Methane emission flux analysis method, device, equipment and storage medium

Abstract

The invention discloses a methane emission flux analysis method, device and equipment and a storage medium, and belongs to the technical field of atmosphere remote sensing monitoring, and the method comprises the following steps: obtaining methane vertical spectrum data and auxiliary observation data under a target area; based on the methane vertical spectrum data and the auxiliary observation data, separately calculating a source methane concentration field in a target height range; performing spatial-temporal characteristic analysis on the source methane concentration field to obtain a plurality of groups of spatial-temporal orthogonal characteristics, the spatial-temporal orthogonal characteristics being used for representing methane column concentration background change characteristics in a target area and dynamic difference of methane column concentration on a transmission path; and performing energy emission source analysis on the multiple groups of space-time orthogonal characteristics to obtain methane emission intensity distribution of the target area. According to the method, the analysis of the methane concentration can be realized through the statistical relationship, and the accurate analysis of the methane emission flux and the emission attribution can be realized.

Description

Technical Field

[0001] This application belongs to the technical field of atmospheric remote sensing monitoring, and in particular relates to a method, apparatus, equipment and storage medium for analyzing methane emission flux. Background Technology

[0002] Methane is the world's second largest greenhouse gas after carbon dioxide. Remote sensing satellites, such as the S5P / TROPOMI satellite, can retrieve methane column concentration (XCH4) data covering the globe once a day by utilizing the absorption spectral characteristics of methane in the 1.65μm or 2.3μm bands. This data can be used to conduct quantitative and attribution analyses of methane emissions from different sources.

[0003] Satellite remote sensing-retrieved methane column concentration (XCH4) data includes atmospheric background values, natural and anthropogenic emissions. A forward model posterior estimation method is typically used to quantify the methane emissions of various influencing factors from satellite methane column concentration data. However, this method requires atmospheric chemical models for analysis, which is computationally expensive, demands significant computing resources, and has relatively coarse spatial resolution. Furthermore, in the absence of a prior inventory or with poor-quality prior inventory data, the prior inventory may significantly influence the results of methane emission flux analysis, leading to poor performance. Summary of the Invention

[0004] The methane emission flux analysis method, apparatus, equipment, and storage medium provided in this application embodiment can analyze methane concentration through statistical relationships, and achieve accurate analysis of methane emission flux and emission attribution.

[0005] In a first aspect, embodiments of this application provide a method for analyzing methane emission flux, the method comprising: Acquire vertical spectral data of methane and auxiliary observation data in the target area; Based on the methane vertical spectral data and auxiliary observation data, the source methane concentration field within the target altitude range is separated and calculated; Spatiotemporal feature analysis was performed on the source methane concentration field to obtain multiple sets of spatiotemporal orthogonal features. These spatiotemporal orthogonal features are used to characterize the background variation characteristics of methane column concentration in the target region and the dynamic differences of methane column concentration along the transport path. Energy emission source analysis was performed on the multiple sets of spatiotemporal orthogonal features to obtain the methane emission intensity distribution in the target region.

[0006] Optionally, the step of separating and calculating the source methane concentration field within the target altitude range based on the methane vertical spectral data and auxiliary observation data includes: Obtain the latitude zone and real-time meteorological data of the target area; Within a preset latitudinal band, the methane vertical spectral data are assimilated based on a preset chemical transport model to obtain assimilation results. Based on the preset vertical probability distribution model of methane concentration, determine the probability density function of methane concentration at the target height in the target area; Based on the preset correction coefficient and the probability density function of methane concentration within the height range, the source methane concentration field within the target height range is obtained by separation calculation.

[0007] Optionally, the auxiliary observation data includes the zenith angle. Before performing spatiotemporal characteristic analysis on the source methane concentration field to obtain multiple sets of spatiotemporal orthogonal features, the method further includes: The methane concentration deviation is determined based on the preset atmospheric scattering modulation factor and zenith angle; Based on the methane concentration deviation, the source methane concentration field is corrected to obtain the corrected methane concentration field; The spatiotemporal feature analysis of the source methane concentration field yields multiple sets of spatiotemporal orthogonal features, including: Spatiotemporal feature analysis was performed on the corrected methane concentration field to obtain multiple sets of spatiotemporal orthogonal features.

