A method and system for detecting a concentration profile of a gaseous pollutant

Abstract

The application discloses a kind of gas pollutant concentration distribution detection method and system, it is related to gas detection technical field.The method comprises the following steps: obtaining the interference image of laser beam in the to-be-measured airspace from multiple directions interference;The to-be-measured airspace is divided into multiple sections, and each section is divided into multiple discrete pixel grids;According to the gas pollutant concentration of pixel grid, the propagation distance of laser beam in pixel grid and projection data matrix, a system of linear equations is constructed.The projection data in the system of linear equations is iterated by convergence criterion, and the gas pollutant concentration corresponding to each projection data after iteration is stacked layer by layer, to obtain the three-dimensional distribution of gas pollutant concentration.The application can directly detect the concentration distribution of gas pollutant in the to-be-measured airspace, and provide reliable data support for environmental protection and pollution control work.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas detection, in particular to a method and system for detecting concentration distribution of gas pollutants. BACKGROUND

[0002] With the continuous advancement of industrialization, the large use of fossil energy leads to the increasing emission concentration of various gas pollutants. The large amount of gas pollutants emitted in the process of industrial production and coal-fired power generation seriously threatens human health, which has attracted worldwide attention. Common gas pollutants include sulfur dioxide (SO2), nitrogen oxides (NOx), ozone (O3) and carbon monoxide (CO), etc. The increase of the concentration of atmospheric pollutants will cause acid rain, which will pose a significant threat to human production and life as well as climate and environment. Atmospheric pollution is also one of the main factors inducing certain diseases. The excessive concentration of sulfur dioxide and nitrogen oxides may cause the onset of cardiovascular and respiratory diseases, and even major diseases such as pneumonia, ophthalmic diseases and heart failure. Therefore, the study of the concentration distribution of gas pollutants plays an important role in the monitoring and prevention of gas pollutants.

[0003] In the prior art, the steps of detecting the concentration of gas pollutants in the atmospheric environment mainly include: first, placing a sensor in the environment to be measured, and the internal sensitive element will interact with the target gas pollutants. This interaction can be of chemical nature, such as the reaction of gas molecules with active materials on the surface of the sensor, resulting in changes in electrical properties such as conductivity, resistance or potential; or it can be of optical nature, such as the absorption or scattering of light of a specific wavelength by gas molecules. Subsequently, the sensor converts these changes in physical quantities into electrical signals, and through a pre-set algorithm or calibration curve, the electrical signals are converted into specific gas pollutant concentration values.

[0004] The defect of the above-mentioned prior art is that the process of detecting the concentration of gas pollutants by the sensor is a single-point measurement method, which stays in one-dimensional detection and cannot detect the three-dimensional distribution of the concentration of gas pollutants in the region. SUMMARY

[0005] Therefore, it is necessary to provide a method and system for detecting the concentration distribution of gas pollutants in view of the above technical problems.

[0006] The embodiment of the present application provides a method for detecting the concentration distribution of gas pollutants, which comprises:

[0007] obtaining an interference image of a laser beam interfering from multiple directions in a to-be-measured airspace;

[0008] performing gray scale and binaryzation processing on the interference image to obtain a gray value matrix; and performing noise reduction and interpolation operations on the gray value matrix to obtain a projection data matrix of the interference image;

[0009] The airspace to be measured is divided into multiple sections, and each section is further divided into multiple discrete pixel grids, each pixel grid having an independent concentration of gaseous pollutants;

[0010] Based on the projection data matrix of the interferometric image, the concentration of gaseous pollutants in the pixel grid, and the propagation distance of the laser beam within the pixel grid, a set of linear equations is constructed to characterize the concentration of gaseous pollutants reflected in the interferometric images of laser beams acquired from different directions.

[0011] The projection data in the linear equation system is iterated, and the gaseous pollutant concentrations corresponding to each projection data point after iteration are stacked layer by layer to obtain the three-dimensional distribution of gaseous pollutant concentrations.

