UAV methane source tracing monitoring model inversion method, system and storage medium

Abstract

The present invention discloses a method, system and storage medium for inversion of a UAV methane source tracing monitoring model, and the method includes: S1, demarcating a box-type monitoring space in a study area based on a research point source or an unorganized surface source; S2, hierarchically planning a UAV planning route in the box-type monitoring space in the study area, and starting the UAV to perform the UAV detection task; S3, constructing a box-type monitoring simulation space in the study area in a geographic space model, extracting longitude and latitude, and altitude information as location information, and extracting methane concentration data, air density data, and wind speed data as three attributes of a midpoint in the box-type monitoring simulation space in the study area; S4, subdividing the box-type monitoring simulation space in the study area into a grid and performing interpolation processing; S5, calculating and inverting a methane inversion calculation model to obtain the methane emission flux of the box-type monitoring space in the study area. The present invention realizes quick and accurate acquisition of the methane emission flux over the mining area through hierarchical methane data monitoring and model data inversion.

Description

Technical Field

[0001] The present invention relates to the field of mining area carbon emission monitoring model inversion, and in particular to an unmanned aerial vehicle methane source tracing monitoring model inversion method, system and storage medium. Background Art

[0002] Under the background of "double carbon", the monitoring of carbon emissions will be one of the challenges faced by coal mining enterprises. Carbon emissions from coal mining enterprises mainly come from methane and carbon dioxide, and the greenhouse effect caused by methane in the past two decades is 84 times that of carbon dioxide. It can be seen that methane monitoring is the top priority of carbon monitoring for coal mining enterprises. Methane itself has a relatively small mass and can easily escape to the top of the mining area, so it is extremely important to monitor methane above the mining area. At present, infrared methane sensors and laser methane sensors are mainly used for methane monitoring in small areas. Infrared methane sensors use the selective absorption of infrared light of a specific wavelength to methane molecules to infer the methane content in the air; laser methane sensors use tunable diode laser absorption spectroscopy (TDLAS) technology, which uses the strong absorption of methane molecules to specific laser wavelengths. The methane concentration can be determined by analyzing the attenuation of the laser passing through methane; compared with infrared sensors, laser methane sensors are more sensitive and can monitor low-concentration methane molecules. The above two methane sensors are currently only used for methane monitoring in small areas and small scopes. A single device cannot be applied to large-scale and large-scale mining areas. At present, satellite remote sensing technology is used for methane monitoring in large-scale mining areas. The methane emissions in the air are detected and inverted through information such as bands. Although this method can be used for continuous monitoring and inversion over a large area, the resolution and accuracy of methane monitoring are low, and data often have missing defects. When monitoring carbon emissions in coal mining enterprises or core key areas, satellite remote sensing technology cannot achieve high accuracy. Moreover, satellite remote sensing technology is inverted through information such as bands, not the direct amount of methane emissions (there will be methane inversion errors, such as multiple inversions or missing data inversions). Satellite remote sensing technology will also be affected by satellite monitoring technology and weather, and cannot accurately monitor methane emissions in key areas. Satellite remote sensing technology is a regional scale, so it is even more impossible to achieve fine monitoring of coal mining enterprises or core key areas, and it is impossible to accurately monitor and evaluate the methane emissions of coal mining enterprises, which is not conducive to the accurate monitoring of effective quantitative data of methane emissions by regulatory authorities. Therefore, there is an urgent need for a precise monitoring and data model inversion technology for methane emission flux in small-scale mining areas, coal mining enterprise areas or core key areas to fill the technical gap in the accurate detection of methane emissions in the carbon emissions of coal mining enterprises. Summary of the invention

[0003] The purpose of the present invention is to solve the technical problems pointed out by the background technology and to provide a method, system and storage medium for inversion of a UAV methane source tracing monitoring model.

[0004] The purpose of the present invention is achieved through the following technical solutions:

[0005] A method for inverting a UAV methane source tracing monitoring model includes a UAV monitoring system, wherein the UAV monitoring system includes a UAV and a positioning communication module and a methane sensor carried on the UAV. The method includes:

[0006] S1. Based on the research point source or unorganized surface source, a box-type monitoring space is delineated in the research area. The drone is started to pre-fly in the box-type monitoring space of the research area and the methane concentration is pre-collected through the methane sensor. A meteorological instrument is deployed in the box-type monitoring space of the research area. When the wind speed monitored by the meteorological instrument is stable in the wind speed monitoring range at time interval t1 and the change of the pre-collected methane concentration does not exceed the threshold B, the drone detection task is executed;

[0007] S2. Plan the UAV route in layers in the box-type monitoring space of the study area, and start the UAV to perform the UAV detection task: the UAV flies in layers in the box-type monitoring space of the study area according to the UAV route, and the positioning communication module obtains the positioning data, the positioning data includes the latitude, longitude and altitude information, and the methane sensor records the methane concentration data associated with the positioning data in real time, and the meteorological instrument obtains the wind speed and direction data and air density data associated with the positioning data in real time, and the wind speed and direction data includes the wind speed data;

[0008] S3. Construct a geographic space model with a three-dimensional geographic coordinate system, build a box-type monitoring simulation space for the study area in the geographic space model, extract longitude, latitude and altitude information as the location information of the midpoint of the box-type monitoring simulation space for the study area, extract methane concentration data, air density data, and wind speed data as the three attributes of the midpoint of the box-type monitoring simulation space for the study area, and the three attributes are respectively methane concentration attribute χ m , air density property ρ m , wind speed attribute u m , m represents the number of the midpoint of the box-type monitoring simulation space in the study area;

[0009] S4. In the geographic space model, the box-type monitoring simulation space of the study area is evenly divided into subdivided grids, and all unit grids in the box-type monitoring simulation space of the study area are interpolated using unit grids containing three attribute data until all unit grids in the box-type monitoring simulation space of the study area contain the methane concentration attribute χ n , air density property ρ n , wind speed attribute u n Three attribute data, n represents the number of cells in the box-type monitoring simulation space of the study area;

[0010] S5. Construct a methane inversion calculation model. The methane inversion calculation model is used to invert the methane vertical convection flux E according to the following formulas: CHV and methane horizontal convection flux E CHH :

[0011]

[0012] Where A represents the area covered by the box-type monitoring simulation space in the study area, ω represents the average flow velocity from the bottom to the top of the box-type monitoring simulation space in the study area, and M R is the molar mass ratio of methane to air, represents the average value of air density attributes in the box-type monitoring simulation space of the study area, represents the average value of methane concentration attribute in the box-type monitoring simulation space of the study area, and N represents the total number of cells in the box-type monitoring simulation space of the study area;

[0013] The methane inversion calculation model obtains the methane emission flux E in the box-shaped monitoring space of the study area according to the following formula: CH :E CH =E CHV +E CHH .

