CN117405563A - Method and device for monitoring pollutants in fuel combustion greenhouse effect - Google Patents

Method and device for monitoring pollutants in fuel combustion greenhouse effect Download PDF

Info

Publication number
CN117405563A
CN117405563A CN202311713169.7A CN202311713169A CN117405563A CN 117405563 A CN117405563 A CN 117405563A CN 202311713169 A CN202311713169 A CN 202311713169A CN 117405563 A CN117405563 A CN 117405563A
Authority
CN
China
Prior art keywords
smoke
image
monitoring
flue gas
target image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202311713169.7A
Other languages
Chinese (zh)
Other versions
CN117405563B (en
Inventor
王杨
赵昱森
郭松
田亮
刘磊
郑东博
傅学庆
许焰松
鲍晓磊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hebei Normal University
Original Assignee
Hebei Normal University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Hebei Normal University filed Critical Hebei Normal University
Priority to CN202311713169.7A priority Critical patent/CN117405563B/en
Publication of CN117405563A publication Critical patent/CN117405563A/en
Application granted granted Critical
Publication of CN117405563B publication Critical patent/CN117405563B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N13/00—Investigating surface or boundary effects, e.g. wetting power; Investigating diffusion effects; Analysing materials by determining surface, boundary, or diffusion effects
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/0004—Gaseous mixtures, e.g. polluted air
    • G01N33/0009—General constructional details of gas analysers, e.g. portable test equipment
    • G01N33/0027—General constructional details of gas analysers, e.g. portable test equipment concerning the detector
    • G01N33/0031—General constructional details of gas analysers, e.g. portable test equipment concerning the detector comprising two or more sensors, e.g. a sensor array
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/0464—Convolutional networks [CNN, ConvNet]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N13/00—Investigating surface or boundary effects, e.g. wetting power; Investigating diffusion effects; Analysing materials by determining surface, boundary, or diffusion effects
    • G01N2013/003—Diffusion; diffusivity between liquids
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Theoretical Computer Science (AREA)
  • Chemical & Material Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • Immunology (AREA)
  • Pathology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Software Systems (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Medicinal Chemistry (AREA)
  • Combustion & Propulsion (AREA)
  • Food Science & Technology (AREA)
  • Incineration Of Waste (AREA)
  • Investigating Or Analysing Materials By Optical Means (AREA)

Abstract

The invention discloses a method and a device for monitoring pollutants of a fuel combustion greenhouse effect, and belongs to the technical field of flue gas monitoring. The method comprises the following steps: the diffusion position at the current moment is determined based on an image recognition and tracking method by taking the diffusion position at the previous moment as a tracking point; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere; collecting smoke at the diffusion position at the current moment; and monitoring the smoke collected at the current moment, and determining the monitoring result at the current moment. The invention carries out tracking of the diffusion position of the smoke in the real atmosphere environment based on the image recognition and tracking method, and monitors the diffusion position, thereby realizing monitoring of fuel combustion emission in the real atmosphere environment.

