Domestic satellite full-time-space continuous atmospheric formaldehyde analysis and completion method and domestic satellite full-time-space continuous atmospheric formaldehyde analysis and completion device

Abstract

The invention discloses a domestic satellite full-space-time continuous atmospheric formaldehyde analysis and completion method and device, and the method comprises the steps: obtaining formaldehyde concentration data, meteorological data, related substance emission data and humanistic geographic data obtained through the observation and inversion of a domestic satellite, and constructing earth space-time information data after the data are matched and aligned; performing partial random masking on formaldehyde concentration data in the earth spatio-temporal information data, inputting the formaldehyde concentration data into a neural operator designed according to a data type, and performing formaldehyde concentration data completion on the random mask through calculation of the neural operator; semi-supervised learning is carried out based on a reconstruction error between the complemented formaldehyde concentration data and the observed formaldehyde concentration data, and neural operator parameters are optimized; and analyzing and complementing the observed formaldehyde concentration data by using a parameter-optimized neural operator to realize efficient and reliable prediction of the observed formaldehyde concentration data.

Description

Technical Field

[0001] The invention belongs to the technical field of environmental monitoring, and specifically relates to a domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion method and device. Background Art

[0002] Formaldehyde (HCHO) is an important reactive carbon gas in the atmosphere, and its concentration level is regarded as a key indicator for assessing air quality and monitoring photochemical pollution. Accurate monitoring of the spatiotemporal distribution of atmospheric formaldehyde is crucial to environmental science and atmospheric chemistry research. Traditional ground-based sampling is constrained by limited spatial coverage, while emerging satellite remote sensing technology provides new opportunities for formaldehyde observations on a global scale.

[0003] Hyperspectral satellites have the ability to observe atmospheric formaldehyde and other gas components. However, some hyperspectral satellites still need to improve in terms of temporal and spatial resolution and spectral accuracy. Due to the limitations of observation capabilities, cloud cover, and fluctuations in atmospheric conditions encountered by some hyperspectral satellites during operation, the formaldehyde observation data obtained by satellites often have a large number of missing and discontinuous problems. Even in areas with observations, a large number of pixels in the daily inversion products of formaldehyde have noise and outliers. This data loss not only affects the daily monitoring effect, but also brings great trouble to the trend analysis and root tracing applications of long-term data series.

[0004] Although some methods try to fill in data gaps through statistical interpolation or numerical model simulation and assimilation, these methods usually require additional assumptions or a large amount of auxiliary data and may not fully capture complex atmospheric chemical processes. Anthropogenic volatile organic compounds (AVOCs) and biogenic volatile organic compounds (BVOCs) are one of the main sources of formaldehyde. The chemical reaction of formaldehyde involves complex thermodynamic and kinetic processes that are difficult to fully understand and simulate. In addition, these methods may face problems of high computational cost and low efficiency when dealing with large-scale data sets.

[0005] Deep learning technology has made significant progress in the field of data processing and pattern recognition. This method has achieved remarkable results in satellite data repair. However, since the space where formaldehyde data is located is spherical coordinates, and current research on formaldehyde is far less in-depth than that of nitrogen dioxide and ozone, the most important thing is that there is currently a lack of full coverage labels for neural networks to conduct supervised learning.

[0006] The emergence of neural operators (NO) has brought partial differential equation neural network fitting to a new level of accuracy. For example, the Fourier neural operator (FNO) uses the characteristics of the Fourier transform to combine a linear global integral operator with a nonlinear local activation function to learn complex nonlinear mapping relationships. This unique structure enables excellent performance when dealing with problems involving infinite-dimensional space, especially for operations in function space. The ability of neural operators is not only reflected in learning complex mappings and combining linearity and nonlinearity, but also in extracting and integrating physical knowledge, strong generalization capabilities, and the potential to adapt to new advances. It has demonstrated outstanding capabilities in multiple scientific and engineering problems, opening up new possibilities for the application of deep learning technology. Summary of the invention

[0007] In view of the above, the purpose of the present invention is to provide a method and device for analyzing and completing the continuous atmospheric formaldehyde concentration of domestic satellites in all time and space, using semi-supervised learning and neural operators to solve the problem of missing formaldehyde data, and to achieve high-precision analysis of the full spatial domain distribution of atmospheric formaldehyde concentration of domestic hyperspectral satellites, and obtain seamless global concentration distribution.

