Three-dimensional tomography method and system for gas concentration space distribution detection based on unmanned aerial vehicle

Abstract

The invention belongs to the technical field of gas concentration detection, and discloses a three-dimensional tomography method and system for gas concentration space distribution detection based on an unmanned aerial vehicle, and the method comprises the steps: 1, building a space rectangular coordinate system, and converting latitude and longitude coordinates into XYZ coordinates; 2, dividing view field grids and grid numbers; 3, calculating the number of intersecting grids of the light beams and the view field and the length of intersecting line segments; 4, calculating a weight coefficient matrix and an integral concentration vector; and 5, reconstructing gas three-dimensional concentration distribution by using an SART algorithm. The system combines the unmanned aerial vehicle and the telemetry technology, realizes gas cloud scanning and three-dimensional gas cloud concentration distribution reconstruction based on the unmanned aerial vehicle, has the characteristics of non-contact measurement, high imaging speed, high precision, autonomous detection, strong environmental adaptability and the like, can monitor and predict the gas diffusion trend in real time, and has a wide application prospect. And a new solution is provided for realizing accurate traceability of methane gas leakage and reconstruction of leakage space distribution in a complex scene.

Description

Technical Field

[0001] The present invention relates to the technical field of gas concentration detection, and in particular to a three-dimensional tomography method and system for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle. Background Art

[0002] With the rapid development of industrialization and urbanization, air pollution and air governance issues are becoming increasingly severe. How to timely monitor the emission of polluted gases and locate pollution sources is of great significance for the control of air pollution. Infrared monitoring technology has the characteristics of high selectivity, non-contact, long life and easy maintenance, and is widely used in the field of gas detection. According to different working methods, infrared gas detection technology can be divided into active and passive. Among them, the passive detection method has a simple structure, a large detection range, and strong real-time performance, but it has disadvantages such as low sensitivity and short detection distance; the active detection method has high sensitivity, strong anti-interference ability, and long working distance, but compared with the passive detection method, it has high cost and fewer types of gases to detect.

[0003] Tunable diode laser absorption spectroscopy (TDLAS) is a commonly used active gas detection method. It changes the laser output wavelength so that the laser scans the absorption peak of the target gas, thereby detecting the gas concentration. However, this method can only measure the path-integrated concentration of the gas, but cannot obtain the internal concentration distribution of the gas, so it does not have distance resolution capability. Tomography (CL) is a method that uses multi-angle scanning and image reconstruction methods to obtain the internal structure of the target object. In gas detection, the tomography method can be used to obtain the two-dimensional or three-dimensional distribution of the internal concentration of the gas cloud, thereby locating the gas leak source, visualizing the gas diffusion path, and quantitatively analyzing the concentration distribution. Summary of the invention

[0004] The purpose of the present invention is to provide a three-dimensional tomography method and system for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle, so as to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle, comprising: Step 1, establish a spatial rectangular coordinate system and convert the longitude and latitude coordinates into XYZ coordinates; Step 2, dividing the field of view into grids and numbering the grids; Step 3, calculating the grid number and the length of the intersection line segment where the light beam intersects the field of view; Step 4, calculating the weight coefficient matrix and the integrated concentration vector; Step 5: Use the SART algorithm to reconstruct the three-dimensional gas concentration distribution.

[0007] Furthermore, the step 1 specifically includes: Step 1.1, select a point on the ground near the gas cloud As the origin, and are the latitude and longitude of the point respectively, and the location of the drone is recorded as ,in, and are the latitude and longitude of the drone, is the height above the ground measured by the ranging module; Step 1.2, establish a spatial rectangular coordinate system with the longitude and latitude directions as the X-axis and Y-axis respectively, and the vertical upward direction as the Z-axis; Step 1.3, calculate the coordinates of the drone in the rectangular coordinate system of the space: Latitude difference between the drone and the origin , longitude difference , using local tangent plane projection, we can get the UAV Coordinate x A and Coordinate y A : Where R is the radius of the earth; According to the return value of the ranging module, the z coordinate of the drone is obtained A : .

