Gas pollutant concentration distribution detection method and system

Abstract

The invention discloses a gas pollutant concentration distribution detection method and system, and relates to the technical field of gas detection. Comprising the following steps: acquiring interference images of a laser beam interfering from multiple directions in a to-be-measured airspace; dividing an airspace to be measured into a plurality of sections, and dividing each section into a plurality of discrete pixel grids; constructing a linear equation set according to the gas pollutant concentration of the pixel grid, the propagation distance of the laser beam in the pixel grid and the projection data matrix; and performing iteration on the projection data in the linear equation set through a convergence criterion, and stacking the gas pollutant concentration corresponding to each piece of projection data after iteration layer by layer to obtain a three-dimensional distribution condition of the gas pollutant concentration. According to the invention, the concentration distribution of the gas pollutants in the airspace to be detected can be visually detected, and reliable data support is provided for environmental protection and pollution abatement work.

Description

Technical Field

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

[0002] Common gaseous pollutants include sulfur dioxide (SO2), nitrogen oxides (NOx), ozone (O3) and carbon monoxide (CO). Increased concentrations of atmospheric pollutants can cause acid rain. Therefore, studying the concentration distribution of gaseous pollutants plays an important role in the monitoring and prevention of gaseous pollutants.

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

[0004] The defects of the above-mentioned prior art are: the process of detecting the concentration of gas pollutants through sensors belongs to a single-point measurement method, and its measurement range remains in one-dimensional detection, and it is impossible to detect the three-dimensional distribution of the concentration of gas pollutants in the area. Summary of the invention

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

[0006] An embodiment of the present invention provides a method for detecting gas pollutant concentration distribution, comprising:

[0007] Obtaining interference images of laser beams interfering from multiple directions in the airspace to be measured;

[0008] The interference image is grayscaled and binarized to obtain a grayscale value matrix; the grayscale value matrix is ​​then subjected to noise reduction and interpolation operations to obtain a projection data matrix of the interference image;

[0009] The airspace to be tested is divided into multiple cross sections, and each cross section is divided into multiple discrete pixel grids, each pixel grid having an independent gas pollutant concentration;

[0010] 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 group is constructed to characterize the gas pollutant concentration reflected by the laser beam interference image obtained from different directions;

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

[0012] Optionally, a linear equation group is constructed to characterize the concentration of gas pollutants reflected by the laser beam interference images obtained from different directions, and its expression is:

[0013]

[0014] Among them, n i is the gas pollutant concentration of the pixel grid, L ij is the propagation distance of the laser beam within the pixel grid, q j is the projection data of the laser beam, N is the number of laser beams, M is the number of pixel grids, and i and j are formal parameters.

[0015] Optionally, the projection data in the linear system of equations is iterated using a convergence criterion, which is expressed as:

[0016]

[0017] Among them, q j is the projection data of the laser beam, L j is the propagation distance of the laser beam within the pixel grid of the same cross section, n (k) is the gas pollutant concentration of the pixel grid, M is the number of pixel grids;

[0018] when The iteration is stopped when it is less than the set threshold ε.

[0019] Optionally, the gas pollutant concentration corresponding to each projection data after iteration specifically includes:

[0020] Set the initial values ​​for the gas pollutant concentrations on the pixel grid:

[0021]

[0022] The gas pollutant concentration corresponding to each projection data after iteration is determined according to the initial value of the gas pollutant concentration. The calculation formula is:

[0023]

[0024] Where k is the number of iterations, 1≤j≤N, λ is the relaxation factor, n i is the gas pollutant concentration of the pixel grid, L ij is the propagation distance of the laser beam within the pixel grid, q iis the projection data of the laser beam, N is the number of laser beams, M is the number of pixel grids, and t, i and j are formal parameters.

