A methane emission rate calculation method and device, electronic equipment and storage medium

Abstract

The application discloses a kind of methane emission rate measurement method, device, electronic equipment and storage medium, it is related to methane emission measurement technical field.The method comprises: obtaining unmanned aerial vehicle measurement data and vehicle measurement data obtained by measuring target emission source respectively;According to the first position data in unmanned aerial vehicle measurement data, flight trajectory is constructed and is layered according to altitude, according to the first emission attribute data corresponding to each layer flight trajectory, the line flux integral value corresponding to each layer flight trajectory is determined, and the first methane emission rate is determined according to line flux integral value;Based on vehicle measurement data, the initial grid flux of each grid in emission section is calculated using mass balance method, and the initial grid flux is integrated after being expanded by Gaussian plume model, to obtain the second methane emission rate;The first methane emission rate and the second methane emission rate are added to determine the final methane emission rate.The above technical solution improves the accuracy of methane emission rate measurement.

Description

Technical Field

[0001] This application relates to the field of atmospheric environment monitoring technology, and more particularly to the field of methane emission measurement technology, specifically to a method, apparatus, electronic device, and storage medium for calculating methane emission rates. Background Technology

[0002] Methane is a potent greenhouse gas that has a significant impact on global warming. Accurately calculating methane emission rates is of great importance for greenhouse gas management, carbon emission reduction accounting, and ecological environmental protection.

[0003] Currently, methane emission rate calculations mainly employ methods such as in-situ sensor monitoring, UAV remote sensing observation, and atmospheric model inversion. Among these, in-situ monitoring involves deploying sensors to collect local concentration data and then combining it with emission factors or simplified models to estimate emissions. However, the monitoring range is limited, failing to reflect the spatial diffusion distribution of methane, resulting in poor accuracy of the calculation results. While UAV remote sensing and traditional atmospheric inversion schemes can achieve regional monitoring, they are easily affected by idealized assumptions in the models, leading to significant errors in the inversion process.

[0004] Therefore, existing technologies generally suffer from inaccurate calculations of methane emission rates and insufficient reliability of results in practical applications. Summary of the Invention

[0005] This application provides a method, apparatus, electronic device, and storage medium for calculating methane emission rates, which improves the accuracy of methane emission rate calculation.

[0006] According to one aspect of this application, a method for calculating methane emission rates is provided, comprising: Acquire drone measurement data and vehicle-mounted measurement data obtained from the measurement of the target emission source by drone-borne equipment and vehicle-mounted equipment, respectively. A flight trajectory is constructed based on the first position data of each trajectory point in the UAV measurement data. The flight trajectory is then layered according to the altitude in the first position data. Based on the first emission attribute data corresponding to the trajectory points in each layer of the flight trajectory in the UAV measurement data, the line flux integral value corresponding to each layer of the flight trajectory is determined. The first methane emission rate of the target emission source is then determined based on the line flux integral value corresponding to each layer of the flight trajectory. The first emission attribute data includes the first methane gas concentration, the first three-dimensional wind speed data, and the first meteorological data. Based on on-board measurement data, the initial grid flux of each grid in the emission section is calculated using the mass balance method, and the initial grid flux is expanded and integrated using the Gaussian plume model to obtain the second methane emission rate of the target emission source. The first methane emission rate and the second methane emission rate are summed to determine the final methane emission rate corresponding to the target emission source.

[0007] According to another aspect of this application, a methane emission rate measuring device is provided, comprising: The measurement data acquisition module is used to acquire UAV measurement data and vehicle measurement data obtained by UAV-borne equipment and vehicle-borne equipment respectively from the target emission source; The first emission rate determination module is used to construct a flight trajectory based on the first position data of each trajectory point in the UAV measurement data, divide the flight trajectory into layers according to the altitude in the first position data, determine the line flux integral value corresponding to each layer of flight trajectory based on the first emission attribute data corresponding to the trajectory points in the UAV measurement data, and determine the first methane emission rate of the target emission source based on the line flux integral value corresponding to each layer of flight trajectory; wherein, the first emission attribute data includes the first methane gas concentration, the first three-dimensional wind speed data, and the first meteorological data; The second emission rate determination module is used to calculate the initial grid flux of each grid in the emission section based on on-board measurement data using the mass balance method, and to expand and integrate the initial grid flux using a Gaussian plume model to obtain the second methane emission rate of the target emission source. The final emission rate determination module is used to sum the first methane emission rate and the second methane emission rate to determine the final methane emission rate corresponding to the target emission source.

[0008] According to another aspect of this application, an electronic device is provided, the electronic device comprising: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the methane emission rate calculation method according to any embodiment of this application.

[0009] According to another aspect of this application, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions for causing a processor to execute and implement the methane emission rate calculation method according to any embodiment of this application.

[0010] According to another aspect of this application, a computer program product is provided, comprising a computer program that, when executed by a processor, implements any of the methane emission rate calculation methods provided in the embodiments of this application.

[0011] The technical solution of this application embodiment constructs a layered flight trajectory based on UAV measurement data and calculates the linear flux integral value along the flight trajectory to obtain the first methane emission rate, thereby avoiding spatial interpolation errors caused by cross-sectional integration and improving calculation accuracy. At the same time, based on vehicle-mounted measurement data, the initial grid flux is calculated using the mass balance method, and the second methane emission rate is obtained by integrating the data after expanding and compensating the areas not covered by the initial grid flux using a Gaussian plume model. This compensates for measurement blind spots and reduces errors while retaining the constraints of measured data. The first and second methane emission rates are fused, and the complementarity between UAV high-altitude data and vehicle-mounted near-ground data is utilized to achieve more complete spatial coverage of the measurement data, thereby improving the accuracy and reliability of the methane emission rate calculation results.