[0012] Optionally, a system of linear equations characterizing the concentration of gaseous pollutants reflected in laser beam interferometry images obtained from different directions is constructed, expressed in the following form:

[0013]

[0014] in, The concentration of gaseous pollutants in the pixel grid. The distance the laser beam travels within the pixel grid. The projection data of the laser beam. The number of laser beams, The number of pixel grids, i and Formal parameters.

[0015] Optionally, the convergence criterion is used to iterate the projected data in the linear equation system, and its expression is:

[0016]

[0017] in, The projection data of the laser beam. The propagation distance of the laser beam within the pixel grid. For the first k The concentration of gaseous pollutants in the next iteration of the pixel grid. The number of laser beams, The number of pixel grids, This represents the number of iterations.

[0018] when Less than the set threshold Stop iterating when the time comes.

[0019] Optionally, the concentration of gaseous pollutants corresponding to each projection data after iteration specifically includes:

[0020] Setting initial values of gas pollutant concentrations of pixel grids:

[0021]

[0022] Determining the gas pollutant concentration corresponding to each projection data after iteration according to the initial values of the gas pollutant concentration, and the calculation formula is:

[0023]

[0024] wherein, is the iteration number, , is the relaxation factor, is the gas pollutant concentration of the pixel grid, is the propagation distance of the laser beam in the pixel grid, is the projection data of the laser beam, is the number of laser beams, is the number of pixel grids, t, i and is the form parameter.

[0025] The embodiment of the present application also provides a gas pollutant concentration distribution detection system, comprising: an intelligent cloud platform, a first unmanned aerial vehicle and a second unmanned aerial vehicle;

[0026] The first unmanned aerial vehicle and the second unmanned aerial vehicle are used for acquiring interference images of laser beams interfering from multiple directions in a to-be-detected airspace;

[0027] The intelligent cloud platform is used for performing the following operations:

[0028] Controlling the first unmanned aerial vehicle and the second unmanned aerial vehicle to move from a starting area to the to-be-detected airspace; and sending a control instruction to control the running state of the unmanned aerial vehicle;

[0029] Performing gray scale and binary processing on the interference images to obtain a gray value matrix; and performing noise reduction and interpolation operations on the gray value matrix to obtain a projection data matrix of the interference images;

[0030] Dividing the to-be-detected airspace into multiple cross sections, and dividing each cross section into multiple discrete pixel grids, wherein the pixel grids have independent gas pollutant concentrations;

[0031] According to the projection data matrix of the interference images, the gas pollutant concentrations of the pixel grids and the propagation distance of the laser beams in the pixel grids, a linear equation set is constructed to represent the gas pollutant concentrations reflected by the interference images acquired from different directions;

[0032] Iterating the projection data in the linear equation set, and stacking the gas pollutant concentrations corresponding to each projection data after iteration layer by layer to obtain a three-dimensional distribution of the gas pollutant concentrations.

[0033] Optionally, the first unmanned aerial vehicle comprises a laser emitting device and a laser beam expanding device;

[0034] the laser emitting device is configured to emit a laser beam to the airspace to be measured;

[0035] the laser beam expanding device is configured to expand the diameter of the laser beam emitted by the laser emitting device.

[0036] Optionally, the second unmanned aerial vehicle comprises an interference generating device and an image acquisition device;

[0037] the interference generating device is configured to control the laser beam to interfere;

[0038] the image acquisition device is configured to acquire an interference image when the laser beam interferes.