[0014] In order to better implement the present invention, in step S1, the box-shaped monitoring space in the study area is demarcated according to the unorganized surface source; and the following method is also included:

[0015] S6. Set a number of background concentration sampling points outside the box-shaped monitoring space area of ​​the study area according to the stratification of the planned UAV route, and start the UAV to hover and measure at the background concentration sampling points respectively;

[0016] S7. The methane inversion calculation model has a background concentration methane flux calculation module. The background concentration methane flux calculation module calculates the background concentration methane flux φ by inversion according to the following method. CH :

[0017] Where λ represents the conversion factor, P represents the total number of layers of the planned route of the drone, and X p represents the average methane concentration of all the background concentration sampling points corresponding to the pth layer of the planned UAV route, Z p represents the height of the pth layer of the planned UAV route, W represents the short side length of the box-shaped monitoring space coverage area of ​​the study area, L represents the long side length of the box-shaped monitoring space coverage area of ​​the study area, and U Lp It represents the wind speed obtained by the meteorological instrument when monitoring the background concentration sampling point, which is decomposed into the wind speed of the long side of the box-shaped monitoring space coverage area in the study area, U WPIt indicates the wind speed obtained by the meteorological instrument when monitoring the background concentration sampling point, which is decomposed into the wind speed of the short side of the box-shaped monitoring space coverage area in the study area;

[0018] S8, the methane emission flux E calculated in step S5 CH Subtract the background concentration methane flux φ CH , and obtain the final methane emission flux of the box-type monitoring space in the study area where unorganized surface sources are demarcated.

[0019] Preferably, in step S6, the hovering measurement of the background concentration sampling point is performed after the wind speed monitored by the meteorological instrument is stably within the wind speed monitoring range during time interval t1 and the pre-collected methane concentration change does not exceed threshold B, and before executing the UAV detection task.

[0020] Preferably, the drone route planning in the box-shaped monitoring space of the study area selects one of the following two methods:

[0021] Method A: The total number of layers of the planned UAV routes in the box-shaped monitoring space of the study area is P layers. The boundary contour of each layer of routes is a rectangle or square, and the boundary contour takes the east direction, the south direction, the west direction, and the north direction as the boundary contour line. Each layer takes the boundary contour line as the route;

[0022] Method B: The total number of layers of UAV planning routes in the box-shaped monitoring space of the study area is P layers. The boundary contour of each layer of routes is a rectangle or a square, and the boundary contour uses the east, south, west, and north directions as boundary contour lines. A number of parallel routes are evenly divided within the boundary contour of each layer of routes, and each layer of routes is connected from the starting point to the end point to form a continuous route for each layer.

[0023] Preferably, among all background concentration sampling points, those located outside the box-type monitoring space area of ​​the study area and with a horizontal distance of more than 3 km account for more than one-third, of which those located within 180 degrees upwind of the box-type monitoring space area of ​​the study area account for more than 50%.

[0024] Preferably, in step S4, the interpolation process adopts the nearest neighbor pixel interpolation method, which is as follows:

[0025] Locate the unit grid that does not contain the methane concentration attribute, and use the existing methane concentration attributes of the adjacent unit grids for interpolation processing until all unit grids contain the methane concentration attribute; locate the unit grid that does not contain the air density attribute, and use the existing air density attributes of the adjacent unit grids for interpolation processing until all unit grids contain the air density attribute; locate the unit grid that does not contain the wind speed attribute, and use the existing wind speed attributes of the adjacent unit grids for interpolation processing until all unit grids contain the wind speed attribute.

[0026] Preferably, the wind speed monitoring range is the average wind speed range of 0 to 7 m / s, and the threshold B is 5%; the selection range of the time interval t1 is 5 to 10 minutes, or the time interval t1 is selected as the estimated duration of executing the drone detection task.

[0027] As a preferred embodiment, when the study area is a research point source, the box-shaped monitoring space of the study area is delineated according to the research point source. The box-shaped monitoring space of the study area delineates a square ground monitoring range A with the research point source as the center, and then the ground monitoring range A is vertically extended to the ground by a height h1 to form the box-shaped monitoring space of the study area. Then, the drone planning route of the research point source is planned according to method A.

[0028] When the study area is an unorganized surface source, the box-shaped monitoring space is delineated according to the unorganized surface source. The box-shaped monitoring space in the study area is delineated according to the unorganized surface source, which surrounds the unorganized surface source and extends outward by K1, and a rectangular ground monitoring range B is delineated. The ground monitoring range B is then extended vertically to the ground by a height h1 to form the box-shaped monitoring space in the study area. The UAV planning route for the unorganized surface source is then planned according to method B.

[0029] A UAV methane source tracing monitoring model inversion system includes an UAV monitoring system, a meteorological instrument, a study area box-type monitoring space demarcation and route planning module, a geographic space model and a methane inversion calculation model. The UAV monitoring system includes a UAV and a positioning communication module and a methane sensor carried by the UAV; the study area box-type monitoring space demarcation and route planning module is used to demarcate the study area box-type monitoring space based on the research point source or unorganized surface source and plan the UAV planning route data, the study area box-type monitoring space demarcation and route planning module transmits the UAV planning route data to the UAV monitoring system, the UAV in the UAV monitoring system flies in the study area box-type monitoring space in layers in turn according to the UAV planning route, the positioning communication module obtains positioning data, and the positioning data Including longitude, latitude and altitude information, at the same time, the methane sensor records the methane concentration data associated with the positioning data in real time, and the meteorological instrument obtains the wind speed and direction data and air density data associated with the positioning data in real time, and the wind speed and direction data include wind speed data; the geographic space model has a built-in three-dimensional geographic coordinate system and constructs a box-type monitoring simulation space of the study area in the geographic space model. The geographic space model is used to associate and collect positioning data, methane concentration data, wind speed and direction data and air density data. The geographic space model extracts longitude, latitude and altitude information as the position information of the midpoint of the box-type monitoring simulation space of the study area, extracts methane concentration data, air density data and wind speed data as three attributes of the midpoint of the box-type monitoring simulation space of the study area, and the three attributes are respectively methane concentration attribute χ m , air density property ρ m , wind speed attribute u m, m represents the number of the midpoint in the box-type monitoring simulation space of the study area; the geographic space model has a grid division and attribute calculation module and an interpolation processing module, the grid division and attribute calculation module is used to subdivide the box-type monitoring simulation space of the study area into uniform grids; the interpolation processing module uses the unit grid containing the three attribute data to interpolate all the unit grids in the box-type monitoring simulation space of the study area until all the unit grids in the box-type monitoring simulation space of the study area contain the methane concentration attribute x n , air density property ρ n , wind speed attribute u n Three attribute data, n represents the number of cells in the box-type monitoring simulation space of the study area; the methane inversion calculation model is inverted according to the following formula to obtain the methane vertical convection flux E CHV and methane horizontal convection flux E CHH :

[0030]

[0031] Where A represents the area covered by the box-type monitoring simulation space in the study area, ω represents the average flow velocity from the bottom to the top of the box-type monitoring simulation space in the study area, and M R is the molar mass ratio of methane to air, represents the average value of air density attributes in the box-type monitoring simulation space of the study area, represents the average value of methane concentration attribute in the box-type monitoring simulation space of the study area, and N represents the total number of cells in the box-type monitoring simulation space of the study area; the methane inversion calculation model obtains the methane emission flux E in the box-type monitoring space of the study area according to the following formula: CH :E CH =E CHV +E CHH .