[0008] Furthermore, the step 2 specifically includes: Step 2.1, select the center of the field of view F according to the location of the gas cloud. The coordinates of F are , the lengths of the field of view in the three directions of XYZ are W, L and H respectively, the number of grids divided into the field of view in the three directions of XYZ are N, M and K respectively, and the total number of grids in the field of view is NMK; The spatial resolution of the field of view in the three directions of XYZ is △x, △y, △z respectively. According to the size of the field of view and the number of grid divisions, we get: , , ; Step 2.2, grid numbering, the numbering method is as follows: start numbering from point S, the position closest to the origin of the coordinate system, divide the field of view into 1-K layers according to the Z-axis coordinate, and number them from low to high layers. In each layer, it is divided into 1-M rows along the Y-axis direction, and numbered from low row numbers to high row numbers. In each row, it is divided into 1-N columns along the X-axis direction, and numbered from low column numbers to high column numbers. Step 2.3, conversion of coordinates and grid numbers. According to the coordinates of point F at the center of the field of view and the size of the field of view, the coordinates of point S are obtained as follows: , , In the formula, x S is the x coordinate of point S, y S is the y coordinate of point S, z S is the z coordinate of point S; For any point in the field of view , its coordinates relative to point S are: , , In the formula, , and They are the XYZ coordinates of point P relative to point S; Get the grid number where point P is located The calculation is as follows: Among them, "[]" means rounding down. .

[0009] Furthermore, the step 3 specifically includes: Step 3.1, calculate the intersection coordinates and intersection matrix according to the beam equation and the field of view plane equation; the intersection coordinates are obtained by the line equation and the plane equation, and the plane equations are as follows: A plane perpendicular to the X axis: ; A plane perpendicular to the Y axis: ; A plane perpendicular to the Z axis: ; According to the pitch angle and heading angle of the drone and the angle data returned by the galvanometer, the beam is obtained. Azimuth and elevation , , where T represents the number of effective beams that intersect the field of view; the position of the drone is , and then get the beam The parametric equation of the straight line is: Substitute the plane equation into the line parametric equation to obtain the parameter t, and then obtain the coordinates of the intersection point; Store all intersection coordinates into the intersection matrix ; Remove the matrix Repeat the intersection points in the code, and then sort all the intersection points in XYZ coordinate order to get the intersection coordinate matrix ,in, is the total number of intersections between the line and all planes; Step 3.2, calculate the length of the grid line segment where the beam and the field of view intersect. intersection, there is Line segment, The length of the line segment d u The calculation is as follows: Step 3.3, calculate the grid number where the beam and the field of view intersect, The midpoint V coordinate of the line segment (V x ,V y ,V z )for: Step 3.4, according to the grid number where point P is located Calculation method and midpoint Coordinates, get The grid number where the line segment is located .

[0010] Furthermore, the step 4 specifically includes: Calculate the weight coefficient matrix and the integral concentration vector, and store the length of the intersecting line segments into the weight coefficient matrix according to the number The corresponding position of ; The gas concentration of all grids in the field of view is represented by a concentration vector of length NMK Indicates that the beam The corresponding path-integrated concentration is , then the following relationship exists: In the formula, is the length of the line segment of beam i in the jth grid, is the average gas concentration in the jth grid; Will Transformed into matrix multiplication form, expressed as: Use symbols separately , and Representing the matrix and vector of the above equation, the above equation can be written as: in, is the integrated concentration vector, is the weight coefficient matrix, is the concentration vector.

[0011] Furthermore, the step 5 specifically includes: Step 5.1, first transform the concentration vector Initialize to zero vector; Step 5.2: According to the weight coefficient matrix and the integral concentration vector, the concentration vector Perform iterative calculations as follows: Where p is the number of iterations, is the relaxation factor, used to control the iteration speed; Step 5.3: Determine whether to stop iteration based on the iteration error and select a threshold As the judgment condition for stopping iteration, when the root mean square error of the concentration distribution of two adjacent iterations is less than the threshold, the iteration is stopped, that is: Step 5.4, reconstruct the three-dimensional concentration distribution of the gas, and convert the obtained concentration vector into Restored to a three-dimensional spatial concentration distribution and stored in a size of The host computer displays the internal concentration distribution of the gas cloud in the form of three-dimensional point cloud, horizontal section and vertical section.

[0012] The present invention also provides a three-dimensional tomography system for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle, which is used to implement the three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle as described above, including a GNSS positioning module, a ranging module and a gas cloud scanning system, wherein the GNSS positioning module measures the longitude and latitude coordinates of the location of the unmanned aerial vehicle, and the ranging module measures the height information of the unmanned aerial vehicle above the ground. The measurement data of the GNSS positioning module and the ranging module are used to establish a spatial rectangular coordinate system, and the gas cloud scanning system measures the gas cloud integral concentration image, which is used to calculate the grid number of the intersection of the light beam and the field of view, the length of the intersection line segment, the weight coefficient matrix and the integral concentration vector.