[0039] Optionally, further comprising an airborne 5G wireless communication unit, an airborne positioning unit and an airborne image transmission unit;

[0040] the airborne positioning unit is configured to acquire real-time position information of the first unmanned aerial vehicle and the second unmanned aerial vehicle;

[0041] the airborne 5G wireless communication unit is configured to control the first unmanned aerial vehicle and the second unmanned aerial vehicle to make a circular motion perpendicular to the rotation axis with the center of the distance between the two as the rotation axis; taking the initial position of the first unmanned aerial vehicle as 0° and the clockwise direction as the positive direction, interference images of the first unmanned aerial vehicle making counterclockwise circular motion relative to the initial position 0°, 60°, 90°, 150° and 300° of the first unmanned aerial vehicle are acquired respectively;

[0042] the airborne image transmission unit is configured to feed back the interference images to the intelligent cloud platform.

[0043] Optionally, the airborne 5G wireless communication unit comprises a transmitter, a receiver, a signal processor, a data processor and a control unit;

[0044] the transmitter is configured to transmit the detection information of the airborne positioning unit to the intelligent cloud platform;

[0045] the receiver is configured to receive the control instruction signal of the intelligent cloud platform;

[0046] the signal processor is configured to control the first unmanned aerial vehicle and the second unmanned aerial vehicle to make a circular motion perpendicular to the rotation axis with the center of the distance between the two as the rotation axis;

[0047] the data processor is configured to process the transmitted data;

[0048] the control unit is configured to control the working of the airborne 5G wireless communication unit.

[0049] The gas pollutant concentration distribution detection method and system provided by the embodiment of the present application have the following advantages compared with the prior art.

[0050] In the to-be-detected airspace, the laser beams interfere from multiple directions to form interference images, which are divided into multiple sections and pixel grids, and each pixel grid is assigned a gas pollutant concentration. Subsequently, the interference images are subjected to grayscale processing to generate a projection data matrix. Based on the projection data matrix, the gas pollutant concentration of the pixel grid, and the propagation distance of the laser beam in the pixel grid, a linear equation set is constructed. The linear equation set can effectively represent the gas pollutant concentration reflected by the laser beam interference images obtained from different directions, which increases the measurement range of the gas pollutant concentration detection from one dimension to three dimensions, and enables the detection of the three-dimensional distribution of the gas pollutant concentration in the region.

[0051] Further, by iteratively processing the projection data in the linear equation set, a three-dimensional distribution of the gas pollutant concentration is constructed, which can more intuitively reflect the concentration distribution of the gas pollutant and provide reliable data support for environmental protection and pollution control work. BRIEF DESCRIPTION OF DRAWINGS

[0052] Figure 1 A structural principle diagram of a gas pollutant concentration distribution detection system provided in an embodiment;

[0053] Figure 2 An optical path principle diagram of a gas pollutant concentration distribution detection system provided in an embodiment;

[0054] Figure 3 An interference image diagram of a gas pollutant concentration distribution detection system provided in an embodiment;

[0055] Figure 4 A flowchart of a gas pollutant concentration distribution detection method provided in an embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and do not limit the present application.

[0057] In an embodiment, a gas pollutant concentration distribution detection system is provided, as shown in Figure 1 The system includes an intelligent cloud platform, an airborne 5G wireless communication unit, an airborne positioning unit, a laser emitting device, a laser beam expanding device, an interference generating device, an image acquisition device, an airborne image transmission unit, and a group of unmanned aerial vehicles.

[0058] Further, the intelligent cloud platform is configured to perform the following operations:

[0059] The first and second unmanned aerial vehicles are controlled to move from a starting area to the airspace to be measured, and control instructions are sent to control the operating state of the unmanned aerial vehicles.

[0060] The interference image is subjected to grayscale and binary processing to obtain a grayscale value matrix, and the grayscale value matrix is subjected to noise reduction and interpolation to obtain a projection data matrix of the interference image.

[0061] The airspace to be measured is divided into multiple cross sections, and each cross section is divided into multiple discrete pixel grids, and the pixel grids have independent gas pollutant concentrations.

[0062] According to the projection data matrix of the interference image, the gas pollutant concentration of the pixel grid, and the propagation distance of the laser beam in the pixel grid, a linear equation set is constructed to represent the gas pollutant concentration reflected by the interference image obtained from different directions.