[0032] A storage medium stores a computer program, which, when executed by a processor, implements the steps of the unmanned aerial vehicle methane source tracing monitoring model inversion method of the present invention.

[0033] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0034] (1) The present invention is mainly used for precise monitoring of methane emission flux and model data inversion in small-scale mining areas, coal mining enterprise areas or core key areas. With small-scale mining areas, coal mining enterprise areas or core key areas as research areas, through stratified methane data monitoring and model data inversion, high-precision methane emission flux can be quickly obtained, and the methane emission flux above the mining area can be obtained quickly and accurately, filling the technical gap of precise detection of methane emissions in carbon emissions of coal mining enterprises, and facilitating regulatory authorities to conduct precise monitoring and evaluation of methane emissions.

[0035] (2) The present invention also obtains the background concentration methane flux through rapid method model inversion, evaluates the background methane concentration, and finally calculates the final methane emission flux of the box-type monitoring space in the study area to facilitate the calculation of the new emissions in the study area. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 This is a method flow chart of the inversion method of the UAV methane source tracing monitoring model of the present invention;

[0037] Figure 2 A simplified structural diagram of a drone-mounted device of a drone monitoring system in an embodiment;

[0038] Figure 3 It is a schematic diagram of the principle of planning a route with the emission point source of interest as the research point source in the embodiment;

[0039] Figure 4 This is a schematic diagram of the principle of single-layer planning route using an open-pit coal mine as a research object for unorganized surface sources in the embodiment;

[0040] Figure 5 This is a software output interface diagram of the results obtained by calculation according to the invention method using the exhaust air well as the research point source in the embodiment;

[0041] Figure 6 This is a software output interface diagram of the results obtained by calculating according to the invention method using an open-pit coal mine as a research object in the embodiment;

[0042] Figure 7 This is a principle structure block diagram of the UAV methane source tracing monitoring model inversion system of the present invention.

[0043] The names corresponding to the reference numerals in the accompanying drawings are:

[0044] 1-UAV, 2-positioning and communication module, 3-methane sensor, 4-emission point source, 5-point source route planning, 6-point source monitoring range, 7-unorganized surface source, 8-unorganized surface source route planning, 9-unorganized surface source monitoring range. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below in conjunction with embodiments:

[0046] Embodiment 1

[0047] like Figure 1 As shown, a UAV methane source tracing monitoring model inversion method includes a UAV monitoring system, such as Figure 2As shown, the UAV monitoring system includes a UAV 1 (the UAV 1 of this embodiment uses DJI Jingwei 300 / 350RTK, with a maximum wind speed of 12m / s, and is equipped with a positioning communication module 2 and a methane sensor 3, and can also carry a fixed frame and a meteorological instrument. The meteorological instrument of this embodiment can be carried on the UAV 1) and a positioning communication module 2 carried on the UAV 1 (the UAV route planning, geographic space model, etc. of this embodiment can be placed in a ground computer or server, and data is exchanged with the UAV monitoring system through the positioning communication module 2. The positioning communication module 2 has a built-in GPS or Beidou positioning module to achieve accurate positioning of the UAV 1), a methane sensor 3 (the methane sensor 3 of this embodiment can use an inhalation methane sensor to inhale methane near the UAV and measure it. The concentration unit is ppm. The tunable diode laser absorption spectroscopy technology is used. The measurement range is 0-10000ppm and the resolution is 1ppm). The method includes:

[0048] S1. Based on the research point source (mainly the main emission point source in the underground mining area, such as the mining shaft, etc.), a box-type monitoring space is delineated in the research area (the box-type monitoring space in the research area is delineated according to the research point source. The box-type monitoring space in the research area is delineated with the research point source as the center to set a square ground monitoring range A, and then the ground monitoring range A is vertically extended to the ground by a height h1 to form the box-type monitoring space in the research area). The drone 1 is started to pre-fly in the box-type monitoring space in the research area and the methane concentration is pre-collected through the methane sensor 3. A meteorological instrument is arranged in the box-type monitoring space in the research area (the meteorological instrument of this embodiment can be carried on the drone 1, and the meteorological data at the location of the drone can be monitored. The meteorological instrument can obtain the wind speed and direction data and air density data of the area near the drone, such as temperature. When the wind speed monitored by the meteorological instrument is stable in the wind speed monitoring range at time interval t1 (at this time, the meteorological instrument can be arranged at the ground center of the box-type monitoring space in the study area, or it can be carried on a drone and hovered in the central area of ​​the box-type monitoring space in the study area for monitoring) and the change of the pre-collected methane concentration does not exceed the threshold value B, the drone detection task is performed (setting such conditions is not only to monitor the methane of the research point source well, but also to facilitate the subsequent data model inversion. At this time, methane monitoring depends on the horizontal wind and vertical wind flux changes. The subsequent methane vertical convection and methane horizontal convection data inversion is more scientific and reasonable, and the data is more accurate, which is conducive to finally obtaining the most accurate methane data that fits the actual situation). In some embodiments, the wind speed monitoring range is an average wind speed in the range of 0 to 7 m / s, and the threshold B is 5%; the selection range of the time interval t1 is 5 to 10 minutes, or the time interval t1 is selected as the estimated time required to perform the drone detection task.

[0049] like Figure 3As shown, in this embodiment, the exhaust air well is used as the research point source. Each layer of drone routes is aligned with the four directions of east, west, south and north. The exhaust air well is located in the center of the square area. The route area is set to 300m×300m. At 25m--200m above the emission point source, the same route planning is carried out every 25 meters. There are 12 flight altitudes in total.

[0050] S2. Plan the UAV route in layers in the box-type monitoring space of the study area, and start UAV 1 to perform the UAV detection task: UAV 1 flies in layers in the box-type monitoring space of the study area according to the UAV planned route, and the positioning communication module 2 obtains the positioning data, which includes latitude, longitude and altitude information. At the same time, the methane sensor 3 records the methane concentration data associated with the positioning data in real time, and the meteorological instrument obtains the wind speed and direction data and air density data associated with the positioning data in real time, and the wind speed and direction data include wind speed data. The air density in the meteorological instrument can be calculated according to the following formula:

[0051] where ρ air is the density of air carrying water vapor, F is the atmospheric pressure monitored by the meteorological instrument, T is the thermodynamic temperature monitored by the meteorological instrument, ψ is the relative humidity of air monitored by the meteorological instrument (%), and Q is the saturated water vapor pressure (kPa).

[0052] The UAV route planning for the box-shaped monitoring space in the study area chooses one of the following two methods:

[0053] Method A: The total number of layers of UAV planning routes in the box-shaped monitoring space of the study area is P layers. The boundary contour of each layer of routes is a rectangle or square, and the boundary contour is in the east, south, west, and north directions as the boundary contour lines. Each layer uses the boundary contour line as the route.

