Open-pit coal mine methane emission inversion estimation method based on unmanned aerial vehicle monitoring data

Abstract

The application discloses an open coal mine methane emission amount inversion estimation method based on unmanned aerial vehicle monitoring data, which comprises the following steps: S1, planning flight over the study mining area and collecting methane concentration monitoring data and meteorological data containing position information; S2, a reverse Lagrangian random diffusion model is used to release Np virtual particles at the observation point and simulate the reverse motion trajectory of the particles in the study mining area by using the meteorological data to simulate the footprint sensitivity corresponding to the observation point of the grid of the study mining area, and the transfer coefficient of the observation point is obtained by accumulating the footprint sensitivity of all grids in the study mining area; S3, the average emission rate of the relevant local route affected by the wind direction is calculated, and the total emission rate of the study mining area is calculated. The application realizes the emission tracing of the study mining area and the accurate estimation of the total emission rate and the emission amount, and is convenient for quantitatively evaluating the methane emission distribution and emission intensity of the study mining area, and provides data support for the carbon emission accounting and environmental monitoring of the study mining area.

Description

Technical Field

[0001] This invention relates to the field of methane emission inversion in open-pit coal mines, and in particular to a method for estimating methane emissions from open-pit coal mines based on UAV monitoring data. Background Technology

[0002] Carbon emissions from coal mining enterprises mainly come from methane and carbon dioxide. Methane has contributed 84 times more to the greenhouse effect than carbon dioxide over the past two decades, highlighting the critical importance of methane monitoring in my country's coal mining industry. Methane, with its relatively small mass, easily escapes over mining areas. Accurately identifying and quantifying methane emission sources is crucial for environmental regulation and climate change research. Traditional ground-based observation stations and satellite remote sensing have limitations in spatial coverage and resolution. However, drones equipped with methane sensors can detect methane concentrations at low altitudes with high resolution, providing a new means for tracing the sources of air pollution. However, current technologies, whether relying on ground-based observation stations to monitor and collect methane concentrations at specific locations or using satellite remote sensing data to obtain airborne methane concentrations in open-pit mines, cannot identify the methane emission sources and intensity in open-pit mines, making quantitative prediction of emissions impossible. Therefore, researching a method for identifying emission sources in mining areas to effectively derive emission intensity is extremely important for understanding methane emission characteristics and tracing the sources of air pollution. Summary of the Invention

[0003] The purpose of this invention is to solve the technical problems pointed out in the background art and provide a method for inverting and estimating methane emissions from open-pit coal mines based on UAV monitoring data. This method collects methane concentration monitoring data and meteorological data containing location information over the study mining area. A reverse Lagrange random diffusion model releases virtual particles using the methane concentration monitoring data as observation points and uses meteorological data to randomly simulate the reverse motion trajectory of the particles within the study mining area. This simulation obtains the footprint sensitivity of the study mining area grid or pixel corresponding to observation point j. It achieves the reverse tracking of methane concentration at locations above the study mining area to the ground surface of the study mining area and inverts the total emission rate of methane emissions from the study mining area. It can calculate the daily, monthly, and annual emissions of the study mining area.

[0004] The objective of this invention is achieved through the following technical solution:

[0005] A method for estimating methane emissions from open-pit coal mines based on UAV monitoring data, the method comprising:

[0006] S1. Use drones equipped with methane sensors and meteorological instruments to plan flights over the research mining area and collect methane concentration monitoring data and meteorological data containing location information;

[0007] S2, the research mining area is divided into a grid, a reverse Lagrangian random diffusion model is constructed, methane concentration monitoring data containing position information is extracted as an observation point, the reverse Lagrangian random diffusion model releases Np virtual particles at the observation point and randomly simulates the reverse motion trajectory of the particles in the research mining area to simulate the grid of the research mining area The footprint sensitivity corresponding to the observation point j The total sensitivity of the observation point j is obtained by accumulating the footprint sensitivities of all grids in the research mining area ;

[0008] S3, the average methane concentration of the local flight path k planned by the unmanned aerial vehicle from the methane concentration monitoring data is calculated, and the background concentration is set to obtain a concentration difference value The total sensitivity of all observation points of the local flight path k is summarized to obtain The average emission rate of the research mining area to the local flight path k is , , The time interval of the methane concentration frequency collected by the methane sensor is obtained, the average emission rate of the relevant local flight path affected by the wind direction is obtained, and the average value is calculated The total emission rate of the research mining area is obtained according to the following formula : , The coverage area of the research mining area.

[0009] In order to better realize the present application, in the method S1, the unmanned aerial vehicle is provided with a GPS positioning system or / and a Beidou positioning system, the unmanned aerial vehicle flies at a height h above the research mining area to plan a flight plane covering the ground area of the research mining area, a flight path covering the flight plane is planned on the flight plane, and the meteorological data includes temperature, humidity, air pressure and wind speed and direction information.