[0012] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this application, nor is it intended to limit the scope of this application. Other features of this application will become readily apparent from the following description. Attached Figure Description

[0013] Figure 1 This is a flowchart of a method for calculating methane emission rate according to Embodiment 1 of this application; Figure 2 This is a flowchart of another method for calculating methane emission rate according to Embodiment 1 of this application; Figure 3 This is a schematic diagram of a methane emission rate measuring device provided in Embodiment 3 of this application; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the methane emission rate calculation method of the embodiments of this application. Detailed Implementation

[0014] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0015] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0016] Example 1 Figure 1 This is a flowchart of a methane emission rate calculation method according to Embodiment 1 of this application. This embodiment is applicable to application scenarios for quantitative monitoring of methane emissions from various industrial sites. The method can be executed by a methane emission rate calculation device, which can be implemented in hardware and / or software and can be configured in a computer device. Figure 1 As shown, the method includes: S101. Obtain UAV measurement data and vehicle measurement data obtained from UAV-borne equipment and vehicle-mounted equipment respectively measuring the target emission source.

[0017] For example, the UAV-borne equipment consists of a multi-rotor UAV platform, a positioning module, a methane analyzer, a 3D anemometer, meteorological parameter sensors, and a data acquisition and transmission module. The vehicle-mounted equipment consists of a vehicle, a multi-altitude sampling device, a positioning module, a methane analyzer, a 3D anemometer, meteorological parameter sensors, and a data acquisition and transmission module. The multi-altitude sampling device is fixed at the front of the vehicle, and its air inlet height is adjustable to sample air at different altitudes above the ground. In this embodiment, the methane analyzer measures the methane gas concentration; the 3D anemometer measures 3D wind speed data; and the data acquisition and transmission module transmits the measurement data collected by the UAV-borne or vehicle-mounted equipment to a ground data platform for storage.

[0018] The target emission source refers to the site where the methane emission rate is to be measured. For example, this site includes, but is not limited to, industrial sites with methane emission characteristics such as compressor stations, gas storage facilities, oil storage facilities, and liquefied natural gas receiving terminals. UAV measurement data refers to data acquired by measuring the target emission source using UAV-borne equipment. For example, a flight path is planned based on the boundary of the oil and gas pipeline station, and the UAV-borne equipment performs multi-altitude, three-dimensional orbital flight around the oil and gas pipeline station. The initial flight altitude is approximately 15 meters, and the maximum flight altitude is 150 to 200 meters to ensure complete capture of the emission plume; the flight layer interval is 5 to 10 meters, for a total of 10 to 20 layers; the flight speed is controlled at 4 to 8 meters per second. Vehicle-mounted measurement data refers to data acquired by measuring the target emission source using vehicle-mounted equipment. For example, measurements are taken along roads surrounding the oil and gas pipeline station or a predetermined planned route, based on the boundary of the oil and gas pipeline station. When the station area is large, the oil and gas pipeline station can be divided into multiple sub-areas for separate measurements. During the measurement process, the vehicle speed was maintained at 4 to 6 kilometers per hour, and each sub-area was continuously circled for measurement at least 10 times, with a total measurement time of 3 to 8 hours. It should be noted that the UAV measurement data and vehicle-mounted measurement data collected during the measurement process include data used to characterize the spatial location of the measurement points, as well as data used to assist in the calculation of methane emission rates, such as methane gas concentration, three-dimensional wind speed and direction, and meteorological data.

[0019] Specifically, the study acquires UAV-based and vehicle-mounted measurement data from the same target emission source, respectively. The UAV-based equipment collects high-altitude data while orbiting the target emission source, while the vehicle-mounted equipment collects near-ground data while orbiting the source. These separate UAV and vehicle-mounted measurement data, taken from different spatial altitudes, together constitute a complete observation of the target emission source. This air-to-ground collaborative orbital measurement method provides more comprehensive spatial coverage of the collected data, offering a data foundation for subsequent methane emission rate calculations that simultaneously encompasses both high-altitude diffusion and near-ground concentration distribution.

[0020] S102. Construct a flight trajectory based on the first position data of each trajectory point in the UAV measurement data, divide the flight trajectory into layers according to the altitude in the first position data, determine the line flux integral value corresponding to each layer of flight trajectory based on the first emission attribute data corresponding to the trajectory points in the UAV measurement data, and determine the first methane emission rate of the target emission source based on the line flux integral value corresponding to each layer of flight trajectory.

[0021] The first emission attribute data includes the first methane gas concentration, the first three-dimensional wind speed data, and the first meteorological data.

[0022] The first position data refers to the spatial coordinate information corresponding to each trajectory point during the UAV's measurement process, used to determine the specific location and altitude of the trajectory point. This first position information includes longitude, latitude, and altitude. The flight trajectory is constructed by connecting the horizontal positions of each trajectory point in the UAV's measurement data to form a planar path, and by establishing a mapping relationship between curve length and altitude based on the altitude of each trajectory point, thus creating a continuous spatial trajectory.

[0023] In one optional implementation, the step of constructing a flight trajectory based on the first position data of each trajectory point in the UAV measurement data includes: placing the first position data corresponding to each trajectory point in the UAV measurement data on a two-dimensional plane with longitude as the horizontal axis and latitude as the vertical axis, and fitting each trajectory point to obtain a flight trajectory fitting curve; determining the functional relationship between the curve length and the longitude and latitude based on the flight trajectory fitting curve; and establishing a two-dimensional flight cross section as the flight trajectory based on the functional relationship and the altitude corresponding to the curve length.

[0024] Specifically, by mapping the trajectory points in the UAV measurement data to a two-dimensional plane with longitude as the horizontal axis and latitude as the vertical axis and performing curve fitting, discrete trajectory points are transformed into continuous flight path curves, i.e., flight path fitting curves. Based on the flight path fitting curves, a curve length parameter is introduced to establish a functional relationship between the spatial position of the trajectory points and the curve length, thereby realizing the parameterized expression of the trajectory. Combined with the altitude corresponding to each trajectory point, a two-dimensional flight cross section is constructed with the curve length as the horizontal axis and the altitude as the vertical axis, thereby characterizing the flight trajectory of the UAV.

[0025] It should be noted that because the UAV-borne equipment performs orbital flight missions at different altitudes during actual measurements, the UAV measurement data exhibits a layered acquisition structure that varies with altitude. Therefore, the flight trajectory can be divided into multiple independent altitude layers based on the altitude of each trajectory point in the first position data. The first emission attribute data, excluding the first position data, comprises attribute information related to methane emission rate inversion from the UAV measurement data, including the first methane gas concentration, first three-dimensional wind speed data, and first meteorological data; the first emission attribute data and the first position data together constitute the UAV measurement data. The linear flux integral value refers to the cumulative amount obtained by integrating the methane flux along the flight trajectory, used to characterize the overall transport intensity of methane flux at the corresponding altitude layer. The first methane emission rate refers to the methane emission rate corresponding to the target emission source, calculated through inversion based on the UAV measurement data.