[0063] The projection data in the linear equation set is iterated, and the gas pollutant concentration corresponding to each projection data after iteration is stacked layer by layer to obtain a three-dimensional distribution of the gas pollutant concentration.

[0064] Further, the airborne 5G wireless communication unit is configured to perform bidirectional data exchange with the intelligent cloud platform, and send the state information (such as position information, power, flight speed, etc.) of the unmanned aerial vehicle to the intelligent cloud platform, and receive control instructions from the intelligent cloud platform.

[0065] Further, the airborne positioning unit is configured to obtain real-time position information of the unmanned aerial vehicle, including position coordinates, flight height, etc. The airborne image transmission unit is configured to feed back the interference image to the intelligent cloud platform.

[0066] Further, the group of unmanned aerial vehicles includes a first unmanned aerial vehicle and a second unmanned aerial vehicle, which are configured to obtain interference images of laser beams interfering from multiple directions in the airspace to be measured.

[0067] The first unmanned aerial vehicle carries a laser emitting device and a laser beam expanding device. The laser emitting device is configured to emit a laser beam to the airspace to be measured, and the laser beam expanding device is configured to expand the diameter of the laser beam emitted by the laser emitting device.

[0068] The second unmanned aerial vehicle carries an interference generating device and an image acquisition device. The interference generating device is configured to control the interference of the laser beam, and the image acquisition device is configured to acquire the interference image of the interference of the laser beam.

[0069] The laser beam expanding device includes an expander mirror and a first convex lens, and the interference generating device includes a second convex lens and a beam splitter.

[0070] The laser beam emitted by the laser emitting device is expanded to the first convex lens through the beam expander. The laser beam emitted by the laser emitting device is expanded to the second convex lens through the first convex lens. The laser beam emitted by the laser emitting device is expanded to the beam splitter through the second convex lens. The laser beam emitted by the laser emitting device generates a first laser beam and a second laser beam through the beam splitter, and interference occurs in the overlapping area. The image acquisition device acquires the interference image of the laser beam interference

[0071] As shown in Figure 2 The specific optical path and detection structure of the gas pollutant concentration distribution online detection system based on unmanned aerial vehicle cooperation are presented.

[0072] In an embodiment, the intelligent cloud platform can be an Internet of Things platform composed of the operating systems and hardware of servers in a data center. After configuration, they can provide cloud computing services for customers.

[0073] In an embodiment, the airborne 5G wireless communication unit is a 5G wireless communication module that can be carried on an unmanned aerial vehicle and is composed of multiple components, including a transmitter, a receiver, a signal processor, a data processor, and a control unit. The transmitter and receiver are responsible for signal transmission and reception, the signal processor is responsible for processing received signals, the data processor is responsible for processing transmitted data, and the control unit is responsible for controlling the entire module.

[0074] In an embodiment, the airborne positioning unit can be a GPS locator or other navigation positioning system that can monitor the flight height, direction, and speed of the unmanned aerial vehicle in real time, track in real time, report the latitude and longitude speed in real time, and play back the track.

[0075] In an embodiment, the first unmanned aerial vehicle and the second unmanned aerial vehicle can be unmanned aircraft controlled by radio remote control equipment and self-provided program control devices, or operated completely or intermittently by a vehicle-mounted computer.

[0076] In an embodiment, the laser emitting device is a laser. In actual application, the laser refers to a device that can emit laser. The laser can be any one of solid-state lasers, semiconductor lasers, and free electron lasers with different working media. Preferably, the interference effect formed by the laser emitted by the semiconductor laser with an output wavelength of 670 nm is better.

[0077] In an embodiment, the laser beam expander includes a beam expander and a first convex lens. In addition, in another embodiment, the laser beam expander can use any device that can achieve the function of expanding the laser beam.

[0078] In an embodiment, the laser beam passes through the airspace to be measured after being expanded by the laser beam expander; the airspace to be measured is located between the first convex lens and the second convex lens.