[0010] Preferably, the virtual particles are released at the observation point in the reverse Lagrangian random diffusion model to simulate the reverse motion trajectory, and the reverse Lagrangian random diffusion model is constructed as follows:

[0011] The calculation basis of the particle random diffusion model is the generalized Langevin equation, which is based on the following assumptions: the particle position and the velocity evolve together as a Markov process:

[0012]

[0013] Wherein and are functions of , and is an average value of 0 with a variance of a random increment chosen from a Gaussian distribution, when ≠ , and are mutually independent;

[0014] In the backward time frame of the inverse Lagrangian stochastic diffusion model, the time coordinate is used, where is an arbitrary transformation constant, and assuming , the particle velocity in the backward time frame is:

[0015]

[0016] In the coordinate system, based on the generalized Langevin equation, the expression of the particle velocity evolution trajectory is as follows:

[0017]

[0018] A essential constraint of the Langevin equation coefficient is that the Eulerian velocity probability density function must satisfy the Fokker-Planck equation corresponding to equation (1):

[0019]

[0020] In the coordinate system, it is noted that , and ,

[0021]

[0022] where and take values at ; it is required that and satisfy this Fokker-Planck equation, and a fully mixed backward model can be obtained;

[0023] where represents ; under the condition that is a Gaussian distribution and is independent of , the special solution is:

[0024]

[0025] Here is the instantaneous velocity of the particle, is the average velocity of the background flow; since the amplitude of the particle random velocity variation is the same in both time frames, the following equation is obtained:

[0026]

[0027] where is the Eulerian vertical velocity variance, while is the Lagrangian decorrelation timescale of

[0028] Equations (2), (3) and (5) constitute the inverse Lagrangian stochastic dispersion model.

[0029] Preferably, in the method S3, the total emission rate is obtained as follows:

[0030] Since any emission source is located at position and time the ensemble-averaged methane concentration at the emission source is expressed by the following expression, with units :

[0031]

[0032] where is the transition probability density, defined as is the probability of finding a particle initially at in the monitoring domain centered at at time ; The calculation of is a function of the Lagrangian model;

[0033] Particles are released at and collected at giving an estimate of the backward-in-time conditional probability density

[0034]

[0035] In the concentration calculation is replaced by ;

[0036] For a continuous emission source, whose emission rate is uniform over the plane, the concentration can be determined by calculating the ensemble-averaged "residence time" of the particles: i.e. the ensemble-averaged "residence time" of the particles released from within the monitoring domain centered at : ​

[0037]

[0038] From the time a particle spends in the source volume centered at , :

[0039]

[0040] where ; for a stationary atmosphere, and have:

[0041]

[0042] In practice, the volume-averaged concentration is calculated from the sample mean of the individual particle residence times:

[0043]

[0044] where particles are released uniformly from a monitoring domain , and denotes the residence time of an individual particle;

[0045] For a surface source with an emission rate , it is convenient to consider it as a thin volume source of infinitesimal height above the ground with an equivalent volume emission rate ; then the contribution to from a particle that impacts the ground within the source boundary with a vertical ground speed and passes through up and down in one time step is ; the concentration can then be expressed as:

[0046]

[0047] where the summation is over all ground impacts within the source region; the concentration calculation thus uses a backward Lagrangian model that releases particles uniformly from the monitoring domain and sums the reciprocals of all ground impacts within the source region;

[0048] Let be the sensitivity of the monitoring point to any grid cell of the source region, which is the footprint function, then the total sensitivity of the monitoring point to all grid cells of the source region is:

[0049] ​

[0050] The parameters can be given by the previously constructed backward trajectory model when atmospheric stability, friction velocity , surface roughness length z0, boundary layer height H, virtual particle number N, time step Δt, source region grid resolution Δx, Δy parameters are given, and the emission rate is:

[0051]

[0052] In the flight path monitoring of the unmanned aerial vehicle, the line average concentration of the flight path is taken as the basis for inverting the emission rate ; the footprint function is generalized: the is simulated as the average value of the concentration of points distributed equidistantly along the monitoring path; and a group of backward trajectories is calculated from each of the points, and the total sensitivity of the source region to all grid cells of the monitoring flight path is obtained by averaging the point footprint function :

[0053]

[0054] is the total number of particles released from each measurement point, and the inner summation only includes landing events within the source;

[0055] At this time, the formula (10) is generalized, and the emission rate is:

[0056] ; wherein is the background concentration value.

[0057] Preferably, the random differential equation expression of the virtual particle in the inverse Lagrangian random diffusion model with respect to the inverse time t is as follows: Wherein and are functions of , and is a random increment selected from a Gaussian distribution with a mean value of 0 and a variance of , when ≠ , and are independent of each other.

[0058] Preferably, the footprint sensitivity in the footprint function simulated by the inverse Lagrangian random diffusion model is expressed as follows:

[0059] , wherein is the total number of particles released from each measurement point, the inner summation only includes landing events within the source of the study mine.

[0060] Preferably, the total sensitivity of observation point j The expression is as follows:

[0061] where j is the flight path monitoring point mark, is the total number of monitoring points.

[0062] Preferably, the unmanned aerial vehicle plans a flight path over the study mine area to be segmented into several local flight paths, and the average value of the average emission rate of the relevant local flight path affected by the wind direction The expression is as follows: , is the total number of relevant local flight paths affected by the wind direction.