[0026] Specifically, based on the first position data from UAV measurement data, flight trajectories are constructed by connecting and modeling various trajectory points. These trajectories are then layered according to the altitude corresponding to each trajectory point to clarify the observation paths within different altitude ranges. Building upon this, the flux changes along the path direction of each flight trajectory layer are calculated by combining the first emission attribute data corresponding to the trajectory points in the UAV measurement data. This yields the linear flux integral value for each layer, which is then used to integrate the methane emissions from the target emission source, ultimately determining the first methane emission rate of the target emission source. This method achieves a layered characterization of methane emission rates at different altitudes. Compared to overall averaging or cross-sectional integration-based estimation methods, the linear integral form along the flight trajectory directly utilizes continuous data from the actual observation path for cumulative calculation, effectively improving the accuracy and stability of emission rate inversion and enhancing the ability to cope with non-uniform methane emission distribution under complex airflow conditions.

[0027] S103. Based on the on-board measurement data, the initial grid flux of each grid in the emission section is calculated using the mass balance method, and the initial grid flux is expanded and integrated using the Gaussian plume model to obtain the second methane emission rate of the target emission source.

[0028] The mass balance method, based on the principle of mass conservation, uses methane concentration and wind field information obtained from onboard measurement data to constrain the gas transport process within a spatial region constructed around the target emission source. It calculates the overall gas flux balance to inversely derive the emission source intensity. The emission cross-section refers to a closed or nearly closed spatial measurement area surrounding the target emission source, used to carry measurement data such as methane gas concentration and three-dimensional wind speed at different locations, serving as the spatial carrier for flux calculation and mass balance analysis. The initial grid flux refers to the gas flux distribution per unit area calculated based on onboard measurement data corresponding to each grid location after the emission cross-section is gridded, used to characterize the methane emission intensity at different locations within the emission cross-section. The second methane emission rate is the methane emission rate corresponding to the target emission source, calculated through inversion based on onboard measurement data.

[0029] Specifically, based on vehicle-mounted measurement data, within an emission cross-section constructed around the target emission source, the methane gas concentration and three-dimensional wind speed data at each measurement location are uniformly calculated using a mass balance method to obtain the initial grid flux corresponding to each grid in the emission cross-section. Building upon this, to address the issue of incomplete spatial coverage of the vehicle-mounted measurement data during the measurement process, a Gaussian plume model is introduced to spatially expand the initial grid flux. The expanded grid flux is then integrated globally to obtain the second methane emission rate corresponding to the target emission source. This approach, using vehicle-mounted measurement data as a highly reliable source of fundamental data, and prioritizing the retention of measured data constraints while only compensating for uncovered areas through model expansion, effectively reduces model-introduced errors and improves the accuracy and reliability of methane emission rate estimation results compared to methods that rely entirely on model assumptions or fit and reconstruct the entire domain.

[0030] S104. Sum the first methane emission rate and the second methane emission rate to determine the final methane emission rate corresponding to the target emission source.

[0031] Specifically, the first methane emission rate derived from UAV measurement data is summed with the second methane emission rate calculated from vehicle-mounted measurement data to determine the final methane emission rate corresponding to the target emission source. This method integrates methane emission information acquired by UAV-borne equipment in the high-altitude range with methane emission information acquired by vehicle-mounted equipment in the near-surface layer, enabling unified utilization of measurement data from different spatial ranges. Compared to calculation methods based on a single data source, this effectively compensates for the measurement blind spots of both UAV-borne and vehicle-mounted equipment, improves the completeness of measurement data acquisition, and thus enhances the accuracy and reliability of the final methane emission rate calculation results.

[0032] The technical solution of this application embodiment constructs a layered flight trajectory based on UAV measurement data and calculates the linear flux integral value along the flight trajectory to obtain the first methane emission rate, thereby avoiding spatial interpolation errors caused by cross-sectional integration and improving calculation accuracy. At the same time, the initial grid flux is calculated based on vehicle-mounted measurement data using the mass balance method, and the second methane emission rate is obtained by integrating the data after expanding and compensating the areas not covered by the initial grid flux using a Gaussian plume model. This compensates for measurement blind spots and reduces errors while retaining the constraints of measured data. The first and second methane emission rates are fused, and the complementarity between UAV high-altitude data and vehicle-mounted near-ground data is utilized to achieve more complete spatial coverage of the measurement data, thereby improving the accuracy and reliability of the methane emission rate calculation results.

[0033] Example 2 Figure 2This is a flowchart of another methane emission rate calculation method provided in Embodiment 1 of this application. The technical solution of this embodiment further refines the method for determining the first methane emission rate based on the technical solution of the above embodiment. For example... Figure 2 As shown, the method includes: S201. Obtain UAV measurement data and vehicle measurement data obtained from UAV-borne equipment and vehicle-mounted equipment respectively measuring the target emission source.

[0034] S202. Construct flight trajectories based on the first position data of each trajectory point in the UAV measurement data, divide the flight trajectories into layers according to the altitude in the first position data, and determine the linear flux integral value corresponding to each layer of flight trajectories based on the first emission attribute data of the trajectory points in each layer of flight trajectories in the UAV measurement data.

[0035] S203. Based on the first methane gas concentration in the first emission attribute data corresponding to each flight path, divide the altitude range into at least two altitude intervals, and divide each flight path into its corresponding altitude interval based on the altitude corresponding to each flight path.

[0036] S204. Perform statistical averaging on the linear flux integral values ​​corresponding to each flight path in each altitude range to obtain the average linear flux value for each altitude range.

[0037] S205. The average linear flux corresponding to each altitude interval is integrated piecewise along the altitude to obtain the first methane emission rate.