[0079] In an embodiment, the laser beam passes through the space to be measured and reaches the interference generating device.

[0080] In an embodiment, the interference generating device comprises a second convex lens and a beam splitter. In addition, in another embodiment, the interference generating device can use any device that can achieve the function of interference of the laser beam after being split.

[0081] In an embodiment, the laser beam passes through the interference generating device and reaches the image acquisition device.

[0082] In an embodiment, the image acquisition device comprises a CCD area array photosensor. Since the image acquisition device uses a CMOS or CCD area array photosensor, the measurement accuracy of the system only depends on the depth of field of each pixel.

[0083] As shown in FIG. 6, the interference image in an embodiment of the gas pollutant concentration distribution online detection system based on UAV cooperation of the present application is shown, and the collected interference image presents alternating light and dark stripes. Figure 3

[0084] The airborne positioning unit obtains the position information and state information of the current UAV, which is transmitted by the airborne 5G wireless communication unit to the intelligent cloud platform, and controls the first UAV and the second UAV to move from the starting area to the space to be measured.

[0085] Step 1: The airborne 5G wireless communication unit receives the instructions sent by the intelligent cloud platform to control the laser emitting device to emit a laser beam, and the laser beam passes through the space to be measured.

[0086] Specifically, the laser beam is emitted by the laser emitting device. In practical application, the laser refers to a device that can emit laser, and the laser can be any one of solid-state laser, semiconductor laser and free electron laser of different working media.

[0087] Step 2: The laser beam is expanded by using the laser beam expander.

[0088] Step 3: The laser beam is interfered by using the interference generating device.

[0089] Specifically, the laser beam emitted by the laser emitting device generates a first laser beam and a second laser beam through the beam splitter, and the interference occurs in the overlapping area.

[0090] Step 4: The airborne 5G wireless communication unit receives the instructions sent by the intelligent cloud platform to control the image acquisition device to collect the interference image.

[0091] ​Step five: the airborne 5G wireless communication unit receives the instruction sent by the intelligent cloud platform to control the first and second drones to make circular motion perpendicular to the rotation axis (horizontal rotation) and vertical motion along the rotation axis (vertical rotation) with the center of the distance between the two as the rotation axis. At the same time, the airborne 5G wireless communication unit sends the position information obtained by the airborne positioning unit to the intelligent cloud platform in real time. In addition, the rotation axis can be any orientation in any three-dimensional space as needed, in addition to the horizontal rotation given in this embodiment, rotation in the vertical direction is also possible.

[0092] Specifically, when the first and second drones make circular motion perpendicular to the rotation axis, the initial position of the first drone is 0°, the initial position of the second drone is 180° from the initial position of the first drone, and the second drone always maintains an angle of 180° with the first drone during the circular motion; taking the clockwise direction as the positive direction, the image acquisition device carried by the second drone respectively acquires interference images of the first drone at 0°, 60°, 90°, 150°, and 300° relative to the initial position of the first drone when the first drone makes counterclockwise (or clockwise) circular motion.

[0093] After each set of multi-angle interference images is collected, the first and second drones return to the initial position and then make vertical motion along the rotation axis. Taking vertical upward motion as an example, the first and second drones immediately move a fixed distance vertically upward along the rotation axis after returning to the initial position; part of the measurement area can be set to overlap each time a fixed distance is moved upward to verify the accuracy of the measurement results; vertical downward motion is the same as above.

[0094] Repeat step five until all interference images of the airspace to be measured are collected.

[0095] Based on the same inventive concept, the present application also provides a gas pollutant concentration distribution detection method, as shown in Figure 4 The method comprises:

[0096] Obtaining interference images of laser beams interfering from multiple directions in the airspace to be measured. That is, the image acquisition device respectively acquires interference images of the first drone at 0°, 60°, 90°, 150°, and 300° relative to the initial position of the first drone when the first drone makes counterclockwise (or clockwise) circular motion.

