Secondary organic aerosol source analysis method, device, equipment and medium based on chemical transmission model

Abstract

The embodiment of the invention discloses a secondary organic aerosol source analysis method based on a chemical transmission model, and belongs to the technical field of environmental sciences, and the method comprises the following steps: obtaining information, carrying out chemical transmission simulation and carrying out post-processing analysis, a brand-new path-based integral method is constructed by using a mesoscale weather numerical model, a pollutant emission list and an air quality chemical transmission model, and the generation contributions of different industries in a target area to the secondary organic aerosol are obtained through simulation. Visibly, through the scheme of the invention, the contribution of different industries to the pollutants in the target area can be obtained, so that an important theoretical basis is provided for continuously improving the air quality of the urban area, the result of the implementation process is obtained according to a direct data model, and the analysis result is more accurate.

Description

Technical Field

[0001] The disclosed embodiments relate to the field of environmental science and technology, and specifically to a method, device, equipment, and medium for analyzing the source of secondary organic aerosol based on a chemical transport model. Background Art

[0002] The information disclosed in this background technology section is only intended to increase the understanding of the overall background of the present disclosure, and shall not necessarily be regarded as an admission or any form of suggestion that the information constitutes the relevant technology that has been known to ordinary technicians in the field.

[0003] With the rapid development of my country's industry and the increase in the number of road mobile sources, the amount of nitrogen oxides (NOx) and volatile organic compounds (VOCs) emitted by anthropogenic sources in the near-ground atmosphere is increasing day by day. Many complex air pollution problems are seriously affecting human health and the ecological environment. In recent years, the governance of air pollution has become more and more in-depth, with fine particulate matter (PM 2.5 ) has been greatly improved, but with the decline in emissions of primary particulate matter, sulfur dioxide, nitrogen oxides, etc., PM 2.5 The proportion of organic components (OAs) is becoming increasingly prominent, of which 20% to 70% of OAs are formed by volatile (VOCs), semi-volatile (SVOCs) and intermediate volatile organic compounds (IVOCs) being oxidized in the atmosphere and then forming secondary organic aerosols through gas-solid distribution. Secondary organic aerosols have stronger polarity and hygroscopicity, and have a greater impact on the optical properties, nucleation ability and health effects of particles. It can be seen that studying the sources of secondary organic aerosols and quantitatively characterizing the relative contribution ratios of various sources of secondary organic aerosols are important for continuously improving the relative air quality of cities.

[0004] In the related technologies, the sensitivity analysis method and species tracing method are mainly used to identify the source of particulate matter using air quality models at home and abroad. The source-based sensitivity analysis methods mainly include the brute force method (BFM) and the decoupled direct method (DDM), but the brute force method is simple in concept and easy to operate, and requires a large amount of calculation, and there is no way to reflect the nonlinear relationship between the precursor emission source and the secondary pollutant. DDM is only applicable to emission source changes that have a small impact on the sensitivity of pollutants. If the emission source changes too much, this method cannot establish a nonlinear relationship between the emission source and the pollutant concentration. The species tracing method is a particulate matter source tracing and analysis method based on the tracer labeling method. The source analysis method based on this method cannot analyze the "indirect" impact of pollutant emissions on the generation of secondary organic aerosols, so its results have certain uncertainties. Therefore, in order to improve the accuracy of the secondary organic aerosol source analysis results, a new method for quantifying the contribution of precursor emissions to pollutant generation is proposed to make up for the shortcomings of the above two methods. Summary of the invention

[0005] To this end, the embodiments of the present disclosure provide a secondary organic aerosol source analysis method based on a chemical transport model to solve the problem of excessive uncertainty in analysis results caused by the use of nonlinear analysis methods in related technologies.