[0008] To achieve the above-mentioned purpose of the invention, the embodiment provides a method for analyzing and completing the atmospheric formaldehyde of a domestic satellite in full space and time, comprising the following steps:

[0009] Obtain formaldehyde concentration data, meteorological data, related substance emission data, and human geography data obtained from domestic satellite observation inversion, and align these data to construct earth's spatiotemporal information data;

[0010] The formaldehyde concentration data in the earth's spatiotemporal information data is partially randomly masked and input into the neural operator designed according to the data type. The formaldehyde concentration data is completed by the random mask after calculation by the neural operator, and semi-supervised learning is performed based on the reconstruction error between the completed formaldehyde concentration data and the observed formaldehyde concentration data to optimize the neural operator parameters.

[0011] The observed formaldehyde concentration data is analyzed and completed using a neural operator with optimized parameters to obtain the completed formaldehyde concentration data.

[0012] Preferably, the relevant substance emission data include formaldehyde emission data and emission data of other relevant species that are interrelated with the chemical transformation process of formaldehyde in the atmosphere;

[0013] The human geography data includes population, leaf area index, and topographic data.

[0014] Preferably, the meteorological data includes temperature, humidity, pressure and wind speed.

[0015] Preferably, the neural operator includes an encoder, a neural operator layer, and a decoder connected in sequence, wherein the neural operator layer adopts a spherical Fourier neural operator (SFNO), an orthogonal neural operator (ONO), a Transolver or a general neural operator transformer (GNOT).

[0016] Preferably, the reconstruction error adopts mean square error, L2 norm or absolute error.

[0017] Preferably, when performing semi-supervised learning on the neural operator, the earth's spatiotemporal information data is coarsely sampled in a coarse-resolution grid, and the neural operator is learned using the coarsely sampled sample data. At the same time, some random mask areas are filled with constants, and null value areas are skipped when calculating the reconstruction error.

[0018] Preferably, when performing semi-supervised learning on the neural operator, after learning the neural operator using coarsely sampled sample data, fine sampling of fine-resolution grids is performed on the earth's spatiotemporal information data, and the neural operator is learned using the finely sampled sample data.

[0019] In order to achieve the above-mentioned invention purpose, the embodiment also provides a domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion device, comprising:

[0020] The data acquisition and processing module is used to obtain formaldehyde concentration data, meteorological data, related substance emission data, and human geography data obtained by inversion of domestic satellite observations, and to match and align these data to construct earth's spatiotemporal information data;

[0021] A model building and training module, which is used to perform partial random masking on the formaldehyde concentration data in the earth's spatiotemporal information data and input it into a neural operator designed according to the data type, complete the formaldehyde concentration data with the random mask through neural operator calculation, and perform semi-supervised learning based on the reconstruction error between the completed formaldehyde concentration data and the observed formaldehyde concentration data to optimize the neural operator parameters;

[0022] The parsing and completion module is used to parse and complete the observed formaldehyde concentration data using a neural operator with optimized parameters to obtain the completed formaldehyde concentration data.

[0023] To achieve the above-mentioned purpose of the invention, an embodiment also provides a computing device, including a memory and one or more processors, wherein the memory stores executable code, and when the one or more processors execute the executable code, they are used to implement the above-mentioned domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion method.

[0024] In order to achieve the above-mentioned purpose of the invention, the embodiment also provides a computer-readable storage medium, on which a program is stored. When the program is executed by a processor, the above-mentioned domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion method is implemented.