[0054] Method B: The total number of layers of UAV route planning in the box-shaped monitoring space of the study area is P layers. The boundary contour of each layer of routes is a rectangle or square, and the boundary contour uses the east, south, west and north directions as boundary contour lines. Several parallel routes are evenly divided within the boundary contour of each layer of routes, and each layer of routes is connected from the starting point to the end point to form a continuous route for each layer.

[0055] This embodiment is implemented with the study area as the study point source. The box-shaped monitoring space of the study area is demarcated according to the study point source. The box-shaped monitoring space of the study area is demarcated with the study point source as the center to form a square ground monitoring range A (such as Figure 3As shown, the ground monitoring range A is the point source monitoring range 6 on the ground. The point source monitoring range 6 is demarcated with the emission point source 4 as the center. The point source monitoring range 6 is a square [area] divided by the east, south, west and north directions as boundary contour lines. Then, the ground monitoring range A is vertically extended to the ground by a height h1 to form a box-shaped monitoring space in the study area. Then, the UAV planning route for the study point source is planned according to method A. The total number of layers of the UAV planning route division of the box-shaped monitoring space in the study area is P layers. The boundary contour of each layer of the route is a rectangle or square, and the boundary contour uses the east, south, west and north directions as boundary contour lines. Each layer uses the boundary contour line as the route. Figure 3 The routes of some sites are listed (each layer uses the boundary contour line as the route, and corresponds to the point source monitoring range 6). In some embodiments, each layer of the route is designed according to a square, with the starting and ending points at the northeast corner of the square. It is required that regardless of the wind direction, the routes are aligned in the four directions of due east, due south, due west, and due north, and the borders around the routes should be kept at a distance of more than 100m from the emission point source (depending on the specific situation of the mining area); on the premise of ensuring safety, the routes of the same area are designed in the same way at multiple layers (more than 10 layers) above the emission point source, and finally a box-shaped area is formed above the emission source, and the emission source should be located at the center of the frame-shaped area.

[0056] S3. Construct a geographic space model with a three-dimensional geographic coordinate system, build a box-type monitoring simulation space for the study area in the geographic space model, extract longitude, latitude and altitude information as the location information of the midpoint of the box-type monitoring simulation space for the study area, extract methane concentration data, air density data, and wind speed data as the three attributes of the midpoint of the box-type monitoring simulation space for the study area, and the three attributes are respectively methane concentration attribute χ m , air density property ρ m , wind speed attribute u m , m represents the number of the midpoint of the box-type monitoring simulation space in the study area.

[0057] S4. In the geographic space model, the box-type monitoring simulation space of the study area is evenly divided into subdivided grids (the subdivided grid size in this embodiment is 1mx1m), and all the unit grids in the box-type monitoring simulation space of the study area are interpolated using the unit grids containing the three attribute data until all the unit grids in the box-type monitoring simulation space of the study area contain the methane concentration attribute x n , air density property ρ n , wind speed attribute u n Three attribute data, n represents the number of cells in the box-type monitoring simulation space of the study area. In some embodiments, the interpolation process adopts the nearest neighbor pixel interpolation method, and the method is as follows:

[0058] Locate the unit grid that does not contain the methane concentration attribute, and use the existing methane concentration attributes of the adjacent unit grids for interpolation processing until all unit grids contain the methane concentration attribute; locate the unit grid that does not contain the air density attribute, and use the existing air density attributes of the adjacent unit grids for interpolation processing until all unit grids contain the air density attribute; locate the unit grid that does not contain the wind speed attribute, and use the existing wind speed attributes of the adjacent unit grids for interpolation processing until all unit grids contain the wind speed attribute.

[0059] S5. Construct a methane inversion calculation model. The methane inversion calculation model is used to invert the methane vertical convection flux E according to the following formulas: CHV and methane horizontal convection flux E CHH :

[0060]

[0061] Where A represents the area covered by the box-type monitoring simulation space in the study area (i.e. Figure 3 Point source monitoring range 6), in an embodiment using the exhaust air well as the research point source, A = 90000m 2 ω represents the average flow velocity from the bottom to the top of the box-type monitoring simulation space in the study area, M R is the molar mass ratio of methane to air (M R =0.55368), represents the average value of air density attributes in the box-type monitoring simulation space of the study area, represents the average value of the methane concentration attribute in the box-type monitoring simulation space of the study area, and N represents the total number of cells in the box-type monitoring simulation space of the study area. In this embodiment, the average flow velocity ω is obtained according to the following method:

[0062]

[0063]

[0064] Where E air_M represents the air circulation in the box-type monitoring space in the study area, E air_H represents the horizontal convection flux of air in the box-type monitoring space of the study area; Δt represents the time interval from the first route to the last route; ΔF represents the change in air pressure from the beginning to the end of the mission in the box-type monitoring simulation space of the study area; F 均 represents the average pressure in the box-type monitoring simulation space of the study area; ΔT represents the temperature change in the box-type monitoring simulation space of the study area; T 均 represents the average temperature in the box-type monitoring simulation space of the study area; ρ p均represents the average air density of the pth layer of the box-type monitoring simulation space in the study area, and P represents the total number of layers of the planned route of the drone. In an embodiment in which the ventilation well is used as the research point source, the average flow velocity ω is calculated to be 1.206 m / s.

[0065] The methane inversion calculation model uses formula F to obtain the methane emission flux E of the box-shaped monitoring space in the study area. CH :E CH =E CHV +E CHH .like Figure 5 As shown, in the embodiment using the exhaust air well as the research point source, the horizontal convection flux of methane E is obtained. CHH is 0.080325 kg / s, and the vertical convection flux of methane is E CHV is 0.132987 kg / s, and the methane emission flux E CH It is 0.213312kg / s.

[0066] In some embodiments, a number of background concentration sampling points are set outside the box-type monitoring space area of ​​the study area according to the stratification of the planned route of the drone, and the drone 1 is started to hover and measure at the background concentration sampling points. Among all the background concentration sampling points, the background concentration sampling points located outside the box-type monitoring space area of ​​the study area and with a horizontal distance of more than 3 km account for more than one-third, and the background concentration sampling points located within 180 degrees upwind of the box-type monitoring space area of ​​the study area account for more than 50%.

[0067] The methane inversion calculation model has a background concentration methane flux calculation module. The background concentration methane flux calculation module calculates the background concentration methane flux φ by inversion according to the following method: CH :

[0068] Where λ represents the conversion factor, P represents the total number of layers of the planned route of the drone, and X p represents the average methane concentration of all the background concentration sampling points corresponding to the pth layer of the planned UAV route, Zp represents the layer height of the pth layer of the planned UAV route, W represents the short side length of the box-shaped monitoring space coverage area of ​​the study area, L represents the long side length of the box-shaped monitoring space coverage area of ​​the study area, and U Lp It represents the wind speed obtained by the meteorological instrument when monitoring the background concentration sampling point, which is decomposed into the wind speed of the long side of the box-shaped monitoring space coverage area in the study area, U WP It indicates the wind speed obtained by the meteorological instrument when monitoring the background concentration sampling point, which is decomposed into the wind speed of the short side of the box-shaped monitoring space coverage area of ​​the study area. Preferably, the hovering measurement of the background concentration sampling point is performed after the wind speed monitored by the meteorological instrument is stably within the wind speed monitoring range during the time interval t1 and the change of the pre-collected methane concentration does not exceed the threshold value B, and before the drone gun measurement task is performed.