[0013] Furthermore, the gas cloud scanning system includes a main control board, a laser, a detector, a signal generator, an acquisition card, a receiving optical system and a galvanometer, wherein the main control board is electrically connected to the signal generator, the acquisition card, the ranging module and the galvanometer respectively, the signal generator is electrically connected to the laser, and the acquisition card is electrically connected to the detector; The signal generator is used to generate a modulation signal to drive the laser to emit a scanning laser; The laser is used to emit laser light for scanning the gas cloud; The acquisition card is used to sample and demodulate the detector output signal; The detector is used to receive the echo signal gathered by the optical system, convert the optical signal into an electrical signal and output it to the acquisition card; The galvanometer is used to change the laser emission direction to scan the gas cloud; The optical system is used to receive the echo signal of the scanning laser and focus the reflected laser onto the detector.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention combines drones and telemetry technology, realizes drone-based gas cloud scanning and three-dimensional gas cloud concentration distribution reconstruction, has the characteristics of non-contact measurement, fast imaging speed, high precision, autonomous detection, strong environmental adaptability, etc., can monitor and predict gas diffusion trends in real time, and provide a new solution for accurate tracing of methane gas leaks and reconstruction of leakage spatial distribution in complex scenarios. The present invention can be applied to the fields of industrial safety and environmental monitoring, public safety, air pollution control and urban management, monitors flammable, explosive and toxic gas leaks, quickly locates the leak source and evaluates the pollution diffusion range. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 It is a flow chart of a three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle of the present invention.

[0016] Figure 2 It is a multi-view scanning schematic diagram of a three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle of the present invention.

[0017] Figure 3 It is a schematic diagram of the principles of field of view gridding and laser path integral concentration calculation in a three-dimensional tomography method for detecting spatial distribution of gas concentration based on an unmanned aerial vehicle of the present invention.

[0018] Figure 4 It is a structural block diagram of a three-dimensional tomography system for detecting spatial distribution of gas concentration based on an unmanned aerial vehicle of the present invention. The black solid arrows in the figure represent electrical signals, and the arrows with blank spaces inside represent optical signals. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] See also Figure 1-Figure 3 , a three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle, comprising the following steps:

[0021] Step 1: Establish a spatial rectangular coordinate system and convert the longitude and latitude coordinates into XYZ coordinates. Specifically include:

[0022] Step 1.1, select a point on the ground near the gas cloud As the origin, and is the latitude and longitude of the point. The drone scans and images the gas cloud at different angles, such as Figure 2 The position of the drone is recorded as ,in, and is the latitude and longitude of the drone, It is the height above the ground measured by the ranging module.

[0023] Step 1.2, establish a spatial rectangular coordinate system with the longitude and latitude directions as the X-axis and Y-axis respectively, and the vertical upward direction of the ground as the Z-axis.

[0024] Step 1.3, the coordinates of the drone in the rectangular coordinate system of the space are calculated as follows:

[0025] The latitude difference between the drone and point O , longitude difference , using local tangent plane projection, we can get the UAV Coordinate x A and Coordinate y A :

[0026] (1)

[0027] (2)

[0028] According to the return value of the ranging module, the z coordinate of the drone is obtained A :

[0029]

[0030] Among them, R is the radius of the earth, which is 6371000 meters here.

[0031] Step 2: Divide the field of view into grids and grid numbers, such as Figure 3 As shown, the specific steps are as follows:

[0032] Step 2.1, select the center of the field of view F according to the location of the gas cloud. The coordinates of F are The lengths of the field of view in the XYZ directions are W, L and H respectively, and the number of grids divided into the field of view in the XYZ directions are N, M and K respectively. Therefore, the total number of grids in the field of view is NMK.

[0033] The spatial resolutions of the field of view in the three directions of XYZ are △x, △y, and △z respectively. According to the size of the field of view and the number of grid divisions, the spatial resolutions of the field of view in the three directions of XYZ are: , , The spatial resolution of the field of view indicates the minimum spatial size that can distinguish the concentration of gas clouds. The smaller the resolution, the more detailed information about the spatial distribution of gas cloud concentration is included, but it also places higher requirements on the system positioning longitude, detection data and gas detection longitude.

[0034] Step 2.2, grid numbering. The numbering method is as follows: start numbering from point S, which is closest to the origin of the coordinate system. Figure 2 As shown. The field of view is divided into 1-K layers according to the Z-axis coordinate, numbered from low to high layers. In each layer, it is divided into 1-M rows along the Y-axis direction, numbered from low row numbers to high row numbers. In each row, it is divided into 1-N columns along the X-axis direction, numbered from low column numbers to high column numbers.

[0035] First, the first row of the first layer is numbered from 1-N, and then one row is added along the Y axis, numbering from N+1 to 2N. And so on, the last grid of the first layer is numbered NM. Then one layer is added along the Z axis, that is, the second layer, and the numbering starts from NM+1. The last grid of the second layer is numbered 2NM. Then the 3-K layers are numbered according to the above method, and the last grid of the K layer is numbered NMK.