[0097] Using Python software to perform grayscale and binarization processing on the interference images to obtain a grayscale value matrix; and performing noise reduction and interpolation operations on the grayscale value matrix to obtain a projection data matrix of the interference images.

[0098] Dividing the airspace to be measured into multiple cross sections, and dividing each cross section into a discrete pixel grid, and the pixel grid has an independent gas pollutant concentration.

[0099] According to the projection data matrix of the interference image, the gas pollutant concentration of the pixel grid, and the propagation distance of the laser beam in the pixel grid, a linear equation set is constructed to represent the gas pollutant concentration reflected by the laser beam interference image acquired from different directions:

[0100]

[0101] wherein, is the gas pollutant concentration of the pixel grid, is the propagation distance of the laser beam in the pixel grid, is the projection data of the laser beam, is the number of the laser beams, is the number of the pixel grids, i and is the form parameter.

[0102] The initial value of the gas pollutant concentration is set as:

[0103]

[0104] The correction value of the gas pollutant concentration is calculated as:

[0105]

[0106] wherein, is the iteration number; ; is the relaxation factor , is the gas pollutant concentration of the pixel grid, is the propagation distance of the laser beam in the pixel grid, is the projection data of the laser beam, is the number of the laser beams, is the number of the pixel grids, t, i and is the form parameter. In order to better suppress artifacts, for the pixel , make . When all the projection data are used up, a complete iteration is completed.

[0107] The projection data in the linear equation set is iterated to check whether the convergence criterion is met.

[0108]

[0109] wherein, is the projection data of the laser beam, is the propagation distance of the laser beam in the pixel grid is the first ka gas pollutant concentration of a sub-iteration pixel grid, is a number of laser beams, is a number of pixel grids, is a number of iterations.

[0110] when is less than a set threshold value iteration is stopped;

[0111] According to optical coherence tomography technology, a gas pollutant concentration corresponding to each projection data after iteration is stacked layer by layer to obtain a three-dimensional distribution of the gas pollutant concentration.

[0112] The above-described embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are within the scope of the present application. Therefore, the scope of protection of the present application patent should be subject to the appended claims.

Claims

1. A method for detecting the concentration distribution of gaseous pollutants, characterized in that, include: Acquire interference images of laser beams interfering from multiple directions in the space under test; The interference image is processed by grayscale and binarization to obtain a grayscale value matrix; Then, noise reduction and interpolation operations are performed on the grayscale matrix to obtain the projection data matrix of the interference image; The airspace to be measured is divided into multiple sections, and each section is further divided into multiple discrete pixel grids, each pixel grid having an independent concentration of gaseous pollutants; Based on the projection data matrix of the interferometric image, the concentration of gaseous pollutants in the pixel grid, and the propagation distance of the laser beam within the pixel grid, a set of linear equations is constructed to characterize the concentration of gaseous pollutants reflected in the interferometric images of laser beams acquired from different directions. The system of linear equations that characterizes the concentration of gaseous pollutants reflected in laser beam interferometry images obtained from different directions is expressed in the following form: in, The concentration of gaseous pollutants in the pixel grid. The distance the laser beam travels within the pixel grid. The projection data of the laser beam. The number of laser beams, The number of pixel grids, i and Formal parameters; The projection data in the linear equation system is iterated, and the gaseous pollutant concentrations corresponding to each projection data are stacked layer by layer to obtain the three-dimensional distribution of gaseous pollutant concentrations.

2. The method for detecting the concentration distribution of gaseous pollutants as described in claim 1, characterized in that, The iteration of the projected data in the linear equation system adopts a convergence criterion, the expression of which is: in, The projection data of the laser beam. The distance the laser beam travels within the pixel grid. For the first k The concentration of gaseous pollutants in the next iteration of the pixel grid. The number of laser beams, The number of pixel grids, This represents the number of iterations. when Less than the set threshold Stop iterating when the time comes.