[0025] An embodiment of the present invention further provides a gas pollutant concentration distribution detection system, comprising: an intelligent cloud platform, a first drone and a second drone;

[0026] The first UAV and the second UAV are used to obtain interference images of laser beams interfering from multiple directions in the airspace to be measured;

[0027] Intelligent cloud platform for performing the following operations:

[0028] Control the first UAV and the second UAV to move from the departure area to the airspace to be tested; send control instructions to control the operation status of the UAVs;

[0029] The interference image is grayscaled and binarized to obtain a grayscale value matrix; the grayscale value matrix is ​​then subjected to noise reduction and interpolation operations to obtain a projection data matrix of the interference image;

[0030] The airspace to be tested is divided into multiple cross sections, and each cross section is divided into multiple discrete pixel grids, each pixel grid having an independent gas pollutant concentration;

[0031] A linear equation system is constructed to characterize the concentration of gaseous pollutants reflected by the interference images obtained from different directions according to the projection data matrix of the interference image, the concentration of gaseous pollutants in the pixel grid and the propagation distance of the laser beam in the pixel grid;

[0032] The projection data in the linear equation group are iterated, and the gas pollutant concentration corresponding to each projection data after the iteration is stacked layer by layer to obtain the three-dimensional distribution of the gas pollutant concentration.

[0033] Optionally, the first UAV includes: a laser emitting device and a laser beam expanding device;

[0034] A laser emitting device, used for emitting a laser beam to the airspace to be measured;

[0035] The laser beam expansion device is used to enlarge the diameter of the laser beam emitted by the laser emitting device.

[0036] Optionally, the second UAV includes: an interference generating device and an image acquisition device;

[0037] An interference generating device, used for controlling the laser beam to interfere;

[0038] The image acquisition device is used to obtain the interference image when the laser beams interfere.

[0039] Optionally, it also includes an airborne 5G wireless communication unit, an airborne positioning unit and an airborne image transmission unit;

[0040] An airborne positioning unit, used to obtain real-time position information of the first UAV and the second UAV;

[0041] The airborne 5G wireless communication unit is used to control the first UAV and the second UAV to make a circular motion perpendicular to the rotation axis with the center of the distance between the two UAVs as the rotation axis; with the initial position of the first UAV as 0° and the clockwise direction as the positive direction, respectively collect interference images of 0°, 60°, 90°, 150°, and 300° relative to the initial position of the first UAV when the first UAV makes a counterclockwise circular motion;

[0042] The onboard image transmission unit is used to feed back the interference image to the intelligent cloud platform.

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

[0044] A transmitter, used to transmit detection information of the airborne positioning unit to the intelligent cloud platform;

[0045] A receiver, used to receive control command signals from the intelligent cloud platform;

[0046] A 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 the first UAV and the second UAV as the rotation axis;

[0047] A data processor for processing the transmitted data;

[0048] A control unit is used to control the operation of the onboard 5G wireless communication unit.

[0049] Compared with the prior art, the gas pollutant concentration distribution detection method and system provided by the embodiment of the present invention have the following beneficial effects:

[0050] In the present invention, in the airspace to be measured, the laser beam interferes from multiple directions, and the formed interference image is divided into multiple sections and pixel grids, and each pixel grid is assigned a gas pollutant concentration. Subsequently, these interference images are gray-scaled 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 system is constructed. The linear equation system can effectively characterize the gas pollutant concentration reflected by the laser beam interference image obtained from different directions. This process increases the measurement range of gas pollutant concentration detection from one dimension to three dimensions, and can realize the detection of the three-dimensional distribution of gas pollutant concentration in the region.

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

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

[0053] Figure 2 A light path principle diagram of a gas pollutant concentration distribution detection system provided in one embodiment;

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

[0055] Figure 4 A schematic flow chart of a method for detecting gas pollutant concentration distribution provided in an embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In one embodiment, a gas pollutant concentration distribution detection system is provided, such as Figure 1 As shown, the system includes: an intelligent cloud platform, an airborne 5G wireless communication unit, an airborne positioning unit, a laser emitting device, a laser beam expansion device, an interference generating device, an image acquisition device, an airborne image transmission unit and a group of drones.

[0058] Furthermore, the intelligent cloud platform is used to perform the following operations:

[0059] Control the first UAV and the second UAV to move from the departure area to the airspace to be tested; send control instructions to control the operating status of the UAVs.

[0060] The interference image is grayscaled and binarized to obtain a grayscale value matrix; the grayscale value matrix is ​​then subjected to noise reduction and interpolation operations to obtain a projection data matrix of the interference image.

[0061] The airspace to be tested is divided into multiple sections, and each section is divided into multiple discrete pixel grids, each pixel grid having an independent gas pollutant concentration.