[0008] Optionally, the spatiotemporal orthogonal features include a first orthogonal feature and a second orthogonal feature. The first orthogonal feature is used to characterize the variation characteristics of the methane column concentration background concentration, and the second orthogonal feature is used to characterize the dynamic differences of the methane column concentration along the transport path. The spatiotemporal feature analysis of the source methane concentration field yields multiple sets of spatiotemporal orthogonal features, including: The target area is divided into grids to obtain multiple grid points; For each grid point, the methane concentration data of that grid point is reconstructed into a spatiotemporal matrix; The spatiotemporal matrix is ​​subjected to singular value decomposition to obtain multiple first orthogonal features and second orthogonal features. The first orthogonal features include a first mode and a first time coefficient, and the second orthogonal features include a second mode and a second time coefficient.

[0009] Optionally, after performing singular value decomposition on the spatiotemporal matrix to obtain multiple first orthogonal features and second orthogonal features, the method further includes: For each group of first orthogonal features and / or second orthogonal features, the first orthogonal features and / or second orthogonal features are filtered according to preset filtering conditions to obtain the transmission mode; The spatiotemporal matrix is ​​modified based on the transmission mode to obtain the final spatiotemporal matrix.

[0010] Optionally, the step of performing energy emission source analysis on the multiple sets of spatiotemporal orthogonal features to obtain the methane emission intensity distribution of the target region includes: For the target area, obtain the concentration value of at least one pollutant within the target area; For any grid point within the target area, construct the correlation matrix between methane and pollutants; For each pollutant, source feature regions are identified based on the correlation matrix to obtain at least one energy emission source region; Based on the energy emission source areas, the methane emission intensity distribution of the target area is determined.

[0011] Optionally, before determining the methane emission intensity distribution of the target area based on the energy emission source area, the method further includes: Based on a preset radiative transfer correction relationship, the methane column concentration is corrected to obtain the corrected methane column concentration. Based on the preset mass balance equation and the corrected methane column concentration, a distribution map of energy methane emission intensity is drawn.

[0012] Secondly, embodiments of this application provide a methane emission flux analysis device, the device comprising: The acquisition module is used to acquire vertical spectral data of methane and auxiliary observation data in the target area; The separation calculation module is used to separate and calculate the source methane concentration field within the target altitude range based on the methane vertical spectral data and auxiliary observation data. The analysis module is used to perform spatiotemporal feature analysis on the source methane concentration field to obtain multiple sets of spatiotemporal orthogonal features. These spatiotemporal orthogonal features are used to characterize the background variation characteristics of methane column concentration in the target region and the dynamic differences of methane column concentration along the transport path. The analysis module is used to analyze the multiple sets of spatiotemporal orthogonal features to perform energy emission source analysis and obtain the methane emission intensity distribution of the target region.

[0013] Thirdly, embodiments of this application provide an electronic device, the device including: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the methane emission flux analysis method as described in any one of the first aspects.

[0014] Fourthly, embodiments of this application provide a computer storage medium storing computer program instructions, which, when executed by a processor, implement the methane emission flux analysis method as described in any one of the first aspects.

[0015] The methane emission flux analysis method, apparatus, equipment, and computer storage medium described in this application can separate the methane column concentration in the target area by acquiring vertical hyperspectral data and auxiliary observation data of methane in the target area. Then, it performs spatiotemporal characteristic analysis on the methane concentration field, i.e., decoupling the spatiotemporal signals, avoiding the dependence on atmospheric chemical transport models in the prior art. At the same time, it achieves adaptive correction of transport time through spatiotemporal orthogonal characteristics. Then, it performs energy emission source analysis on the spatiotemporal orthogonal characteristics to infer the methane emission source type and eliminate the interference of other pollutants in the target area on the energy methane emission flux. Thus, it can quickly and accurately analyze the methane emission flux and achieve high-efficiency methane emission attribution calculation. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic flowchart of the methane emission flux analysis method in a preferred embodiment of this application; Figure 2 This is a schematic diagram of the structure of the methane emission flux analysis device in a preferred embodiment of this application; Figure 3 This is a schematic diagram of the structure of the electronic device in a preferred embodiment of this application. Detailed Implementation

[0018] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0020] To address the problems of existing technologies, this application provides a method, apparatus, device, and storage medium for methane emission flux analysis. In this application, methane column concentration within the target area is separated by acquiring vertical hyperspectral data and auxiliary observation data of methane in the target area. Then, spatiotemporal characteristic analysis of the methane concentration field is performed, i.e., spatiotemporal signal decoupling, avoiding the dependence on atmospheric chemical transport models in existing technologies. Simultaneously, adaptive correction of transport time is achieved through spatiotemporal orthogonal features. Energy emission source analysis is then performed on the spatiotemporal orthogonal features to infer the methane emission source type, eliminating interference from other pollutants in the target area on energy methane emission flux. This enables rapid and accurate analysis of methane emission flux and achieves highly efficient methane emission attribution calculation.