[0063] Preferably, in method S3, the total emission rate represents the total emission of the study mine area per unit time in seconds, and the total emission rate is μg / s, and the total emission rate is used to calculate the monthly and annual emissions of the study area, respectively.

[0064] An open-pit coal mine methane emission inversion estimation system based on unmanned aerial vehicle monitoring data, comprising an unmanned aerial vehicle detection and collection system, a reverse Lagrangian random diffusion model, and a methane emission estimation module, wherein the unmanned aerial vehicle detection and collection system comprises an unmanned aerial vehicle carrying a methane sensor and a meteorological instrument, the unmanned aerial vehicle is provided with a GPS positioning system or / and a Beidou positioning system and is used for positioning position information, the unmanned aerial vehicle of the unmanned aerial vehicle detection and collection system plans a flight over the study mine area, the methane sensor is used for collecting methane concentration monitoring data, and the meteorological instrument is used for collecting meteorological data; the methane concentration monitoring data containing position information and the meteorological data are communicated and transmitted to the reverse Lagrangian random diffusion model by the unmanned aerial vehicle detection and collection system; the reverse Lagrangian random diffusion model is internally constructed with a grid division and a study mine area with position information, extracts the methane concentration monitoring data containing position information as an observation point, releases Np virtual particles from the observation point by the reverse Lagrangian random diffusion model, and randomly simulates the reverse motion trajectory of the particles in the study mine area to simulate the grid footprint sensitivity corresponding to observation point j , and the total sensitivity of observation point j is obtained by accumulating the footprint sensitivities of all grids of the study mine area; the methane emission estimation module divides the flight path planned by the unmanned aerial vehicle into several local flight paths, calculates the average methane concentration of the local flight path k, and obtains the concentration difference value The total sensitivity of all observation points of the local flight line k is summarized to obtain The average emission rate of the study mine area to the local flight line k is studied , , The time interval of the methane concentration frequency collected by the methane sensor is obtained, the average emission rate of the relevant local flight line affected by the wind direction is obtained, and the average value is obtained The total emission rate of the study mine area is obtained by the methane emission estimation module according to the following formula : , The coverage area of the study mine area.

[0065] Compared with the prior art, the present application has the following advantages and beneficial effects:

[0066] (1) The present application collects methane concentration monitoring data containing position information above the study mine area and meteorological data, and the inverse Lagrangian random diffusion model releases virtual particles as observation points using methane concentration monitoring data and randomly simulates the reverse motion trajectory of the particles in the study mine area using meteorological data to simulate the footprint sensitivity corresponding to the observation point j of the grid or pixel of the study mine area, realizes the reverse tracking of the methane concentration position above the study mine area to the ground of the study mine area, and inversely obtains the total emission rate of the methane emission of the study mine area, and can calculate the daily emission, monthly emission and annual emission of the study mine area.

[0067] (2) The present application realizes the accurate estimation of the total emission rate and the emission amount of the study mine area by the inverse Lagrangian random diffusion model for reverse simulation in the study mine area using methane concentration monitoring data as observation points, which is convenient for quantitative evaluation of the methane emission distribution and emission intensity of the study mine area, and provides data support for carbon emission accounting and environmental monitoring of the study mine area.

[0068] (3) The present application fills the technical blank of coal mine methane emission source tracing and emission amount quantitative estimation, can accurately invert the methane emission distribution and emission intensity of the mine area, and improves the accuracy of methane detection and carbon monitoring activities. BRIEF DESCRIPTION OF DRAWINGS

[0069] Figure 1 It is a method flow diagram of the present application of coal mine methane emission inversion estimation method;

[0070] Figure 2 It is a schematic diagram of the equipment carried by the unmanned aerial vehicle of the embodiment;

[0071] Figure 3 It is a schematic diagram of the planned flight of the study mine area in the embodiment example.

[0072] In the drawings, the names corresponding to the reference signs in the drawings are:

[0073] 1 - UAV, 2 - methane extraction module, 3 - methane sensor, 4 - meteorological instrument, 5 - planned flight path, 6 - compass, 7 - wind vane, 8 - methane emission source. DETAILED DESCRIPTION

[0074] The present application will be further described in conjunction with the following examples: EXAMPLE

[0075] As shown in Figure 1 , a method for estimating the methane emission of an open-pit coal mine based on UAV monitoring data, the method comprising:

