Enterprise point source carbon emission inversion method based on underway mobile monitoring data

Abstract

The invention belongs to the technical field of enterprise point source carbon emission inversion, and provides an enterprise point source carbon emission inversion method based on underway mobile monitoring data, and the method comprises the steps: 1, cleaning the underway monitoring data, and taking the speed of 30Km / h of an underway vehicle as a threshold value; 2, monitoring concentration enhancement amount accounting, and determining a concentration background value and a voyage high value concentration by comparing CO2 / CH4 concentrations in voyage monitoring data; 3, enterprise point source CO2 / CH4 emission simulation based on the plume diffusion model; 4, simulating concentration enhancement amount accounting, and providing track point coordinates according to the underway monitoring data; according to the method, through the steps of real-time underway monitoring, data cleaning, concentration enhancement amount accounting, plume diffusion model simulation, simulation concentration matching, optimal square method inversion and the like, the problems of inaccurate, lagged and incomplete data and the like in a traditional carbon emission monitoring method are effectively solved.

Description

Technical Field

[0001] The present invention belongs to the technical field of enterprise point source carbon emission inversion, and specifically relates to a method for inverting enterprise point source carbon emissions based on mobile monitoring data during vehicle cruising. Background Art

[0002] With the increasingly severe global climate change, the accurate monitoring and assessment of carbon emissions have become an important topic in the environmental protection field. Traditional carbon emission monitoring methods mostly rely on historical emission data reported by enterprises themselves. However, these data often have problems such as inaccuracy, lag, and incompleteness, and it is difficult to meet the requirements for real-time and accurate monitoring of carbon emissions.

[0003] Therefore, those skilled in the art have proposed a method for inverting enterprise point source carbon emissions based on mobile monitoring data during vehicle cruising to solve the problems raised in the background art. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method for inverting enterprise point source carbon emissions based on mobile monitoring data during vehicle cruising to solve the problems that existing carbon emission monitoring methods mostly rely on historical emission data reported by enterprises themselves, and these data often have problems such as inaccuracy, lag, and incompleteness, and it is difficult to meet the requirements for real-time and accurate monitoring of carbon emissions.

[0005] A method for inverting enterprise point source carbon emissions based on mobile monitoring data during vehicle cruising includes:

[0006] Step 1: Cleaning the mobile monitoring data, using the vehicle cruising speed of 30 Km / h as a threshold, and selecting the monitoring data during non-congested periods as valid data;

[0007] Step 2: Calculating the enhanced monitoring concentration, by comparing the CO2 / CH4 concentrations in the mobile monitoring data, determining the background concentration value and the high cruising concentration value, and calculating the enhanced concentration;

[0008] Step 3: Simulating the CO2 / CH4 emissions of enterprise point sources based on the plume diffusion model, using the historical emissions of the enterprise, wind speed, wind direction, and chimney height parameters to simulate the CO2 / CH4 emission diffusion process;

[0009] Step 4: Calculating the simulated enhanced concentration, according to the trajectory point coordinates provided by the mobile monitoring data, performing coordinate matching in the simulation results of the plume diffusion model to obtain the simulated enhanced concentration;

[0010] Step 5: Inverting the carbon emissions of enterprise point sources during the cruising period using the optimal least squares method, constructing a covariance matrix, and calculating the CO2 / CH4 emissions of the enterprise during the cruising period through the weighted least squares method combined with the covariance matrix.

[0011] Preferably, in step S1, the cleaning of the mobile monitoring data uses a data validity screening function to quantify the validity of the data.

[0012] Preferably, in step S2, after obtaining the valid mobile monitoring data, the system further performs the following operations to determine the concentration background value and the mobile high-value concentration: First, select the average CO2 / CH4 monitoring concentration less than the lower decile as the concentration background value of this mobile monitoring; then, define the CO2 / CH4 monitoring concentration higher than the upper decile as the mobile monitoring high-value concentration, and mark the position where the mobile high-value concentration appears as the plume landing position; finally, by calculating the difference between the mobile high-value concentration and the mobile background concentration, obtain the enhanced amount of the monitoring concentration caused by the diffusion of the enterprise point source plume.

[0013] Preferably, the calculation formula for the concentration enhancement amount is used to quantify the change in CO2 / CH4 concentration caused by the enterprise point source emission.