[0021] The method for analyzing methane emission flux provided in the embodiments of this application will be introduced first below.

[0022] Figure 1 A schematic flowchart of a methane emission flux analysis method according to an embodiment of this application is shown. Figure 1 As shown, the methane emission flux analysis method may include S101-S104: S101, acquire vertical spectral data of methane in the target area and auxiliary observation data.

[0023] In this embodiment of the application, the electronic device can acquire methane vertical spectral data through limb observation satellites such as infrared atmospheric sounding interferometers. The methane vertical spectral data can include the concentration distribution characteristics of different atmospheric layers, which can include the troposphere and stratosphere. For easier understanding of this application, the troposphere is used as an example below.

[0024] As an example, since satellite observation data differs depending on latitude, it is necessary to establish different vertical probability distribution models of methane concentration at different altitudes in different latitude zones. This can be determined by fitting a large amount of experimental data.

[0025] In this embodiment of the application, the auxiliary observation data may include satellite zenith angle and cloud top height data.

[0026] It is worth noting that the methane column concentration can be obtained by assimilating methane vertical spectral data in conjunction with a chemical transport model (CTM). It can be understood that the methane column concentration can be the concentration column of different atmospheric layers in the target area.

[0027] In addition, in order to quickly obtain the source methane concentration field for each latitudinal zone, a vertical probability distribution model of methane concentration can be constructed according to the latitudinal zone to facilitate obtaining the methane column concentration for each latitudinal zone.

[0028] S102, based on methane vertical spectral data and auxiliary observation data, separates and calculates the source methane concentration field within the target altitude range.

[0029] In this embodiment of the application, S102 may specifically include: Obtain the latitude zone and real-time meteorological data of the target area; Within a preset latitudinal band, methane vertical spectral data are assimilated based on a preset chemical transport model to obtain assimilation results. Based on the preset vertical probability distribution model of methane concentration, determine the probability density function of methane concentration at the target height in the target area; Based on the preset correction coefficient and the probability density function of methane concentration within the altitude range, the source methane concentration field within the target altitude range is obtained by separation calculation.

[0030] In this embodiment of the application, in order to separate the tropospheric methane column concentration and obtain the source methane concentration field of the target area within the target altitude range, the latitude zone and real-time meteorological data of the target area can be obtained. Then, the vertical spectral data of methane can be assimilated by a preset chemical transport model. The assimilation result is the methane column concentration obtained above. Then, the methane concentration probability density function can be obtained by a preset methane concentration vertical probability distribution model, which facilitates the acquisition of the methane column concentration.

[0031] Specifically, the methane concentration probability density function can be determined using the following formula (1): (1) in, It is the probability density function of methane concentration at height z (unit: ppb / km). These are the weighting coefficients of the i-th Gaussian sub-distribution (determined by least squares fitting). It is the altitude (in km) of the i-th concentration peak. It is the vertical profile parameter of the i-th concentration peak (unit: km).

[0032] In the embodiments of this application, the methane concentration within the target altitude range can be determined by the above formula (1). For different latitude zones, there can be different parameter changes. The methane concentration can be easily and quickly found by measuring a large amount of experimental data in the early stage.

[0033] In some other embodiments, after obtaining the methane concentration according to the above formula (1), the tropospheric height Ht can be determined based on real-time meteorological data, and then the tropospheric methane column concentration ratio can be calculated.

[0034] Specifically, the following formula (2) can be used for settlement: (2) in, arrive It is the range of the effective observation boundary height of the atmosphere (unit: km). Latitude The seasonal correction factor (summer > 1.0, winter < 1.0).

[0035] It is understood that the TCR mentioned above is the ratio of methane column concentration to total column concentration, representing the source methane concentration field.

[0036] In this embodiment, a probability distribution model of tropospheric methane column concentration and total column concentration is established using adjacent satellite vertical contour data. The tropospheric concentration is separated by a preset vertical probability distribution model of methane concentration, thus avoiding dependence on the atmospheric chemical transport model (CTM) and achieving accurate extraction of tropospheric methane concentration.