[0076] S1, use the UAV 1 equipped with methane sensor 3 and meteorological instrument 4 to plan flight and collect methane concentration monitoring data and meteorological data containing position information above the study area. In some embodiments, the UAV is equipped with a GPS positioning system or / and a Beidou positioning system (to obtain the latitude and longitude information of the device), as shown in Figure 2 , the UAV 1 is equipped with a methane extraction module 2, a methane sensor 3 (or a methane gas analyzer) and a meteorological instrument 4 (or a meteorological module), the UAV 1 is selected from the model DJI Weidu 350RTK, the maximum take-off weight is 9kg, the maximum wind speed is 12m / s, and is equipped with a fixed frame that can carry a methane sensor and a meteorological instrument. The methane extraction module of the methane extraction module 2 is combined with the methane sensor 3 to realize air extraction and detection of methane concentration in the air, adopts tunable diode laser absorption spectroscopy technology, the range is 0-10000ppm, the resolution is 1ppm, the detection limit is 1ppm, the response time is 1s, and is equipped with a power line data transmission line to connect the meteorological instrument and the UAV device. The UAV 1 flies at a height h above the methane emission source 8 (the emission source inside the study area, or the working surface of the open-pit coal mine) to plan a flight plane covering the ground area of the study area, the flight path of the flight plane is planned, and the meteorological data contains temperature, humidity, air pressure and wind speed and direction information. As shown in Figure 3 , preferably, the height h is 50-80 meters above the methane emission source 8 (i.e. the working surface of the open-pit coal mine), and the flight plane is expanded at least 100 meters (specifically determined according to the situation of the study area) from the methane emission source 8 (or the working surface of the open-pit coal mine), and the planned flight area is larger than the methane emission source 8 or the working surface of the open-pit coal mine, i.e. the planned flight area is located in the peripheral area vertically above the methane emission source 8, which facilitates the reverse Lagrangian random diffusion model to simulate the reverse motion trajectory of the particles in the study area. Referring to Figure 3 , the example of the present embodiment constructs a flight path of the UAV 1 in the area above the methane emission source 8 or the working surface of the open-pit coal mine expanded by 100 meters, and the example of the flight path is Figure 3The rectangular route shown, with the starting and ending points being the northeast corners of the rectangle, requires that the compass 3, the wind vane 7, the planned route 5, and the methane emission source 8 be aligned with the four cardinal directions, north, south, east, and west, regardless of the wind direction Figure 3 The example shows that the route direction is aligned with the four cardinal directions (so as to facilitate the determination of the wind direction affected route, for example, if the wind direction is southwest, then the wind direction affected route is the east route and the north route, in the reverse motion trajectory simulation of the inverse Lagrangian stochastic diffusion model, the methane sensor 3 of the UAV in the range of the east route and the north route will receive methane concentration monitoring data, the south route and the west route are in the upwind area, and the methane sensor 3 of the UAV in the range of the south route and the west route receives little methane concentration monitoring data, which is not considered in this embodiment, and only the wind direction affected route is considered), and the flight height is 50 meters relative to the working face. The UAV performs experiments according to the planned route, and in the execution process, the methane sensor 3 extracts the surrounding air every 1s to monitor the methane content (ppm); the meteorological instrument 4 obtains the temperature, humidity, air pressure, wind speed, and wind direction data at the current time; the GPS synchronously obtains the current longitude and latitude information, and the UAV 1 is also provided with a 4G module, which transmits data in real time.

[0077] S2, divide the research mine area into grids, construct an inverse Lagrangian stochastic diffusion model, extract methane concentration monitoring data containing position information as observation points, and release Np virtual particles from the observation points and simulate the reverse motion trajectory of the particles in the research mine area using meteorological data to obtain the grid The footprint sensitivity corresponding to the observation point j The total sensitivity of the observation point j is obtained by accumulating the footprint sensitivities of all grids in the research mine area .

[0078] In some embodiments, the footprint sensitivity in the footprint function simulated by the inverse Lagrangian stochastic diffusion model is The expression is as follows:

[0079] wherein is the total number of particles released from each measurement point, and the inner summation only includes the particles corresponding to the landing events or the wind direction affected route within the source of the research mine area. The wind direction affected route is determined as follows: the meteorological instrument 4 of the UAV 1 monitors meteorological data, which includes temperature, humidity, air pressure, wind speed, and wind direction information, and the UAV obtains the wind direction information and determines whether the route is affected by the wind direction when flying along the route. If the route is affected by the wind direction, for example, if the wind direction is north (blowing from the north), then the south route is affected by the wind direction. In Figure 3In the route example, the route direction is aligned with the four directions of east, west, north and south (so as to facilitate the determination of the route affected by the wind direction, for example, if the wind direction is southwest, the routes affected by the wind direction are the east route and the north route, and in the reverse trajectory simulation of the inverse Lagrangian stochastic dispersion model, the methane sensor 3 of the unmanned aerial vehicle in the east route and the north route range will receive the methane concentration monitoring data, the south route and the west route are in the upwind area, and the methane sensor 3 in the range of the south route and the west route receives little methane concentration monitoring data, which is not considered in this embodiment, and only the routes related to the wind direction are considered.

[0080] In some embodiments, the total sensitivity of the observation point j is The expression is as follows:

[0081] Wherein j is the route monitoring point mark, is the total number of monitoring points.