Description

Method and device for monitoring pollutants in fuel combustion greenhouse effect
Technical Field
The invention relates to the technical field of flue gas monitoring, in particular to a method and a device for monitoring pollutants with a fuel combustion greenhouse effect.
Background
Coping with climate change is a significant challenge for human development. Black carbon, CO 2 、CH 4 The emission of atmospheric pollutants with greenhouse effect has an important influence on global climate change. The artificial greenhouse effect pollutants are mainly generated by burning fossil fuel and biomass fuel, and civil fuel combustion emission in rural areas is one of important sources. At present, some researches are carried out on observing atmospheric pollutants discharged by rural domestic fuel combustion, but a laboratory smoke box combustion simulation method is mainly adopted, so that few researches are carried out on the rural domestic fuel combustion in a real atmospheric diffusion environment, and the method is mainly limited by the observation method.
The existing method has the following problems when observing black carbon generated by rural domestic fuel combustion: firstly, the types, the concentrations, the reaction conditions and the like of reactants in the smoke box combustion simulation system are different from the actual and complex atmospheric environment, and the simplification of the atmospheric diffusion process limits the accuracy of the result to a certain extent. Secondly, the smoke box combustion simulation method generally adopts a dilution channel method for sampling, the sampling method is forced dilution, the dilution factor of smoke is higher than the actual dilution condition under natural conditions, the components to be tested possibly enter the gas state to keep phase balance, and certain deviation can be generated on the result.
Disclosure of Invention
The invention aims to provide a method and a device for monitoring pollutants of a fuel combustion greenhouse effect, so as to monitor fuel combustion emission in a real atmospheric diffusion environment.
In order to achieve the above object, the present invention provides the following solutions:
the invention provides a method for monitoring pollutants of a fuel combustion greenhouse effect, which comprises the following steps:
the diffusion position at the current moment is determined based on an image recognition and tracking method by taking the diffusion position at the previous moment as a tracking point; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere;
and collecting the smoke at the diffusion position at the current moment, monitoring the smoke collected at the current moment, and determining the monitoring result at the current moment.
Optionally, the determining the diffusion position at the current time based on the image recognition and tracking method by using the diffusion position at the previous time as the tracking point specifically includes:
taking the tracking point as a center, and performing rotation shooting to obtain a plurality of images with different angles;
respectively carrying out smoke identification on each image, and determining an image containing smoke as a target image;
carrying out smoke segmentation on the target image to obtain the central position of the smoke outline;
and determining an actual position corresponding to the central position of the smoke outline as a diffusion position at the current moment based on the angle of the target image, the parameters of a camera shooting the target image and the central position of the smoke outline in the target image.
Optionally, smoke identification is performed on each image, and an image containing smoke is determined as a target image, which specifically includes:
respectively inputting each image into a smoke identification network model, and respectively determining whether each image contains smoke or not; the smoke recognition network model is obtained by training a convolutional neural network model;
the image containing the smoke is set as the target image.
Optionally, the flue gas segmentation is performed on the target image to obtain a central position of a flue gas contour, which specifically includes:
transmitting the target image to a smoke segmentation model to obtain a smoke outline in the target image; the flue gas segmentation model is obtained by training a YOLOv5s-seg network model;
and determining the central position of the smoke profile.
A fuel-fired greenhouse effect contaminant monitoring device, the device comprising: the system comprises an unmanned aerial vehicle, and an image acquisition and transmission device, a microcontroller, a smoke acquisition device and a greenhouse effect pollutant monitoring device which are assembled on the unmanned aerial vehicle;
the image acquisition and transmission equipment comprises an FPV camera and a picture transmission module, wherein the FPV camera is used for acquiring a plurality of images with different angles, and the picture transmission module is used for transmitting the images with different angles back to the microcontroller;
the microcontroller is in wireless connection with the unmanned aerial vehicle control terminal, and is used for determining the diffusion position at the current moment based on an image recognition and tracking method according to a plurality of images at different angles and sending the diffusion position at the current moment to the unmanned aerial vehicle control terminal; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere;
the microcontroller is also connected with the control end of the smoke collection device and is also used for controlling the diffusion position of the smoke collection device at the current moment to collect smoke;
the greenhouse effect pollutant monitoring equipment is connected with the microcontroller and is used for monitoring the flue gas collected at the current moment to obtain a monitoring result and sending the monitoring result to the microcontroller.
Optionally, the greenhouse effect pollutant monitoring device comprisesBlack carbon monitor and CO 2 Monitoring device and CH 4 Monitoring equipment;
the flue gas collection device includes: a carbon fiber tube, a black carbon sampling tube, a gas sampling tube and a gas observation chamber;