[0038] An altitude range refers to a continuous altitude range obtained by dividing the altitude range. Each flight trajectory is assigned to a corresponding altitude range based on its corresponding altitude. The altitude range refers to the altitude range covered by the flight trajectory measurement data. Specifically, based on the obtained linear flux integral values ​​for each flight trajectory, the continuous altitude range is divided into at least two altitude ranges according to the first methane gas concentration in the first emission attribute data corresponding to each flight trajectory. Each flight trajectory is then assigned to its corresponding altitude range to achieve segmented processing of the linear flux integral values ​​for flight trajectories within different altitude ranges. Within each altitude range, the linear flux integral values ​​for each flight trajectory within that range are statistically averaged to reduce the impact of single-layer data fluctuations on the overall result, yielding the average linear flux for each altitude range. Based on this, the average linear flux for each altitude range is segmented and integrated along the altitude direction to achieve cumulative calculation of the emission contribution in the entire vertical direction, thus obtaining the first methane emission rate. This method transforms discrete, layered data into a piecewise smoothed, continuous integration process. Compared to directly accumulating data based on a single layer, this effectively reduces measurement noise and local anomalies, improving the accuracy of methane emission rate calculations.

[0039] In one optional embodiment, dividing the altitude range into at least two altitude intervals based on the first methane gas concentration in the first emission attribute data corresponding to each flight trajectory includes: determining the average methane gas concentration corresponding to each flight trajectory based on the first methane gas concentration in the first emission attribute data corresponding to the trajectory points in each flight trajectory; dividing the altitude range into multiple basic altitude intervals according to a preset altitude step size; determining the interval concentration based on the average methane gas concentration corresponding to the flight trajectory within each basic altitude interval; and further subdividing or merging the basic altitude intervals according to the interval concentration to obtain multiple altitude intervals with different altitude interval widths.

[0040] In this embodiment, the average methane gas concentration refers to the concentration value representing the overall emission level of the flight trajectory, obtained by statistically averaging the first methane gas concentration corresponding to each trajectory point within the same flight trajectory. For example, if the methane gas concentrations collected at an altitude of approximately 150m for a certain flight trajectory are 2.8ppm, 3.0ppm, and 3.2ppm, then the average methane gas concentration corresponding to that flight trajectory is 3.0ppm. The preset altitude step size refers to the fixed altitude interval used when initially dividing the altitude range. For example, if the altitude range of 0-300m is divided into steps of 50m, then the preset altitude step size is 50m, corresponding to basic altitude intervals such as [0, 50] and [50, 100]. The interval concentration refers to the concentration value representing the overall emission level of a certain altitude range, obtained by statistically averaging the average methane gas concentration of all flight paths falling within that basic altitude range. For example, if there are three flight paths within the basic altitude range [150, 200] with average methane gas concentrations of 3.0 ppm, 3.3 ppm, and 3.1 ppm, then the interval concentration can be the average of the three, approximately 3.13 ppm.

[0041] Specifically, the average methane concentration of the flight trajectory at each trajectory point is calculated based on the first methane concentration data, which characterizes the overall emission level of that altitude layer. Within the altitude range covered by the entire flight trajectory measurement data, multiple basic altitude intervals are initially divided according to a preset altitude step size. The average methane concentration of flight trajectories falling within each basic altitude interval is then aggregated to determine the interval concentration. Based on this, the basic altitude intervals are adjusted according to their concentrations: in areas with higher concentrations, subdivisions are performed to narrow the interval width; in areas with lower concentrations, merging is performed to widen the interval width, resulting in multiple altitude intervals with different widths. This method ensures that the altitude interval division matches the actual distribution characteristics of methane gas, increasing the fineness of the division in high-emission areas and reducing redundant divisions in low-emission areas, thereby improving the representativeness of subsequent linear flux integral values ​​within each altitude interval while maintaining computational efficiency.

[0042] Optionally, subdividing or merging the base height intervals based on interval concentration refers to the process of adjusting the base height intervals based on a preset interval concentration threshold. Specifically, when the interval concentration of a certain base height interval is higher than the preset interval concentration threshold, a subdivision operation is performed on the base height interval; when the interval concentration of a certain base height interval is lower than or equal to the preset interval concentration threshold, a merging operation is performed on the base height interval. Specifically, the subdivision operation divides a single base height interval into multiple smaller continuous height intervals according to preset subdivision rules. The subdivision rules can be equal intervals or division according to a preset minimum interval width. For example, when the interval concentration of the base height interval [150, 200] is 3.2 ppm and higher than the preset interval concentration threshold of 2.5 ppm, it can be equally subdivided into [150, 170] and [170, 200]. The merging operation combines adjacent base height intervals to form a larger continuous height interval. The merging rule is to continuously merge adjacent base height intervals whose interval concentrations are all lower than or equal to the preset interval concentration threshold. For example, when the interval concentrations of the base height intervals [0, 50] and [50, 100] are 1.0 ppm and 1.2 ppm respectively and are both lower than the preset interval concentration threshold of 2.5 ppm, they can be merged into [0, 100], thereby reducing the redundancy of dividing low-concentration areas and improving computational efficiency.

[0043] S206. Based on the vehicle-mounted measurement data, the initial grid flux of each grid in the emission section is calculated using the mass balance method, and the initial grid flux is expanded and integrated using the Gaussian plume model to obtain the second methane emission rate of the target emission source.

[0044] S207. Sum the first methane emission rate and the second methane emission rate to determine the final methane emission rate corresponding to the target emission source.

[0045] The technical solution of this embodiment further refines the method for determining the first methane emission rate. By dividing the altitude range into at least two altitude intervals and dividing each flight trajectory into its corresponding altitude interval, the linear flux integral value corresponding to each flight trajectory is statistically averaged within each altitude interval, and then piecewise integral calculation is performed along the altitude direction. This achieves piecewise smoothing and continuous accumulation calculation of the vertical emission contribution. In this process, by averaging the data of multiple layers within the same altitude interval, random measurement noise is mutually canceled within the group, and the amplified influence of individual abnormal layers on the overall result is weakened. This avoids the cumulative error introduced by the direct participation of single-layer data fluctuations in the integration. Compared with the method of directly accumulating layer by layer, the obtained methane emission rate is more stable, continuous, and closer to the actual emission situation, effectively improving the accuracy and reliability of the calculation results.