[0082] In some embodiments, the virtual particles released by the observation point in the inverse Lagrangian stochastic dispersion model are used to simulate the reverse trajectory, and the inverse Lagrangian stochastic dispersion model is constructed as follows:

[0083] The calculation basis of the particle random diffusion model is the generalized Langevin equation, which is based on the following assumptions: the particle position

[0084] And the velocity Evolve together as a Markov process:

[0085] ;

[0086] Wherein And are functions of , and is a random increment selected from a Gaussian distribution with a mean of 0 and a variance of , when ≠ , and are independent of each other; this embodiment discretizes formula (1) and uses it to calculate a large number of particle trajectories emitted from the emission source, and then determines the volume average concentration according to the residence time of the particles in the monitoring point range (for the case of continuous emission source).

[0087] One of the essential constraints of the Langevin equation coefficient is that the Euler velocity probability density function Must satisfy the Fokker-Planck equation (FPE) corresponding to formula (1):

[0088] ;

[0089] in express ;exist It is a Gaussian distribution and and Particular solution under irrelevant conditions:

[0090] ;

[0091] It is the instantaneous velocity of the particle. It is the average velocity of the background flow;

[0092] ;

[0093] In the backward time frame of the inverse Lagrange stochastic diffusion model, time coordinates are used. ,in It is an arbitrary transformation constant, assuming The particle velocity in the backward time frame is:

[0094] ;

[0095] exist In the coordinate system, based on the generalized Langevin equation, the expression for the particle velocity evolution trajectory is as follows:

[0096] ;

[0097] An essential constraint on the coefficients of the Langevin equation is the Euler velocity probability density function. The Fokker-Planck equation corresponding to formula (1) must be satisfied:

[0098] ;

[0099] exist In coordinate system representation, note , ,and ,

[0100] ;

[0101] in and exist The value is taken at the specified location; requirements are as follows: and Satisfying this Fokker-Planck equation yields a fully mixed backward model;

[0102] in express ; in is Gaussian and is independent of the particular solution:

[0103] ;

[0104] Here is the instantaneous velocity of the particle, is the mean velocity of the background flow; since the amplitude of the particle's random velocity variation is the same in both time frames, we obtain the following equation:

[0105] ;

[0106] where is the Eulerian vertical velocity variance, while is the Lagrangian decorrelation timescale of ;

[0107] The inverse Lagrangian stochastic dispersion model is constructed by combining equations (2), (4), and (5).

[0108] S3, calculating the average methane concentration from the methane concentration monitoring data according to the local flight path k planned by the unmanned aerial vehicle, and subtracting the set background concentration to obtain a concentration difference , and the total sensitivity of all observation points on the local flight path k is summarized to obtain , then the average emission rate of the study mine area to the local flight path k is , is the time interval of the methane concentration frequency collected by the methane sensor, and the average emission rate of the relevant local flight path affected by the wind direction is obtained and averaged . In some embodiments, the unmanned aerial vehicle plans a flight path over the study mine area and divides it into several local flight paths, and the average value of the average emission rate of the relevant local flight path affected by the wind direction (the wind direction affected can use the wind direction affected judgment method, or use the inverse Lagrangian stochastic dispersion model to judge the associated flight path corresponding to the particle landing event) The expression is as follows: , is the total number of relevant local flight paths affected by the wind direction. The wind direction affected judgment direction is as follows: the meteorological instrument 4 of the unmanned aerial vehicle monitors meteorological data, which includes temperature, humidity, air pressure, wind speed, and wind direction information. The unmanned aerial vehicle judges whether the flight path is affected by the wind direction when it flies along the flight path. If the flight path is affected by the wind direction, such as north wind (blowing from the north), then the southward flight path is affected by the wind direction. In Figure 3In the route example of FIG. 1, the route directions are aligned with the four cardinal directions (east, west, north, and south) (so as to facilitate route determination in the presence of wind direction, for example, if the wind direction is southwest, then the routes affected by the wind direction are the east route and the north route, and in the reverse Lagrangian stochastic dispersion model, the methane sensor 3 of the UAV in the east route and the north route will receive methane concentration monitoring data, and the south route and the west route are upwind routes, and the methane sensor 3 of the UAV in the south route and the west route will receive very little methane concentration monitoring data, which is not considered in this embodiment, and only the routes affected by the wind direction are considered.

[0109] In some embodiments, in the method S3, the total emission rate The method is as follows:

[0110] Since any emission source At The ensemble average methane concentration expression at the location and time is expressed as follows, and the emission source The unit is :

[0111] ;

[0112] Where is the transition probability density, defined as is the probability of finding a particle initially at in the monitoring domain centered at at time ; The calculation of

[0113] Particles are released at and collected at to give an estimate of the backward-in-time conditional probability density :

[0114] ;

[0115] In the concentration calculation, replace with ;

[0116] For a continuous emission source, the emission rate is uniform in the plane, and the concentration is determined by calculating the ensemble average "residence time" of the particles: that is, the ensemble average "residence time" of the particles released from in the monitoring domain centered at :

[0117] ​ ;

[0118] from The released particles are located at the center source volume Time spent inside :

[0119] ;

[0120] in For a still atmosphere, and have:

[0121] ;

[0122] In practice, The volume average concentration can be calculated from the sample average of the residence time of individual particles. The expression is:

[0123] ;

[0124] Among them, from the monitoring domain Even release One particle, Indicates the residence time of a single particle;