[0006] In order to achieve the above objectives, the embodiments of the present disclosure provide the following technical solutions:

[0007] In a first aspect of the embodiments of the present disclosure, a method for secondary organic aerosol source analysis based on a chemical transport model is provided, comprising the following steps:

[0008] S101. Obtain information: The information includes WRF (mesoscale weather numerical model) and SMOKE (air pollutant emission source inventory processing model) of GFS meteorological data or FNL reanalysis meteorological data;

[0009] S102, Chemical transport simulation: Chemical transport model construction, input the results obtained from the chemical model into PIM, and obtain the initial gridded simulation concentration of SOAs (secondary organic aerosol) and pollution source emission contribution in the target area;

[0010] S103: Post-processing analysis: Based on the results of the chemical transport model simulation, the spatial distribution of SOAs (secondary organic aerosol) concentration and the contribution ratio of different industries to SOAs (secondary organic aerosol) are displayed through a drawing script.

[0011] In one embodiment, a gridded pollutant emission inventory, a natural source gridded emission inventory, an emission inventory with emission variation coefficients, a natural source emission inventory processing model MEGAN, an air quality chemical transport model CAMx / DDM, and a PIM method integration are constructed based on the information;

[0012] The emission industries of SOAs (secondary organic aerosol) are tracked through chemical transport models to obtain simulated SOAs concentrations and pollution source contributions.

[0013] In one embodiment, obtaining the initial gridded simulated concentration of SOAs (secondary organic aerosols) and the emission contribution of pollution sources in the target area includes:

[0014] Obtain the initial gridded simulated concentration of SOAs (secondary organic aerosol) in the target area and its first-order sensitivity coefficient to different industries;

[0015] The obtained first-order sensitivity coefficient of SOAs (secondary organic aerosol) is input into PIM to obtain the contribution ratio of different industries to SOAs (secondary organic aerosol).

[0016] In one embodiment, the method for obtaining the initial gridded simulated concentration of SOAs (secondary organic aerosol) in the target area and its first-order sensitivity coefficients for different industries includes:

[0017] The meteorological field simulated by the mesoscale weather numerical model WRF, the local gridded pollutant emission inventory constructed by the SMOKE (air pollutant emission source inventory processing model), and the natural source gridded emission inventory constructed by the natural source emission inventory processing model MEGAN after correction of the emission coefficient are input into the air quality chemical transport model CAMx / DDM to obtain the initial gridded simulated concentration of SOAs (secondary organic aerosol) in the target area and its first-order sensitivity coefficient to different industries;

[0018] The list of air pollutant emission sources is modified through the mrguam merger script for each industry, and each is multiplied by the corresponding emission coefficient, which is calculated by the Gaussian formula.

[0019] In one embodiment, the method for acquiring the meteorological field of the target area includes:

[0020] The WRF (mesoscale weather numerical model) obtains the meteorological field of the target area through the downloaded meteorological data GFS or FNL reanalysis data, ground data such as topography, land use, soil type, etc. of the target area, meteorological initial field and physical parameterization scheme.

[0021] In one embodiment, the method for obtaining the gridded pollutant emission inventory includes:

[0022] By using the localized emission inventory as emission data and distributing it in time and space through the natural source emission inventory processing model MEGAN, a gridded pollutant emission inventory suitable for the target area is obtained.

[0023] In one embodiment, the method for obtaining the gridded natural source emission inventory includes:

[0024] By spatially distributing the downloaded leaf area index, vegetation cover type index, land cover type and other data, a gridded emission inventory of natural sources suitable for the target area is obtained.

[0025] In a second aspect of the embodiments of the present disclosure, a secondary organic aerosol source analysis device based on a chemical transport model is provided, characterized in that it includes:

[0026] Information acquisition module: used to acquire information, including the mesoscale weather numerical model WRF and SMOKE (air pollutant emission source inventory processing model) of GFS meteorological data or FNL reanalysis meteorological data;

[0027] Chemical transport simulation module: used to construct a chemical transport model, input the results obtained from the chemical model into PIM, and obtain the initial gridded simulation concentration of SOAs (secondary organic aerosol) and the emission contribution of pollution sources in the target area;

[0028] Post-processing analysis module: It is used to display the spatial distribution of SOAs (secondary organic aerosol) concentration and the contribution ratio of different industries to SOAs (secondary organic aerosol) through drawing scripts based on the results of chemical transport model simulation.