the carbon fiber tube is erected on the unmanned aerial vehicle, the black carbon sampling tube is arranged in the carbon fiber tube, and the gas production tube is fixed on the outer side of the carbon fiber tube in parallel;
the air inlet of the black carbon sampling tube is spaced from the unmanned aerial vehicle by a preset distance, and the air outlet of the black carbon sampling tube is connected with a black carbon monitor;
the air inlet of the air extraction pipe is spaced by a preset distance from the unmanned aerial vehicle, the air outlet of the air extraction pipe is connected with the gas observation chamber, and CO 2 Monitoring device and CH 4 The monitoring equipment is arranged in the gas observation chamber;
when the flue gas is collected, the air inlet of the black carbon sampling tube and the air inlet of the gas production tube are both positioned at the diffusion position at the current moment.
Optionally, a particulate filter is arranged at the front end of the gas inlet of the gas observation chamber, and the gas outlet of the gas observation chamber is connected with the air pump.
Optionally, in determining the diffusion position at the current moment based on the image recognition and tracking method according to the images of a plurality of different angles, the microcontroller is specifically configured to:
respectively carrying out smoke identification on each image, and determining an image containing smoke as a target image;
carrying out smoke segmentation on the target image to obtain the central position of the smoke outline;
and determining an actual position corresponding to the central position of the smoke outline as a diffusion position at the current moment based on the angle of the target image, the parameters of a camera shooting the target image and the central position of the smoke outline in the target image.
Optionally, smoke identification is performed on each image, and an image containing smoke is determined as a target image, which specifically includes:
respectively inputting each image into a smoke identification network model, and respectively determining whether each image contains smoke or not; the smoke recognition network model is obtained by training a convolutional neural network model;
the image containing the smoke is set as the target image.
Optionally, the flue gas segmentation is performed on the target image to obtain a central position of a flue gas contour, which specifically includes:
inputting the target image into a smoke segmentation model to obtain a smoke outline in the target image; the flue gas segmentation model is obtained by training a YOLOv5s-seg network model;
and determining the central position of the smoke profile.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the embodiment of the invention provides a method and a device for monitoring pollutants of a fuel combustion greenhouse effect, wherein the method comprises the following steps: the diffusion position at the current moment is determined based on an image recognition and tracking method by taking the diffusion position at the previous moment as a tracking point; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere; collecting smoke at the diffusion position at the current moment; and monitoring the smoke collected at the current moment, and determining the monitoring result at the current moment. The invention carries out tracking of the diffusion position of the smoke in the real atmosphere environment based on the image recognition and tracking method, and monitors the diffusion position, thereby realizing monitoring of fuel combustion emission in the real atmosphere environment.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method for monitoring pollutants in a fuel combustion greenhouse effect provided by an embodiment of the invention;
FIG. 2 is a schematic diagram of a fuel combustion greenhouse effect pollutant monitoring device according to an embodiment of the present invention;
reference numerals illustrate:
1. an unmanned aerial vehicle landing gear; 2. a microcontroller; 3. a gas observation chamber; 4. a particulate filter; 5. a black carbon monitor; 6. a battery compartment; 7. an image acquisition and transmission device; 8. a carbon fiber tube; 9. a particulate matter measuring chamber.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention aims to provide a method and a device for monitoring pollutants of a fuel combustion greenhouse effect, so as to monitor fuel combustion emission in a real atmospheric diffusion environment.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
Example 1
The embodiment 1 of the invention provides a method for monitoring pollutants of a fuel combustion greenhouse effect, which is shown in fig. 1, and comprises the following steps:
step 101, using a diffusion position at the previous moment as a tracking point, and determining the diffusion position at the current moment based on an image recognition and tracking method; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere.
Step 101, determining the diffusion position at the current moment based on an image recognition and tracking method by taking the diffusion position at the previous moment as a tracking point; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere, and specifically comprises: and taking the tracking point as a center, and performing rotation shooting to obtain a plurality of images with different angles.
Respectively carrying out smoke identification on each image, and determining an image containing smoke as a target image; carrying out smoke segmentation on the target image to obtain the central position of the smoke outline; and determining an actual position corresponding to the central position of the smoke outline as a diffusion position at the current moment based on the angle of the target image, the parameters of a camera shooting the target image and the central position of the smoke outline in the target image.
The embodiment of the invention adopts a smoke recognition network model for smoke recognition, and the smoke recognition model is obtained by training a convolutional neural network model. In the embodiment of the invention, a smoke segmentation model is adopted for smoke segmentation, and the smoke segmentation model is obtained by training a Yolov5s-seg network model.