[0125] For emission rates of The surface source is considered as an infinitesimally small height above the ground. It constitutes a thin volumetric source and has an equivalent volumetric emission rate. Then when the particle falls at a vertical velocity... It impacts the ground within the source boundary and travels upwards and downwards within one time step. At that time, its response to The amount of contribution is The concentration at this point can be expressed as:

[0126] ;

[0127] The summation applies to all ground-landing events within the source region (simulated using a backward Lagrange stochastic diffusion model, where the flight paths of the ground-landing event particles are those influenced by wind direction); therefore, the concentration calculation uses a backward Lagrange model within the monitoring domain. The particles are uniformly released within the source region, and all landing events within the source region are monitored. Sum of reciprocals;

[0128] Will Defined as the monitoring point for any grid cell in the source region. If the sensitivity is the footprint function, then the total sensitivity of this monitoring point to all grid cells in the source region is:

[0129] ;

[0130] This parameter can be related to atmospheric stability and friction speed. Given parameters such as surface roughness length z0, boundary layer height H, virtual particle number N, time step Δt, and source region mesh resolution Δx, Δy, etc., which are obtained from the previously constructed backward trajectory model, the emission rate... have: ;

[0131] When using drones for flight path monitoring, the average concentration along the flight path is used. As an inversion emission rate The foundation; generalizing the footprint function: The simulation is based on the distribution of equally spaced data along the monitoring path. The average point concentration; and from each of these points, calculate a set of backward trajectories, averaging the point footprint function, to obtain the total sensitivity of the monitoring route to all grid cells in the source region. :

[0132] ;

[0133] It is the total number of particles released from each measuring point, and the internal summation only includes landing events within the source;

[0134] At this point, formula (10) is generalized, and the emission rate is... (i.e., total emission rate) )have:

[0135] ;in This represents the background concentration value.

[0136] In some embodiments, the stochastic differential equation expression of the virtual particle with respect to the reverse time t in the reverse Lagrange stochastic diffusion model is as follows:

[0137] in and It is about The function, and It is from a value with a mean of 0 and a variance of The random increment selected from the Gaussian distribution, when hour, and They are independent of each other.

[0138] The present invention obtains the total emission rate of the research mining area according to the following formula. , To study the mining area coverage. In some embodiments, the total emission rate This represents the total emissions per unit time (second) in the research mining area, and the total emission rate. The unit is μg / s, using the total emission rate. The monthly and annual emissions for the study area were calculated separately. In this embodiment, the methane sensor 3 on the UAV monitored at a frequency of 1 second. The expression for monthly emissions (tons / month) is as follows:

[0139] , in the formula of for The unit is μg / s.

[0140] The expression for annual emissions (tons / month) is as follows:

[0141] , in the formula of for The unit is μg / s.

[0142] A system for estimating methane emissions from open-pit coal mines based on UAV monitoring data includes a UAV detection and acquisition system, a reverse Lagrange random diffusion model, and a methane emission estimation module. The UAV detection and acquisition system comprises a UAV equipped with a methane sensor and a meteorological instrument. The UAV is equipped with a GPS positioning system and / or a BeiDou positioning system for location information. The UAV plans its flight over the study mining area. The methane sensor collects methane concentration monitoring data, and the meteorological instrument collects meteorological data. The UAV detection and acquisition system communicates and transmits the methane concentration monitoring data containing location information and the meteorological data to the reverse Lagrange random diffusion model. The reverse Lagrange random diffusion model constructs a gridded study mining area with location information. The methane concentration monitoring data containing location information is extracted as observation points. The reverse Lagrange random diffusion model releases Np virtual particles at the observation points and uses meteorological data to randomly simulate the inverse motion trajectory of the particles within the study mining area to obtain the grid of the study mining area. Footprint sensitivity corresponding to observation point j The total sensitivity of observation point j is obtained by accumulating the sensitivity of all grid footprints in the research mining area. The methane emission estimation module divides the planned flight path of the UAV into several local flight paths and calculates the average methane concentration of local flight path k. The difference between this average and the set background concentration is then used to obtain the concentration difference value. The total sensitivity of all observation points along local route k is obtained by summing up the results. The average emission rate of the local flight line k of the study mine area is , The time interval of the methane concentration frequency collected by the methane sensor is obtained, the average emission rate of the local flight line affected by the wind direction is obtained, and the average value is obtained ; The total emission rate of the study mine area is obtained by the methane emission estimation module according to the following formula , is the coverage area of the study mine area.