[0029] In a third aspect of the embodiments of the present disclosure, there is provided an electronic device, comprising an input device and an output device, characterized in that it also comprises

[0030] a processor adapted to implement one or more instructions; and

[0031] A computer-readable storage medium having stored thereon at least one instruction, at least one program, a code set or an instruction set, wherein the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the steps of any of the above methods.

[0032] In a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, characterized in that at least one instruction, at least one program, a code set or an instruction set is stored on the computer-readable storage medium, and the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the steps of any of the above methods.

[0033] According to the embodiments of the present disclosure, compared with the existing methods, the method has the following advantages: including the steps of obtaining information, chemical transport simulation and post-processing analysis, by obtaining grid data of the target area or multiple adjacent areas, using a mesoscale weather numerical model, a pollutant emission inventory and an air quality chemical transport model, a new path integral method is constructed to simulate the contribution of different industries in the target area to the generation of secondary organic aerosols. It can be seen that through the scheme of the present invention, the contribution of different industries to pollutants in the target area can be obtained, thereby providing an important theoretical basis for continuously improving the air quality in urban areas. The results of its implementation process are obtained based on a direct data model, and the analysis results are more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the related technologies, the following briefly introduces the drawings required for the embodiments or the related technical descriptions. Obviously, the drawings described below are only exemplary, and for ordinary technicians in this field, other implementation drawings can be derived from the provided drawings without creative work.

[0035] Figure 1 is a flow chart of a method for secondary organic aerosol source analysis based on a chemical transport model according to an exemplary embodiment;

[0036] Figure 2 The present invention is a system structure diagram of an implementation method of a secondary organic aerosol source analysis method based on a chemical transport model according to an exemplary embodiment;

[0037] Figure 3 It is a structural diagram of a secondary organic aerosol source analysis device based on a chemical transport model according to an exemplary embodiment. DETAILED DESCRIPTION

[0038] The following is a description of the implementation methods of the present disclosure by specific specific embodiments. People familiar with the technology can easily understand other advantages and effects of the present disclosure from the contents disclosed in this specification. Obviously, the described embodiments are part of the embodiments of the present disclosure, but not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in the field without creative work are within the scope of protection of the present disclosure.

[0039] The method of this embodiment can be implemented by means of software plus a necessary general hardware platform, or by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present disclosure, or the part that contributes to the relevant technology, can be embodied in the form of a software product, and cannot be associated with the limitation of the implementation of the method of the present disclosure due to the limitation of the hardware structure platform. Therefore, the embodiments of the present method are all applicable to the electronic device and storage medium, and can achieve the same or similar beneficial effects.

[0040] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0041] Before this application was submitted, the source analysis of SOAs (secondary organic aerosols) often used source analysis techniques such as the brute force method, sensitivity analysis method and species tracing method in the third-generation air quality model. These source analysis techniques have problems such as large computational complexity and inability to reflect the nonlinear relationship between precursor emission sources and pollutants, which will lead to inaccurate source analysis results.

[0042] In order to solve this problem, the present invention provides a method and system for secondary organic aerosol source analysis based on a chemical transport model, which is described in detail below in conjunction with the accompanying drawings.

[0043] like Figure 1 As shown, it shows a flow chart of the secondary organic aerosol source analysis method based on the chemical transport model provided by an embodiment of the present disclosure, and the specific steps include:

[0044] S101. Obtain information: The information includes the mesoscale weather numerical model (WRF) of GFS meteorological data or FNL reanalysis meteorological data and the atmospheric pollutant emission source inventory processing model (SMOKE);

[0045] S102, Chemical Transport Simulation: Chemical transport model construction, input the results obtained from the chemical model into PIM, and obtain the initial gridded simulated concentration of secondary organic aerosols (SOAs) in the target area and the emission contribution of pollution sources;

[0046] S103: Post-processing analysis: Based on the results of the chemical transport model simulation, the spatial distribution of SOAs concentration and the contribution ratio of different industries to SOAs are displayed through a drawing script.

[0047] Specifically, the spatial distribution map, bar graph or line graph of SOAs in different industries can be obtained by using the nc file post-processing program of NCL or Python.