[0069] Embodiment 2

[0070] like Figure 1 As shown, a UAV methane source tracing monitoring model inversion method includes a UAV monitoring system, such as Figure 2 As shown, the UAV monitoring system includes a UAV 1 (the UAV 1 of this embodiment uses DJI Jingwei 300 / 350RTK, with a maximum wind speed of 12m / s, and is equipped with a positioning communication module 2 and a methane sensor 3, and can also carry a fixed frame and a meteorological instrument. The meteorological instrument of this embodiment can be carried on the UAV 1) and a positioning communication module 2 carried on the UAV 1 (the UAV route planning, geographic space model, etc. of this embodiment can be placed in a ground computer or server, and data is exchanged with the UAV monitoring system through the positioning communication module 2. The positioning communication module 2 has a built-in GPS or Beidou positioning module to achieve accurate positioning of the UAV 1), a methane sensor 3 (the methane sensor 3 of this embodiment can use an inhalation methane sensor to inhale methane near the UAV and measure it. The concentration unit is ppm. The tunable diode laser absorption spectroscopy technology is used. The measurement range is 0-10000ppm and the resolution is 1ppm). The method includes:

[0071] S1. Based on the unorganized surface source, a box-type monitoring space is delineated in the study area, and the drone 1 is started to pre-fly in the box-type monitoring space of the study area and pre-collect the methane concentration through the methane sensor 3. A meteorological instrument is arranged in the box-type monitoring space of the study area. When the wind speed monitored by the meteorological instrument is stable in the wind speed monitoring range at the time interval t1 and the change of the pre-collected methane concentration does not exceed the threshold value B, the drone detection task is executed. In some embodiments, the wind speed monitoring range is an average wind speed in the range of 0 to 7 m / s (i.e., an average wind speed less than 7 m / s), and the threshold value B is 5%; the selection range of the time interval t1 is 5 to 10 minutes, or the time interval t1 is selected as the estimated time required to execute the drone detection task.

[0072] S1. Based on the unorganized surface sources, a box-type monitoring space is delineated in the study area (the box-type monitoring space in the study area is delineated according to the unorganized surface sources. For example, an open-pit coal mine or a mining area in a certain area is selected as the unorganized surface source. The box-type monitoring space in the study area is surrounded by the unorganized surface source and extends outward by K1 and a rectangular ground monitoring range B is delineated. Then, the ground monitoring range B is vertically extended to the ground by a height h1 to form the box-type monitoring space in the study area). The drone 1 is started to pre-fly in the box-type monitoring space in the study area and the methane concentration is pre-collected through the methane sensor 3. A meteorological instrument is arranged in the box-type monitoring space in the study area (the meteorological instrument in this embodiment The meteorological instrument can be mounted on the drone 1, and can realize the meteorological data monitoring at the location of the drone. The meteorological instrument can obtain the wind speed and direction data and air density data of the area near the drone, such as temperature, humidity, air pressure, wind speed and direction, etc.). When the wind speed monitored by the meteorological instrument is stable in the wind speed monitoring range at the time interval t1 (at this time, the meteorological instrument can be arranged at the ground center of the box-type monitoring space in the study area, or it can be carried on the drone and hovered in the center area of ​​the box-type monitoring space in the study area for monitoring) and the change of the pre-collected methane concentration does not exceed the threshold value B, the drone detection task is executed. In some embodiments, the wind speed monitoring range is an average wind speed in the range of 0 to 7 m / s, and the threshold value B is 5%; the selection range of the time interval t1 is 5 to 10 minutes, or the time interval t1 is selected as the estimated time required to perform the drone detection task.

[0073] S2. Plan the UAV route in layers in the box-shaped monitoring space of the study area, and start UAV 1 to perform the UAV gun test task: UAV 1 flies in layers in the box-shaped monitoring space of the study area according to the UAV planned route. Example: The unorganized surface source takes an open-pit coal mine in Inner Mongolia as the unorganized emission surface source of methane to invert the unorganized surface source methane emission flux. The selected mining area is about 1900m×650m. Figure 4 The route is planned in a planar manner, with a length of 2100m and a width of 850m, and the route spacing is no more than 50m. In the same way, the routes are planned at four heights above the mining area, 50m, 150m, 250m, and 350m.

[0074] The positioning communication module 2 obtains positioning data, which includes latitude, longitude and altitude information. At the same time, the methane sensor 3 records the methane concentration data associated with the positioning data in real time. The meteorological instrument obtains the wind speed and direction data and air density data associated with the positioning data in real time. The wind speed and direction data includes wind speed data. The air density in the meteorological instrument can be calculated according to the following formula:

[0075] where ρ airis the density of air carrying water vapor, F is the atmospheric pressure monitored by the meteorological instrument, T is the thermodynamic temperature monitored by the meteorological instrument, ψ is the relative humidity of air monitored by the meteorological instrument (%), and Q is the saturated water vapor pressure (kPa).

[0076] The UAV route planning for the box-shaped monitoring space in the study area chooses one of the following two methods:

[0077] Method A: The total number of layers of UAV planning routes in the box-shaped monitoring space of the study area is P layers. The boundary contour of each layer of routes is a rectangle or square, and the boundary contour is in the east, south, west, and north directions as the boundary contour lines. Each layer uses the boundary contour line as the route.

[0078] Method B: The total number of layers of UAV route planning in the box-shaped monitoring space of the study area is P layers. The boundary contour of each layer of routes is a rectangle or square, and the boundary contour is in the east, south, west, and north directions as the boundary contour lines. Several parallel routes are evenly divided within the boundary contour of each layer of routes (see Figure 4 The route of one layer of the box-type monitoring space in the study area can be seen in the unorganized surface source route planning 8. The unorganized surface source route planning 8 includes several parallel routes, which are connected end to end in sequence to form the route of this layer). Each layer of routes is connected from the starting point to the end point to form a continuous route for each layer.

[0079] In this example, the study area is taken as an unorganized surface source (such as Figure 4 As shown in the figure, when the unorganized surface source selects an open-pit coal mine as the unorganized surface source 7 for example, the box-shaped monitoring space is delineated according to the unorganized surface source 7. The box-shaped monitoring space in the study area is designed to surround the unorganized surface source 7 and extend outward by K1 (generally not less than 100m) and delineate a rectangular ground monitoring range B (such as Figure 4 As shown, the ground monitoring range B is Figure 4 The unorganized surface source monitoring range in the study area is 9), and then the ground monitoring range B is extended vertically to the ground by a height h1 to form a box-shaped monitoring space in the study area (see Figure 4 , the route of one layer of the box-type monitoring space in the study area can be seen in the unorganized surface source route planning 8), and then the unorganized surface source drone planning route is planned according to method B.