[0143] In this embodiment, the plane uniform emission source A of a certain open-pit coal mine in Inner Mongolia is an irregular plane, and the area thereof is determined to be about A=5.602×105 m 2 by photogrammetry. The flight line planning of the unmanned aerial vehicle is performed in the manner shown in the accompanying drawings. Figure 2 The unmanned aerial vehicle flies according to the set flight line, and in the execution process, the methane sensor extracts the surrounding air every 1s to monitor the methane content (ppm); the weather instrument obtains the current temperature, humidity, air pressure, wind speed and wind direction data; the wind direction measured on the experimental day is northeast wind, and the wind speed is U=4.5m / s. The average methane concentrations of the four flight lines measured on site are C N =1.9334ppm, C S =1.9353ppm, C W =1.9489ppm, and C E =1.9610ppm. The background concentration C0=1.909 ppm. The related parameters of the inverse Lagrangian random diffusion model are taken as the commonly used default values to establish the parameter system of turbulent diffusion and inverse simulation, and are respectively: the atmospheric stability classification is neutral (Monin-Obukhov stability D class), the friction velocity , the surface roughness length z0=0.1m, the boundary layer height H=1000m, the virtual particle number N p =20000, the time step Δt=1s, and the source area grid resolution Δx, Δy=10m×10m. Since the wind direction on the experimental day is northeast wind, the data of the main methane dispersion direction (south side and west side flight lines) are selected for inversion; the average emission rate of the south side flight line is as follows ; the average emission rate of the west side flight line is as follows: .

[0144] The emission rates of the two directions are integrated with the length proportion of the flight line as the weight:

[0145] , and represent the emission weight coefficients of the south region of the study mine area and the west region of the study mine area, respectively.

[0146] The total emission rate of the study mine area is .

[0147] The monthly emissions (tons / month) are as follows:

[0148] .

[0149] The annual emissions (tons / month) are as follows:

[0150] .

[0151] The above merely provides the preferred embodiments of the present application, and is not used to limit the present application, and any modification, equivalent replacement, and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for estimating methane emissions from open-pit coal mines based on UAV monitoring data, characterized in that: The methods include: S1. Use drones equipped with methane sensors and meteorological instruments to plan flights over the research mining area and collect methane concentration monitoring data and meteorological data containing location information; S2. Divide the study mining area into grids, construct a reverse Lagrange random diffusion model, extract methane concentration monitoring data containing location information as observation points, release Np virtual particles from the observation points in the reverse Lagrange random diffusion model, and use meteorological data to randomly simulate the reverse motion trajectory of the particles within the study mining area to obtain the grid of the study mining area. Footprint sensitivity corresponding to observation point j The total sensitivity of observation point j is obtained by accumulating the sensitivity of all grid footprints in the research mining area. ; S3. Calculate the average methane concentration from the methane concentration monitoring data along the local flight path k planned by the UAV, and subtract the set background concentration to obtain the concentration difference value. The total sensitivity of all observation points along local route k is obtained by summing up the results. Then the average emission rate of the mining area to local flight path k is: , , The time interval for collecting methane concentration data from the methane sensor is used to obtain the average emission rate of the relevant local flight path affected by wind direction, and the average value is calculated. The total emission rate of the studied mining area was obtained using the following formula. : , To study the mining area coverage area; total emission rate The method to obtain it is as follows: Due to any emission source exist Location and time The expression for the ensemble average methane concentration at the emission source is as follows: Units are : ； in It is the transition probability density, defined as It is in time At that time The monitoring domain centered on The discovery of the initial stage The probability of the particle; The calculation is a function of the Lagrange model; Particles in Released, in Collected, providing backward time conditional probability density Estimate: ； In concentration calculation Alternative ; For continuous emission sources, the emission rate is uniform across the plane, and the concentration... It can be determined by calculating the average "residence time" of the particle swarm: that is, from The released particles are located at the center monitoring domain Average "stay time" within the set : ； from The released particles are located at the center source volume Time spent inside : ； in For a still atmosphere, and have: ； In practice, The volume average concentration can be calculated from the sample average of the residence time of individual particles. The expression is: ； Among them, from the monitoring domain Even release One particle, Indicates the residence time of a single particle; For emission rates of The surface source is considered as an infinitesimally small height above the ground. It constitutes a thin volumetric source and has an equivalent volumetric emission rate. Then when the particle falls at a vertical velocity... It impacts the ground within the source boundary and travels upwards and downwards within one time step. At that time, its response to The amount of contribution is The concentration at this point can be expressed as: ； The summation applies to all ground-landing events within the source region; therefore, the concentration calculation uses a backward Lagrange model in the monitoring domain. The particles are uniformly released within the source region, and all landing events within the source region are monitored. Sum of reciprocals; Will Defined as a monitoring point for any grid cell in the source region If the sensitivity is the footprint function, then the total sensitivity of the monitoring point to all grid cells in the source region is: ； This parameter can be related to atmospheric stability and friction speed. Given the surface roughness length z0, boundary layer height H, virtual particle number N, time step Δt, and source region mesh resolution Δx and Δy, which are obtained from the previously constructed backward trajectory model, the emission rate... have: ； When using drones for flight path monitoring, the average concentration along the flight path is used. As an inversion emission rate The foundation; generalizing the footprint function: The simulation is based on the distribution of equally spaced data along the monitoring path. The average concentration at each point is calculated; and a set of backward trajectories is calculated for each of these points, and the average of the point footprint functions is obtained to determine the total sensitivity of the monitoring route to all grid cells in the source region. : ； It is the total number of particles released from each measuring point, and the internal summation only includes landing events within the source; At this point, formula (10) is generalized, and the emission rate is... have: ;in This represents the background concentration value.