[0014] Preferably, step S3 is specifically described as follows: Using the plume diffusion model, combined with the key parameters such as the historical emission data of the enterprise, the wind speed and wind direction information during the mobile monitoring period, and the chimney height of the enterprise, accurately simulate the diffusion process and its spatio-temporal distribution characteristics of the enterprise's CO2 / CH4 emissions during the mobile monitoring period; this model can calculate the diffusion of the CO2 / CH4 emission plume at different horizontal and vertical positions, so as to simulate and obtain the specific distribution of the CO2 / CH4 concentration at the height of the mobile vehicle (i.e., 2 meters); this process provides an important simulation data basis for subsequent concentration matching and inversion calculations.

[0015] Preferably, the plume diffusion model is used to simulate the diffusion process of pollutants in the atmosphere.

[0016] Preferably, step S4 is specifically described as follows: First, construct a covariance matrix by combining the observation time difference of the trajectory points and the uncertainty of the mobile monitoring instrument; this matrix is very crucial because it can comprehensively describe the correlation between each observation value and their respective error ranges; specifically, the diagonal elements in the matrix represent the observation error sizes of each observation value, and the smaller the time difference between the observation values, the higher their correlation in the matrix.

[0017] After constructing the covariance matrix, the system will use the weighted least squares method (also known as the optimal least squares method) to further process the data; this method will make full use of the information provided by the covariance matrix to conduct in-depth mathematical processing on the concentration enhancement amount simulated by the model and the actually monitored concentration enhancement amount; through a series of complex calculations and optimization algorithms, the system can finally accurately calculate the CO2 / CH4 emissions of enterprises during the moving monitoring period; this process not only fully considers the correlation between the observed data, but also accurately quantifies the error range, thus ensuring the accuracy and reliability of the inversion results.

[0018] Preferably, in step four for simulating the concentration enhancement amount accounting and step five for using the optimal least squares method to invert the carbon emissions of enterprise point sources during the moving monitoring period, a covariance matrix needs to be constructed to describe the correlation and error range between the observed values.

[0019] Preferably, the weighted least squares method is used to calculate the CO2 / CH4 emissions of enterprises during the moving monitoring period, and the formula of the weighted least squares method is as follows:

[0020]

[0021] where Q represents the carbon emissions to be inverted, C obs,i represents the observed concentration value, C sim,i (Q) represents the simulated concentration value, and w i represents the weight of the observed value i.

[0022] Preferably, an inversion system for enterprise point source carbon emissions based on moving monitoring data uses the above-mentioned inversion method for enterprise point source carbon emissions based on moving monitoring data, and includes:

[0023] A data cleaning module, which is used to select the monitoring data during non-congested periods as valid data with the speed of the moving vehicle at 30 Km / h as the threshold;

[0024] A concentration enhancement amount accounting module, which is used to compare the CO2 / CH4 concentrations in the moving monitoring data, determine the concentration background value and the high-concentration value during the moving monitoring, and calculate the CO2 / CH4 concentration enhancement amount caused by the enterprise point source emissions;

[0025] An emission simulation module, which uses the plume diffusion model to simulate the CO2 / CH4 emission diffusion process and spatio-temporal distribution during the moving monitoring period according to the historical emissions of enterprises, wind speed, wind direction, and chimney height parameters, calculates the CO2 / CH4 emission plume diffusion at different horizontal and vertical positions, and simulates the CO2 / CH4 concentration distribution at the height of the moving vehicle (2 m);

[0026] The simulated concentration matching module performs coordinate matching in the simulation results of the plume diffusion model based on the trajectory point coordinates provided by the mobile monitoring data during the cruise, and obtains the simulated concentration enhancement amount.

[0027] The emission inversion module inversely calculates the enterprise point-source carbon emissions during the cruise using the optimal least squares method. By constructing a covariance matrix to describe the correlation and error range between the observed values, and using the weighted least squares method in combination with the covariance matrix, the enterprise CO2 / CH4 emissions during the cruise are calculated.

[0028] A processor is configured to execute an enterprise point-source carbon emission inversion system based on the above mobile monitoring data during the cruise.

[0029] A computer-readable storage medium stores a computer program thereon. When the computer program is executed by a processor, it implements the above enterprise point-source carbon emission inversion system based on the mobile monitoring data during the cruise.

[0030] Compared with the prior art, the present invention has the following beneficial effects:

[0031] 1. Through the real-time mobile monitoring data during the cruise, the present invention can obtain the carbon emission situation of enterprise point sources in real time, greatly improving the real-time performance and accuracy of carbon emission monitoring.