[0080] S3. Construct a geographic space model with a three-dimensional geographic coordinate system, build a box-type monitoring simulation space for the study area in the geographic space model, extract longitude, latitude and altitude information as the location information of the midpoint of the box-type monitoring simulation space for the study area, extract methane concentration data, air density data, and wind speed data as the three attributes of the midpoint of the box-type monitoring simulation space for the study area, and the three attributes are respectively methane concentration attribute χ m , air density property ρ m , wind speed attribute um , m represents the number of the midpoint of the box-type monitoring simulation space in the study area.

[0081] S4. In the geographic space model, the box-type monitoring simulation space of the study area is evenly divided into subdivided grids (the subdivided grid size in this embodiment is 1mx1m), and all the unit grids in the box-type monitoring simulation space of the study area are interpolated using the unit grids containing the three attribute data until all the unit grids in the box-type monitoring simulation space of the study area contain the methane concentration attribute x n , air density property ρ n , wind speed attribute u n Three attribute data, n represents the number of cells in the box-type monitoring simulation space of the study area. In some embodiments, the interpolation process adopts the nearest neighbor pixel interpolation method, and the method is as follows:

[0082] Locate the unit grid that does not contain the methane concentration attribute, and use the existing methane concentration attributes of the adjacent unit grids for interpolation processing until all unit grids contain the methane concentration attribute; locate the unit grid that does not contain the air density attribute, and use the existing air density attributes of the adjacent unit grids for interpolation processing until all unit grids contain the air density attribute; locate the unit grid that does not contain the wind speed attribute, and use the existing wind speed attributes of the adjacent unit grids for interpolation processing until all unit grids contain the wind speed attribute.

[0083] S5. Construct a methane inversion calculation model. The methane inversion calculation model is used to invert the methane vertical convection flux E according to the following formulas: CHV and methane horizontal convection flux E CHH :

[0084]

[0085] Where A represents the area covered by the box-type monitoring simulation space in the study area, ω represents the average flow velocity from the bottom to the top of the box-type monitoring simulation space in the study area, and M R is the molar mass ratio of methane to air, represents the average value of air density attributes in the box-type monitoring simulation space of the study area, represents the average value of the methane concentration attribute in the box-type monitoring simulation space of the study area, and N represents the total number of cells in the box-type monitoring simulation space of the study area. In this embodiment, the average flow velocity ω is obtained according to the following method:

[0086]

[0087] Where E air_M represents the air circulation in the box-type monitoring space in the study area, E air_Hrepresents the horizontal convection flux of air in the box-type monitoring space of the study area; Δt represents the time interval from the first route to the last route; ΔF represents the change in air pressure from the beginning to the end of the mission in the box-type monitoring simulation space of the study area; F 均 represents the average pressure in the box-type monitoring simulation space of the study area; ΔT represents the temperature change in the box-type monitoring simulation space of the study area; T 均 represents the average temperature in the box-type monitoring simulation space of the study area; ρ p均 represents the average air density of the pth layer in the box-type monitoring simulation space of the study area, and P represents the total number of layers of the planned UAV route.

[0088] The methane inversion calculation model obtains the methane emission flux E in the box-shaped monitoring space of the study area according to the following formula: CH :E CH =E CHV +E CHH .

[0089] S6. Set a number of background concentration sampling points outside the box-type monitoring space area of ​​the study area according to the stratification of the planned UAV route, and start the UAV 1 to hover and measure at the background concentration sampling points respectively. Preferably, among all the background concentration sampling points, the background concentration sampling points located outside the box-type monitoring space area of ​​the study area and with a horizontal distance of more than 3 km account for more than one-third, and those within 180 degrees upwind of the box-type monitoring space area of ​​the study area account for more than 50%.

[0090] S7, the methane inversion calculation model has a background concentration methane flux calculation module (the background concentration methane flux can be obtained by the method of steps S2 to S6. In order to obtain it more quickly, this embodiment adopts a fast inversion estimation method to obtain the background concentration methane flux φ CH ), the background concentration methane flux calculation module inverts and calculates the background concentration methane flux φ according to the following method CH :

[0091] Where λ represents the conversion coefficient (in the embodiment, λ is 7.1428×〖10〗^(-7)), P represents the total number of layers of the planned route of the drone, and X p represents the average methane concentration of all sampling points corresponding to the background concentration in the pth layer of the planned UAV route (taking an open-pit coal mine in Inner Mongolia as an example of an unorganized surface source, assuming that the total number of layers is four, the average methane concentrations of the four layers are 1.834m, 1.825ppm, 1.836ppm, and 1.824ppm respectively), Z p represents the height of the pth layer of the planned UAV route, W represents the short side length of the box-shaped monitoring space coverage area of ​​the study area, L represents the long side length of the box-shaped monitoring space coverage area of ​​the study area, and U LpIt represents the wind speed obtained by the meteorological instrument when monitoring the background concentration sampling point, which is decomposed into the wind speed of the long side of the box-shaped monitoring space coverage area in the study area, U WP It indicates that when the background concentration sampling point is monitored, the meteorological instrument obtains the wind speed decomposed to the wind speed of the short side of the box-shaped monitoring space coverage area of ​​the study area. In step S6, the hovering measurement of the background concentration sampling point is performed after the wind speed monitored by the meteorological instrument is stable in the wind speed monitoring range during the time interval t1 and the change of the pre-collected methane concentration does not exceed the threshold B, and before the drone detection task is performed.

[0092] S8, the methane emission flux E calculated in step S5 CH Subtract the background concentration methane flux φ CH , and obtain the final methane emission flux of the box-type monitoring space in the study area where the unorganized surface source is demarcated (taking the open-pit coal mine as the unorganized surface source for research, such as Figure 6 For data acquisition and inversion result output, see Figure 6 Data software interface). Taking an open-pit coal mine in Inner Mongolia as an example of an unorganized surface source, the long side length L is 2100m and the short side length is 850m. The background concentration methane flux φ is calculated. CH is 1.895 kg / s. According to the previous method, the methane emission flux E of an open-pit coal mine in Inner Mongolia is taken as an unorganized surface source. CH The calculated result is 1.909 kg / s, so the final methane emission flux is 0.0014 kg / s.