The method comprises the specific steps of training a Yolov5s-seg network model:
a1, constructing a flue gas image data set: shooting images of typical rural domestic fuel combustion flue gas in a field environment, and extracting at least 400 images from the images for manual labeling to train a flue gas segmentation model.
A2, using the data set, and performing model training based on the YOLOv5s-seg network.
And A3, deploying the smoke segmentation model obtained after training into a microcontroller, and carrying out real-time smoke segmentation.
And 102, collecting and monitoring the smoke at the diffusion position at the current moment to obtain the monitoring result at the current moment.
Example 2
Embodiment 2 of the present invention provides a device for monitoring pollutants of fuel combustion greenhouse effect, as shown in fig. 2, the device includes: the system comprises an unmanned aerial vehicle, and a microcontroller 2, a smoke acquisition device, greenhouse effect pollutant monitoring equipment and image acquisition and transmission equipment 7 which are assembled on the unmanned aerial vehicle; the image acquisition and transmission device 7 comprises an FPV camera and a picture transmission module, wherein the FPV camera is used for acquiring a plurality of images with different angles, and the picture transmission module is used for transmitting the images with different angles back to the microcontroller 2; the microcontroller 2 is in wireless connection with the unmanned aerial vehicle control terminal, and the microcontroller 2 is used for determining the diffusion position at the current moment based on an image recognition and tracking method according to a plurality of images at different angles and sending the diffusion position at the current moment to the unmanned aerial vehicle control terminal; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere; the greenhouse effect pollutant monitoring equipment is connected with the microcontroller 2 and is used for monitoring the flue gas collected at the current moment, sending the monitoring result to the microcontroller 2 and transmitting the monitoring result to the user side in real time through the WIFI base station.
Wherein the greenhouse effect pollutant monitoring equipment comprises a black carbon monitor 5 and CO 2 Monitoring device and CH 4 Monitoring equipment.
The flue gas collection device includes: a carbon fiber tube 8, a black carbon sampling tube, a gas sampling tube and a gas observation chamber 3; the carbon fiber tube 8 is erected on the monitoring platform, the black carbon sampling tube is arranged in the carbon fiber tube 8, and the gas sampling tube is fixed on the outer side of the carbon fiber tube 8 in parallel.
The air inlet of the black carbon sampling tube is spaced from the unmanned aerial vehicle by a preset distance, and the air outlet of the black carbon sampling tube is connected with the black carbon monitor 5; the air inlet of the air extraction pipe is spaced by a preset distance from the unmanned aerial vehicle, the air outlet of the air extraction pipe is connected with the gas observation chamber 3, and the CO is provided with a gas sensor 2 Monitoring device and CH 4 The monitoring device is arranged in the gas observation chamber 3. Illustratively, the front end of the air inlet of the gas observation chamber 3 is provided with a particulate filter 4, the air outlet of the gas observation chamber is connected with an air pump, and collected flue gas firstly passes through a particulate measuring chamber 9 for particulate monitoring before entering the gas observation chamber 3 through the particulate filter 4.
According to the embodiment of the invention, a carbon fiber tube 8 with the length of 1 meter is fixed on a monitoring platform, a black carbon sampling tube of a black carbon monitor 5 is penetrated inside the carbon fiber tube 8, an air inlet is flush with the front end of the carbon fiber tube 8, and an air outlet is connected with an air inlet of the black carbon monitor 5. CO is processed by 2 、CH 4 Is fixed with the carbon fiber tube 8 and connected withThe air pump collects the flue gas to the gas observation chamber 3, and a particulate filter 4 is added in front of the gas observation chamber 3. The gas sampling pipe and the black carbon sampling pipe are both positioned at the lower part of the unmanned aerial vehicle, and the gas inlet is 1 meter away from the unmanned aerial vehicle, so that the gas flow interference caused by the operation of the rotor wing of the unmanned aerial vehicle can be effectively avoided. Realize simultaneous monitoring of black carbon and CO 2 、CH 4 Three kinds of greenhouse effect pollutants.
The black carbon sampling tube in the embodiment of the invention is an antistatic silica gel hose, the carbon fiber tube 8 is a supporting tube, and the length of the carbon fiber tube can be 1 meter, and the carbon fiber tube is used for supporting the black carbon sampling tube and the gas sampling tube. The microcontroller 2 employed in the embodiment of the invention is a raspberry pie.
The greenhouse effect pollutant monitoring equipment and the monitoring data acquisition equipment are integrated on a self-made monitoring platform, are connected with a smoke acquisition device and are fixed with the landing gear 1 of the unmanned aerial vehicle, and in order to avoid the impact when the unmanned aerial vehicle falls, the monitoring platform is supported by a bracket and is 10 cm higher than the landing gear of the unmanned aerial vehicle.
The microcontroller in the embodiment of the invention is communicated with a user side PC or a mobile phone through a WIFI base station and is used for sending the monitoring result to a user in real time, and the microcontroller also receives an image shot by an FPV camera through a picture transmission module and transmits the smoke identification result to the unmanned aerial vehicle control terminal in real time.
In the embodiment of the invention, the raspberry pie and the air pump are powered by a mobile power supply, and the mobile power supply is arranged in the battery compartment 6.