[0046] In one optional embodiment, the first emission attribute data includes a first methane gas concentration, a first three-dimensional wind speed data, and first meteorological data; determining the linear flux integral value corresponding to each flight trajectory based on the first emission attribute data corresponding to the trajectory points in the UAV measurement data of each flight trajectory includes: determining the vertical wind speed vector corresponding to the trajectory points in each flight trajectory based on the first three-dimensional wind speed data; determining the air density corresponding to the trajectory points in each flight trajectory based on the first meteorological data; and determining the linear flux integral value corresponding to each flight trajectory based on the vertical wind speed vector, the air density, and the first methane gas concentration corresponding to the trajectory points in each flight trajectory.

[0047] It should be noted that the UAV measurement data collected by the UAV-borne equipment also includes UAV attitude data. During the data preprocessing stage, the three-dimensional wind speed data is processed using the first position data and the UAV attitude data to obtain the corresponding horizontal wind speed and direction, which are used to characterize the horizontal wind vector. Based on the first three-dimensional wind speed data, the vertical wind speed vector corresponding to the trajectory points in each flight path is determined, specifically as follows: the horizontal wind vector is decomposed into northward and eastward components. and ,and The length of the curve along the flight path is expressed in latitude and longitude. and The function, i.e. , This represents the altitude of the corresponding trajectory point. Therefore, the vertical wind vector... The formula for calculation is: .

[0048] First-level meteorological data refers to meteorological data collected by the UAV during flight, including meteorological parameters such as temperature, air pressure, and dew point temperature. Based on this first-level meteorological data, the air density corresponding to the trajectory points in each flight path is determined as follows: According to the moist air state equation, the air density corresponding to the trajectory points in each flight path is... The calculation formula is ,in, , , These represent the temperature, air pressure, and dew point temperature in the first meteorological data, respectively. Indicates relative moisture; Represents the gas constant of dry air, taking... ; This represents the empirical constant used in calculating water vapor saturation pressure, and is taken as... ; This represents the temperature-dependent constant in the calculation of water vapor saturation pressure, taken as... ; This represents the ratio of the gas constant of dry air to that of water vapor, taking... .

[0049] Based on the vertical wind speed vector, air density, and the first methane gas concentration corresponding to the trajectory points in each flight path, the linear flux integral value corresponding to each flight path is determined. Specifically, at each altitude level, the corresponding linear flux integral value is calculated based on each trajectory point in the flight path. The calculation formula is as follows: ; in, This indicates the altitude corresponding to the flight trajectory. Indicates the length of the curve along the flight path. Indicates an altitude of The curve length is The first methane gas concentration at the location, This represents the ratio of the molar mass of methane to the molar mass of air. It is the distance flown within a 1-second time interval for each measurement. It is the distance of a complete flight trajectory.

[0050] Specifically, for each trajectory point in the flight path at each level, the corresponding vertical wind speed vector is determined based on the first three-dimensional wind speed data; the corresponding air density is determined based on the first meteorological data; and based on this, the vertical wind speed vector, air density, and first methane gas concentration are correlated and calculated together with the first methane gas concentration corresponding to each trajectory point. This is then accumulated along the flight path for each trajectory point to determine the linear flux integral value corresponding to each flight path. This method enables the determination of the linear flux integral value based on the first emission attribute data corresponding to the trajectory points in each flight path, ensuring consistency between the calculation process and the UAV measurement data, thereby improving the accuracy of the linear flux integral value determination.

[0051] In one optional implementation, the step of calculating the initial grid flux of each grid in the emission cross section based on vehicle-mounted measurement data using the mass balance method includes: constructing an emission cross section with the vehicle travel distance as the horizontal axis and the measurement height as the vertical axis based on the second position data of each trajectory point in the vehicle-mounted measurement data; dividing the emission cross section into grids, and using the mass balance method combined with the second emission attribute data corresponding to each trajectory point in the UAV measurement data to determine the initial grid flux corresponding to each grid; wherein, the second emission attribute data includes second meteorological data, second three-dimensional wind speed data, and a second methane gas concentration corresponding to a preset measurement height.

[0052] The second emission attribute data is the attribute information related to methane emission rate inversion in the vehicle-mounted measurement data, excluding the second location data. It includes the second methane gas concentration, the second three-dimensional wind speed data, and the second meteorological data. The second emission attribute data and the second location data together constitute the UAV measurement data.

[0053] Optionally, during the actual measurement process of the on-board equipment, two different driving modes can be preset: one is to circle the target emission source and obtain the gas emission cross-section of the entire outer perimeter surrounding the target emission source; the other is to obtain the gas emission cross-section downwind of the target emission source on a road downwind of the emission source, provided that there are no obvious methane interference sources around the emission source. The second methane gas concentration data measured according to the two different driving modes are both three-dimensional data composed of longitude, latitude, and altitude. To simplify processing, the two-dimensional data of longitude and latitude is converted from a preset starting point. Initial vehicle-mounted equipment travel distance The formula for calculating the distance between points refers to the spherical semi-positive vector formula. After the conversion, the spatial gas concentration data is transformed into two-dimensional data consisting of the driving distance L and altitude h, while the second meteorological data is transformed into one-dimensional data consisting of the driving distance L. On the two-dimensional plane consisting of the driving distance L and altitude h, it is used as the emission section, with the driving distance as 0 (i.e., the preset starting point). The point with a height of 0 is designated as the origin of the emission cross-section, and the second methane emission rate will be calculated based on this cross-section. After generating the emission cross-section, it is divided into grids starting from the origin. For each grid, the second meteorological data and the second methane gas concentration values ​​within each grid cell can be approximated as being consistent, and the value at the center point of that grid cell is used as the representative value for that grid.

[0054] Specifically, based on the second location data of each trajectory point in the vehicle-mounted measurement data, an emission cross-section is constructed with the vehicle's travel distance on the horizontal axis and the measurement altitude on the vertical axis. This emission cross-section is divided into grid cells, and within each grid, the initial grid flux is calculated using the mass balance method, incorporating the second emission attribute data corresponding to each trajectory point in the UAV measurement data. This yields the baseline emission values ​​for each grid. The second emission attribute data includes second meteorological data, second three-dimensional wind speed data, and the second methane gas concentration at the corresponding preset measurement altitude. This ensures that the flux calculation for each grid cell simultaneously considers the combined effects of wind speed, meteorological conditions, and methane concentration. This method allows for independent calculation of the flux for each grid cell on the emission cross-section, and, combined with meteorological data and wind speed information at the measurement altitude, forms the initial grid flux for each grid, providing foundational data for subsequent emission rate calculations.