[0032] 2. In the data preprocessing stage, by taking the speed of the mobile vehicle during the cruise at 30 Km / h as a threshold and selecting the monitoring data during non-congested periods as valid data, the present invention effectively excludes the monitoring data errors caused by factors such as traffic congestion, improving the data quality.

[0033] 3. By using the plume diffusion model and combining parameters such as the enterprise's historical emissions, wind speed, wind direction, and chimney height, the present invention can accurately simulate the diffusion process and its spatio-temporal distribution characteristics of enterprise CO2 / CH4 emissions during the cruise; this method is not only scientific and rigorous but also can quantitatively analyze the impact of enterprise point-source emissions on the surrounding environment.

[0034] 4. By comparing the CO2 / CH4 concentrations in the mobile monitoring data, determining the concentration background value and the high-concentration value during the cruise, and calculating the concentration enhancement amount, the present invention can accurately quantify the change in CO2 / CH4 concentration caused by enterprise point-source emissions, providing a reliable basis for carbon emission inversion.

[0035] 5. The present invention inversely calculates the enterprise point-source carbon emissions during the cruise using the optimal least squares method (weighted least squares method), and describes the correlation and error range between the observed values by constructing a covariance matrix, further optimizing the inversion algorithm and improving the accuracy and reliability of carbon emission inversion.

[0036] 6. The present invention is not only applicable to the inversion of enterprise point-source carbon emissions, but also can be extended and applied to the monitoring and assessment of other similar pollution sources, with wide applicability and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a flowchart of the method for inverting enterprise point-source carbon emissions based on mobile monitoring data of the present invention;

[0038] Figure 2 It is a schematic diagram of mobile monitoring of the mobile vehicle of the present invention;

[0039] Figure 3 It is a schematic diagram of the concentration monitoring data of the mobile vehicle in the enterprise park of the present invention;

[0040] Figure 4 It is a schematic diagram of plume diffusion of the present invention;

[0041] Figure 5 It is a schematic diagram of the comparison of emissions during the inversion mobile vehicle operation and the prior emissions of the present invention;

[0042] Figure 6 It is a framework diagram of the system for inverting enterprise point-source carbon emissions based on mobile monitoring data of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0043] The following further describes in detail the embodiments of the present invention in conjunction with the drawings and examples. The following examples are used to illustrate the present invention, but cannot be used to limit the scope of the present invention.

[0044] As Figures 1 to 5 shown:

[0045] Example: The present invention provides a method for inverting enterprise point-source carbon emissions based on mobile monitoring data. As Figure 1 shown, it includes:

[0046] Step 1: Cleaning of mobile monitoring data. Taking the speed of the mobile vehicle at 30 Km / h as the threshold, the monitoring data in non-congested periods is selected as valid data. As Figure 2 shown;

[0047] Step 2: Calculation of the enhanced amount of monitoring concentration. By comparing the CO2 / CH4 concentrations in the mobile monitoring data, the background concentration value and the high concentration value during mobile monitoring are determined, and the enhanced amount of concentration is calculated;

[0048] Step 3: Simulation of enterprise point-source CO2 / CH4 emissions based on the plume diffusion model. Using parameters such as the historical emissions of the enterprise, wind speed, wind direction, and chimney height, the CO2 / CH4 emission diffusion process is simulated. As Figure 3 shown;

[0049] Step 4: Simulated concentration enhancement calculation. According to the trajectory point coordinates provided by the mobile monitoring data, coordinate matching is performed in the simulation results of the plume diffusion model to obtain the simulated concentration enhancement, as Figure 4 shown;

[0050] Step 5: Use the optimal least squares method to invert the carbon emissions of enterprise point sources during the mobile monitoring period. Construct a covariance matrix, and combine the covariance matrix through the weighted least squares method to calculate the CO2 / CH4 emissions of enterprises during the mobile monitoring period, as Figure 5 shown.

[0051] As can be seen from the above, through steps such as real-time mobile monitoring, data cleaning, concentration enhancement calculation, plume diffusion model simulation, simulated concentration matching, and optimal least squares inversion, problems such as inaccurate, lagging, and incomplete data in traditional carbon emissions monitoring methods are effectively solved; this method not only improves the real-time and accuracy of carbon emissions monitoring, but also significantly improves work efficiency through systematic and automated processing methods, providing a more scientific and reliable technical means for the environmental protection field, helping to more accurately evaluate the carbon emissions of enterprises, and promoting the realization of carbon emission reduction goals.