The FPV camera is connected with the image transmission module, the camera shoots a smoke image, and the image is transmitted to a smoke identification model preset in the raspberry group for analysis through the image transmission module. The embodiment of the invention provides a flue gas identification method based on deep learning, which is used for identifying the position and the outline of diffused flue gas in a field atmospheric environment, guiding the flight route and the position of an unmanned aerial vehicle and ensuring that a sampling air inlet is always monitored in the flue gas.
In the embodiment of the invention, the target image is firstly determined, then the target image is segmented to obtain the smoke outline, and further the embodiment of the invention adopts the YOLOv5s-seg network to segment the smoke, and the specific steps are as follows:
b1, constructing a flue gas identification data set: shooting images of typical rural domestic fuel combustion flue gas in a field environment, and extracting at least 400 images from the images for manual labeling to train a flue gas segmentation model.
And B2, performing model training based on the YOLOv5s-seg network by using the data set.
And B3, deploying the trained smoke segmentation model to a raspberry group. And inputting an image shot by the camera as a model to perform real-time smoke profile recognition, and visualizing a recognition result into a graph.
And B4, transmitting the identification result back to the unmanned aerial vehicle control terminal in the monitoring process, wherein a user can refer to the flue gas identification result to acquire the flue gas diffusion position, and control the unmanned aerial vehicle to reach the accurate azimuth, so that the sampling tube air inlet is ensured to be always collected and monitored at the flue gas center.
The unmanned aerial vehicle provided by the embodiment of the invention adopts a multi-rotor unmanned aerial vehicle, and the microcontroller 2, the smoke collection device, the greenhouse effect pollutant monitoring equipment and the image collection and transmission equipment are integrated on the monitoring platform and fixed on the landing gear 1 of the unmanned aerial vehicle, can be disassembled, and is convenient and simple to disassemble.
After rural domestic fuel burns and ignites, the flue gas is discharged continuously, along with the diffusion of the flue gas, the further the flue gas is from the emission source, the lighter the flue gas color is, at the moment, the camera shoots towards the diffusion direction of the flue gas, the image is transmitted back to the raspberry pie, the unmanned aerial vehicle is guided to fly to the diffusion position of the flue gas according to the result of flue gas identification, the unmanned aerial vehicle position is adjusted in real time according to the identification result, and the sampling pipe orifice is ensured to be always collected and monitored at the center of the flue gas. The flue gas is monitored at the diffusion positions with different distances from the flue gas discharge port, and black carbon and CO with different mixing degrees with the ambient atmosphere can be obtained 2 CH (CH) 4 Is provided.
In summary, the embodiment of the invention has the following beneficial effects:
(1) The unmanned aerial vehicle is used for tracking and observing, the problem that smoke monitoring cannot be tracked by ground fixed-point observation is solved, the unmanned aerial vehicle can be flexibly controlled to track smoke for observing, and the observation result is more comprehensive. And the smoke is acquired and observed in a field real atmospheric diffusion environment, so that the smoke box combustion simulation system is closer to the real state of pollutants in the atmosphere, and the observed data is more real and accurate.
(2) The flue gas segmentation model based on the YOLOv5s-seg is provided, the FPV image is used for identifying the flue gas diffusion position in real time in the monitoring process, the flight track and the position of the unmanned aerial vehicle are guided, and the purpose of more accurate pollutant collection and monitoring results is achieved.
(3) The design flue gas collection system contains a carbon fiber tube 8 of 1 meter length and fixes at the monitoring platform front end, wears black carbon appearance sampling pipe inside carbon fiber tube 8, and the air inlet is parallel and level with carbon fiber tube 8 front end. CO is processed by 2 、CH 4 The gas production pipe is fixed with a carbon fiber pipe 8 and is connected with a gas pump to collect the flue gas to a gas observation chamber 3, and a particulate filter 4 is added in front of the gas observation chamber. The sampling pipe air inlet is 1 meter away from unmanned aerial vehicle, can effectively avoid the air current interference that unmanned aerial vehicle rotor during operation brought. Can realize simultaneous monitoring of black carbon and CO 2 、CH 4 Three kinds of greenhouse effect pollutants.
(4) The high-power outdoor WIFI base station is used for carrying out remote communication with the microcontroller, so that a user can check monitoring data in real time at a PC end or a mobile phone end when the unmanned aerial vehicle works in the air.
(5) Use portable power source to send raspberry and aspiration pump power supply to reduce unmanned aerial vehicle's power consumption output, extension unmanned aerial vehicle flight time.
(6) The monitoring system has strong operability, can be suitable for monitoring rural and civil fuel combustion pollutants by most rotor unmanned aerial vehicles, effectively improves the defects of the traditional observation method, greatly improves the accuracy and the representativeness of observation data, has stable and reliable data acquisition and transmission, and has wide application prospect.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The principles and embodiments of the present invention have been described herein with reference to specific examples, the description of which is intended only to assist in understanding the methods of the present invention and the core ideas thereof; also, it is within the scope of the present invention to be modified by those of ordinary skill in the art in light of the present teachings. In view of the foregoing, this description should not be construed as limiting the invention.