[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 group characterizing the gas pollutant concentration reflected by the interference images obtained from different directions is constructed.

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

[0064] Furthermore, the onboard 5G wireless communication unit is used to conduct two-way data exchange with the intelligent cloud platform, sending the drone's status information (such as location information, battery level, flight speed, etc.) to the intelligent cloud platform, and receiving control commands from the intelligent cloud platform.

[0065] Furthermore, the airborne positioning unit is used to obtain the real-time location information of the drone, including location coordinates, flight altitude, etc. The airborne image transmission unit is used to feed back the interference image to the intelligent cloud platform.

[0066] Furthermore, a group of drones includes a first drone and a second drone, which are used to obtain interference images of laser beams interfering from multiple directions in the airspace to be measured.

[0067] The first UAV carries a laser emitting device and a laser beam expanding device. The laser emitting device is used to emit a laser beam to the airspace to be measured, and the laser beam expanding device is used to enlarge the diameter of the laser beam emitted by the laser emitting device.

[0068] The second UAV carries an interference generating device and an image acquisition device. The interference generating device is used to control the interference of the laser beams, and the image acquisition device is used to acquire the interference image of the interference of the laser beams.

[0069] The laser beam expansion device comprises a beam expander and a first convex lens, and the interference generating device comprises a second convex lens and a beam splitter.

[0070] The laser beam emitted by the laser emitting device is expanded by a beam expander to the first convex lens. The laser beam emitted by the laser emitting device is expanded by the first convex lens to the second convex lens. The laser beam emitted by the laser emitting device is transmitted by the second convex lens to the beam splitter. The laser beam emitted by the laser emitting device is generated by the beam splitter to form a first laser beam and a second laser beam, which interfere in the overlapping area. The image acquisition device acquires the interference image of the interference of the laser beams.

[0071] like Figure 2 As shown, the specific optical path and detection structure of the online detection system for gas pollutant concentration distribution based on UAV collaboration are presented.

[0072] In one embodiment, the intelligent cloud platform may be an Internet of Things platform, which is composed of the operating system and hardware of each server in a data center, which can provide cloud computing services to customers after being configured.

[0073] In one embodiment, the airborne 5G wireless communication unit is a 5G wireless communication module that can be carried on a drone, and is composed of multiple components, including a transmitter, a receiver, a signal processor, a data processor, and a control unit. The transmitter and the receiver are responsible for transmitting and receiving signals, 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 operation of the entire module.

[0074] In one embodiment, the airborne positioning unit can be a GPS locator or other navigation and positioning system that can monitor the flight altitude, direction, and speed of the drone in real time, and provide real-time online tracking, one-button alarm, remote monitoring, trajectory playback, and real-time reporting of latitude, longitude, and speed.

[0075] In one embodiment, the first drone and the second drone may be unmanned aircraft controlled by a radio remote control device and a self-contained program control device, or may be fully or intermittently autonomously operated by an onboard computer.

[0076] In one embodiment, the laser emitting device is a laser. In practical applications, a laser refers to a device that can emit laser light, and the laser can be any laser with different working media such as a solid laser, a semiconductor laser, and a free electron laser. Preferably, the interference effect formed by the laser emitted by a semiconductor laser with an output wavelength of 670nm is better.

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

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

[0079] In one embodiment, the laser beam reaches the interference generating device after passing through the airspace to be measured.

[0080] In one embodiment, the interference generating device includes a second convex lens and a beam splitter. In addition, in another embodiment, the interference generating device can also use any device that can realize the interference function in the overlapping area after the laser beam is split.

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

[0082] In one embodiment, the image acquisition device includes a CCD area array photoelectric sensor. Since the image acquisition device uses a CMOS or CCD area array photoelectric sensor, the measurement accuracy of the system depends only on the light-sensitive depth of each pixel.

[0083] like Figure 3 As shown, it shows the interference image of the online detection system for gas pollutant concentration distribution based on drone cooperation in one embodiment of the present invention. The collected interference image shows alternating light and dark stripes.

[0084] The airborne positioning unit obtains the location information and status information of the current UAV, which is transmitted to the intelligent cloud platform by the airborne 5G wireless communication unit to control the first UAV and the second UAV to move from the departure area to the airspace to be tested.

