 2 in that:
the first wheel moves forward, the second wheel is a driven wheel, and the same area on the filter membrane sequentially passes through the lower sides of the first imaging unit, the sampling tube, the second imaging unit and the beta-ray particulate matter detection device;
each region of the filter is sequentially blank imaged, sampled, imaged, and possibly beta-ray particulate detected, i.e., each region can be sampled and detected only once.
Claims (6)
1. The system comprises a walking vehicle, a sampling tube and a particle collection unit, wherein the sampling tube penetrates through the top wall of the walking vehicle, the particle collection unit comprises a filter membrane, a driving module, a first wheel and a second wheel, one end of the filter membrane is wound on the first wheel, the other end of the filter membrane is wound on the second wheel, and the driving module drives the first wheel to rotate forwards; the utility model is characterized in that, walk-behind formula particulate matter detecting system still includes:
the optical particle detection device comprises a first imaging unit, a second imaging unit, an analysis module, a correction module and a memory, wherein the first imaging unit and the second imaging unit are respectively arranged at the side part of the sampling tube and used for imaging the filter membrane before and after sampling; the analysis module is used for obtaining the calculated concentration C of the particulate matters on the filter membrane according to the imaging 1 The method comprises the steps of carrying out a first treatment on the surface of the The correction module is used for calculating concentration C according to the calculation 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient; the memory is used for storing a correction coefficient k;
beta-ray particle detection device for obtaining the content C of particles in the correction area on the filter membrane during correction β ；
The calculation module is used for obtaining a new correction coefficient according to the output of the analysis module and the beta-ray particulate matter detection deviceAnd sending the new correction coefficients to the memory;
the control module is used for replacing the correction coefficient in the memory with the new correction coefficient;
the analysis module obtains the calculated concentration C 1 The method comprises the following steps:
the analysis module obtains a gray value H of a particulate matter gathering area on the filter membrane according to the images of the filter membrane before and after sampling 1 Obtaining the concentration C' of the particulate matters according to the mapping relation between the concentration of the particulate matters and the gray values of the particulate matters;
in a standard pattern gallery of gray values, the gray value H is found 1 Most matched gray value patternThe gray value H 'of the gray value pattern' 1 And gray value H 1 The deviation between the two parts is minimum;
the analysis module obtains the calculated concentration
The mapping relation is obtained in the following way:
setting shooting frame rates and image resolutions of the first imaging unit and the second imaging unit;
the analysis module obtains gray values of a particulate matter aggregation area on the filter membrane according to images of the filter membrane before and after sampling, and draws a particulate matter gray value pattern diagram under the concentration;
introducing a neural network for learning the relation between the gray value of the particulate matter aggregation area and the particulate matter concentration, updating a gray value model pattern library, and establishing a mapping relation between the gray value of the particulate matter and the particulate matter concentration
2. The navigable particulate detection system of claim 1, wherein the drive module drives the reverse rotation of the second wheel according to the instruction of the control module.
3. The system according to claim 1 or 2, wherein the calculation module is further configured to calculate an average value of the correction concentrations, and obtain a deviation between the correction concentration and the average value; the walk-behind particulate matter detection system further includes:
the judging module is used for judging whether the deviation reaches a threshold value or not, and sending a judging result to the control module;
and if the deviation reaches a threshold value, the control module controls the beta-ray particulate matter detection device to detect the particulate matters corresponding to the correction concentration.
4. The method for detecting the walking type particulate matters comprises the following steps:
(A1) In the course of the navigation vehicle, outside air passes through the sampling tube and enters the navigation vehicle;
(A2) A first imaging unit positioned at one side of the sampling tube acquires an image of a first area of a blank filter membrane, and two ends of the filter membrane are respectively wound on a first wheel and a second wheel;
(A3) A first wheel rotates positively, the filter membrane moves positively, and the first area is positioned at the lower side of the sampling tube and collects particles;
(A4) The first wheel rotates positively, the filter membrane moves positively, a first area of the filter membrane moves out of the lower side of the sampling tube, and the second imaging unit obtains an image of the first area;
(A5) The analysis module obtains the calculated concentration C of the particulate matters according to the images of the first area before and after sampling 1 ；
(A6) The correction module calculates the concentration C according to the calculation 1 Obtaining correction concentration C 2 ＝k·C 1 K is a correction coefficient;
the analysis module obtains the calculated concentration C 1 The method comprises the following steps:
the analysis module obtains a gray value H of a particulate matter gathering area on the filter membrane according to the images of the filter membrane before and after sampling 1 Obtaining the concentration C' of the particulate matters according to the mapping relation between the concentration of the particulate matters and the gray values of the particulate matters;
in a standard pattern gallery of gray values, the gray value H is found 1 A best matching gray value pattern, the gray value H 'of the gray value pattern' 1 And gray value H 1 The deviation between the two parts is minimum;
the analysis module obtains the calculated concentration
The mapping relation is obtained in the following way:
setting shooting frame rates and image resolutions of the first imaging unit and the second imaging unit;
the analysis module obtains gray values of a particulate matter aggregation area on the filter membrane according to images of the filter membrane before and after sampling, and draws a particulate matter gray value pattern diagram under the concentration;
the neural network is introduced to learn the relation between the gray value of the particle aggregation area and the particle concentration, update the gray value pattern library and establish the mapping relation between the particle gray value and the particle concentration.
5. The method for detecting airborne particulate matter of claim 4, further comprising the steps of:
(A7) The calculation module calculates the average value of the correction concentration for a plurality of times and obtains the correction concentration C 2 And a deviation from the average;
(A8) The judging module judges whether the deviation reaches a threshold value or not;
if the deviation reaches a threshold value, the beta-ray particulate matter detection device obtains the particulate matter concentration C of the first area β ；
(A9) The calculation module calculates the correction concentration C according to the correction concentration C 2 And particle concentration C β Obtaining new correction coefficientAnd sending the new correction coefficient to a memory;
(A10) The control module replaces the correction coefficient in the memory with the new correction coefficient.
6. The method of claim 4, wherein in step (A3), the first imaging unit obtains an image of the second region of the blank filter;
in step (A4), the second region is on the underside of the sampling tube and collects particulate matter; a second wheel rotates reversely, the filter membrane moves reversely, the first area moves to the lower side of the sampling tube, particles are collected again, the second area moves out of the lower side of the sampling tube, and the first imaging unit obtains an image of the second area;
in step (A5), the analysis moduleObtaining a calculated concentration C of particulate matter from an image of a second region before and after sampling 1 。
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