3. The method for detecting the concentration distribution of gaseous pollutants as described in claim 1, characterized in that, The concentration of gaseous pollutants corresponding to each projection data after the iteration specifically includes: Set the initial values ​​for the gaseous pollutant concentration of the pixel grid: The concentration of gaseous pollutants corresponding to each projection data point after iteration is determined based on the initial value of the gaseous pollutant concentration. The calculation formula is as follows: in, For the number of iterations, , As a relaxation factor, The concentration of gaseous pollutants in the pixel grid. The distance the laser beam travels within the pixel grid. The projection data of the laser beam. The number of laser beams, The number of pixel grids, t、i and Formal parameters.

4. A gaseous pollutant concentration distribution detection system applied to the gaseous pollutant concentration distribution detection method according to any one of claims 1-3, characterized in that, include: Intelligent cloud platform, first drone and second drone; The first UAV and the second UAV are used to acquire interference images of laser beams interfering from multiple directions in the airspace to be tested; The intelligent cloud platform is used to perform the following operations: Control the first and second UAVs to move from the departure area to the airspace to be measured; send control commands to control the operation status of the UAVs; The interference image is processed by grayscale and binarization to obtain a grayscale value matrix; Then, noise reduction and interpolation operations are performed on the grayscale matrix to obtain the projection data matrix of the interference image; The airspace to be measured is divided into multiple sections, and each section is further divided into multiple discrete pixel grids, each pixel grid having an independent concentration of gaseous pollutants; Based on the projection data matrix of the interferometric image, the concentration of gaseous pollutants in the pixel grid, and the propagation distance of the laser beam within the pixel grid, a set of linear equations is constructed to characterize the concentration of gaseous pollutants reflected in the interferometric images obtained from different directions. The projection data in the linear equation system is iterated, and the gaseous pollutant concentrations corresponding to each projection data are stacked layer by layer to obtain the three-dimensional distribution of gaseous pollutant concentrations.

5. The gaseous pollutant concentration distribution detection system as described in claim 4, characterized in that, The first UAV includes: a laser emitting device and a laser beam expander; The laser emitting device is used to emit a laser beam into the space to be measured; The laser beam expander is used to enlarge the diameter of the laser beam emitted by the laser emitting device.

6. The gaseous pollutant concentration distribution detection system as described in claim 5, characterized in that, The second UAV includes: an interference generator and an image acquisition device; The interference generating device is used to control the laser beam to generate interference; The image acquisition device is used to acquire interference images when laser beams interfere.

7. The gaseous pollutant concentration distribution detection system as described in claim 4, characterized in that, It also includes an airborne 5G wireless communication unit, an airborne positioning unit, and an airborne image transmission unit; The airborne positioning unit is used to acquire the real-time location information of the first UAV and the second UAV; The airborne 5G wireless communication unit is used to control the first UAV and the second UAV to perform circular motion perpendicular to the rotation axis with the center of the distance between them as the rotation axis; taking the initial position of the first UAV as 0° and the clockwise direction as the positive direction, it collects interference images relative to the initial position of the first UAV at 0°, 60°, 90°, 150° and 300° when the first UAV is performing counterclockwise circular motion. The airborne image transmission unit is used to feed back the interferometric images to the intelligent cloud platform.

8. The gaseous pollutant concentration distribution detection system as described in claim 7, characterized in that, The airborne 5G wireless communication unit includes a transmitter, a receiver, a signal processor, a data processor, and a control unit; The transmitter is used to transmit the detection information of the airborne positioning unit to the intelligent cloud platform; The receiver is used to receive control command signals from the intelligent cloud platform; The signal processor is used to control the first UAV and the second UAV to perform circular motion perpendicular to the rotation axis with the center of the distance between them as the rotation axis. The data processor is used to process the transmitted data; The control unit is used to control the operation of the airborne 5G wireless communication unit.

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