[0055] For example, since the actual measurement points for the second meteorological data and the second methane gas concentration cannot cover all grid cells of the emission section, interpolation is required. For second meteorological data such as atmospheric temperature and atmospheric pressure, the vertical changes are not significant in the lower layers of the Earth's surface (within 15m). Therefore, the measurement data obtained by the meteorological parameter sensor at a single altitude can be directly used for the grid cells at each altitude, while linear interpolation is used to complete the data for each grid cell in the direction of travel. For the measurement data in the direction of travel, since the platform and the multi-altitude measurement device move in the same direction, the vertical changes have a small impact. Therefore, linear interpolation is also used to supplement the data in the horizontal direction.

[0056] For example, three-dimensional wind speed data is typically measured only at a single height, but surface wind speed fluctuates vertically and cannot be directly substituted. Therefore, empirical atmospheric vertical profile relationships are used to extend the calculation of wind speed at the same location. The calculation formula is as follows: ;in, and These are the target height and the measured height, respectively. and These represent the wind speeds at corresponding altitudes. This is an empirical coefficient for the atmospheric vertical profile, related to surface roughness, and typically taken as approximately 0.2 for urban surfaces. After expanding the three-dimensional wind speed data at different vertical heights, linear interpolation is then used to horizontally interpolate the three-dimensional wind speed data at each height into the emission section grid.

[0057] For example, the distribution of methane gas concentration in the atmosphere is influenced by multiple factors and exhibits a complex distribution across the emission cross-section, making simple one-dimensional linear interpolation unsuitable. Therefore, ordinary kriging interpolation is employed to interpolate discrete measurement points into a regular grid across the entire emission cross-section. Based on the spatial distribution characteristics of methane gas concentration in the atmosphere, a spherical kriging function is used for fitting, and the calculation formula is as follows: ; in, The distance between two points The corresponding semivariance, , and These are the nugget value, sill value, and range value for Kriging interpolation. These parameters determine the spatial correlation of gas concentrations at different grid locations for the emission source. After obtaining the fitting coefficients of the Kriging interpolation, the semivariance can be calculated using the distance between two points, thereby estimating the methane gas concentration within the unknown grid. By calculating for each grid point of the emission cross-section grid, the methane gas concentration distribution within the entire emission cross-section grid can be obtained.

[0058] For example, the initial grid emissions are calculated using the mass balance method, including: the second methane gas concentration at each grid point of the emission cross-section. Since the concentration measured by the instrument is generally in volume fraction (ppm), it is necessary to combine it with temperature. and atmospheric pressure The unit conversion is performed using the ideal gas law, and the calculation formula is as follows: ;in, Let be the molar mass of the gas. After completing the concentration unit conversion, the initial grid emission flux within each grid is calculated using the following formula: ;in, Let be the mass concentration of methane gas at grid point (i,j). The background value of gas concentration during a single measurement is taken from the lowest 10% quantile. The wind speed value is the value for the grid. The unit normal vector of the concentration cross section facing outward from the emission source is determined by the angle between the vehicle's driving direction and the wind direction. This represents the grid area, in cubic meters. Note that in the formula... This represents the increase in the concentration of the target gas during the measurement period, and in principle, it should not be less than zero. However, since the background value is taken from the lowest 10% quantile, there may be cases where the concentration increase is less than zero. To ensure the rationality of the calculation, values ​​less than zero are replaced with zero, thereby avoiding the impact of negative values ​​on the emission flux calculation.

[0059] In one optional embodiment, the step of expanding and integrating the initial grid flux using a Gaussian plume model to obtain the second methane emission rate of the target emission source includes: determining a Gaussian plume region based on the emission cross-section; obtaining a lateral diffusion coefficient by fitting the center of the Gaussian plume region along the horizontal direction, and determining the lateral range of the plume; integrating the second methane gas concentration corresponding to each preset measurement height within the lateral range of the plume to obtain an integrated concentration value; fitting the integrated concentration value in the vertical direction to obtain a vertical diffusion coefficient; expanding the initial grid flux in the vertical direction based on the lateral diffusion coefficient and the vertical diffusion coefficient to obtain an expanded grid flux; and integrating the expanded grid flux to obtain the second methane emission rate.

[0060] A Gaussian plume refers to a concentration cloud region formed by methane gas emitted from a target emission source in the atmosphere under the influence of wind and turbulence. This region exhibits a Gaussian distribution in both the horizontal and vertical directions, and its diffusion characteristics can be obtained by fitting a finite number of measurement points. The lateral diffusion coefficient describes the degree of horizontal diffusion of the plume and can be obtained by fitting the concentration change along the horizontal direction at the plume center using a Gaussian distribution. The lateral extent of the plume refers to the spatial width of the plume with a significant concentration distribution in the horizontal direction, determined by the lateral diffusion coefficient. The vertical diffusion coefficient describes the intensity and height distribution characteristics of the vertical diffusion of the plume and can be obtained by fitting the integrated concentration values ​​in the vertical direction. The extended grid flux refers to the flux value after spatially expanding the flux according to the lateral and vertical diffusion coefficients based on the initial grid flux. It includes the initial portion of the original measurement and the portion extended according to the Gaussian plume model, and is used to reflect the continuous distribution of gas within the emission cross-section.

[0061] Specifically, at the emission cross-section, based on the second methane gas concentration at each grid point, a Gaussian distribution fitting method is used to determine the Gaussian plume region formed by methane emissions. The methane concentration distribution is then fitted horizontally along the plume center to obtain the lateral diffusion coefficient, simultaneously determining the lateral range of the plume. Within the lateral range of the plume, the second methane gas concentration corresponding to each preset measurement height is integrated vertically to obtain the integrated concentration value. This integrated concentration value is then fitted vertically to obtain the vertical diffusion coefficient. Based on the lateral and vertical diffusion coefficients, the initial grid flux is extended vertically to form a complete extended grid flux. This extended grid flux is then integrated to obtain the second methane emission rate. This method allows for the reasonable supplementation of methane emission information in areas not covered by onboard measurement data based on the initial grid flux, making the final calculated methane emission rate more complete and continuous, thus improving the spatial accuracy and overall accuracy of methane emission rate measurement.