[0025] Compared with the prior art, the present invention has the following beneficial effects:

[0026] By purposefully introducing gaps in the dataset and using neural operators to learn and predict these gaps, the model can be trained without the need for real labels. This process does not rely on a complete formaldehyde concentration dataset, but instead makes full use of the data's inherent structure and pattern information to achieve efficient and reliable prediction of data gaps, providing strong support for environmental monitoring, weather forecasting, and climate change research. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0028] Figure 1 It is a flow chart of a method for analyzing and completing the domestic satellite atmospheric formaldehyde in full space and time continuously provided by an embodiment;

[0029] Figure 2 It is a flowchart of a method for analyzing and completing atmospheric formaldehyde in a domestic satellite in all time and space continuously provided by an embodiment;

[0030] Figure 3 is a structural diagram of a neural operator model provided in an embodiment;

[0031] Figure 4 is a distribution diagram of the original formaldehyde tropospheric column concentration monitored by satellite provided in the embodiment;

[0032] Figure 5 is the formaldehyde tropospheric column concentration distribution diagram after analysis and completion provided in the embodiment;

[0033] Figure 6 It is the effect verification of filling the vacant formaldehyde tropospheric column concentration provided by the embodiment;

[0034] Figure 7 This is the ground-based MAX-DOAS instrument verification for reconstructing the formaldehyde tropospheric column concentration provided in the embodiment, wherein the left side is the verification result of the original satellite data, and the right side is the verification result after analysis and completion;

[0035] Figure 8It is a structural schematic diagram of a domestic satellite full-time and space continuous atmospheric formaldehyde analysis device provided in an embodiment. DETAILED DESCRIPTION

[0036] 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 accompanying drawings and embodiments. It should be understood that the specific implementation methods described herein are only used to explain the present invention and do not limit the scope of protection of the present invention.

[0037] The inventive concept of the present invention is: when the existing technology reconstructs the spatiotemporal distribution of formaldehyde, the accuracy of the results is low, especially when the data is missing or incomplete. Based on this, in order to solve the technical problem of missing formaldehyde data in satellite observations, the embodiment of the present invention provides a domestic satellite full-time and space continuous atmospheric formaldehyde analysis method and device.

[0038] like Figure 1 and Figure 2 As shown, the embodiment provides a method for analyzing and completing the atmospheric formaldehyde in a domestic satellite in all time and space, comprising the following steps:

[0039] S1, obtain the formaldehyde concentration data, meteorological data, related substance emission data, and human geography data obtained by inverting domestic satellite observations, and then match and align these data to construct the earth's spatiotemporal information data.

[0040] In an embodiment, observed formaldehyde concentration data is obtained and cleaned and standardized. Specifically, the observed data can be obtained from a trace gas differential absorption spectrometer carried on a satellite, and outliers in the formaldehyde concentration data in the observed data are removed and standardized and then sorted in chronological order.

[0041] In the embodiment, meteorological data is obtained from a high-resolution global reanalysis meteorological field, wherein the meteorological data includes wind speed, temperature, air pressure, humidity, and potential, etc., and the obtained meteorological data is interpolated to a result with the same resolution as the satellite. The present invention uses wind speed, temperature, humidity, air pressure, and potential height of 1000hPa, 850hPa, 700hPa, and 500hPa as meteorological input.

[0042] In the embodiment, the relevant substance emission data includes formaldehyde emission data, wherein formaldehyde emissions include natural sources (vegetation, wildfire emissions) and anthropogenic sources (industry, transportation, and daily life emissions). According to the characteristics of the study area, selecting an appropriate emission inventory for replacement can more accurately reflect the formaldehyde emissions in the region. When making the emission inventory, formaldehyde emissions from vegetation, biomass combustion, and anthropogenic sources are added. At the same time, in addition to formaldehyde itself, it is also necessary to consider the emissions of other related species, such as volatile organic compounds (VOCs) emissions, nitrogen oxides (NOx ) and particulate matter emissions. Emission inventories of these species usually need to be used together with formaldehyde emission inventories because their chemical transformation processes in the atmosphere are interrelated and jointly determine the concentration and distribution of formaldehyde.