[0037] S103, spatiotemporal characteristic analysis of the source methane concentration field was performed to obtain multiple sets of spatiotemporal orthogonal characteristics.

[0038] In this embodiment, the spatiotemporal orthogonal feature is used to characterize the background variation characteristics of methane column concentration in the target region and the dynamic differences of methane column concentration along the transport path.

[0039] To achieve more accurate spatiotemporal feature analysis, prior to S103, the method may also include: The methane concentration deviation is determined based on the preset atmospheric scattering modulation factor and zenith angle; Based on the methane concentration deviation, the source methane concentration field is corrected to obtain the corrected methane concentration field. In this embodiment, the source methane concentration field can be corrected to enable more accurate analysis of methane emission flux during subsequent spatiotemporal analysis. Specifically, each latitude zone has corresponding satellite observation zenith angle and cloud top height data, so a correction equation can be constructed based on satellite observations combined with parameters, as shown in the following formula (3): (3) in, It is the zenith angle observed by satellite (unit: degrees). is the altitude corresponding to the cloud top pressure (unit: hPa), and k is the atmospheric scattering modulation factor (laboratory calibration value 0.15-0.35). It is the atmospheric elevation (typical value 8km).

[0040] In the embodiments of this application, the source methane concentration field can be corrected using the above correction formula to obtain a corrected methane concentration field. The corrected methane concentration field can extract the methane concentration more accurately, thereby achieving accurate analysis.

[0041] After obtaining the corrected methane concentration field, S103 can be specifically defined as follows: Spatiotemporal characteristic analysis of the corrected methane concentration field yielded multiple sets of spatiotemporal orthogonal characteristics.

[0042] In this embodiment, the empirical orthogonal function (EOF) component analysis method can be used to analyze the spatiotemporal characteristics of satellite tropospheric methane column concentration. This technique decomposes the dataset corresponding to the corrected methane concentration field into multiple sets of orthogonal signals, consisting of spatial modes (EOF) and temporal components (PCA). The dominant signal that contributes the most to the total variance of the dataset is selected, representing the unique phenomenon controlling the overall distribution pattern of satellite tropospheric methane column concentration. The first mode EOF1 and its time coefficient PC1 mainly characterize the variation characteristics of the background concentration of satellite tropospheric methane column concentration. The spatial distribution of the second mode EOF2 exhibits a significant dipole structure. This spatial pattern of coexisting positive and negative outlier regions reveals the dynamic differences in satellite tropospheric methane column concentration between the source region and the transport path. Specifically, the positive value region of EOF2 usually corresponds to strong emission sources, while the negative value region characterizes the methane transport path and deposition region. This spatial distribution clearly reflects the transport process of methane from the source region to downstream via atmospheric advection. The extreme points of the PC2 time series have a significant correlation with strong methane transport events. By analyzing the statistical characteristics of PC2, the key time points of these transmission events can be accurately identified.

[0043] Specifically, the spatiotemporal orthogonal features include a first orthogonal feature and a second orthogonal feature. The first orthogonal feature is used to characterize the variation characteristics of the methane column concentration and background concentration, and the second orthogonal feature is used to characterize the dynamic differences of the methane column concentration along the transport path. S103 may specifically include: The target area is divided into grids, resulting in multiple grid points; For each grid point, the methane concentration data of the grid point is reconstructed into a spatiotemporal matrix; Singular value decomposition is performed on the spatiotemporal matrix to obtain multiple first orthogonal features and second orthogonal features. The first orthogonal features include the first mode and the first time coefficient, and the second orthogonal features include the second mode and the second time coefficient.

[0044] In this embodiment, the target region is first divided into grids to obtain multiple grid points. Then, the methane column concentration at each grid point is reorganized to obtain an m*n spatiotemporal matrix. The spatiotemporal matrix X can be represented as: (4)

[0045] in, It is the first i The spatial lattice point at the th ... j Methane column concentration (in ppbv) for each time slice. m It is the total number of spatial grids. n It is the length of the time series.

[0046] In this embodiment, by decoupling the data, it is convenient to find out the concentration distribution of methane in the target area and the dynamic differences between the source area and the transport path, wherein the source area can be the area where methane is produced.

[0047] In some other embodiments, the singular value decomposition of the normalized matrix can be performed using the following formula (5): (5)

[0048] in, It is the first k Spatial mode (EOF) vector, It is the k-th time coefficient (PC) vector. It is the singular value of the k-th mode (the variance contribution is proportional to) ).