[0052] Furthermore, in step S1, the mobile monitoring data cleaning uses a data validity screening function to quantify the data validity. The formula of the data validity screening function is as follows:

[0053]

[0054] Among them, V(t) represents the data validity (1 means valid, 0 means invalid) of the carbon emissions inversion method of enterprise point sources based on mobile monitoring data at time point t, and v((t) represents the speed of the mobile vehicle at time point t.

[0055] As can be seen from the above, this data cleaning strategy significantly improves the quality of monitoring data, effectively excluding data during low-speed driving periods caused by traffic congestion, etc. These data often contain large errors and cannot truly reflect the carbon emissions of enterprise point sources; through this screening process, it ensures that the data for subsequent analysis is more accurate and reliable, thereby improving the accuracy of carbon emissions inversion and laying a solid foundation for scientific simulation and quantitative analysis in subsequent steps.

[0056] Further, the specific description of Step 2 is as follows: After obtaining the effective cruise monitoring data, the system further performs the following operations to determine the concentration background value and the cruise high-value concentration: First, select the average value of the CO2 / CH4 monitoring concentrations that are less than the lower decile, and use it as the concentration background value for this cruise monitoring; then, define the CO2 / CH4 monitoring concentrations that are higher than the upper decile as the cruise monitoring high-value concentrations, and mark the positions where the cruise high-value concentrations appear as the plume landing positions; finally, by calculating the difference between the cruise high-value concentration and the cruise background concentration, obtain the enhancement amount of the monitoring concentration caused by the diffusion of the enterprise point source plume.

[0057] As can be seen from the above, first, selecting the average value of the CO2 / CH4 monitoring concentrations that are less than the lower decile as the concentration background value can effectively exclude the influence of abnormal low values on the background concentration, making the background value more representative; second, defining the concentrations that are higher than the upper decile as the cruise monitoring high-value concentrations and marking their positions as the plume landing positions helps to accurately identify the impact area of enterprise point source emissions on the surrounding environment; finally, by calculating the difference between the cruise high-value concentration and the background concentration, obtaining the enhancement amount of the monitoring concentration caused by the diffusion of the enterprise point source plume, this quantitative indicator directly reflects the actual impact of enterprise emissions, providing an important basis for the subsequent inversion of carbon emissions; the whole process is not only scientific and rigorous, but also can accurately quantify the contribution of enterprise emissions to the environment, which is of great significance for accurately evaluating the carbon emissions of enterprises.

[0058] Further, the concentration enhancement amount calculation formula is used to quantify the change in CO2 / CH4 concentration caused by enterprise point source emissions, and its formula is as follows:

[0059] ΔC = C * - C′;

[0060] where ΔC represents the concentration enhancement amount, C * represents the high-value concentration in the cruise monitoring, and C′ represents the concentration background value.

[0061] As can be seen from the above, by calculating the concentration enhancement amount, environmental supervision departments and enterprises themselves can more accurately understand the actual contribution of emission sources, which helps to formulate more effective emission reduction measures and management strategies; in addition, this formula also enhances the interpretability and application value of monitoring data, providing strong support for the subsequent inversion of carbon emissions and environmental protection work.

[0062] Further, the specific description of step S3 is as follows: Using the plume diffusion model, combined with key parameters such as the historical emission data of the enterprise, the wind speed and wind direction information during the mobile monitoring period, and the chimney height of the enterprise, to accurately simulate the diffusion process and its spatio-temporal distribution characteristics of the CO2 / CH4 emissions of the enterprise during the mobile monitoring period; this model can calculate the diffusion of the CO2 / CH4 emission plume at different horizontal and vertical positions, so as to simulate and obtain the specific distribution of the CO2 / CH4 concentration at the driving height (2m) of the mobile vehicle.

[0063] As can be seen from the above, through this method, it is possible to comprehensively consider actual environmental factors such as historical emissions, wind speed, wind direction, and chimney height, to carefully simulate the diffusion of the emission plume, and thus obtain the specific concentration distribution at the driving height of the mobile vehicle; not only improves the accuracy and reliability of the simulation results, but also provides important data support for concentration matching and carbon emission inversion in subsequent steps; in addition, this method can also help researchers better understand the specific impact of enterprise emissions on the surrounding environment, and provide a scientific basis for environmental protection and the implementation of emission reduction measures.