2. The method for estimating methane emissions from open-pit coal mines based on UAV monitoring data according to claim 1, characterized in that: In method S1, the UAV is equipped with a GPS positioning system and / or a Beidou positioning system. The UAV plans a flight plane covering the ground area of ​​the research mining area at a flight altitude h above the research mining area, and plans a flight route covering the flight plane. The meteorological data includes temperature, humidity, air pressure, and wind speed and direction information.

3. The method for estimating methane emissions from open-pit coal mines based on UAV monitoring data according to claim 1, characterized in that: In the reverse Lagrange random diffusion model, virtual particles are released from the observation point to simulate their inverse motion trajectory. The method for constructing the reverse Lagrange random diffusion model is as follows: The computational basis of the random diffusion model of particles is the generalized Langevin equation, which is based on the following assumptions: particle position... and speed Co-evolution as Markov processes: ； in and It is about The function, and It is from a value with a mean of 0 and a variance of The random increment selected from the Gaussian distribution, when ≠ hour, and They are independent of each other; In the backward time frame of the inverse Lagrange stochastic diffusion model, time coordinates are used. ,in It is an arbitrary transformation constant, assuming The particle velocity in the backward time frame is: ； exist In the coordinate system, based on the generalized Langevin equation, the expression for the particle velocity evolution trajectory is as follows: ； An essential constraint on the coefficients of the Langevin equation is the Euler velocity probability density function. The Fokker-Planck equation corresponding to formula (1) must be satisfied: ； exist In coordinate system representation, note , ,and , ； in and exist The value is taken at the specified location; requirements are as follows: and Satisfying this Fokker-Planck equation yields a fully mixed backward model; in express ;exist It is a Gaussian distribution and and Particular solution under irrelevant conditions: ； here It is the instantaneous velocity of the particle. This is the average velocity of the background flow; since the magnitude of the random velocity variation of particles is the same in both time frames, the following formula is obtained: ； in It is the variance of Euler's vertical velocity, and yes The Lagrange terminology is relevant to the time scale; Formulas (2), (4) and (5) constitute the inverse Lagrange random diffusion model.

4. The method for estimating methane emissions from open-pit coal mines based on UAV monitoring data according to claim 1, characterized in that: The stochastic differential equation expression for the virtual particle with respect to the reverse time t in the reverse Lagrange stochastic diffusion model is as follows: in and It is about The function, and It is from a value with a mean of 0 and a variance of The random increment selected from the Gaussian distribution, when ≠ hour, and They are independent of each other.

5. The method for estimating methane emissions from open-pit coal mines based on UAV monitoring data according to claim 1 or 2, characterized in that: The drone's planned flight path over the research mining area was segmented to obtain several local flight paths. The average emission rate of the relevant local flight paths was affected by wind direction. The expression is as follows: , This represents the total number of local flight routes affected by wind direction.

6. The method for estimating methane emissions from open-pit coal mines based on UAV monitoring data according to claim 1, characterized in that: In method S3, the total emission rate This represents the total emissions per unit time (second) in the research mining area, and the total emission rate. The unit is μg / s, using the total emission rate. Calculate the monthly and annual emissions for the study area separately.

7. A system for estimating methane emissions from open-pit coal mines based on UAV monitoring data, implementing the method for estimating methane emissions from open-pit coal mines as described in claim 1, characterized in that: The system comprises a drone detection and acquisition system, a reverse Lagrange random diffusion model, and a methane emission estimation module. The drone detection and acquisition system includes a drone equipped with a methane sensor and a meteorological instrument. The drone is equipped with a GPS positioning system and / or a BeiDou positioning system for location information. The drone plans its flight over the study mining area. The methane sensor collects methane concentration monitoring data, and the meteorological instrument collects meteorological data. The drone detection and acquisition system communicates and transmits the methane concentration monitoring data, including location information, and the meteorological data to the reverse Lagrange random diffusion model. The reverse Lagrange random diffusion model constructs a gridded study mining area with location information. The methane concentration monitoring data containing location information is extracted as observation points. The reverse Lagrange random diffusion model releases Np virtual particles at the observation points and uses meteorological data to randomly simulate the inverse motion trajectory of the particles within the study mining area to obtain the grid of the study mining area. Footprint sensitivity corresponding to observation point j The total sensitivity of observation point j is obtained by accumulating the sensitivity of all grid footprints in the research mining area. The methane emission estimation module divides the planned flight path of the UAV into several local flight paths and calculates the average methane concentration of local flight path k. The difference between this average and the set background concentration is then used to obtain the concentration difference value. The total sensitivity of all observation points along local route k is obtained by summing up the results. Then the average emission rate of the mining area to local flight path k is: , , The time interval for collecting methane concentration data from the methane sensor is used to obtain the average emission rate of the relevant local flight path affected by wind direction, and the average value is calculated. The methane emission estimation module obtains the total emission rate of the studied mining area according to the following formula. : , To study the coverage area of ​​the mining area.

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