[0048] The method obtains grid data of the target area or multiple adjacent areas, and uses a mesoscale weather numerical model, a pollutant emission inventory, and an air quality chemical transport model to construct a new path integral method to simulate the contribution of different industries in the target area to the generation of secondary organic aerosols. It can be seen that through the scheme of the present invention, the contribution of different industries to pollutants in the target area can be obtained, thereby providing an important theoretical basis for continuously improving the air quality in urban areas. The results of its implementation process are obtained based on a direct data model, and the analysis results are more accurate.

[0049] like Figure 2 As shown, it is a system structure diagram of the implementation method of the secondary organic aerosol source analysis method based on the chemical transport model in the embodiment of the present disclosure.

[0050] First: Based on the information, a gridded pollutant emission inventory, a natural source gridded emission inventory, an emission inventory with emission variation coefficients, MEGAN (natural source emission inventory processing model), CAMx / DDM (air quality chemical transport model), and PIM method integration were constructed;

[0051] The SOAs emission industries are tracked through the chemical transport model to obtain simulated SOAs concentrations and pollution source contributions.

[0052] In one embodiment, obtaining the initial gridded simulated concentrations of SOAs and the emission contributions of pollution sources in the target area includes:

[0053] Obtain the initial gridded simulated concentration of SOAs in the target area and its first-order sensitivity coefficients to different industries;

[0054] Secondly: Use the CAMx / DDM model to obtain the gridded concentration of SOAs components and the first-order sensitivity coefficients of different industries’ contributions to them, and then use PIM to obtain the contribution ratio of SOAs. The specific implementation method is as follows:

[0055] Carry out meteorological simulation of the target area based on WRF: According to the resolution of the target area, select the simulation period, collect the initial meteorological field, terrain and land use data required for meteorological simulation, and simulate the target area.

[0056] Among them, the method for obtaining the meteorological field of the target area includes:

[0057] The WRF model obtains the meteorological field of the target area through the downloaded meteorological data GFS or FNL reanalysis data, ground data such as topography, land use, soil type, meteorological initial field and physical parameterization scheme.

[0058] By using the localized emission inventory as emission data and distributing it in time and space through the MEGAN model, a gridded pollutant emission inventory suitable for the target area is obtained.

[0059] (1) Use the SMOKE model to generate gridded pollutant emission inventories for different emission industries: The pollutant emission inventory adopts a localized emission inventory established based on the emission factor method, material balance and other methods. It is then further refined and allocated in time and space through the SMOKE model, and localized and the emission coefficient parameters are modified to generate a gridded pollutant emission inventory for each industry suitable for CAMx / DDM model simulation.

[0060] (3) Generate a gridded pollutant emission inventory of natural sources using the MEGAN model: The natural source emission inventory is generated by downloading relevant data such as leaf area index, vegetation cover type and land type, and simulating the natural source emission inventory through the MEGAN model, which is suitable for CAMx / DDM model simulation.

[0061] (4) The CAMx / DDM model was used to simulate the initial gridded simulated concentration of SOAs components in the target area and the first-order sensitivity coefficients of the contribution of pollution sources from different industries to them: the results of meteorological simulation, the emission inventory generated by the SMOKE model, and the natural source emission inventory generated by the MEGAN model were input into the CAMx / DDM model to simulate the SOAs concentration and the first-order sensitivity coefficients of the contribution of different emission industries to them.

[0062] The list of air pollutant emission sources is merged through the mrguam script, and the source list of each industry is multiplied by the corresponding emission factor for correction. The emission factor is derived from historical experience and the purpose is to reduce emissions accordingly.

[0063] (5) Input the obtained first-order sensitivity coefficient of SOAs into PIM to obtain the contribution ratio of different industries to SOAs.

[0064] The simulation results were integrated using the PIM post-processing method: the first-order sensitivity coefficients of SOAs were input into the PIM post-processing method to obtain the contribution of SOAs to source categories in different emission industries.

[0065] The PIM formula is as follows:

[0066]

[0067] In the formula: where is the simulated concentration of target pollutant i, background emissions are the simulated scenario with only natural source emissions. After adding the anthropogenic source emission inventory, the difference between the two is the pollutant concentration increment caused by anthropogenic source emissions.