[0093] like Figure 7As shown, a UAV methane source tracing monitoring model inversion system includes a UAV monitoring system, a meteorological instrument, a study area box-type monitoring space demarcation and route planning module, a geographic space model and a methane inversion calculation model. The UAV monitoring system includes a UAV 1 and a positioning communication module 2 and a methane sensor 3 carried on the UAV 1; the study area box-type monitoring space demarcation and route planning module is used to demarcate the study area box-type monitoring space based on the research point source or unorganized surface source and plan the UAV planning route data. The study area box-type monitoring space demarcation and route planning module transmits the UAV planning route data to the UAV monitoring system. In the UAV monitoring system, the UAV 1 flies in layers in the study area box-type monitoring space according to the UAV planning route, and the positioning communication module 2 obtains the positioning data. The positioning data includes longitude, latitude and altitude information. At the same time, the methane sensor 3 records the methane concentration data associated with the positioning data in real time. The meteorological instrument obtains the wind speed and direction data and air density data associated with the positioning data in real time. The wind speed and direction data include wind speed data. The geographic space model has a built-in three-dimensional geographic coordinate system and constructs a box-type monitoring simulation space of the study area in the geographic space model. The geographic space model is used to associate and collect positioning data, methane concentration data, wind speed and direction data and air density data. The geographic space model extracts longitude, latitude and altitude information as the location information of the midpoint of the box-type monitoring simulation space of the study area, extracts methane concentration data, air density data and wind speed data as three attributes of the midpoint of the box-type monitoring simulation space of the study area, respectively. The three attributes are methane concentration attribute χ m , air density property ρ m , wind speed attribute u m , m represents the number of the midpoint in the box-type monitoring simulation space of the study area; the geographic space model has a grid division and attribute calculation module and an interpolation processing module. The grid division and attribute calculation module is used to subdivide the box-type monitoring simulation space of the study area into evenly subdivided grids; the interpolation processing module uses the unit grids containing three attribute data to interpolate all unit grids in the box-type monitoring simulation space of the study area until all unit grids in the box-type monitoring simulation space of the study area contain the methane concentration attribute χ n , air density property ρ n , wind speed attribute u n Three attribute data, n represents the number of cells in the box-type monitoring simulation space of the study area; the methane inversion calculation model is inverted according to the following formula to obtain the methane vertical convection flux E CHV and methane horizontal convection flux E CHH :

[0094]

[0095] Where A represents the area covered by the box-type monitoring simulation space in the study area, ω represents the average flow velocity from the bottom to the top of the box-type monitoring simulation space in the study area, and M R is the molar mass ratio of methane to air, represents the average value of air density attributes in the box-type monitoring simulation space of the study area, represents the average value of methane concentration attribute in the box-type monitoring simulation space of the study area, and N represents the total number of cells in the box-type monitoring simulation space of the study area; the methane inversion calculation model obtains the methane emission flux E in the box-type monitoring space of the study area according to the following formula: CH :E CH =E CHV +E CHH .like Figure 5 As shown in the example of taking the exhaust air well as the research point source (taking the exhaust air well as the research point source, each layer of drone route is aligned with the four directions of east, west, south and north, the exhaust air well is located in the center of the square area, and the route area is set to 300m×300m. At 25m--200m above the emission point source, the same route planning is carried out every 25 meters. There are 12 flight altitudes in total), the horizontal convection flux of methane E is obtained. CHH is 0.080325 kg / s, and the vertical convection flux of methane is E CHV is 0.132987 kg / s, and the methane emission flux E CH The data of the present invention is verified by artificial monitoring of methane with dense distribution in a small area, and the accuracy of the present invention reaches more than 94%.

[0096] A storage medium stores a computer program, which, when executed by a processor, implements the steps of the unmanned aerial vehicle methane source tracing monitoring model inversion method of the present invention.

[0097] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A UAV methane source tracing monitoring model inversion method, characterized by: The invention comprises an unmanned aerial vehicle monitoring system, wherein the unmanned aerial vehicle monitoring system comprises an unmanned aerial vehicle (1), a positioning communication module (2) and a methane sensor (3) carried on the unmanned aerial vehicle (1), and the method comprises: S1. Based on the research point source or unorganized surface source, a box-shaped monitoring space is demarcated in the research area, and a drone (1) is started to pre-fly in the box-shaped monitoring space of the research area and pre-collect methane concentration through a methane sensor (3). A meteorological instrument is arranged in the box-shaped monitoring space of the research area. When the wind speed monitored by the meteorological instrument is stably within the wind speed monitoring range at time interval t1 and the change of the pre-collected methane concentration does not exceed the threshold value B, the drone detection task is executed; S2. A UAV route is planned in layers in the box-shaped monitoring space of the study area, and the UAV (1) is started to perform the UAV detection task: the UAV (1) flies in layers in the box-shaped monitoring space of the study area according to the UAV route, and the positioning communication module (2) obtains positioning data, the positioning data includes latitude, longitude and altitude information, and the methane sensor (3) records the methane concentration data associated with the positioning data in real time, and the meteorological instrument obtains the wind speed and direction data and air density data associated with the positioning data in real time, and the wind speed and direction data includes wind speed data; S3. Construct a geographic space model with a three-dimensional geographic coordinate system, build a box-type monitoring simulation space for the study area in the geographic space model, extract longitude, latitude and altitude information as the location information of the midpoint of the box-type monitoring simulation space for the study area, extract methane concentration data, air density data, and wind speed data as the three attributes of the midpoint of the box-type monitoring simulation space for the study area, and the three attributes are respectively methane concentration attribute χ m , air density property ρ m , wind speed attribute u m , m represents the number of the midpoint of the box-type monitoring simulation space in the study area; S4. In the geographic space model, the box-type monitoring simulation space of the study area is evenly divided into subdivided grids, and all unit grids in the box-type monitoring simulation space of the study area are interpolated using unit grids containing three attribute data until all unit grids in the box-type monitoring simulation space of the study area contain the methane concentration attribute χ n , air density property ρ n , wind speed attribute u n Three attribute data, n represents the number of cells in the box-type monitoring simulation space of the study area; S5. Construct a methane inversion calculation model. The methane inversion calculation model is used to invert the methane vertical convection flux E according to the following formulas: CHV and methane horizontal convection flux E CHH : Where A represents the area covered by the box-type monitoring simulation space in the study area, ω represents the average flow velocity from the bottom to the top of the box-type monitoring simulation space in the study area, and M R is the molar mass ratio of methane to air, represents the average value of air density attributes in the box-type monitoring simulation space of the study area, represents the average value of methane concentration attribute in the box-type monitoring simulation space of the study area, and N represents the total number of cells in the box-type monitoring simulation space of the study area; The methane inversion calculation model obtains the methane emission flux E in the box-shaped monitoring space of the study area according to the following formula: CH :E CH =E CHV +E CHH .

2. The method for inverting the UAV methane source tracing monitoring model according to claim 1 is characterized in that: In step S1, the box-shaped monitoring space in the study area is demarcated according to the unorganized surface source; and the following method is also included: S6. Set a number of background concentration sampling points outside the box-shaped monitoring space area of ​​the study area according to the stratification of the planned UAV route, and start the UAV (1) to hover and measure at the background concentration sampling points respectively; S7. The methane inversion calculation model has a background concentration methane flux calculation module. The background concentration methane flux calculation module calculates the background concentration methane flux φ by inversion according to the following method. CH : Where λ represents the conversion factor, P represents the total number of layers of the planned route of the drone, and X p represents the average methane concentration of all the background concentration sampling points corresponding to the pth layer of the planned UAV route, Z p represents the height of the pth layer of the planned UAV route, W represents the short side length of the box-shaped monitoring space coverage area of ​​the study area, L represents the long side length of the box-shaped monitoring space coverage area of ​​the study area, and U Lp It represents the wind speed obtained by the meteorological instrument when monitoring the background concentration sampling point, which is decomposed into the wind speed of the long side of the box-shaped monitoring space coverage area in the study area, U WP It indicates the wind speed obtained by the meteorological instrument when monitoring the background concentration sampling point, which is decomposed into the wind speed of the short side of the box-shaped monitoring space coverage area in the study area; S8, the methane emission flux E calculated in step S5 CH Subtract the background concentration methane flux φ CH , and obtain the final methane emission flux of the box-type monitoring space in the study area where unorganized surface sources are demarcated.