[0049] In this embodiment, singular value decomposition can more accurately extract the first orthogonal feature and the second orthogonal feature, so as to analyze the concentration evolution of methane in the target area, thereby laying the foundation for subsequent removal of pollutant concentrations.

[0050] It is worth noting that the first mode EOF1 and its first time coefficient PC1 primarily characterize the variation characteristics of the background concentration of methane column in the satellite's troposphere. The spatial distribution of the second mode EOF2 exhibits a significant dipole structure. This spatial pattern of coexisting positive and negative outlier regions reveals the dynamic differences in the methane column concentration in the satellite's troposphere between the source region and the transport path. Specifically, the positive regions of EOF2 typically correspond to strong emission sources, while the negative regions characterize methane transport paths and deposition areas. This spatial distribution clearly reflects the transport process of methane from the source region to downstream areas via atmospheric advection. The extreme points of the second time coefficient PC2 show a significant correlation with intense methane transport events. By analyzing the statistical characteristics of PC2, the key time nodes of these transport events can be accurately identified.

[0051] In other embodiments, the specific path and scope of methane transport can be further analyzed by combining the wind field data at that time. For example, when the extreme point of PC2 is identified, the transport trajectory of methane from the emission source to the downstream area can be accurately tracked by analyzing the spatial distribution characteristics (such as plume morphology and concentration gradient) of satellite tropospheric methane column concentration data and the matching relationship with the wind field (wind direction and wind speed). This allows the externally transported methane signal to be extracted from the satellite tropospheric methane column concentration data.

[0052] It is worth noting that extracting methane information from external sources by combining wind field data can be done manually or by building a neural network model with a large amount of data; no specific method is specified here.

[0053] In some embodiments, after performing singular value decomposition on the spatiotemporal matrix to obtain multiple first orthogonal features and second orthogonal features, the method further includes: For each group of first orthogonal features and / or second orthogonal features, the first orthogonal features and / or second orthogonal features are filtered according to preset filtering conditions to obtain the transmission mode; The spatiotemporal matrix is ​​corrected based on the transmission mode to obtain the final spatiotemporal matrix.

[0054] Specifically, preset filtering criteria can be based on the range of variance contribution, for example, selecting those with a variance contribution of 20%-30%. k =2-mode construction of transmission signals: (6)

[0055] in, It is the modal confidence weight (based on wind field correlation 0.7 < 0.7). <0.9), It is the transmission signal matrix to be eliminated. It is the first 2 Spatial mode (EOF) vector, It is the second time coefficient (PC) vector. It is a singular value of the second mode.

[0056] In this embodiment, after removing external methane concentrations based on wind field signals, the final spatiotemporal matrix can be represented as: .

[0057] In this embodiment of the application, by using spatiotemporal decoupling and transmission extraction technology, the source region transmission path can be identified by utilizing the dipole structure of EOF decomposition. By combining the PC2 extreme point with the wind field trajectory matching, adaptive correction of transmission time can be achieved, which can accurately extract the methane column concentration of the emission source in the target region.

[0058] S104 analyzes the energy emission sources of multiple sets of spatiotemporal orthogonal features to obtain the methane emission intensity distribution in the target region.

[0059] In some embodiments, since multiple associated pollutants may exist in the target area, S104 may specifically include the following in order to determine the methane emission intensity of the emission source: For the target area, obtain the concentration value of at least one pollutant within the target area; For any grid point within the target area, construct a correlation matrix between methane and pollutants; For each pollutant, source feature regions are identified based on the correlation matrix to obtain at least one energy emission source region; Based on energy emission source regions, the distribution of methane emission intensity in the target area is determined.

[0060] In this embodiment, after removing the external methane concentration, the remaining methane column concentration is the methane emission flux generated by the emission source. However, there are still some associated pollutants emitted by the energy industry, so further correction is needed to obtain a more accurate methane concentration.

[0061] Specifically, associated pollutants can include carbon monoxide, nitrogen oxides, sulfur dioxide, and light-absorbing aerosols. Therefore, multi-component observation data from the TROPOMI / S5P satellite can be integrated with high-precision measurement results from ground stations (such as the TCCON and EMEP networks) to establish a multi-species collaborative analysis framework. The chemical oxidation of methane is often accompanied by a coordinated equilibrium conversion process involving multiple components such as CO2 and CO. Since the methane to CO emission ratio (CH4 / CO) varies significantly across scenarios such as coal mining (typical ratio > 5), natural gas leaks (pure leak ratio >> 10), and conventional combustion (ratio ≈ 0.1), this difference can be used to determine the differences in individual pollutants, thereby eliminating corresponding energy interference.