[0062] Example 3 Figure 3 This is a schematic diagram of a methane emission rate calculation device according to Embodiment 3 of this application. This embodiment is applicable to applications that quantitatively monitor methane emissions from various industrial sites. The methane emission rate calculation device can be implemented in hardware and / or software and can be configured in a computer device. Figure 3 As shown, the methane emission rate measuring device 300 includes: The measurement data acquisition module 310 is used to acquire UAV measurement data and vehicle measurement data obtained by UAV-borne equipment and vehicle-borne equipment respectively measuring the target emission source. The first emission rate determination module 320 is used to construct a flight trajectory based on the first position data of each trajectory point in the UAV measurement data, divide the flight trajectory into layers according to the altitude in the first position data, determine the line flux integral value corresponding to each layer of flight trajectory based on the first emission attribute data corresponding to the trajectory points in the UAV measurement data, and determine the first methane emission rate of the target emission source based on the line flux integral value corresponding to each layer of flight trajectory; wherein, the first emission attribute data includes a first methane gas concentration, a first three-dimensional wind speed data, and a first meteorological data; The second emission rate determination module 330 is used to calculate the initial grid flux of each grid in the emission section based on the on-board measurement data using the mass balance method, and to expand and integrate the initial grid flux using the Gaussian plume model to obtain the second methane emission rate of the target emission source. The final emission rate determination module 340 is used to sum the first methane emission rate and the second methane emission rate to determine the final methane emission rate corresponding to the target emission source.

[0063] In one alternative embodiment, the first emission rate determination module 320 is specifically used for: Based on the first methane gas concentration in the first emission attribute data corresponding to each flight path, the altitude range is divided into at least two altitude intervals, and each flight path is divided into its corresponding altitude interval based on the altitude corresponding to each flight path. The linear flux integral values ​​corresponding to each flight path in each altitude range are statistically averaged to obtain the average linear flux value for each altitude range. The first methane emission rate is obtained by integrating the average linear flux corresponding to each altitude interval piecewise along the altitude range.

[0064] In one alternative embodiment, the first emission rate determination module 320 is specifically used for: The average methane concentration corresponding to each flight trajectory is determined based on the first methane gas concentration in the first emission attribute data corresponding to the trajectory points in each flight trajectory. The altitude range is divided into multiple basic altitude intervals based on the preset altitude step size; The interval concentration is determined based on the average methane gas concentration corresponding to the flight trajectory within each basic altitude interval. The basic height interval is subdivided or merged based on the interval concentration to obtain multiple height intervals with different height interval widths.

[0065] In one alternative embodiment, the second emission rate determination module 330 is specifically used for: The Gaussian plume region is determined based on the emission cross section; The lateral diffusion coefficient is obtained by fitting the center of the Gaussian plume region along the horizontal direction, and the lateral range of the plume is determined. Within the lateral range of the plume, the concentration of the second methane gas corresponding to each preset measurement height is integrated to obtain the integrated concentration value; The vertical diffusion coefficient is obtained by fitting the integrated concentration value in the vertical direction. Based on the lateral diffusion coefficient and the vertical diffusion coefficient, the initial grid flux is expanded in the vertical direction to obtain the expanded grid flux; Integrating the extended grid flux yields the second methane emission rate.

[0066] In one optional embodiment, the first emission rate determination module 320 is specifically used to: the first emission attribute data includes a first methane gas concentration, a first three-dimensional wind speed data, and a first meteorological data; Based on the first three-dimensional wind speed data, determine the vertical wind speed vector corresponding to the trajectory points in each flight trajectory layer; Based on the first meteorological data, determine the air density corresponding to the trajectory points in each flight path; Based on the vertical wind speed vector, the air density, and the first methane gas concentration corresponding to the trajectory points in each flight path, the linear flux integral value corresponding to each flight path is determined.

[0067] In one alternative embodiment, the first emission rate determination module 320 is specifically used for: Place the first position data corresponding to each trajectory point in the UAV measurement data into a two-dimensional plane with longitude as the horizontal axis and latitude as the vertical axis, and fit each trajectory point to obtain the trajectory fitting curve. Based on the fitted curve of the flight path, determine the functional relationship between the curve length and latitude and longitude; A two-dimensional flight cross section is established based on the functional relationship and the altitude corresponding to the curve length, serving as the flight trajectory.

[0068] In one alternative embodiment, the second emission rate determination module 330 is specifically used for: Based on the second position data of each trajectory point in the vehicle measurement data, an emission cross section is constructed with the horizontal axis representing the vehicle's travel distance and the vertical axis representing the measurement height. The emission cross section is divided into grids, and the initial grid flux corresponding to each grid is determined by using the mass balance method combined with the second emission attribute data corresponding to each trajectory point in the UAV measurement data; wherein, the second emission attribute data includes second meteorological data, second three-dimensional wind speed data and second methane gas concentration corresponding to the preset measurement height.

[0069] The methane emission rate calculation device provided in this application embodiment can execute the methane emission rate calculation method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects of the method execution.

[0070] This application also provides an electronic device, a readable storage medium, and a computer program product. The computer-readable storage medium stores a computer program that, when executed by a processor, implements the arbitrary methane emission rate calculation method of this application.

[0071] Example 4 Figure 4 This is a schematic diagram of the structure of an electronic device that implements the methane emission rate calculation method of the embodiments of this application. Figure 4 A schematic diagram of an electronic device 410 that can be used to implement embodiments of this application is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the application described and / or claimed herein.

[0072] like Figure 4 As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory (ROM) 412 or a random access memory (RAM) 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the ROM 412 or loaded from storage unit 418 into the RAM 413. The RAM 413 may also store various programs and data required for the operation of the electronic device 410. The processor 411, ROM 412, and RAM 413 are interconnected via a bus 414. An input / output (I / O) interface 415 is also connected to the bus 414.

[0073] Multiple components in electronic device 410 are connected to I / O interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of displays, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0074] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as the methane emission rate calculation method.

[0075] In some embodiments, the methane emission rate calculation method may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program may be loaded and / or installed on electronic device 410 via ROM 412 and / or communication unit 419. When the computer program is loaded into RAM 413 and executed by processor 411, one or more steps of the methane emission rate calculation method described above may be performed. Alternatively, in other embodiments, processor 411 may be configured to perform the methane emission rate calculation method by any other suitable means (e.g., by means of firmware).