3. The UAV methane source tracing monitoring model inversion method according to claim 2 is characterized by: In step S6, the hovering measurement of the background concentration sampling point is performed after the wind speed monitored by the meteorological instrument is stable in the wind speed monitoring range during the time interval t1 and the pre-collected methane concentration change does not exceed the threshold B, and before the drone detection task is performed.

4. The method for inverting the UAV methane source tracing monitoring model according to claim 1 is characterized in that: The UAV route planning for the box-shaped monitoring space in the study area is selected by one of the following two methods: Method A: The total number of layers of the planned UAV routes in the box-shaped monitoring space of the study area is P layers, and the boundary contour of each layer of routes is a rectangle or square, and the boundary contour takes the east, south, west, and north directions as the boundary contour lines, and each layer takes the boundary contour line as the route; Method B: The total number of layers of UAV planning routes in the box-shaped monitoring space of the study area is P layers. The boundary contour of each layer of routes is a rectangle or a square, and the boundary contour uses the east, south, west, and north directions as boundary contour lines. A number of parallel routes are evenly divided within the boundary contour of each layer of routes, and each layer of routes is connected from the starting point to the end point to form a continuous route for each layer.

5. The method for inverting the UAV methane source tracing monitoring model according to claim 3 is characterized in that: Among all the background concentration sampling points, those located outside the box-type monitoring space of the study area and with a horizontal distance of more than 3 kmm account for more than one-third, of which those located within 180 degrees upwind of the box-type monitoring space of the study area account for more than 50%.

6. The method for inverting the UAV methane source tracing monitoring model according to claim 1 is characterized in that: In step S4, the interpolation process adopts the nearest pixel interpolation method, which is as follows: Locate the unit grid that does not contain the methane concentration attribute, and use the existing methane concentration attributes of the adjacent unit grids for interpolation processing until all unit grids contain the methane concentration attribute; locate the unit grid that does not contain the air density attribute, and use the existing air density attributes of the adjacent unit grids for interpolation processing until all unit grids contain the air density attribute; locate the unit grid that does not contain the wind speed attribute, and use the existing wind speed attributes of the adjacent unit grids for interpolation processing until all unit grids contain the wind speed attribute.

7. The UAV methane source tracing monitoring model inversion method according to claim 1 is characterized by: The wind speed monitoring range is an average wind speed in the range of 0 to 7 m / s, and the threshold B is 5%; the selection range of the time interval t1 is 5 to 10 minutes, or the time interval t1 is selected as the estimated time required to perform the drone detection task.

8. The method for inverting the UAV methane source tracing monitoring model according to claim 4 is characterized in that: When the study area is a research point source, the box-shaped monitoring space of the study area is delineated according to the research point source. The box-shaped monitoring space of the study area is delineated with the research point source as the center to form a square ground monitoring range A. Then, the ground monitoring range A is vertically extended to the ground by a height h1 to form the box-shaped monitoring space of the study area. Then, the UAV planning route of the research point source is planned according to method A. When the study area is an unorganized surface source, the box-shaped monitoring space is delineated according to the unorganized surface source. The box-shaped monitoring space in the study area is delineated according to the unorganized surface source, which surrounds the unorganized surface source and extends outward by K1, and a rectangular ground monitoring range B is delineated. The ground monitoring range B is then extended vertically to the ground by a height h1 to form the box-shaped monitoring space in the study area. The UAV planning route for the unorganized surface source is then planned according to method B.

9. A UAV methane source tracing monitoring model inversion system, characterized by: The invention comprises an unmanned aerial vehicle monitoring system, a meteorological instrument, a module for demarcating a study area box-type monitoring space and a route planning module, a geographic space model and a methane inversion calculation model. The unmanned aerial vehicle monitoring system comprises an unmanned aerial vehicle (1), a positioning communication module (2) and a methane sensor (3) mounted on the unmanned aerial vehicle (1); the module for demarcating a study area box-type monitoring space and a route planning module is used to demarcate a study area box-type monitoring space based on a study point source or an unorganized surface source and to plan unmanned aerial vehicle planned route data; the module for demarcating a study area box-type monitoring space and a route planning module transmits the unmanned aerial vehicle planned route data to the unmanned aerial vehicle monitoring system; in the unmanned aerial vehicle monitoring system, the unmanned aerial vehicle (1) flies in layers in the study area box-type monitoring space in accordance with the unmanned aerial vehicle planned route; the positioning communication module (2) obtains positioning data, and the positioning data packet is sent to the unmanned aerial vehicle monitoring system; The geospatial model has a built-in three-dimensional geographic coordinate system and a box-type monitoring simulation space of the study area is constructed in the geospatial model. The geospatial model is used to associate and collect positioning data, methane concentration data, wind speed and direction data and air density data. The geospatial model extracts longitude, latitude and altitude information as the position information of the midpoint of the box-type monitoring simulation space of the study area, extracts methane concentration data, air density data and wind speed data as three attributes of the midpoint of the box-type monitoring simulation space of the study area, and the three attributes are respectively methane concentration attribute x, methane concentration attribute y, methane concentration attribute y, methane concentration attribute y, methane concentration attribute y and methane concentration attribute y. m , air density property ρ m , wind speed attribute u m , m represents the number of the midpoint in the box-type monitoring simulation space of the study area; the geographic space model has a grid division and attribute calculation module and an interpolation processing module, the grid division and attribute calculation module is used to subdivide the box-type monitoring simulation space of the study area into uniform grids; the interpolation processing module uses the unit grid containing the three attribute data to interpolate all the unit grids in the box-type monitoring simulation space of the study area until all the unit grids in the box-type monitoring simulation space of the study area contain the methane concentration attribute x n , air density property ρ n , wind speed attribute u n Three attribute data, n represents the number of cells in the box-type monitoring simulation space of the study area; the methane inversion calculation model is inverted according to the following formula to obtain the methane vertical convection flux E CHV and methane horizontal convection flux E CHH : Where A represents the area covered by the box-type monitoring simulation space in the study area, ω represents the average flow velocity from the bottom to the top of the box-type monitoring simulation space in the study area, and M R is the molar mass ratio of methane to air, represents the average value of air density attributes in the box-type monitoring simulation space of the study area, represents the average value of methane concentration attribute in the box-type monitoring simulation space of the study area, and N represents the total number of cells in the box-type monitoring simulation space of the study area; the methane inversion calculation model obtains the methane emission flux E in the box-type monitoring space of the study area according to the following formula: CH :E CG =E CHV +E CHH .

10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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