[0043] In this way, the real chemical processes in the atmosphere can be more accurately reflected, thereby improving the reliability and predictive ability of the simulation. These emission inventories are processed into multi-channel two-dimensional data so that they can be merged with meteorological data. The emission flux over the ocean is mostly 0, and is usually not considered to be a major emission source. When normalizing, the magnitude of the emission inventory needs to be considered to avoid a large number of low values ​​affecting the normalization effect. A constant can be multiplied at the same time to make the emission flux reach a relative distribution of 0-1000. At the same time, human geography data such as population, leaf area index, and altitude data can also be added and processed to the same resolution as auxiliary data.

[0044] The embodiment also performs data point alignment and matching of formaldehyde concentration data, meteorological data, related substance emission data, and human geography data according to time sequence and spatial position to obtain earth time and space information data.

[0045] S2, the formaldehyde concentration data in the earth's spatiotemporal information data is partially randomly masked and input into the neural operator model designed according to the data type. The formaldehyde concentration data of the random mask is completed through neural operator calculation, and semi-supervised learning is performed based on the reconstruction error between the completed formaldehyde concentration data and the observed formaldehyde concentration data to optimize the neural operator parameters.

[0046] In the embodiment, the constructed neural operator model consists of three main parts: an encoder, a neural operator layer and a decoder. It is a mapping from input function space x to output function space y. Neural operator This mapping is learned by parameters θ:

[0047]

[0048] Among them, the encoder maps the input function u to a high-dimensional feature space, Indicates passing The processing of the neural operator layer.

[0049] Among them, the neural operators in the neural operator layer convert the data into a spherical grid suitable for earth coordinates. The spherical Fourier neural operator (SFNO) can be used. SFNO takes advantage of the advantages of Fourier transform in processing periodic and global data, and is suitable for simulating continuous space systems such as the atmosphere. When designing a neural operator model, the most suitable operator network can be selected according to the needs of the specific problem. It is not limited to Fourier neural operators, and other operator networks can be selected. Neural operators such as the orthogonal neural operator (ONO) and the general neural operator transformer (GNOT) can also project and transform data into a spherical coordinate system to meet the spatial continuity characteristics of the three-dimensional atmosphere. When selecting and adjusting neural operators, researchers need to consider the characteristics of the data, the complexity of the model, the requirements for computing resources, and the goals of model performance, and reasonably select the number of features and layers of the model. Specifically, the designed neural operator model such as Figure 3 As shown, it includes an earth spatiotemporal information input layer, an encoding layer, an earth spatiotemporal feature extraction layer, a neural operator layer, a nonlinear activation layer, a decoding layer, and an earth spatiotemporal information output layer.

[0050] The purpose of supervised learning of the neural operator model is to learn the representation of data by reconstructing the missing parts of the formaldehyde concentration data in the earth's spatiotemporal information data. During the supervised learning process, a part of the formaldehyde concentration data is randomly selected and masked, and the masking ratio can be adjusted according to the actual situation, such as selecting a masking ratio of 25-75%. The neural operator model will learn how to fill in the missing parts of the formaldehyde data in the original data set. This process involves optimizing the parameters of the neural operator so that the model can accurately fill in the data.

[0051] The specific steps of supervised learning are:

[0052] (a) Initial data sampling: First, the Earth's spatiotemporal information data is coarsely sampled at a coarse resolution grid, that is, gridded at a resolution of 1°. This step is intended to create a coarse geospatial grid for subsequent modeling and analysis. The 1° resolution balances the needs of spatial detail and computing resources while ensuring that the neural network can converge quickly and learn the chemical change characteristics reflected in the coarse-resolution physical and related material emission data.