[0064] Further, the plume diffusion model is used to simulate the diffusion process of pollutants in the atmosphere, and the formula of the plume diffusion model is as follows:

[0065]

[0066] Among them, C(x, y, z, H) represents the pollutant concentration at the position (x, y, z), Q represents the source strength, u represents the wind speed, H represents the chimney height, σ y and σ z respectively represent the horizontal and vertical diffusion parameters.

[0067] Further, the specific description of step S4 is as follows: First, construct a covariance matrix by combining the observation time difference of the trajectory points and the uncertainty of the mobile monitoring instrument. The diagonal elements in the matrix represent the observation error sizes of each observation value, and the smaller the time difference between the observation values, the higher their correlation in the matrix; then, through the weighted least squares method (optimal least squares method) combined with the covariance matrix, conduct in-depth mathematical processing on the concentration enhancement amount simulated by the model and the actually monitored concentration enhancement amount, and then obtain the CO2 / CH4 emissions of the enterprise during the mobile monitoring period.

[0068] As can be seen from the above, by constructing a covariance matrix and combining the weighted least squares method (optimal least squares method) for data processing, the accuracy of enterprise CO2 / CH4 emissions inversion has been significantly improved. This method first constructs a covariance matrix using the observation time difference of trajectory points and the uncertainty of the mobile monitoring instrument, comprehensively describing the correlation between observation values and their respective error ranges. Subsequently, through the weighted least squares method, the information provided by the covariance matrix is fully utilized to perform mathematical optimization on the concentration enhancement amount simulated by the model and the actual monitoring values, effectively reducing error accumulation and improving the reliability of the inversion results. This process not only enhances the scientificity and rigor of data processing but also provides important technical support for accurately evaluating enterprise carbon emissions and formulating effective environmental protection policies.

[0069] Further, in step four for calculating the simulated concentration enhancement amount and step five for inverting the enterprise point source carbon emissions during the mobile monitoring period using the optimal least squares method, a covariance matrix needs to be constructed to describe the correlation and error range between observation values. The formula for the covariance matrix is as follows:

[0070]

[0071] where Cov(i,j) represents the covariance between observation values i and j, and represent the variances of observation values i and j respectively, and ρ ij represents the correlation coefficient between observation values i and j.

[0072] As can be seen from the above, through the covariance matrix, the internal relationship between each observation value and its respective error level can be comprehensively and accurately quantified. It not only helps to understand the complex relationship between data but also fully considers these relationships during the inversion process. Through optimization algorithms such as the weighted least squares method, the model simulation results and actual monitoring data can be more effectively integrated, thereby improving the accuracy and robustness of carbon emissions inversion. At the same time, the construction of the covariance matrix also enhances the transparency and repeatability of data processing, providing a solid data foundation for scientific research and environmental protection decision-making.

[0073] Further, the weighted least squares method is used to calculate the enterprise CO2 / CH4 emissions during the mobile monitoring period. The formula for the weighted least squares method is as follows:

[0074]

[0075] where Q represents the carbon emissions to be inverted, C obs,i represents the observed concentration value, C sim,i (Q) represents the simulated concentration value, and w i represents the weight of observation value i.

[0076] As can be seen from the above, by introducing the weights of the observed values, the accuracy and reliability of the inversion results are significantly improved; this method not only considers the differences between the observed values and the simulated values, but also fully takes into account the uncertainties of each observed value itself and the correlations between them, and accurately calculates the weights of each observed value through the covariance matrix; this processing method effectively reduces the influence of the observed values with large errors on the final results, making the inversion results closer to the real situation; therefore, the weighted least squares method not only improves the accuracy of carbon emission inversion, but also provides more scientific and effective technical support for environmental monitoring and pollution control.

[0077] Working principle: Obtain the CO2 / CH4 concentration data in the enterprise point source emission area through real-time mobile monitoring. After data cleaning and calculation of the concentration enhancement amount, use the plume diffusion model to simulate the emission diffusion process in combination with parameters such as historical emissions, wind speed, wind direction, and chimney height, then match the coordinates to simulate the concentration enhancement amount, and finally use the weighted least squares method combined with the covariance matrix to inversely calculate the enterprise point source carbon emissions during the mobile monitoring period.