[0036] Step 2.3, conversion of coordinates and grid numbers. According to the coordinates of point F at the center of the field of view and the size of the field of view, the coordinates of point S can be obtained as:

[0037] , ,

[0038] In the formula, x S is the x coordinate of point S, y S is the y coordinate of point S, z S is the z coordinate of point S.

[0039] For any point in the field of view , its coordinates relative to point S are:

[0040] , ,

[0041] You can get the grid number where point P is located The calculation is as follows:

[0042] (3)

[0043] Among them, "[]" means rounding down. .

[0044] Step 3, calculate the grid number and the length of the intersection line segment where the beam intersects the field of view. The specific steps are as follows:

[0045] Step 3.1, calculate the intersection coordinates and intersection matrix according to the beam equation and the field of view plane equation. The intersection coordinates can be obtained by the line equation and the plane equation. According to the grid division in step 2, there are three types of planes, namely, the plane perpendicular to the X-axis, the plane perpendicular to the Y-axis, and the plane perpendicular to the Z-axis. The plane equations are as follows:

[0046] A plane perpendicular to the X axis: ;

[0047] A plane perpendicular to the Y axis:

[0048] A plane perpendicular to the Z axis:

[0049] Here n is an integer between 0 and N and has no specific physical meaning. The same is true for m and k. According to the pitch angle and heading angle of the drone and the angle data returned by the galvanometer, the beam can be obtained. Azimuth and elevation , , where T represents the number of effective beams that intersect the field of view. The position of the drone is , and then we can get the beam The parametric equation of the straight line is:

[0050] (4)

[0051] Substitute the plane equation into the line parametric equation to obtain the parameter t, and then obtain the intersection coordinates. The intersection point of the line and the plane perpendicular to the X-axis is calculated as follows:

[0052] The x coordinate of the intersection point is:

[0053]

[0054] Will Substitute into the equation , get the parameters for:

[0055] (5)

[0056] Further, Substitute the parametric equation of the line to obtain the y-coordinate and z-coordinate, which are as follows:

[0057] (6)

[0058] (7)

[0059] The intersection point of a line and a plane perpendicular to the Y axis is calculated as follows:

[0060] The y coordinate of the intersection point is:

[0061]

[0062] Will Substitute into the equation , get the parameters for:

[0063]

[0064] Further, Substitute the parametric equation of the line to obtain the x-coordinate and z-coordinate, which are as follows:

[0065]

[0066]

[0067] The intersection point of a line and a plane perpendicular to the Z axis is calculated as follows:

[0068] The z coordinate of the intersection point is:

[0069]

[0070] Will Substitute into the equation , get the parameters for:

[0071]

[0072] Further, Substitute the parametric equation of the line to obtain the x-coordinate and y-coordinate, which are as follows:

[0073]

[0074]

[0075] Store all intersection coordinates into the intersection matrix Since the straight line may intersect with the intersection line of the plane, it will appear at repeated intersection points, so it is necessary to remove the matrix Repeat the intersection points in the code, and then sort all the intersection points in XYZ coordinate order to get the intersection coordinate matrix ,in, is the total number of intersections of the line and all planes.

[0076] Step 3.2, calculate the length of the grid line segment where the beam and the field of view intersect. intersection, there is Line segment, The length of the line segment d u The calculation is as follows:

[0077] (8)

[0078] Step 3.3, calculate the grid number where the beam and the field of view intersect. The midpoint V coordinate of the line segment (V x ,V y ,V z )for:

[0079]

[0080]

[0081]

[0082] Step 3.4, according to formula (3) and the midpoint Coordinates can be obtained The grid number where the line segment is located .

[0083] Step 4, calculate the weight coefficient matrix and the integrated concentration vector. Specifically include:

[0084] Store the length of the intersecting line segments into the weight coefficient matrix according to the number The corresponding position of The gas concentration of all grids in the field of view is represented by a concentration vector of length NMK. Indicates that the beam The corresponding path-integrated concentration is , then the following relationship exists:

[0085] (9)

[0086] in, is the length of the line segment of beam i in the jth grid, is the average gas concentration in the jth grid.

[0087] Formula (9) can be expressed as:

[0088] (10)

[0089] Use symbols separately , and Representing the matrix and vector of formula (10), formula (10) can be written as:

[0090] (11)

[0091] in, is the integrated concentration vector, is the weight coefficient matrix, is the concentration vector.

[0092] Step 5: Use the SART algorithm to reconstruct the three-dimensional gas concentration distribution. The specific method is as follows:

[0093] Step 5.1, first transform the concentration vector Initialize to zero vector.

[0094] Step 5.2: According to the weight coefficient matrix and the integral concentration vector, the concentration vector Perform iterative calculations as follows:

[0095] (12)

[0096] Here, k is the number of iterations. is the relaxation factor, which is used to control the iteration speed.