[0076] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0077] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0078] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0079] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0080] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0081] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0082] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0083] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for calculating methane emission rate, characterized in that, include: Acquire drone measurement data and vehicle-mounted measurement data obtained from the measurement of the target emission source by drone-borne equipment and vehicle-mounted equipment, respectively. A flight trajectory is constructed based on the first position data of each trajectory point in the UAV measurement data. The flight trajectory is layered according to the altitude in the first position data. Based on the first emission attribute data corresponding to the trajectory points in each layer of the flight trajectory in the UAV measurement data, the line flux integral value corresponding to each layer of the flight trajectory is determined. The first methane emission rate of the target emission source is determined based on the line flux integral value corresponding to each layer of the flight trajectory. Based on on-board measurement data, the initial grid flux of each grid in the emission section is calculated using the mass balance method, and the initial grid flux is expanded and integrated using the Gaussian plume model to obtain the second methane emission rate of the target emission source. The first methane emission rate and the second methane emission rate are summed to determine the final methane emission rate corresponding to the target emission source.

2. The method according to claim 1, characterized in that, The step of determining the first methane emission rate of the target emission source based on the linear flux integral value corresponding to each flight trajectory includes: Based on the first methane gas concentration in the first emission attribute data corresponding to each flight path, the altitude range is divided into at least two altitude intervals, and each flight path is divided into its corresponding altitude interval based on the altitude corresponding to each flight path. The linear flux integral values ​​corresponding to each flight path in each altitude range are statistically averaged to obtain the average linear flux value for each altitude range. The first methane emission rate is obtained by integrating the average linear flux corresponding to each altitude interval piecewise along the altitude range.

3. The method according to claim 2, characterized in that, The altitude range is divided into at least two altitude intervals based on the first methane gas concentration in the first emission attribute data corresponding to each flight trajectory, including: The average methane concentration corresponding to each flight trajectory is determined based on the first methane gas concentration in the first emission attribute data corresponding to the trajectory points in each flight trajectory. The altitude range is divided into multiple basic altitude intervals based on the preset altitude step size; The interval concentration is determined based on the average methane gas concentration corresponding to the flight trajectory within each basic altitude interval. The basic height interval is subdivided or merged based on the interval concentration to obtain multiple height intervals with different height interval widths.

4. The method according to claim 1, characterized in that, The step of integrating the expanded initial grid flux using a Gaussian plume model to obtain the second methane emission rate of the target emission source includes: The Gaussian plume region is determined based on the emission cross section; The lateral diffusion coefficient is obtained by fitting the center of the Gaussian plume region along the horizontal direction, and the lateral range of the plume is determined. Within the lateral range of the plume, the concentration of the second methane gas corresponding to each preset measurement height is integrated to obtain the integrated concentration value; The vertical diffusion coefficient is obtained by fitting the integrated concentration value in the vertical direction. Based on the lateral diffusion coefficient and the vertical diffusion coefficient, the initial grid flux is expanded in the vertical direction to obtain the expanded grid flux; Integrating the extended grid flux yields the second methane emission rate.

5. The method according to claim 1, characterized in that, The first emission attribute data includes the first methane gas concentration, the first three-dimensional wind speed data, and the first meteorological data; based on the first emission attribute data corresponding to the trajectory points in each flight trajectory and the UAV measurement data, the linear flux integral value corresponding to each flight trajectory is determined, including: Based on the first three-dimensional wind speed data, determine the vertical wind speed vector corresponding to the trajectory points in each flight trajectory layer; Based on the first meteorological data, determine the air density corresponding to the trajectory points in each flight path; Based on the vertical wind speed vector, the air density, and the first methane gas concentration corresponding to the trajectory points in each flight path, the linear flux integral value corresponding to each flight path is determined.

6. The method according to claim 1, characterized in that, The step of constructing a flight trajectory based on the first position data of each trajectory point in the UAV measurement data includes: Place the first position data corresponding to each trajectory point in the UAV measurement data into a two-dimensional plane with longitude as the horizontal axis and latitude as the vertical axis, and fit each trajectory point to obtain the trajectory fitting curve. Based on the fitted curve of the flight path, determine the functional relationship between the curve length and latitude and longitude; A two-dimensional flight cross section is established based on the functional relationship and the altitude corresponding to the curve length, serving as the flight trajectory.

7. The method according to claim 1, characterized in that, The calculation of the initial grid flux of each grid in the emission section based on on-board measurement data and using the mass balance method includes: Based on the second position data of each trajectory point in the vehicle measurement data, an emission cross section is constructed with the horizontal axis representing the vehicle's travel distance and the vertical axis representing the measurement height. The emission cross section is divided into grids, and the initial grid flux corresponding to each grid is determined by using the mass balance method combined with the second emission attribute data corresponding to each trajectory point in the UAV measurement data; wherein, the second emission attribute data includes second meteorological data, second three-dimensional wind speed data and second methane gas concentration corresponding to the preset measurement height.

8. A methane emission rate measuring device, characterized in that, include: The measurement data acquisition module is used to acquire UAV measurement data and vehicle measurement data obtained by UAV-borne equipment and vehicle-borne equipment respectively from the target emission source; The first emission rate determination module is used to construct a flight trajectory based on the first position data of each trajectory point in the UAV measurement data, divide the flight trajectory into layers according to the altitude in the first position data, determine the line flux integral value corresponding to each layer of flight trajectory based on the first emission attribute data corresponding to the trajectory points in the UAV measurement data, and determine the first methane emission rate of the target emission source based on the line flux integral value corresponding to each layer of flight trajectory; wherein, the first emission attribute data includes the first methane gas concentration, the first three-dimensional wind speed data, and the first meteorological data; The second emission rate determination module is used to calculate the initial grid flux of each grid in the emission section based on on-board measurement data using the mass balance method, and to expand and integrate the initial grid flux using a Gaussian plume model to obtain the second methane emission rate of the target emission source. The final emission rate determination module is used to sum the first methane emission rate and the second methane emission rate to determine the final methane emission rate corresponding to the target emission source.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the methane emission rate calculation method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that cause a processor to execute the method for calculating the methane emission rate according to any one of claims 1-7.

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