Claims (10)

1. A method for monitoring pollutants of the greenhouse effect of fuel combustion, which is characterized by comprising the following steps:
the diffusion position at the current moment is determined based on an image recognition and tracking method by taking the diffusion position at the previous moment as a tracking point; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere;
and collecting the smoke at the diffusion position at the current moment, monitoring the smoke collected at the current moment, and determining the monitoring result at the current moment.
2. The method for monitoring the pollution of the fuel combustion greenhouse effect according to claim 1, wherein the diffusion position at the current moment is determined based on the image recognition and tracking method by taking the diffusion position at the previous moment as a tracking point, and specifically comprises the following steps:
taking the tracking point as a center, and performing rotation shooting to obtain a plurality of images with different angles;
respectively carrying out smoke identification on each image, and determining an image containing smoke as a target image;
carrying out smoke segmentation on the target image to obtain the central position of the smoke outline;
and determining an actual position corresponding to the central position of the smoke outline as a diffusion position at the current moment based on the angle of the target image, the parameters of a camera shooting the target image and the central position of the smoke outline in the target image.
3. The method for monitoring the pollution of the fuel combustion greenhouse effect according to claim 2, wherein the flue gas identification is performed on each image, and the image containing the flue gas is determined as the target image, and specifically comprises the following steps:
respectively inputting each image into a smoke identification network model, and respectively determining whether each image contains smoke or not; the smoke recognition network model is obtained by training a convolutional neural network model;
the image containing the smoke is set as the target image.
4. The method for monitoring the pollutants of the fuel combustion greenhouse effect according to claim 2, wherein the flue gas segmentation is performed on the target image to obtain the central position of the flue gas profile, and the method specifically comprises the following steps:
transmitting the target image to a smoke segmentation model to obtain a smoke outline in the target image; the flue gas segmentation model is obtained by training a YOLOv5s-seg network model;
and determining the central position of the smoke profile.
5. A fuel-fired greenhouse effect contaminant monitoring device, the device comprising: the system comprises an unmanned aerial vehicle, and an image acquisition and transmission device, a microcontroller, a smoke acquisition device and a greenhouse effect pollutant monitoring device which are assembled on the unmanned aerial vehicle;
the image acquisition and transmission equipment comprises an FPV camera and a picture transmission module, wherein the FPV camera is used for acquiring a plurality of images with different angles, and the picture transmission module is used for transmitting the images with different angles back to the microcontroller;
the microcontroller is in wireless connection with the unmanned aerial vehicle control terminal, and is used for determining the diffusion position at the current moment based on an image recognition and tracking method according to a plurality of images at different angles and sending the diffusion position at the current moment to the unmanned aerial vehicle control terminal; the diffusion position is a position where flue gas generated by fuel combustion diffuses in the ambient atmosphere;
the microcontroller is also connected with the control end of the smoke collection device and is also used for controlling the diffusion position of the smoke collection device at the current moment to collect smoke;
the greenhouse effect pollutant monitoring equipment is connected with the microcontroller and is used for monitoring the flue gas collected at the current moment to obtain a monitoring result and sending the monitoring result to the microcontroller.
6. The fuel-fired greenhouse effect pollutant monitoring device according to claim 5, wherein the greenhouse effect pollutant monitoring equipment comprises a black carbon monitor, a CO 2 Monitoring device and CH 4 Monitoring equipment;
the flue gas collection device includes: a carbon fiber tube, a black carbon sampling tube, a gas sampling tube and a gas observation chamber;
the carbon fiber tube is erected on the unmanned aerial vehicle, the black carbon sampling tube is arranged in the carbon fiber tube, and the gas production tube is fixed on the outer side of the carbon fiber tube in parallel;
the air inlet of the black carbon sampling tube is spaced from the unmanned aerial vehicle by a preset distance, and the air outlet of the black carbon sampling tube is connected with a black carbon monitor;
the air inlet of the air extraction pipe is spaced by a preset distance from the unmanned aerial vehicle, the air outlet of the air extraction pipe is connected with the gas observation chamber, and CO 2 Monitoring device and CH 4 The monitoring equipment is arranged in the gas observation chamber;
when the flue gas is collected, the air inlet of the black carbon sampling tube and the air inlet of the gas production tube are both positioned at the diffusion position at the current moment.
7. The fuel combustion greenhouse effect pollutant monitoring device according to claim 6, wherein a particulate filter is arranged at the front end of the gas inlet of the gas observation chamber, and the gas outlet of the gas observation chamber is connected with the air pump.
8. The fuel-fired greenhouse effect contaminant monitoring device according to claim 5, wherein the microcontroller is configured to, in determining a diffusion location at a current time based on an image recognition and tracking method from a plurality of images at different angles:
respectively carrying out smoke identification on each image, and determining an image containing smoke as a target image;
carrying out smoke segmentation on the target image to obtain the central position of the smoke outline;
and determining an actual position corresponding to the central position of the smoke outline as a diffusion position at the current moment based on the angle of the target image, the parameters of a camera shooting the target image and the central position of the smoke outline in the target image.
9. The fuel-fired greenhouse effect pollutant monitoring device according to claim 8, wherein the flue gas identification is performed on each image, and the image containing the flue gas is determined as the target image, and specifically comprises:
respectively inputting each image into a smoke identification network model, and respectively determining whether each image contains smoke or not; the smoke recognition network model is obtained by training a convolutional neural network model;
the image containing the smoke is set as the target image.
10. The fuel-fired greenhouse effect pollutant monitoring device according to claim 8, wherein the target image is subjected to flue gas segmentation to obtain a central position of a flue gas profile, and specifically comprising:
inputting the target image into a smoke segmentation model to obtain a smoke outline in the target image; the flue gas segmentation model is obtained by training a YOLOv5s-seg network model;
and determining the central position of the smoke profile.
CN202311713169.7A 2023-12-14 2023-12-14 Method and device for monitoring pollutants in fuel combustion greenhouse effect Active CN117405563B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202311713169.7A CN117405563B (en) 2023-12-14 2023-12-14 Method and device for monitoring pollutants in fuel combustion greenhouse effect