[0097] Step 5.3, determine whether to stop iteration based on the iteration error. Select a threshold As the judgment condition for stopping iteration, when the root mean square error of the concentration distribution of two adjacent iterations is less than the threshold, the iteration is stopped, that is:

[0098] (13)

[0099] Step 5.4, reconstruct the three-dimensional gas concentration distribution. According to the numbering method, the obtained concentration vector Restored to a three-dimensional spatial concentration distribution and stored in a size of The three-dimensional array of the gas cloud is reconstructed according to the field of view resolution. The host computer displays the internal concentration distribution of the gas cloud in the form of three-dimensional point cloud, horizontal section and vertical section. Furthermore, based on the reconstructed three-dimensional gas cloud distribution, the leak source can be accurately traced, the leakage rate can be estimated and the diffusion area can be detected.

[0100] See also Figure 4The present invention also provides a three-dimensional tomography system for detecting the spatial distribution of gas concentration based on a drone. The system is mounted on a drone 2 and is used to implement the three-dimensional tomography method for detecting the spatial distribution of gas concentration based on a drone as described above. The system includes a GNSS positioning module 3, a ranging module 10 and a gas cloud scanning system. The drone 2 is also provided with a drone attitude control module 1.

[0101] The UAV attitude control module 1 is used to control the heading angle, pitch angle and roll angle of the UAV.

[0102] Specifically, the attitude control module controls the flight attitude of the UAV, adjusts the center of the scanning field of view to the center of the gas cloud, and the heading angle, pitch angle, and roll angle are used to calculate the actual angle of the laser emission.

[0103] The GNSS positioning module 3 measures the longitude and latitude of the location of the UAV to obtain the location information of the UAV. The measurement data of the GNSS positioning module 3 is used to establish the spatial rectangular coordinate system in the above-mentioned three-dimensional tomography method.

[0104] Preferably, the system uses a high-precision GNSS positioning module. The module can support multiple satellite systems such as GPS, BDS, GLONASS, etc. The single-point GNSS horizontal positioning error is less than 1.5 meters. Combined with RTK (Real-time kinematic) technology, the positioning error can be controlled at the centimeter level. The module sends the positioning information to UAV 2 through the serial port.

[0105] It should be noted that the coordinates directly returned by the GNSS positioning module 3 are longitude and latitude. In order to facilitate subsequent data processing, it is necessary to use projection transformation to convert them into coordinates in a spatial rectangular coordinate system.

[0106] The spatial rectangular coordinate system is established as follows: select a point on the ground near the target gas cloud as the coordinate origin, measure its longitude and latitude, use the extension directions of the longitude and latitude as the X-axis and Y-axis respectively, and the direction perpendicular to the ground as the Z-axis to establish a new spatial rectangular coordinate system.

[0107] When the drone 1 is flying, the GNSS positioning module 3 is used to measure the longitude and latitude coordinates, calculate the relative longitude and latitude between the coordinates and the origin, and then calculate the X and Y coordinates in the spatial rectangular coordinate system based on the obtained relative longitude and latitude.

[0108] The distance measurement module measures the height information of the UAV 1 above the ground, and the measurement data of the distance measurement module is used to establish the spatial rectangular coordinate system in the above-mentioned three-dimensional tomography method.

[0109] Preferably, the distance measurement module 10 uses laser distance measurement to accurately measure distance information.

[0110] Specifically, the distance measurement module 10 measures the distance vertically to the ground to obtain the height of the UAV 1 relative to the ground. The height data and the plane coordinates obtained by the GNSS positioning module 3 constitute the spatial coordinates of the UAV, which are used for the positioning of the UAV and the laser optical path positioning.

[0111] The gas cloud scanning system emits a scanning infrared laser and receives the echo signal after gas absorption, and measures the gas cloud integral concentration image. The gas cloud integral concentration image is used to calculate the grid number of the intersection of the light beam and the field of view, the length of the intersection line segment, the weight coefficient matrix and the integral concentration vector in the above-mentioned three-dimensional tomography method.

[0112] The gas cloud scanning system includes a main control board 5, a laser 7, a detector 8, a signal generator 4, an acquisition card 6, a receiving optical system 11 and a galvanometer 9. The main control board 5 is electrically connected to the drone 1, the signal generator 4, the acquisition card 6, the ranging module 10 and the galvanometer 9 respectively, the signal generator 4 is electrically connected to the laser 7, and the acquisition card 6 is electrically connected to the detector 8.

[0113] The signal generator 4 is used to generate a modulation signal to drive the laser 7 to emit scanning laser.

[0114] Specifically, according to the principle of wavelength modulation spectroscopy technology, the signal generator 4 generates a low-frequency sawtooth wave and a high-frequency sine wave superposition signal as a driving signal for the laser 7 to control the laser 7 to output a scanning laser. In addition, the signal generator 4 outputs a trigger signal with the same frequency as the low-frequency sawtooth wave as a sampling clock for the acquisition card 6.