[0078] An enterprise point source carbon emission inversion system based on mobile monitoring data during mobile monitoring, as Figure 6 shown, using the above-mentioned enterprise point source carbon emission inversion method based on mobile monitoring data during mobile monitoring, includes:

[0079] The data cleaning module is used to select the monitoring data during non-congested periods as valid data with the speed of the mobile vehicle at 30 Km / h as the threshold;

[0080] The concentration enhancement amount calculation module is used to compare the CO2 / CH4 concentrations in the mobile monitoring data, determine the concentration background value and the mobile high-value concentration, and calculate the CO2 / CH4 concentration enhancement amount caused by enterprise point source emissions;

[0081] The emission simulation module uses the plume diffusion model to simulate the CO2 / CH4 emission diffusion process and spatio-temporal distribution of the enterprise during the mobile monitoring period according to parameters such as enterprise historical emissions, wind speed, wind direction, and chimney height, calculate the CO2 / CH4 emission plume diffusion at different horizontal and vertical positions, and simulate the CO2 / CH4 concentration distribution at the height of the mobile vehicle (2 m);

[0082] The simulated concentration matching module performs coordinate matching in the simulation results of the plume diffusion model according to the trajectory point coordinates provided by the mobile monitoring data to obtain the simulated concentration enhancement amount;

[0083] The emission inversion module inversely calculates the enterprise point source carbon emissions during the mobile monitoring period using the optimal least squares method, describes the correlations and error ranges between the observed values by constructing a covariance matrix, and calculates the enterprise CO2 / CH4 emissions during the mobile monitoring period using the weighted least squares method combined with the covariance matrix.

[0084] As can be seen from the above, the implementation of steps such as data cleaning, calculation of concentration enhancement amount, emission simulation, simulation concentration matching, and emission inversion is systematized and automated, greatly improving work efficiency and monitoring accuracy.

[0085] An embodiment of the present application provides an electronic device, which is applicable to the above-mentioned enterprise point source carbon emission inversion system based on mobile monitoring data during vehicle patrol, and includes:

[0086] A memory for storing computer programs and data;

[0087] A processor for running system programs.

[0088] An embodiment of the present application provides a computer storage medium, which is applicable to the above-mentioned enterprise point source carbon emission inversion system based on mobile monitoring data during vehicle patrol, and performs hierarchical confidentiality management on the above system and data according to the requirements of confidentiality management.

[0089] Those skilled in the art should understand that the embodiments of the present application can be provided as a system or a computer program product. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) containing computer-usable program codes.

[0090] The present application is described with reference to the flowcharts and / or block diagrams of the devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of general-purpose computers, special-purpose computers, embedded processors, or other programmable data processing devices to generate a machine, so that the instructions executed by the processors of the computer or other programmable data processing devices generate means for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0091] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured product including instruction means, and the instruction means implements the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 one block or multiple blocks.

[0092] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0093] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0094] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0095] Computer readable media include permanent and non-permanent, removable and non-removable media, and can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0096] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, commodity or device including the elements.

[0097] Embodiments of the present invention are given for purposes of illustration and description. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for inverting the carbon emissions of enterprise point sources based on mobile monitoring data during cruise, characterized in that: Including: Step 1: Cleaning the mobile monitoring data. Taking the speed of the mobile vehicle at 30 Km / h as the threshold, select the monitoring data during non-congested periods as valid data. Step 2: Calculating the enhanced concentration. By comparing the CO2 / CH4 concentrations in the mobile monitoring data, determine the background concentration and the high-value concentration during the mobile monitoring, and calculate the enhanced concentration. Step 3: Simulating the CO2 / CH4 emissions from enterprise point sources based on the plume diffusion model. Using the historical emissions of the enterprise, wind speed, wind direction, and chimney height parameters, simulate the diffusion process of CO2 / CH4 emissions. Step 4: Calculating the simulated enhanced concentration. According to the trajectory point coordinates provided by the mobile monitoring data, perform coordinate matching in the simulation results of the plume diffusion model to obtain the simulated enhanced concentration. Step 5: Inverting the carbon emissions of enterprise point sources during the mobile monitoring period using the optimal least squares method. Construct a covariance matrix, and through the weighted least squares method combined with the covariance matrix, calculate the CO2 / CH4 emissions of the enterprise during the mobile monitoring period.

2. The enterprise point-source carbon emission inversion method based on moving monitoring data during navigation as claimed in claim 1, wherein: In Step S1, the cleaning of the mobile monitoring data uses a data validity screening function.