[0085] Step 1: The onboard 5G wireless communication unit receives 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 airspace to be measured.

[0086] Specifically, a laser beam is emitted by a laser emitting device. In practical applications, a laser refers to a device that can emit laser light, and the laser can be any laser with different working media such as a solid laser, a semiconductor laser, and a free electron laser.

[0087] Step 2: Expand the laser beam using a laser beam expander.

[0088] Step 3: Use an interference generating device to cause interference of the laser beams.

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

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

[0091] Step 5: The airborne 5G wireless communication unit receives the command sent by the intelligent cloud platform to control the first UAV and the second UAV to perform circular motion perpendicular to the rotation axis, i.e. horizontal rotation, and vertical motion along the rotation axis, i.e. vertical motion, 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 UAV and the second UAV make a circular motion perpendicular to the rotation axis, the initial position of the first UAV is 0°, the initial position of the second UAV and the initial position of the first UAV are at an angle of 180°, and the second UAV and the first UAV always maintain an angle of 180° when making a circular motion; taking the clockwise direction as the positive direction, the image acquisition device carried by the second UAV respectively acquires interference images of 0°, 60°, 90°, 150°, and 300° relative to the initial position of the first UAV when the first UAV makes a counterclockwise (or clockwise) circular motion.

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

[0094] Repeat step 5 until all interference images of the airspace to be tested are collected.

[0095] Based on the same inventive concept, the present application also provides a method for detecting the concentration distribution of gas pollutants, such as Figure 4 As shown, the method includes:

[0096] Obtain interference images of the laser beam interfering from multiple directions in the airspace to be measured. That is, the image acquisition device respectively acquires interference images of 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] Python software is used to grayscale and binarize the interference image to obtain a grayscale value matrix; then the grayscale value matrix is ​​subjected to denoising and interpolation operations to obtain the projection data matrix of the interference image.

[0098] The airspace to be measured is divided into multiple sections, and each section is divided into M = m × m discrete pixel grids, and the pixel grids have independent gas pollutant concentrations.

[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 group is constructed to characterize the gas pollutant concentration reflected by the laser beam interference image obtained from different directions:

[0100]

[0101] Among them, n i is the gas pollutant concentration of the pixel grid, L ijis the propagation distance of the laser beam within the pixel grid, q j is the projection data of the laser beam, N is the number of laser beams, M is the number of pixel grids, and i and j are formal parameters.

[0102] Set the initial value of gas pollutant concentration:

[0103]

[0104] Calculate the correction value of gas pollutant concentration:

[0105]

[0106] Where k is the number of iterations; 1≤j≤N; λ is the relaxation factor (0<λ<2), n i is the gas pollutant concentration of the pixel grid, L ij is the propagation distance of the laser beam within the pixel grid, q i is the projection data of the laser beam, N is the number of laser beams, M is the number of pixel grids, and t, i and j are formal parameters. To better suppress artifacts, for pixel n j <0, then n j = 0. When all the projection data are used, a full iteration is completed.

[0107] Iterate the projection data in the linear system of equations and check whether the convergence criteria are met.

[0108]

[0109] In the formula, when Stop the iteration when it is less than the set threshold ε (0<ε<1);

[0110] Based on the optical coherence tomography technology, the gas pollutant concentration corresponding to each projection data after iteration is stacked layer by layer to obtain the three-dimensional distribution of the gas pollutant concentration.

[0111] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for detecting gas pollutant concentration distribution, characterized in that: include: Obtaining interference images of laser beams interfering from multiple directions in the airspace to be measured; Perform grayscale and binarization processing on the interference image to obtain a grayscale value matrix; Then, the gray value matrix is ​​subjected to noise reduction and interpolation operations to obtain the projection data matrix of the interference image; Dividing the airspace to be measured into a plurality of cross sections, and dividing each cross section into a plurality of discrete pixel grids, wherein the pixel grids have independent gas pollutant concentrations; 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 group is constructed to characterize the gas pollutant concentration reflected by the laser beam interference image obtained from different directions; The projection data in the linear equation group are iterated, and the gas pollutant concentration corresponding to each projection data after the iteration is stacked layer by layer to obtain the three-dimensional distribution of the gas pollutant concentration.