[0115] Preferably, the signal generator 4 uses a DAC with a high signal-to-noise ratio and a 16-bit high-precision, and the main control board 5 communicates with the DAC via the SPI protocol to control the DAC to output a low-frequency sawtooth wave and a high-frequency sine wave superimposed signal.

[0116] According to the principle of wavelength modulation spectroscopy technology and the requirements of imaging time and image spatial resolution, the frequencies of low-frequency sawtooth wave and high-frequency sine wave can be selected as needed. The typical waveform frequencies can be selected as: low-frequency sawtooth wave 50Hz and high-frequency sine wave 10kHz.

[0117] The laser 7 is used to emit laser light for scanning the gas cloud.

[0118] Specifically, the laser 7 is a distributed feedback laser that can emit near-infrared continuous wave laser. The center wavelength of the laser is 1653nm, and the output laser wavelength range is 1652nm~1656nm. The laser uses the modulation signal output by the signal generator as the driving signal, and its output wavelength and driving current are approximately linearly related.

[0119] The strongest absorption peak of methane gas in the near-infrared band is 1653.7 nm, and the tuning band of the laser 7 includes this absorption peak. Therefore, when the gas cloud to be measured is methane, the above laser 7 can be used.

[0120] The acquisition card 6 is used to sample and demodulate the output signal of the detector 8 .

[0121] Preferably, the acquisition card uses an FPGA chip of model EP4CE10F17C8 as the control core, uses a 14-bit high-precision ADC to acquire signals, and uses the SPI communication protocol and the FPGA chip to transmit sampled data. The acquisition card has a DDR4 memory with 2GB of storage space for storing waveforms and demodulated signals acquired by the ADC.

[0122] Specifically, the acquisition card 6 receives the trigger signal of the signal generator 4. When the rising edge of the trigger signal is detected, the acquisition card 6 turns on the ADC to sample the output signal of the detector 8. The sampling period is the same as the period of the low-frequency sawtooth wave, and the typical sampling rate is 100kHz. The waveform data collected by the ADC is stored in the DDR4 memory.

[0123] Furthermore, in the FPGA chip, a digital orthogonal phase-locked amplifier is generated using software to demodulate the collected waveform to obtain the first harmonic 1f signal and the second harmonic 2f signal, respectively, and the first harmonic and second harmonic signals are sent to the main control board 5 through the serial port.

[0124] The detector 8 is used to receive the echo signal gathered by the optical system (11), and convert the optical signal into an electrical signal and output it to the acquisition card 6.

[0125] Preferably, the detector 8 uses an InGaAs single-point photodetector. The detector 8 has a response wavelength range of 800nm-1700nm and a peak response wavelength of 1550nm, which meets the requirements for methane gas detection. The photosensitive area has a diameter of 2mm and an equivalent noise power of 22 , meeting the needs of gas cloud telemetry. The operating temperature range is 10℃-50℃ and can be used at room temperature.

[0126] The galvanometer 9 is used to change the laser emission direction to scan the gas cloud.

[0127] Specifically, a dual-axis galvanometer system is selected, and laser scanning can be achieved in both the X-axis and Y-axis directions through two mirror reflections.

[0128] Preferably, the galvanometer 9 is a well-known technology, and is composed of a motor, a lens, and a motor drive board. The galvanometer 9 controls the rotation of the motor through current, thereby driving the lens to deflect the laser beam. The motor resolution is 15μRad, the maximum scanning angle is ±12.5°, and the maximum zero drift of the angle does not exceed 10μRad / °C. The lens can receive a maximum beam diameter of 5mm, and the mirror surface uses a gold coating material, which can reflect lasers in the 800nm~20um band, meeting the requirements of the scanning band. The motor drive board is connected to the main control board through the serial port, and can output the motor drive current and transmit the galvanometer scanning angle data.

[0129] The optical system 11 is used to receive the echo signal of the scanning laser and focus the reflected laser onto the detector 8 .

[0130] Specifically, the receiving optical system 11 uses a Fresnel condenser lens. Compared with ordinary lenses, the Fresnel condenser lens has a larger numerical aperture, which can improve the reception efficiency of the echo signal. At the same time, the large numerical aperture makes the Fresnel lens have a larger field of view, which increases the field of view range of the gas cloud scanning.

[0131] Preferably, the Fresnel lens is made of acrylic material with a focal length of 76 mm, which ensures the miniaturization and lightness of the system and is easy to be carried on the drone 2.

[0132] The main control board 5 is used to control the drone and each submodule, receive data returned by each module, and complete subsequent data analysis and processing tasks.