[0062] Furthermore, a correlation matrix can be constructed based on the concentration values ​​of at least one pollutant within the target area, using the following formula: (7)

[0063] in, It is the spatial Pearson correlation coefficient between species x and y. R is the concentration anomaly value of the i-th grid (measured value minus regional background value), and R is the correlation matrix.

[0064] Then, the corresponding judgment is made using the above correlation matrix, based on the following criteria: (8)

[0065] Where Ω represents the sub-region of the target analysis. Significant emission threshold (typical value: 100 ppb·km) 2 ).

[0066] The above criteria can be used to screen energy industry emission sources and identify methane concentrations, which will facilitate further correction of methane concentration data and result in a more accurate energy methane emission intensity distribution map.

[0067] Furthermore, before determining the methane emission intensity distribution of the target region based on energy emission source areas, the method also includes: Based on a preset radiative transfer correction relationship, the methane column concentration is corrected to obtain the corrected methane column concentration. Based on the preset mass balance equation and the corrected methane column concentration, a distribution map of energy methane emission intensity is drawn.

[0068] In this embodiment, the methane column concentration can be further corrected by a preset radiative transfer correction relationship to obtain a more accurate methane emission flux.

[0069] Specifically, the pre-defined radiative transfer correction relationship can be expressed by the following formula: \left [ {{CH}_{4}} \right ]_{cor}=\left [ {{CH}_{4}} \right ]_{raw}+\delta \cdot {AOD}_{550}\cdot \frac {\partial \left [ {{CH}_{4}} \right ]} {{\partial}_{\tau}} (9) in, The 550nm aerosol optical thickness was obtained using the TROPOMI UAVI product. Band-dependent correction factor (0.25±0.05 for 1.65µm band). It refers to atmospheric optical thickness.

[0070] Furthermore, the pre-defined mass balance equation can be expressed by the following formula: (10)

[0071] in, For wind field gradient, The wind field vector, It is consumed in chemical reactions.

[0072] In this embodiment, source apportionment can be achieved by using carbon pollution co-source multi-component separation technology and the emission correlation characteristics of pollutants and methane. This eliminates the dependence on low-resolution wind field data, thereby reducing the impact of wind field on concentration calculation. As a result, the methane concentration emission flux can be accurately calculated, enabling the physical separation and quantitative assessment of methane emission signals in the energy industry, and thus improving the spatiotemporal resolution of emission source apportionment.

[0073] Based on the methane emission flux analysis method provided in the above embodiments, this application also provides specific implementation methods for a methane emission flux analysis device. Please refer to the following embodiments.

[0074] First see Figure 2 The methane emission flux analysis device 200 provided in this application embodiment includes the following units: The acquisition module 201 is used to acquire vertical spectral data of methane in the target area and auxiliary observation data; The separation calculation module 202 is used to separate and calculate the source methane concentration field within the target altitude range based on methane vertical spectral data and auxiliary observation data. Analysis module 203 is used to perform spatiotemporal feature analysis on the source methane concentration field and obtain multiple sets of spatiotemporal orthogonal features. The spatiotemporal orthogonal features are used to characterize the background change characteristics of methane column concentration in the target region and the dynamic differences of methane column concentration along the transport path. The analysis module 204 is used to analyze the energy emission source analysis of multiple sets of spatiotemporal orthogonal features to obtain the methane emission intensity distribution of the target area.

[0075] As an optional implementation, the separate computing module 202 can be specifically used for: Obtain the latitude zone and real-time meteorological data of the target area; Within a preset latitudinal band, methane vertical spectral data are assimilated based on a preset chemical transport model to obtain assimilation results. Based on the preset vertical probability distribution model of methane concentration, determine the probability density function of methane concentration at the target height in the target area; Based on the preset correction coefficient and the probability density function of methane concentration within the altitude range, the source methane concentration field within the target altitude range is obtained by separation calculation.

[0076] As an optional implementation, the auxiliary observation data includes a zenith angle separation calculation module, which can be specifically used for: The methane concentration deviation is determined based on the preset atmospheric scattering modulation factor and zenith angle; Based on the methane concentration deviation, the source methane concentration field is corrected to obtain the corrected methane concentration field. Analysis module 203 is specifically used for: Spatiotemporal characteristic analysis of the corrected methane concentration field yielded multiple sets of spatiotemporal orthogonal characteristics.

[0077] As an optional implementation, the spatiotemporal orthogonal features include a first orthogonal feature and a second orthogonal feature. The first orthogonal feature is used to characterize the variation characteristics of the methane column concentration and background concentration, and the second orthogonal feature is used to characterize the dynamic differences in methane column concentration along the transport path. Specifically, the analysis module 203 can be used for: The target area is divided into grids, resulting in multiple grid points; For each grid point, the methane concentration data of the grid point is reconstructed into a spatiotemporal matrix; Singular value decomposition is performed on the spatiotemporal matrix to obtain multiple first orthogonal features and second orthogonal features. The first orthogonal features include the first mode and the first time coefficient, and the second orthogonal features include the second mode and the second time coefficient.

[0078] As an optional implementation, the analysis module 203 can specifically be used for: For each group of first orthogonal features and / or second orthogonal features, the first orthogonal features and / or second orthogonal features are filtered according to preset filtering conditions to obtain the transmission mode; The spatiotemporal matrix is ​​corrected based on the transmission mode to obtain the final spatiotemporal matrix.

[0079] As an optional implementation, the parsing module 204 can specifically be used for: For the target area, obtain the concentration value of at least one pollutant within the target area; For any grid point within the target area, construct a correlation matrix between methane and pollutants; For each pollutant, source feature regions are identified based on the correlation matrix to obtain at least one energy emission source region; Based on energy emission source regions, the distribution of methane emission intensity in the target area is determined.

[0080] As an optional implementation, the parsing module 204 can specifically be used for: Based on a preset radiative transfer correction relationship, the methane column concentration is corrected to obtain the corrected methane column concentration. Based on the preset mass balance equation and the corrected methane column concentration, a distribution map of energy methane emission intensity is drawn.

[0081] Figure 3 A schematic diagram of the hardware structure of the electronic device provided in an embodiment of this application is shown.

[0082] An electronic device may include a processor 301 and a memory 302 storing computer program instructions.

[0083] Specifically, the processor 301 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0084] Memory 302 may include mass storage for data or instructions. For example, and not limitingly, memory 302 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 302 may include removable or non-removable (or fixed) media, or memory 302 may be non-volatile solid-state memory. Memory 302 may be internal or external to the integrated gateway disaster recovery device.

[0085] In one instance, memory 302 may be read-only memory (ROM). In one instance, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0086] Memory 302 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Therefore, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the methane emission flux analysis method according to the first aspect of this disclosure.

[0087] The processor 301 reads and executes computer program instructions stored in the memory 302 to achieve... Figure 1 A method for analyzing methane emission flux in the illustrated embodiment.

[0088] In one example, the electronic device may also include a communication interface 303 and a bus 304. For example, Figure 3 As shown, the processor 301, memory 302, and communication interface 303 are connected through bus 304 and complete communication with each other.

[0089] The communication interface 303 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0090] Bus 304 includes hardware, software, or both, that couples components of an electronic device together. For example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or a combination of two or more of these. Where appropriate, bus 304 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0091] This electronic device can perform the methane emission flux analysis method in the embodiments of this application, thereby achieving a combination of Figures 1-2 The method and apparatus for analyzing methane emission flux are described.

[0092] Furthermore, in conjunction with the methane emission flux analysis methods in the above embodiments, this application embodiment can provide a computer storage medium for implementation. This computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the methane emission flux analysis methods in the above embodiments.

[0093] In an optional embodiment, in conjunction with the methane emission flux analysis method in the above embodiments, this application embodiment can provide a computer program product to implement it. The instructions in the computer program product are executed by the processor of an electronic device, enabling the electronic device to implement any of the methane emission flux analysis methods in the above embodiments.

[0094] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0095] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0096] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0097] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0098] The functional blocks shown in the above-described structural diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0099] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0100] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0101] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for analyzing methane emission flux, characterized in that, include: Acquire vertical spectral data of methane and auxiliary observation data in the target area; Based on the methane vertical spectral data and auxiliary observation data, the source methane concentration field within the target altitude range is separated and calculated; Spatiotemporal feature analysis was performed on the source methane concentration field to obtain multiple sets of spatiotemporal orthogonal features. These spatiotemporal orthogonal features are used to characterize the background variation characteristics of methane column concentration in the target region and the dynamic differences of methane column concentration along the transport path. Energy emission source analysis was performed on the multiple sets of spatiotemporal orthogonal features to obtain the methane emission intensity distribution in the target region.

2. The method according to claim 1, characterized in that, The step of separating and calculating the source methane concentration field within the target altitude range based on the methane vertical spectral data and auxiliary observation data includes: Obtain the latitude zone and real-time meteorological data of the target area; Within a preset latitudinal band, the methane vertical spectral data are assimilated based on a preset chemical transport model to obtain assimilation results. Based on the preset vertical probability distribution model of methane concentration, determine the probability density function of methane concentration at the target height in the target area; Based on the preset correction coefficient and the probability density function of methane concentration within the height range, the source methane concentration field within the target height range is obtained by separation calculation.

3. The method according to claim 2, characterized in that, The auxiliary observation data includes the zenith angle. Before performing spatiotemporal characteristic analysis on the source methane concentration field to obtain multiple sets of spatiotemporal orthogonal features, the method further includes: The methane concentration deviation is determined based on the preset atmospheric scattering modulation factor and zenith angle; Based on the methane concentration deviation, the source methane concentration field is corrected to obtain the corrected methane concentration field; The spatiotemporal feature analysis of the source methane concentration field yields multiple sets of spatiotemporal orthogonal features, including: Spatiotemporal feature analysis was performed on the corrected methane concentration field to obtain multiple sets of spatiotemporal orthogonal features.

4. The method according to claim 1 or 3, characterized in that, The spatiotemporal orthogonal features include a first orthogonal feature and a second orthogonal feature. The first orthogonal feature is used to characterize the variation characteristics of the methane column concentration background concentration, and the second orthogonal feature is used to characterize the dynamic differences of the methane column concentration along the transport path. The spatiotemporal feature analysis of the source methane concentration field yields multiple sets of spatiotemporal orthogonal features, including: The target area is divided into grids to obtain multiple grid points; For each grid point, the methane concentration data of that grid point is reconstructed into a spatiotemporal matrix; The spatiotemporal matrix is ​​subjected to singular value decomposition to obtain multiple first orthogonal features and second orthogonal features. The first orthogonal features include a first mode and a first time coefficient, and the second orthogonal features include a second mode and a second time coefficient.

5. The method according to claim 4, characterized in that, After performing singular value decomposition on the spatiotemporal matrix to obtain multiple first orthogonal features and second orthogonal features, the method further includes: For each group of first orthogonal features and / or second orthogonal features, the first orthogonal features and / or second orthogonal features are filtered according to preset filtering conditions to obtain the transmission mode; The spatiotemporal matrix is ​​modified based on the transmission mode to obtain the final spatiotemporal matrix.

6. The method according to claim 1, characterized in that, The process of performing energy emission source analysis on the multiple sets of spatiotemporal orthogonal features to obtain the methane emission intensity distribution in the target region includes: For the target area, obtain the concentration value of at least one pollutant within the target area; For any grid point within the target area, construct the correlation matrix between methane and pollutants; For each pollutant, source feature regions are identified based on the correlation matrix to obtain at least one energy emission source region; Based on the energy emission source areas, the methane emission intensity distribution of the target area is determined.

7. The method according to claim 6, characterized in that, Before determining the methane emission intensity distribution of the target region based on the energy emission source region, the method further includes: Based on a preset radiative transfer correction relationship, the methane column concentration is corrected to obtain the corrected methane column concentration. Based on the preset mass balance equation and the corrected methane column concentration, a distribution map of energy methane emission intensity is drawn.

8. A methane emission flux analysis device, characterized in that, The device includes: The acquisition module is used to acquire vertical spectral data of methane and auxiliary observation data in the target area; The separation calculation module is used to separate and calculate the source methane concentration field within the target altitude range based on the methane vertical spectral data and auxiliary observation data. The analysis module is used to perform spatiotemporal feature analysis on the source methane concentration field to obtain multiple sets of spatiotemporal orthogonal features. These spatiotemporal orthogonal features are used to characterize the background variation characteristics of methane column concentration in the target region and the dynamic differences of methane column concentration along the transport path. The analysis module is used to analyze the multiple sets of spatiotemporal orthogonal features to perform energy emission source analysis and obtain the methane emission intensity distribution of the target region.

9. An electronic device, characterized in that, The device includes: a processor and a memory storing computer program instructions; When the processor executes the computer program instructions, it implements the methane emission flux analysis method as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions that, when executed by a processor, implement the methane emission flux analysis method as described in any one of claims 1-7.

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