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202311713169.7A CN117405563B (en) 2023-12-14 2023-12-14 Method and device for monitoring pollutants in fuel combustion greenhouse effect

Publications (2)

Publication Number Publication Date
CN117405563A true CN117405563A (en) 2024-01-16
CN117405563B CN117405563B (en) 2024-03-19

Family

ID=89500187

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202311713169.7A Active CN117405563B (en) 2023-12-14 2023-12-14 Method and device for monitoring pollutants in fuel combustion greenhouse effect

Country Status (1)

Country Link
CN (1) CN117405563B (en)

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107941988A (en) * 2017-10-16 2018-04-20 华南理工大学 The unmanned machine equipment and monitoring method of a kind of detection gas pollution sources
CN109901615A (en) * 2019-03-20 2019-06-18 深圳智人环保科技有限公司 A kind of ship discharge detection method and system based on flying platform
WO2020258007A1 (en) * 2019-06-24 2020-12-30 南京邮电大学 Atmospheric pollutant unmanned aerial vehicle tracing system and method based on big data technology
CN213661802U (en) * 2020-12-22 2021-07-09 生态环境部华南环境科学研究所 Rural pollution supervisory systems based on video monitoring and unmanned aerial vehicle patrol
KR102321942B1 (en) * 2021-03-18 2021-11-04 주식회사 에스티엔인포텍 Water resources monitoring system through autonomous mobile unmanned boat and enviroment information analysis using artificial intelligence
KR20210137280A (en) * 2020-05-07 2021-11-17 고려대학교 산학협력단 Method and Apparatus for Detecting Object
JP7040827B1 (en) * 2021-01-14 2022-03-23 株式会社ロックガレッジ Search support system and rescue support program
TWM629332U (en) * 2021-12-13 2022-07-11 管家麗 UAV exhaust gas odor detection and tracking device
CN115526790A (en) * 2022-08-26 2022-12-27 中国人民解放军军事科学院国防科技创新研究院 Spacecraft wreckage search and rescue identification tracking method and system based on neural network
CN116486290A (en) * 2023-06-21 2023-07-25 成都庆龙航空科技有限公司 Unmanned aerial vehicle monitoring and tracking method and device, electronic equipment and storage medium

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN107941988A (en) * 2017-10-16 2018-04-20 华南理工大学 The unmanned machine equipment and monitoring method of a kind of detection gas pollution sources
CN109901615A (en) * 2019-03-20 2019-06-18 深圳智人环保科技有限公司 A kind of ship discharge detection method and system based on flying platform
WO2020258007A1 (en) * 2019-06-24 2020-12-30 南京邮电大学 Atmospheric pollutant unmanned aerial vehicle tracing system and method based on big data technology
KR20210137280A (en) * 2020-05-07 2021-11-17 고려대학교 산학협력단 Method and Apparatus for Detecting Object
CN213661802U (en) * 2020-12-22 2021-07-09 生态环境部华南环境科学研究所 Rural pollution supervisory systems based on video monitoring and unmanned aerial vehicle patrol
JP7040827B1 (en) * 2021-01-14 2022-03-23 株式会社ロックガレッジ Search support system and rescue support program
KR102321942B1 (en) * 2021-03-18 2021-11-04 주식회사 에스티엔인포텍 Water resources monitoring system through autonomous mobile unmanned boat and enviroment information analysis using artificial intelligence
TWM629332U (en) * 2021-12-13 2022-07-11 管家麗 UAV exhaust gas odor detection and tracking device
CN115526790A (en) * 2022-08-26 2022-12-27 中国人民解放军军事科学院国防科技创新研究院 Spacecraft wreckage search and rescue identification tracking method and system based on neural network
CN116486290A (en) * 2023-06-21 2023-07-25 成都庆龙航空科技有限公司 Unmanned aerial vehicle monitoring and tracking method and device, electronic equipment and storage medium