[0133] Preferably, the main control board 5 uses a Raspberry Pi 5 development board. The development board CPU is a quad-core ARM Cortex-A76 with 8GB of memory and a 32GB SD memory card. It has a large number of communication interfaces, including four USB interfaces, an HDMI video display output interface, a MIPI CSI camera interface, a Gigabit Ethernet port, Bluetooth, a serial port, and a general IO port. The Raspberry Pi 5 is powered by 5V and has a size of 85mm×56mm, which can realize the miniaturization of the device and meet the needs of drone detection.

[0134] Raspberry Pi 5 uses a dedicated Linux operating system, Raspberry Pi OS system. The system has good compatibility with the hardware resources on the board, and can use the system's built-in function library to call serial ports, network ports and other interfaces, making it easy to develop host computer programs and control drone platforms and other sub-modules.

[0135] Specifically, the host computer program of the main control board 5 is used to control the modules of the drone and the gas-cloud imaging system, and analyze and process the received data. The host computer controls the drone to fly according to the preset trajectory, and receives the attitude angle data and GNSS positioning data from the drone. The host computer calls the ranging module 10 to measure the height of the drone above the ground. The host computer sends sawtooth wave and sine wave frequencies to the signal generator according to the set parameters to generate the laser drive signal. The host computer initializes the galvanometer and sends the scanning angle range and scanning speed to the galvanometer 9. After the galvanometer 9 starts scanning, when the rising edge of the trigger signal is detected, the angle data is returned to the main control board through the network port. The host computer turns on the acquisition card, and the acquisition card 6 detects the rising edge of the trigger signal and starts working. The main control board 5 receives the first harmonic 1f and second harmonic 2f data returned by the acquisition card 6.

[0136] Furthermore, the host computer receives and processes the data sent by each module. The 1f signal is used to normalize the 2f signal to obtain the normalized 2f curve. According to the peak value of the 2f curve and the laboratory calibration result, the actual gas path integral concentration is inverted. The integral concentration is matched one-to-one with the X-axis and Y-axis angle data returned by the galvanometer to reconstruct a two-dimensional integral concentration image. The image is median filtered to remove noise.

[0137] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle, characterized in that: include: Step 1, establish a spatial rectangular coordinate system and convert the longitude and latitude coordinates into XYZ coordinates; Step 2, dividing the field of view into grids and numbering the grids; Step 3, calculating the grid number and the length of the intersection line segment where the light beam intersects the field of view; Step 4, calculating the weight coefficient matrix and the integrated concentration vector; Step 5: Use the SART algorithm to reconstruct the three-dimensional gas concentration distribution.

2. The three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle according to claim 1, characterized in that: The step 1 specifically includes: Step 1.1, select a point on the ground near the gas cloud As the origin, and are the latitude and longitude of the point respectively, and the location of the drone is recorded as ,in, and are the latitude and longitude of the drone, is the height above the ground measured by the ranging module; Step 1.2, establish a spatial rectangular coordinate system with the longitude and latitude directions as the X-axis and Y-axis respectively, and the vertical upward direction as the Z-axis; Step 1.3, calculate the coordinates of the drone in the rectangular coordinate system of the space: Latitude difference between the drone and the origin , longitude difference , using local tangent plane projection, we can get the UAV Coordinate x A and Coordinate y A : Where R is the radius of the earth; According to the return value of the ranging module, the z coordinate of the drone is obtained A : 。 3. The three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle according to claim 1, characterized in that: The step 2 specifically includes: Step 2.1, select the center of the field of view F according to the location of the gas cloud. The coordinates of F are , the lengths of the field of view in the three directions of XYZ are W, L and H respectively, the number of grids divided into the field of view in the three directions of XYZ are N, M and K respectively, and the total number of grids in the field of view is NMK; The spatial resolution of the field of view in the three directions of XYZ is △x, △y, △z respectively. According to the size of the field of view and the number of grid divisions, we get: , , ; Step 2.2, grid numbering, the numbering method is as follows: start numbering from point S, the position closest to the origin of the coordinate system, divide the field of view into 1-K layers according to the Z-axis coordinate, and number them from low to high layers. In each layer, it is divided into 1-M rows along the Y-axis direction, and numbered from low row numbers to high row numbers. In each row, it is divided into 1-N columns along the X-axis direction, and numbered from low column numbers to high column numbers. Step 2.3, conversion of coordinates and grid numbers. According to the coordinates of point F at the center of the field of view and the size of the field of view, the coordinates of point S are obtained as follows: ， ， In the formula, x S is the x coordinate of point S, y S is the y coordinate of point S, z S is the z coordinate of point S; For any point in the field of view , its coordinates relative to point S are: ， ， In the formula, , and They are the XYZ coordinates of point P relative to point S; Get the grid number where point P is located The calculation is as follows: Among them, "[]" means rounding down. .