[0068] m represents different source types (such as motor vehicles, industrial sources, civil sources, etc.), λ m is the emission change coefficient of the mth emission source, and P is the change path of the overall emissions (it can be the emissions for one year or the emission changes for the difference between two years).

[0069] The partial derivative on the right is the first-order sensitivity coefficient of the concentration of target pollutant i to the change in the emission of the mth type of emission source, which can be obtained by CAMx / DDM model simulation. After that, the integral is performed, and the integral value is the contribution of the mth type of emission source to the increase in the concentration of the target pollutant.

[0070] Finally, based on the results of the chemical transport model simulation, the spatial distribution of SOAs concentration and the contribution ratio of different industries to SOAs are displayed through a drawing script.

[0071] Specifically, based on nc file processing scripts such as NCL or Python, the simulation results are visualized. The specific implementation method is as follows:

[0072] Based on the simulation results of the model, NCL or Python is used to process the nc file, draw the spatial distribution map of SOAs contributed by different industries, and display the simulation results in different forms.

[0073] In a second aspect of the embodiments of the present disclosure, Figure 3 As shown, it is a secondary organic aerosol source analysis device based on a chemical transport model disclosed in the present invention, characterized in that it includes

[0074] Information acquisition module: used to obtain information, including WRF model and SMOKE model of GFS meteorological data or FNL reanalysis meteorological data;

[0075] Chemical transport simulation module: used to build a chemical transport model, input the results obtained from the chemical model into PIM, and obtain the initial gridded simulation concentration of SOAs in the target area and the emission contribution of pollution sources;

[0076] Post-processing analysis module: It is used to display the spatial distribution of SOAs concentration and the contribution ratio of different industries to SOAs through drawing scripts based on the results of chemical transport model simulation.

[0077] In a third aspect of the embodiments of the present disclosure, there is provided an electronic device, comprising an input device and an output device, characterized in that it also comprises

[0078] a processor adapted to implement one or more instructions; and

[0079] A computer-readable storage medium having stored thereon at least one instruction, at least one program, code set or instruction set, wherein the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the steps of any of the above methods.

[0080] In a fourth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, characterized in that at least one instruction, at least one program, code set or instruction set is stored on the computer-readable storage medium, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the steps of any of the above methods.

[0081] For example, to simulate the contribution of emissions from different industries in Changshu City, Jiangsu Province to SOAs, first download GFS meteorological data or FNL analysis data, and use the WRF model to obtain meteorological simulation data; then use the local emission factor method to obtain an emission inventory suitable for localization, and use the SMOKE model emission inventory processing model to obtain a gridded emission inventory; use the MEGAN model to process the natural source emission inventory; input the above simulation results into the CAMx / DDM model for simulation to obtain the first-order sensitivity coefficients of different emission industries to SOAs; input the first-order sensitivity coefficients into PIM for integration to obtain the contribution values ​​of different source types to SOAs; and use NCL or Python to visualize them.

[0082] Through this method, the analysis results of the sources of secondary organic aerosols are more accurate and displayed through an intuitive visualization system, which is conducive to better control of subsequent secondary organic aerosol emission trends and more scientific and reasonable construction of targeted treatment measures for emissions.

[0083] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article 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, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0084] Although the present disclosure has been described in detail above with general descriptions and specific embodiments, it is obvious to those skilled in the art that some modifications or improvements may be made to the present disclosure. Therefore, these modifications or improvements made without departing from the spirit of the present disclosure are within the scope of protection claimed by the present disclosure.

Claims

1. A method for secondary organic aerosol source analysis based on a chemical transport model, characterized in that: The following steps are included S101. Obtaining information: The information includes a mesoscale weather numerical model of GFS meteorological data or FNL reanalysis meteorological data, and an atmospheric pollutant emission source inventory processing model; S102, Chemical Transport Simulation: Chemical transport model construction, input the results obtained from the chemical model into PIM, and obtain the initial gridded simulated concentration of secondary organic aerosol in the target area and the emission contribution of pollution sources; S103: Post-processing analysis: Based on the results of the chemical transport model simulation, the spatial distribution of secondary organic aerosol concentration and the contribution ratio of different industries to secondary organic aerosol are displayed through a drawing script.