Non-Patent Citations (8)

* Cited by examiner, † Cited by third party
Title
MUKHRIDDIN MUKHIDDINOV ET AL: "A Wildfire Smoke Detection System Using Unmanned Aerial Vehicle Images Based on the Optimized YOLOv5", 《SENSORS》, 1 December 2022 (2022-12-01) *
丁承君;宋赛;冯玉伯;陈雪;: "基于无人机的立体大气环境监测系统设计", 传感器与微系统, no. 10, 24 September 2020 (2020-09-24) *
张玄;王猛;杜文乐;: "基于树莓派的无人机环境监测反馈系统", 电子技术与软件工程, no. 07, 6 April 2018 (2018-04-06) *
胡伟等: "基于多旋翼无人机的烟气监测系统研究", 《山东科学》, 31 December 2019 (2019-12-31), pages 89 - 94 *
贺园园;胡小敏;梁腾飞;: "基于深度机器学习的霾污染监测技术", 计算机测量与控制, no. 08, 25 August 2020 (2020-08-25) *
陶磊;洪韬;钞旭;: "基于YOLOv3的无人机识别与定位追踪", 工程科学学报, no. 04, 15 April 2020 (2020-04-15) *
高天禹;马雨婷;韩成哲;蔡博;翁睿;张立宪;: "基于激光测距与单目视觉的微型无人机室内目标人物搜索方法研究", 中国科学:技术科学, no. 07, 28 July 2020 (2020-07-28) *
魏明鑫;黄浩;胡永明;王德志;李岳彬;: "基于深度学习的多旋翼无人机单目视觉目标定位追踪方法", 计算机测量与控制, no. 04, 25 April 2020 (2020-04-25) *

Also Published As

Publication number Publication date
CN117405563B (en) 2024-03-19

Similar Documents

Publication Publication Date Title
CN102445409B (en) Method for measuring dust concentration based on digital image technology
CN109630905A (en) A kind of full intelligent inspection system of oil-gas pipeline based on unmanned aerial vehicle remote sensing and deep learning
CN103091134A (en) Dilution sampling system and sampling method of fixed source particles and volatile organic compounds
CN104777006B (en) Device for testing dust generation and device and method for evaluating purification effect
CN110979677B (en) Multi-region air collection integrated unmanned aerial vehicle and working method
CN110531036A (en) Motor-vehicle tail-gas real-time detecting system
CN105973623A (en) Person working environment occupational safety and health index system simulation test bench
CN115060850B (en) Air-ground double-field coupling atmospheric pollution source tracking and flux measuring device and method
CN117405563B (en) Method and device for monitoring pollutants in fuel combustion greenhouse effect
CN205404218U (en) Simple and easy transient state operating mode method test system of automobile exhaust
CN212622469U (en) Follow-on fume emission continuous monitoring system
CN208140627U (en) A kind of atmosphere quality monitoring system based on unmanned plane
CN205826519U (en) A kind of flue gas on-line measurement device in situ
CN107917860A (en) Construction site air quality detection system based on light scattering method
CN112629802A (en) Wind tunnel experiment device and method for simulating influence of bulk coal combustion atmospheric environment
CN112945891B (en) Air carbon monoxide measuring method and measuring instrument thereof
CN202582959U (en) High-altitude gas acquisition device
CN206790527U (en) A kind of aerial mobile monitoring device of distributed urban quality of air environment
CN214622302U (en) Carbon dioxide gas sampling analysis equipment based on unmanned aerial vehicle
CN213813478U (en) Atmospheric VOCs sampling device based on multi-rotor unmanned aerial vehicle
CN102628757A (en) High-altitude gas collection device
CN217466853U (en) On-spot carbon emission detection device of operation based on 5G and unmanned aerial vehicle
CN212159484U (en) Wisdom building site particulate matter real-time monitoring system
CN220542564U (en) Waste gas sampling device for waste gas detection
CN216525445U (en) Smoke intensity detection device

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant