A method for inverting satellite remote sensing carbon dioxide emissions based on multi-source data

Abstract

In the satellite remote sensing carbon dioxide emission inversion method, device and storage medium based on multi-source data proposed in this application, the factory range of carbon dioxide monitoring is determined, the number of factories within the factory range and the corresponding longitude and latitude position of each factory are obtained, the fossil fuel accounting data and carbon dioxide online monitoring data of the first part of the factories are used to obtain the carbon dioxide online monitoring data of the second part of the factories to obtain the carbon dioxide online monitoring data of each factory, and the satellite remote sensing data and the carbon dioxide online monitoring data of each factory are used to obtain the satellite inversion carbon dioxide model. When performing satellite monitoring applications, the satellite remote sensing data is input into the satellite inversion carbon dioxide model to calculate the carbon dioxide emissions of each factory. Therefore, this application proposes a satellite remote sensing carbon dioxide emission inversion method based on multi-source data fusion, thereby improving the measurement accuracy of carbon dioxide emissions.

Description

Technical Field

[0001] The present application relates to the field of online carbon dioxide monitoring, and in particular to a method, device and storage medium for inverting satellite remote sensing carbon dioxide emissions based on multi-source data. Background Art

[0002] As the global greenhouse effect continues to worsen, environmental issues are becoming increasingly prominent, necessitating the monitoring of carbon emissions to reduce them. Factories are the world's primary source of carbon dioxide emissions. Currently, there are two main methods for monitoring factory carbon dioxide emissions: one is based on fossil fuel consumption data, calculating carbon emissions data using indicators such as calorific value, carbon content per unit calorific value, and carbon oxidation rate, typically calculated on a monthly basis; the other is based on online flue gas monitoring data, calculating carbon emissions data using indicators such as flue gas flow rate and flue gas carbon dioxide concentration, with sampling frequencies as low as minutes or even seconds. However, the current calculation results of fuel data have significant deviations, and the penetration rate of online monitoring is low. Most factories do not monitor carbon dioxide emissions data online, resulting in uneven carbon dioxide emissions monitoring across factories.

[0003] With the launch of carbon satellites by countries around the world, carbon dioxide emissions can now be monitored based on satellite observations. The principle of satellite remote sensing data for CO2 monitoring is to utilize carbon satellites, environmental monitoring satellites, and cloud and aerosol satellites. Based on the principle of atmospheric absorption pools, a characteristic atmospheric absorption spectrum is formed. This spectrum is then combined with air pressure and temperature to eliminate interference factors. Combined with measured data from ground monitoring stations, satellite remote sensing data is then correlated with carbon dioxide concentration data for learning. This allows the concentration distribution and flux changes of carbon dioxide to be inferred from satellite remote sensing data, ultimately leading to the carbon dioxide emissions for each region. However, currently, due to the limited satellite coverage and the paucity of carbon dioxide data from ground monitoring stations, the accuracy of satellite remote sensing data is low.

[0004] In related technologies, by correlating NOx, CO, and CO2 emissions, satellite remote sensing data can be used to invert NOx and CO emissions, and then CO2 emissions can be estimated. However, this method does not perform direct measurements, resulting in inaccurate CO2 data. Alternatively, by using factory carbon emissions as ground monitoring station data, satellite remote sensing models can be used to invert factory carbon dioxide emissions, but research on factory carbon emissions is not in-depth enough. If only the factory carbon emissions obtained by fuel accounting are used, they are inaccurate data in themselves, so the satellite inversion model is also biased. In summary, how to accurately measure carbon dioxide emissions is a technical problem that currently needs to be solved by those skilled in the art. Summary of the Invention

[0005] The present application provides a method, device and storage medium for inverting satellite remote sensing carbon dioxide emissions based on multi-source data to solve the technical problems arising in the above-mentioned related technologies.

[0006] The first embodiment of the present application proposes a method for inverting satellite remote sensing carbon dioxide emissions based on multi-source data, which may include:

[0007] Determine the factory range for carbon dioxide monitoring, and obtain the number of factories within the factory range and the longitude and latitude corresponding to each factory;

[0008] Using the fossil fuel accounting data and carbon dioxide online monitoring data of the first part of the factories, obtaining the carbon dioxide online monitoring data of the second part of the factories, so as to obtain the carbon dioxide online monitoring data of each factory, wherein the first part of the factories and the second part of the factories are all factories within the factory range, and the second part of the factories do not have a carbon dioxide online monitoring function;

[0009] Using satellite remote sensing data and carbon dioxide online monitoring data of each factory, a satellite-derived carbon dioxide model is obtained;

[0010] When satellite monitoring is used, satellite remote sensing data is input into a satellite inversion carbon dioxide model to calculate the carbon dioxide emissions of each factory.

[0011] The second embodiment of the present application proposes a satellite remote sensing carbon dioxide emission inversion device based on multi-source data, which may include:

[0012] A determination module determines the factory range for carbon dioxide monitoring, obtains the number of factories within the factory range and the longitude and latitude corresponding to each factory;

[0013] an acquisition module, configured to acquire the carbon dioxide online monitoring data of the second part of the plants using the fossil fuel accounting data and the carbon dioxide online monitoring data of the first part of the plants, so as to obtain the carbon dioxide online monitoring data of each plant, wherein the first part of the plants and the second part of the plants are all plants within the plant range, and the second part of the plants do not have a carbon dioxide online monitoring function;

[0014] A processing module, configured to obtain a satellite-derived carbon dioxide model using satellite remote sensing data and carbon dioxide online monitoring data of each plant;

[0015] The calculation module is used to calculate the carbon dioxide emissions of each factory by inputting satellite remote sensing data into a satellite inversion carbon dioxide model when performing satellite monitoring applications.

[0016] The computer device proposed in the third embodiment of the present application is characterized in that it may include a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the program, it implements the method described in the first aspect above.

[0017] The computer storage medium proposed in the fourth embodiment of the present application, wherein the computer storage medium stores computer-executable instructions; after the computer-executable instructions are executed by the processor, the method described in the first aspect above can be implemented.

[0018] The technical solutions provided by the embodiments of this application bring at least the following beneficial effects:

[0019] In the satellite remote sensing carbon dioxide emission inversion method, device and storage medium based on multi-source data proposed in this application, the factory range of carbon dioxide monitoring is determined, the number of factories within the factory range and the corresponding longitude and latitude position of each factory are obtained, the fossil fuel accounting data and carbon dioxide online monitoring data of the first part of the factories are used to obtain the carbon dioxide online monitoring data of the second part of the factories to obtain the carbon dioxide online monitoring data of each factory, and the satellite remote sensing data and the carbon dioxide online monitoring data of each factory are used to obtain the satellite inversion carbon dioxide model. When performing satellite monitoring applications, the satellite remote sensing data is input into the satellite inversion carbon dioxide model to calculate the carbon dioxide emissions of each factory. It can be seen from this that this application proposes a satellite remote sensing carbon dioxide emission inversion method that forms a multi-source data fusion by combining factory fossil fuel accounting data, carbon dioxide online monitoring data, and satellite remote sensing data, thereby improving the measurement accuracy of carbon dioxide emissions.

[0020] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0022] Figure 1 A schematic flow chart of a method for inverting satellite remote sensing carbon dioxide emissions based on multi-source data according to one embodiment of the present application;

[0023] Figure 2 This is a schematic structural diagram of a satellite remote sensing carbon dioxide emission inversion device based on multi-source data provided according to one embodiment of the present application. DETAILED DESCRIPTION

[0024] The following describes in detail embodiments of the present application. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0025] The following describes the satellite remote sensing carbon dioxide emission inversion method and apparatus based on multi-source data according to an embodiment of the present application with reference to the accompanying drawings.

[0026] Figure 1 This is a flow chart of a method for inverting satellite remote sensing carbon dioxide emissions based on multi-source data according to one embodiment of the present application. Figure 1 As shown, the following steps may be included:

[0027] Step 101: Determine the factory range for carbon dioxide monitoring, and obtain the number of factories within the factory range and the longitude and latitude corresponding to each factory.

[0028] In one embodiment of the present application, the number of factories within the factory range is obtained, so that the number of factories and the emission sources corresponding to each factory can be used to obtain daily carbon dioxide online monitoring data for each factory.

[0029] For example, in one embodiment of the present application, there are N factories within the factory area, and each factory has m emission sources.

[0030] Furthermore, in one embodiment of the present application, the longitude and latitude positions of each factory within the factory range are obtained so that subsequent satellites can use the longitude and latitude positions of each factory to monitor the carbon dioxide emissions of the factory.

[0031] Step 102: Using the fossil fuel accounting data and carbon dioxide online monitoring data of the first part of the factories, obtain the carbon dioxide online monitoring data of the second part of the factories to obtain the carbon dioxide online monitoring data of each factory.

[0032] In one embodiment of the present application, the emission source is used as the smallest emission statistical unit for fossil fuel accounting and online carbon dioxide monitoring. In addition, the online carbon dioxide monitoring data of each factory in the present application is the daily online carbon dioxide monitoring data of each factory.

[0033] It should be noted that, in one embodiment of the present application, some emission sources in the second part of the factories do not have the function of online carbon dioxide monitoring. Based on this, the emission sources have no online carbon dioxide monitoring data. The first part of the factories and the second part of the factories are all factories within the factory scope.

[0034] Furthermore, in one embodiment of the present application, a method for obtaining the carbon dioxide online monitoring data of the second part of the plants by using the fossil fuel accounting data and the carbon dioxide online monitoring data of the first part of the plants to obtain the carbon dioxide online monitoring data of each plant may include the following steps:

[0035] Step 1021: Obtain fossil fuel accounting data and carbon dioxide online monitoring data for each emission source in the first part of the factory.

[0036] In one embodiment of the present application, the first part of the factory is a factory with a carbon dioxide online monitoring function.

[0037] Furthermore, in one embodiment of the present application, fossil fuel accounting data is generally collected on a daily basis, and the daily fossil fuel accounting emissions of a single emission source are calculated using a formula, wherein the formula is:

[0038] A=B×C×D×E×44 / 12

[0039] Among them, A is the daily fossil fuel accounting emissions of a single emission source, B is the fuel consumption, C is the lower calorific value, D is the carbon content per unit calorific value, and E is the carbon oxidation rate.

[0040] Furthermore, in one embodiment of the present application, the carbon dioxide online monitoring data is generally counted by minute or second, and needs to be accumulated and counted on a daily basis to obtain the daily carbon dioxide online monitoring emission F of a single emission source.

[0041] Step 1022: Obtain fossil fuel accounting data for each emission source in the second part of the factories.

[0042] Step 1023: Using the fossil fuel accounting data and carbon dioxide online monitoring data of each emission source in the first part of the factories, and the fossil fuel accounting data of each emission source in the second part of the factories, obtain the carbon dioxide online monitoring data of the second part of the factories to obtain the carbon dioxide online monitoring data of each factory.

[0043] In one embodiment of the present application, a method for obtaining carbon dioxide online monitoring data of the second part of the plants by using the fossil fuel accounting data and carbon dioxide online monitoring data of each emission source in the first part of the plants, and the fossil fuel accounting data of each emission source in the second part of the plants, to obtain carbon dioxide online monitoring data of each plant may include the following steps:

[0044] Step 1: Use the fossil fuel accounting data and carbon dioxide online monitoring data of each emission source in the first part of the factory to train the initial neural network model to obtain the target neural network module.

[0045] In one embodiment of the present application, the daily fossil fuel accounting emissions A1 and the daily carbon dioxide online monitoring emissions F1 of each emission source in the first part of the factory are divided into pairs into training data sets (A1, F1), and the training data sets (A1, F1) are used to train the initial neural network model to obtain the target neural network module.

[0046] Specifically, in one embodiment of the present application, fossil fuel accounting data A1 is used as input, and carbon dioxide online monitoring data F1 is used as output, and the carbon dioxide online monitoring data is obtained by regressing the fossil fuel accounting data.

[0047] Step 2: Input the fossil fuel accounting data of each emission source in the second part of the factory into the target neural network to obtain the carbon dioxide online monitoring data of each emission source in the second part of the factory.

[0048] In one embodiment of the present application, the daily fossil fuel accounting emissions A2 of each emission source in the second part of the factory are divided into a test data set (A2), and the test data set (A2) is input into the target neural network to obtain the carbon dioxide online monitoring data F2 of each emission source in the second part of the factory.

[0049] Step 3: Accumulate the carbon dioxide online monitoring data of each emission source corresponding to each factory to obtain the carbon dioxide online monitoring data of each factory.

[0050] Step 103: Utilize satellite remote sensing data and the online carbon dioxide monitoring data of each factory to obtain a satellite inversion carbon dioxide model.

[0051] In one embodiment of the present application, the satellite-derived carbon dioxide model includes a CO2 diffusion model and a forward radiation transfer model.

[0052] Furthermore, in one embodiment of the present application, a method for obtaining a satellite-derived carbon dioxide model using satellite remote sensing data and carbon dioxide online monitoring data of each factory may include the following steps:

[0053] Step a: Set the state vector to x0.

[0054] In one embodiment of the present application, the state vector includes surface reflectivity, surface reflectivity slope, temperature profile offset, water vapor profile amplification factor, spectral drift, and 18 layers of carbon dioxide concentration data, y is the satellite spectral signal, and z is the online carbon dioxide monitoring data of each factory.

[0055] Step b: Calculate the CO2 diffusion using the CO2 diffusion model G based on the online carbon dioxide monitoring data:

[0056] z0=G(x0)+σ

[0057] Step c: Perform forward radiation transfer calculation using the forward radiation transfer model F of the atmospheric parameter vector:

[0058] y0=F(x0,b)+ε

[0059] Where b is a vector of other atmospheric parameters that affect radiative transfer and are not inverted.

[0060] Step d: Determine the optimal atmospheric parameter vector b and the error vectors σ and ε corresponding to the CO2 diffusion model and the forward radiative transfer model, respectively, by minimizing the results of the CO2 diffusion model and the forward radiative transfer model ||z-z0||+||y-y0||.

[0061] Step 104: When satellite monitoring is used, satellite remote sensing data is input into a satellite inversion carbon dioxide model to calculate the carbon dioxide emissions of each factory.

[0062] In one embodiment of the present application, when performing satellite monitoring applications, a method for calculating the carbon dioxide emissions of each factory by inputting satellite remote sensing data into a satellite inversion carbon dioxide model may include the following steps:

[0063] Step 1041: Set the initial value x0 of the state vector.

[0064] Step 1042: Update the state vector through LM Newton nonlinear iteration:

[0065]

[0066] in represents the covariance matrix of the solution, is the Jacobian matrix, 1 is the LM coefficient, is the observed error covariance matrix, and is the prior covariance matrix.

[0067] Step 1043: When the maximum number of iterations is reached or ||x i+1 -x i When the preset error is reached, the iteration stops, x i+1 is the state vector after inversion;

[0068] Step 1044: Using the state vector x i+1 Perform CO2 diffusion calculation to obtain the carbon dioxide emissions z of each factory.

[0069] z=G(x i+1 )+σ

[0070] Where σ is the error vector of the CO2 diffusion model.

[0071] In one embodiment of the present application, step 104 can directly monitor the carbon dioxide emissions of the factory using satellite remote sensing data by using the location and emission of the carbon dioxide emission source and a satellite inversion carbon dioxide model constructed using satellite remote sensing data.

[0072] In the satellite remote sensing carbon dioxide emission inversion method based on multi-source data proposed in this application, the factory range of carbon dioxide monitoring is determined, the number of factories within the factory range and the corresponding longitude and latitude position of each factory are obtained, the fossil fuel accounting data and carbon dioxide online monitoring data of the first part of the factories are used to obtain the carbon dioxide online monitoring data of the second part of the factories to obtain the carbon dioxide online monitoring data of each factory, and the satellite remote sensing data and the carbon dioxide online monitoring data of each factory are used to obtain the satellite inversion carbon dioxide model. When performing satellite monitoring applications, the satellite remote sensing data is input into the satellite inversion carbon dioxide model to calculate the carbon dioxide emissions of each factory. It can be seen from this that this application proposes a satellite remote sensing carbon dioxide emission inversion method that forms a multi-source data fusion by combining factory fossil fuel accounting data, carbon dioxide online monitoring data, and satellite remote sensing data, thereby improving the measurement accuracy of carbon dioxide emissions.

[0073] Figure 2 This is a schematic diagram of the structure of a satellite remote sensing carbon dioxide emission inversion device based on multi-source data according to one embodiment of the present application. Figure 2 As shown, this may include:

[0074] Determine the module, determine the factory range of carbon dioxide monitoring, obtain the number of factories within the factory range and the corresponding longitude and latitude positions of each factory;

[0075] an acquisition module, configured to acquire the carbon dioxide online monitoring data of the second part of the plants using the fossil fuel accounting data and carbon dioxide online monitoring data of the first part of the plants, so as to obtain the carbon dioxide online monitoring data of each plant, wherein the first part of the plants and the second part of the plants are all plants within the plant range, and the second part of the plants do not have the carbon dioxide online monitoring function;

[0076] A processing module is used to obtain a satellite-derived carbon dioxide model using satellite remote sensing data and online carbon dioxide monitoring data of each factory;

[0077] The calculation module is used to calculate the carbon dioxide emissions of each factory by inputting satellite remote sensing data into the satellite inversion carbon dioxide model when performing satellite monitoring applications.

[0078] In one embodiment of the present application, the acquisition module is specifically configured to:

[0079] Obtain fossil fuel accounting data and CO2 online monitoring data for each emission source in the first part of the plant;

[0080] Obtain fossil fuel accounting data for each emission source in the second part of the plant, where the first and second parts of the plant are all plants within the plant scope;

[0081] The fossil fuel accounting data and CO2 online monitoring data of each emission source in the first part of the factory, as well as the fossil fuel accounting data of each emission source in the second part of the factory, are used to obtain the CO2 online monitoring data of each factory.

[0082] In the satellite remote sensing carbon dioxide emission inversion method based on multi-source data proposed in this application, the factory range of carbon dioxide monitoring is determined, the number of factories within the factory range and the corresponding longitude and latitude position of each factory are obtained, the fossil fuel accounting data and carbon dioxide online monitoring data of the first part of the factories are used to obtain the carbon dioxide online monitoring data of the second part of the factories to obtain the carbon dioxide online monitoring data of each factory, and the satellite remote sensing data and the carbon dioxide online monitoring data of each factory are used to obtain the satellite inversion carbon dioxide model. When performing satellite monitoring applications, the satellite remote sensing data is input into the satellite inversion carbon dioxide model to calculate the carbon dioxide emissions of each factory. It can be seen from this that this application proposes a satellite remote sensing carbon dioxide emission inversion method that forms a multi-source data fusion by combining factory fossil fuel accounting data, carbon dioxide online monitoring data, and satellite remote sensing data, thereby improving the measurement accuracy of carbon dioxide emissions.

[0083] The computer device provided in the embodiment of the present application includes a memory, a processor, and a computer program stored in the memory and executable on the processor; when the processor executes the program, it can achieve the following Figure 1 The method shown.

[0084] In order to implement the above embodiments, the present application also proposes a computer storage medium.

[0085] The computer storage medium provided in the embodiment of the present application stores computer executable instructions; after the computer executable instructions are executed by the processor, the following can be achieved: Figure 1 The method shown.

[0086] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0087] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.

[0088] Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limitations on the present application. Ordinary technicians in this field can change, modify, replace and modify the above embodiments within the scope of the present application.

Claims

1. A satellite remote sensing carbon dioxide emission inversion method based on multi-source data, characterized in that: include: Determine the factory range for carbon dioxide monitoring, and obtain the number of factories within the factory range and the longitude and latitude corresponding to each factory; Obtain fossil fuel accounting data and CO2 online monitoring data for each emission source in the first part of the plant; Obtain fossil fuel accounting data for each emission source in the second part of the plant; Using the fossil fuel accounting data and carbon dioxide online monitoring data of each emission source in the first part of the plant to train the initial neural network model to obtain the target neural network module; Inputting the fossil fuel accounting data of each emission source in the second part of the factories into the target neural network to obtain the carbon dioxide online monitoring data of each emission source in the second part of the factories; Accumulating the carbon dioxide online monitoring data of each emission source corresponding to each factory to obtain the carbon dioxide online monitoring data of each factory, wherein the first part of factories and the second part of factories are all factories within the factory range, and the second part of factories do not have the carbon dioxide online monitoring function; Using satellite remote sensing data and online carbon dioxide monitoring data of each factory, a satellite-derived carbon dioxide model is obtained, wherein the satellite-derived carbon dioxide model includes a CO2 diffusion model and a forward radiation transfer model; When satellite monitoring is used, satellite remote sensing data is input into a satellite inversion carbon dioxide model, and an inverted state vector is obtained based on LM Newton nonlinear iterative calculation. The carbon dioxide emissions of each plant are calculated using the inverted state vector and the CO2 diffusion model. The method of obtaining a satellite-derived carbon dioxide model by utilizing satellite remote sensing data and the carbon dioxide online monitoring data of each factory includes: Set the state vector to , where the state vector includes surface reflectivity, surface reflectivity slope, temperature profile offset, water vapor profile amplification factor, spectral drift, and 18 layers of carbon dioxide concentration data. is the satellite spectral signal, Provide online carbon dioxide monitoring data for each factory; CO2 diffusion model based on CO2 online monitoring data Perform CO2 diffusion calculations: Forward radiative transfer model via atmospheric parameter vector Perform forward radiation transfer calculations: in, is the other atmospheric parameter vector that affects the radiation transfer and is not inverted; Calculation results by minimizing the CO2 diffusion model and the forward radiation transfer model , determine the optimal atmospheric parameter vector , and the error vectors corresponding to the CO2 diffusion model and the forward radiation transfer model respectively and .

2. The method according to claim 1, wherein When performing satellite monitoring applications, the satellite remote sensing data is input into the satellite inversion carbon dioxide model, and the inverted state vector is obtained based on the LM Newton nonlinear iterative calculation. The carbon dioxide emissions of each factory are calculated using the inverted state vector and the CO2 diffusion model, including: First, set the initial value of the state vector ; The state vector is updated through LM Newton nonlinear iteration, where y is the measured satellite spectral signal: in, represents the covariance matrix of the solution, is the Jacobian matrix, is the observed error covariance matrix, is the prior covariance matrix; When the maximum number of iterations is reached or When the preset error is reached, the iteration stops. is the state vector after inversion; Using the state vector Perform CO2 diffusion calculations to obtain the CO2 emissions of each factory . in, is the error vector of the CO2 diffusion model.

3. A satellite remote sensing carbon dioxide emission inversion device based on multi-source data, characterized in that: include: A determination module determines the factory range for carbon dioxide monitoring, obtains the number of factories within the factory range and the longitude and latitude corresponding to each factory; an acquisition module for acquiring fossil fuel accounting data and carbon dioxide online monitoring data for each emission source in the first part of the factories; acquiring fossil fuel accounting data for each emission source in the second part of the factories; using the fossil fuel accounting data and carbon dioxide online monitoring data for each emission source in the first part of the factories to train an initial neural network model to obtain a target neural network module; inputting the fossil fuel accounting data for each emission source in the second part of the factories into the target neural network to obtain carbon dioxide online monitoring data for each emission source in the second part of the factories; accumulating the carbon dioxide online monitoring data for each emission source corresponding to each factory to obtain carbon dioxide online monitoring data for each factory, wherein the first part of the factories and the second part of the factories are all factories within the factory range, and the second part of the factories does not have a carbon dioxide online monitoring function; A processing module is used to obtain a satellite-derived carbon dioxide model using satellite remote sensing data and the carbon dioxide online monitoring data of each factory, wherein the satellite-derived carbon dioxide model includes a CO2 diffusion model and a forward radiation transfer model; a calculation module for, when performing satellite monitoring applications, inputting satellite remote sensing data into a satellite inversion carbon dioxide model, obtaining an inverted state vector based on LM Newton nonlinear iterative calculation, and calculating the carbon dioxide emissions of each plant using the inverted state vector and a CO2 diffusion model; The method of obtaining a satellite-derived carbon dioxide model by utilizing satellite remote sensing data and the carbon dioxide online monitoring data of each factory includes: Set the state vector to , where the state vector includes surface reflectivity, surface reflectivity slope, temperature profile offset, water vapor profile amplification factor, spectral drift, and 18 layers of carbon dioxide concentration data. is the satellite spectral signal, Provide online carbon dioxide monitoring data for each factory; CO2 diffusion model based on CO2 online monitoring data Perform CO2 diffusion calculations: Forward radiative transfer model via atmospheric parameter vector Perform forward radiation transfer calculations: in, is the other atmospheric parameter vector that affects the radiation transfer and is not inverted; Calculation results by minimizing the CO2 diffusion model and the forward radiation transfer model , determine the optimal atmospheric parameter vector , and the error vectors corresponding to the CO2 diffusion model and the forward radiation transfer model respectively and .

4. A computer device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 2 is implemented.

5. A computer storage medium, wherein: The computer storage medium stores computer-executable instructions; after the computer-executable instructions are executed by the processor, the method according to any one of claims 1 to 2 can be implemented.

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