2. A method for detecting gas pollutant concentration distribution according to claim 1, characterized in that: The linear equations for characterizing the concentration of gas pollutants reflected by the laser beam interference images obtained from different directions are constructed and expressed as follows: Among them, n i is the gas pollutant concentration of the pixel grid, L ij is the propagation distance of the laser beam within the pixel grid, q j is the projection data of the laser beam, N is the number of laser beams, M is the number of pixel grids, and i and j are formal parameters.

3. A method for detecting gas pollutant concentration distribution according to claim 1, characterized in that: The iterative process of the projection data in the linear equation system adopts a convergence criterion, which is expressed as follows: Among them, q j is the projection data of the laser beam, L j is the propagation distance of the laser beam within the pixel grid of the same cross section, n (k) is the gas pollutant concentration of the pixel grid, M is the number of pixel grids; when The iteration is stopped when it is less than the set threshold ε.

4. A method for detecting gas pollutant concentration distribution according to claim 1, characterized in that: The gas pollutant concentration corresponding to each projection data after the iteration specifically includes: Set the initial values ​​for the gas pollutant concentrations on the pixel grid: The gas pollutant concentration corresponding to each projection data after iteration is determined according to the initial value of the gas pollutant concentration. The calculation formula is: Where k is the number of iterations, 1≤j≤N, λ is the relaxation factor, n i is the gas pollutant concentration of the pixel grid, L ij is the propagation distance of the laser beam within the pixel grid, q i is the projection data of the laser beam, N is the number of laser beams, M is the number of pixel grids, and t, i and j are formal parameters.

5. A gas pollutant concentration distribution detection system applied to the gas pollutant concentration distribution detection method according to any one of claims 1 to 4, characterized in that: include: An intelligent cloud platform, a first drone, and a second drone; The first UAV and the second UAV are used to obtain interference images of laser beams interfering from multiple directions in the airspace to be measured; The intelligent cloud platform is used to perform the following operations: Control the first UAV and the second UAV to move from the departure area to the airspace to be tested; send control instructions to control the operation status of the UAVs; Perform grayscale and binarization processing on the interference image to obtain a grayscale value matrix; Then, the gray value matrix is ​​subjected to noise reduction and interpolation operations to obtain the projection data matrix of the interference image; Dividing the airspace to be measured into a plurality of cross sections, and dividing each cross section into a plurality of discrete pixel grids, wherein the pixel grids have independent gas pollutant concentrations; A linear equation system is constructed to characterize the concentration of gaseous pollutants reflected by the interference images obtained from different directions according to the projection data matrix of the interference image, the concentration of gaseous pollutants in the pixel grid and the propagation distance of the laser beam in the pixel grid; The projection data in the linear equation group are iterated, and the gas pollutant concentration corresponding to each projection data after the iteration is stacked layer by layer to obtain the three-dimensional distribution of the gas pollutant concentration.

6. A gas pollutant concentration distribution detection system as claimed in claim 5, characterized in that: The first UAV comprises: a laser emitting device and a laser beam expanding device; The laser emitting device is used to emit a laser beam to the airspace to be measured; The laser beam expansion device is used to enlarge the diameter of the laser beam emitted by the laser emitting device.

7. A gas pollutant concentration distribution detection system as claimed in claim 6, characterized in that: The second UAV includes: an interference generating device and an image acquisition device; The interference generating device is used to control the laser beam to interfere; The image acquisition device is used to obtain an interference image when interference occurs in the laser beams.

8. A gas pollutant concentration distribution detection system as claimed in claim 5, 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 obtain real-time position 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 the first UAV and the second UAV as the rotation axis; with the initial position of the first UAV as 0° and the clockwise direction as the positive direction, respectively collect interference images of the first UAV at 0°, 60°, 90°, 150°, and 300° relative to the initial position of the first UAV when the first UAV performs counterclockwise circular motion; The airborne image transmission unit is used to feed back the interference image to the intelligent cloud platform.

9. A gas pollutant concentration distribution detection system as claimed in claim 8, 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 the control command signal of 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 the first UAV and the second UAV 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 onboard 5G wireless communication unit.

Images