3. The method for inverting the carbon emissions of enterprise point sources based on the data of mobile monitoring during navigation as claimed in claim 1, wherein: The specific description of Step 2 is as follows: After obtaining the valid mobile monitoring data, the system performs the following operations to determine the background concentration and the high-value concentration during the mobile monitoring: First, select the average value of the CO2 / CH4 monitoring concentrations that are less than the lower decile as the background concentration for this mobile monitoring; then, define the CO2 / CH4 monitoring concentrations that are higher than the upper decile as the high-value concentrations during the mobile monitoring, and mark the positions where the high-value concentrations appear as the plume landing positions; finally, calculate the difference between the high-value concentration during the mobile monitoring and the background concentration.

4. The enterprise point-source carbon emission inversion method based on moving monitoring data during cruise as claimed in claim 1, wherein: The formula for calculating the enhanced concentration is used to quantify the change in CO2 / CH4 concentration caused by the emissions from enterprise point sources.

5. The method for inverting the carbon emissions of enterprise point sources based on the data of mobile monitoring during cruise according to claim 1, characterized in that: The specific description of Step S3 is as follows: Using the plume diffusion model, combined with the obtained historical emissions data of the enterprise, the wind speed and wind direction information during the mobile monitoring period, and the key parameter of the enterprise's chimney height, accurately simulate the diffusion process and its spatio-temporal distribution characteristics of the enterprise's CO2 / CH4 emissions during the mobile monitoring period; this model can calculate the diffusion of the CO2 / CH4 emission plume at different horizontal and vertical positions, so as to simulate and obtain the specific distribution of the CO2 / CH4 concentration at the height (2m) of the mobile vehicle.

6. The enterprise point source carbon emission inversion method based on the moving monitoring data during navigation as claimed in claim 1, wherein: The plume diffusion model is used to simulate the diffusion process of pollutants in the atmosphere.

7. The method for inverting the carbon emission of enterprise point sources based on the data of mobile monitoring during cruise according to claim 1, wherein: The specific description of Step S4 is as follows: First, construct a covariance matrix by combining the observation time difference of the trajectory points and the uncertainty of the mobile monitoring instrument. The diagonal elements in the matrix represent the observation error sizes of each observation value, and the smaller the time difference between the observation values, the higher their correlation in the matrix; then, through the weighted least squares method (optimal least squares method) combined with the covariance matrix, perform in-depth mathematical processing on the enhanced concentration simulated by the model and the actually monitored enhanced concentration, and then obtain the CO2 / CH4 emissions of the enterprise during the mobile monitoring period.

8. The method for inverting the carbon emission of enterprise point sources based on the data of mobile monitoring during navigation as claimed in claim 1, wherein: The covariance matrix is used to describe the correlation and error range between observation values.

9. The enterprise point-source carbon emission inversion method based on the vehicle-mounted mobile monitoring data according to claim 1, wherein: The weighted least squares method is used to calculate the CO2 / CH4 emissions of enterprises during the cruising period.

10. An enterprise point source carbon emission inversion system based on mobile monitoring data during shipborne surveys, characterized in that: Using the method for inverting the carbon emissions of enterprise point sources based on cruising mobile monitoring data described in claims 1-9, comprising: A data cleaning module, which uses the cruising vehicle speed of 30 Km / h as a threshold to select the monitoring data during non-congested periods as valid data; A concentration enhancement amount calculation module, which is used to compare the CO2 / CH4 concentrations in the cruising monitoring data, determine the concentration background value and the cruising high-value concentration, and calculate the CO2 / CH4 concentration enhancement amount caused by the emissions of enterprise point sources; An emission simulation module, which uses the plume diffusion model to simulate the CO2 / CH4 emission diffusion process and spatio-temporal distribution of enterprises during the cruising period according to the historical emissions, wind speed, wind direction, and chimney height parameters of the enterprises, calculates the CO2 / CH4 emission plume diffusion at different horizontal and vertical positions, and simulates the CO2 / CH4 concentration distribution at the height of the cruising vehicle (2m); A simulated concentration matching module, which performs coordinate matching in the simulation results of the plume diffusion model according to the trajectory point coordinates provided by the cruising monitoring data to obtain the simulated concentration enhancement amount; An emissions inversion module, which uses the optimal least squares method to invert the carbon emissions of enterprise point sources during the cruising period, constructs a covariance matrix to describe the correlation and error range between the observed values, and combines the covariance matrix with the weighted least squares method to calculate the CO2 / CH4 emissions of enterprises during the cruising period.

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