2. The method for secondary organic aerosol source analysis based on chemical transport model according to claim 1 is characterized in that: Constructing a gridded pollutant emission inventory, a natural source gridded emission inventory, an emission inventory with emission variation coefficients, a natural source emission inventory processing model, an air quality chemical transport model, and a PIM method integration based on the information; The secondary organic aerosol emission industries are tracked through the chemical transport model to obtain the simulated secondary organic aerosol concentration and pollution source contribution.

3. The method for secondary organic aerosol source analysis based on chemical transport model according to claim 2 is characterized in that: The initial gridded simulated concentration of secondary organic aerosol in the target area and the contribution of pollution source emissions include Obtain the initial gridded simulated concentration of secondary organic aerosol in the target area and its first-order sensitivity coefficient to different industries; The obtained first-order sensitivity coefficient of secondary organic aerosol is input into PIM to obtain the contribution ratio of different industries.

4. The method for secondary organic aerosol source analysis based on chemical transport model according to claim 3 is characterized in that: The methods for obtaining the initial gridded simulated concentration of secondary organic aerosol in the target area and its first-order sensitivity coefficients for different industries include The meteorological field simulated by the mesoscale weather numerical model, the local gridded pollutant emission inventory constructed by the atmospheric pollutant emission source inventory processing model, and the natural source gridded emission inventory constructed by the natural source emission inventory processing model after correction of the emission coefficient are input into the air quality chemical transport model to obtain the initial gridded simulated concentration of secondary organic aerosol in the target area and its first-order sensitivity coefficient to different industries; The list of air pollutant emission sources is modified through the mrguam merger script for each industry, and each is multiplied by the corresponding emission coefficient, which is calculated by the Gaussian formula.

5. The method for secondary organic aerosol source analysis based on chemical transport model according to claim 4, characterized in that: The method for acquiring the meteorological field of the target area includes: The mesoscale weather numerical model obtains the meteorological field of the target area through the downloaded meteorological data GFS or FNL reanalysis data, ground data such as topography, land use, soil type, etc. of the target area, meteorological initial field and physical parameterization scheme.

6. The method for secondary organic aerosol source analysis based on chemical transport model according to claim 5, characterized in that: The method for obtaining the gridded pollutant emission inventory includes: By taking the localized emission inventory as the emission data and distributing it in time and space through the natural source emission inventory processing model, a gridded pollutant emission inventory suitable for the target area is obtained.

7. The method for secondary organic aerosol source analysis based on chemical transport model according to claim 6, characterized in that: The method for obtaining the natural source gridded emission inventory includes: By spatially distributing the downloaded leaf area index, vegetation cover type index, land cover type and other data, a gridded emission inventory of natural sources suitable for the target area is obtained.

8. A secondary organic aerosol source analysis device based on a chemical transport model, used to implement the method according to any one of claims 1 to 7, characterized in that: include Information acquisition module: used to acquire information, including mesoscale weather numerical models of GFS meteorological data or FNL reanalysis meteorological data, and atmospheric pollutant emission source inventory processing models; Chemical transport simulation module: used to construct a chemical transport model, input the results obtained from the chemical model into PIM, and obtain the initial gridded simulated concentration of secondary organic aerosols in the target area and the emission contribution of pollution sources; Post-processing analysis module: It is used to display the spatial distribution of secondary organic aerosol concentration and the contribution ratio of different industries to secondary organic aerosol through drawing scripts based on the results of chemical transport model simulation.

9. An electronic device, comprising an input device and an output device, characterized in that: Also includes a processor adapted to implement one or more instructions; and A computer-readable storage medium having stored thereon at least one instruction, at least one program, a code set or an instruction set, wherein the at least one instruction, the at least one program, the code set or the instruction set is loaded and executed by the processor to implement the steps of any method in claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the steps of any method in claims 1-7.

Images