4. The three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle according to claim 3, characterized in that: The step 3 specifically includes: Step 3.1, calculate the intersection coordinates and intersection matrix according to the beam equation and the field of view plane equation; the intersection coordinates are obtained by the line equation and the plane equation, and the plane equations are as follows: A plane perpendicular to the X axis: ; A plane perpendicular to the Y axis: ; A plane perpendicular to the Z axis: ; According to the pitch angle and heading angle of the drone and the angle data returned by the galvanometer, the beam is obtained. Azimuth and elevation , , where T represents the number of effective beams that intersect the field of view; the position of the drone is , and then get the beam The parametric equation of the straight line is: Substitute the plane equation into the line parametric equation to obtain the parameter t, and then obtain the coordinates of the intersection point; Store all intersection coordinates into the intersection matrix ; Remove the matrix Repeat the intersection points in the code, and then sort all the intersection points in XYZ coordinate order to get the intersection coordinate matrix ,in, is the total number of intersections between the line and all planes; Step 3.2, calculate the length of the grid line segment where the beam and the field of view intersect. intersection, there is Line segment, The length of the line segment d u The calculation is as follows: Step 3.3, calculate the grid number where the beam and the field of view intersect, The midpoint V coordinate of the line segment (V x ,V y ,V z )for: Step 3.4, according to the grid number where point P is located Calculation method and midpoint Coordinates, get The grid number where the line segment is located .

5. The three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle according to claim 1, characterized in that: The step 4 specifically includes: Calculate the weight coefficient matrix and the integral concentration vector, and store the length of the intersecting line segments into the weight coefficient matrix according to the number The corresponding position of ; The gas concentration of all grids in the field of view is represented by a concentration vector of length NMK Indicates that the beam The corresponding path-integrated concentration is , then the following relationship exists: In the formula, is the length of the line segment of beam i in the jth grid, is the average gas concentration in the jth grid; Will Transformed into matrix multiplication form, expressed as: Use symbols separately , and Representing the matrix and vector of the above equation, the above equation can be written as: in, is the integrated concentration vector, is the weight coefficient matrix, is the concentration vector.

6. The three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle according to claim 1, characterized in that: The step 5 specifically includes: Step 5.1, first transform the concentration vector Initialize to zero vector; Step 5.2: According to the weight coefficient matrix and the integral concentration vector, the concentration vector Perform iterative calculations as follows: Where p is the number of iterations, is the relaxation factor, used to control the iteration speed; Step 5.3: Determine whether to stop iteration based on the iteration error and select a threshold As the judgment condition for stopping iteration, when the root mean square error of the concentration distribution of two adjacent iterations is less than the threshold, the iteration is stopped, that is: Step 5.4, reconstruct the three-dimensional concentration distribution of the gas, and convert the obtained concentration vector into Restored to a three-dimensional spatial concentration distribution and stored in a size of The host computer displays the internal concentration distribution of the gas cloud in the form of three-dimensional point cloud, horizontal section and vertical section.

7. A three-dimensional tomography system for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle, used to implement a three-dimensional tomography method for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle as claimed in any one of claims 1 to 6, characterized in that: It includes a GNSS positioning module, a ranging module and a gas-cloud scanning system. The GNSS positioning module measures the longitude and latitude coordinates of the location of the UAV, and the ranging module measures the height information of the UAV above the ground. The measurement data of the GNSS positioning module and the ranging module are used to establish a spatial rectangular coordinate system. The gas-cloud scanning system measures the gas-cloud integrated concentration image, and the gas-cloud integrated concentration image is used to calculate the grid number of the intersection of the light beam and the field of view, the length of the intersection line segment, the weight coefficient matrix and the integrated concentration vector.

8. The three-dimensional tomography system for detecting the spatial distribution of gas concentration based on an unmanned aerial vehicle according to claim 7, characterized in that: The gas cloud scanning system includes a main control board, a laser, a detector, a signal generator, an acquisition card, a receiving optical system and a galvanometer. The main control board is electrically connected to the signal generator, the acquisition card, the ranging module and the galvanometer respectively, the signal generator is electrically connected to the laser, and the acquisition card is electrically connected to the detector; The signal generator is used to generate a modulation signal to drive the laser to emit a scanning laser; The laser is used to emit laser light for scanning the gas cloud; The acquisition card is used to sample and demodulate the detector output signal; The detector is used to receive the echo signal gathered by the optical system, convert the optical signal into an electrical signal and output it to the acquisition card; The galvanometer is used to change the laser emission direction to scan the gas cloud; The optical system is used to receive the echo signal of the scanning laser and focus the reflected laser onto the detector.

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