[0053] (b) Model training: Use the reconstruction error as the loss function to measure the difference between the completed formaldehyde concentration data output by the network and the formaldehyde concentration data in the masked area of ​​the original image. The reconstruction error can be the mean square error or absolute difference, etc. For a set of samples, the L2 loss (mean square error, MSE) can be expressed as:

[0054]

[0055] Where n is the number of samples, y iis the true value of the i-th sample, is the predicted value of the ith sample.

[0056] During mask processing, the areas that need to be masked are filled with constants, and the original empty value areas are skipped when calculating the loss function. The model is trained on the coarse resolution dataset for 100 epochs (epoch in neural operator model training refers to the process of iterating all samples of the dataset once). The choice of this training cycle is based on experiments and verification to ensure that the neural operator model can converge quickly without overfitting and quickly capture the key features in the data.

[0057] (c) Fine data training: After 100 epochs of training on the coarse-resolution dataset, the Earth's spatiotemporal information data is finely sampled with a fine-resolution grid, specifically gridded at a resolution of 0.1°. This step enables the model to further learn more detailed features, thereby improving the resolution and prediction accuracy of the neural operator model. The neural operator model is further trained on fine data to further improve the performance and generalization ability of the neural operator model. This step can help the neural operator model better understand subtle changes in the data, thereby improving the neural operator model's ability to fill in missing areas.

[0058] (d) Model evaluation: After retraining is completed, the neural operator model is evaluated, including its performance and generalization ability on an independent test set. The prediction performance of the neural operator model is evaluated on the test set, using appropriate evaluation indicators (such as correlation coefficient, mean square error). The evaluation results should demonstrate that the model can accurately predict and simulate the distribution of formaldehyde in the atmosphere.

[0059] S3, using the parameter-optimized neural operator model to parse and complete the observed formaldehyde concentration data to obtain the completed formaldehyde concentration data.

[0060] In the embodiment, the parameter-optimized neural operator model is used for the missing formaldehyde data in the original data set. The prediction results are post-processed to match the statistical characteristics and spatial distribution of the data set, thereby obtaining a long-term, continuous, and accurate satellite formaldehyde column concentration data set.

[0061] In order to verify the technical effect of the above method, a specific experimental example was conducted, in which the formaldehyde column concentration data obtained by observation inversion from 2018 to 2022 were applied. Due to factors such as cloud cover and satellite orbit, there are about 30% missing and invalid values ​​in the original data.

[0062] Data preprocessing: First, the original satellite observed formaldehyde column concentration data was cleaned, obvious outliers were removed, and arranged in daily chronological order. Then the temperature, humidity, air pressure, and wind speed parameters from 2018 to 2022 were extracted from the meteorological field, and the resolution interpolation was consistent with the formaldehyde column concentration data observed by satellite. When constructing the emission inventory, the CAMS formaldehyde emissions and wildfire emission inventories were combined, including anthropogenic sources of formaldehyde, biomass burning, and vegetation emissions. In addition, population distribution, LAI vegetation index, and terrain altitude were extracted as auxiliary data. Finally, all the above data were gridded and matched to a uniform spatiotemporal resolution. All data were standardized to be suitable for model fitting. Due to the low values ​​of the emission inventory, they were multiplied by 10 8 Then standardize.

[0063] Model construction: The experimental example uses a 6-layer spherical Fourier neural operator (SFNO) as the core. Each layer of SFNO consists of Fourier transform and activation function. The input data first passes through a 3D convolution layer to merge multi-dimensional features into single-channel spherical data. Then the features are extracted through SFNO, and finally a decoding layer is connected to output the prediction results. The Adam optimizer is used, and the initial learning rate is 1e -3 , and the cosine annealing strategy is adopted. The loss function is the square error loss. During the training process, the batch size is 8 and the weight decay coefficient is 1e -5 .

[0064] Semi-supervised training: When sampling training data, the original satellite formaldehyde data and various other data are first gridded at a resolution of 1 degree, and 2018-2021 constitutes the training set, and 2022 is used as the validation set. In training or testing, 50% of the satellite observed formaldehyde data points are randomly selected for masking as the target gaps for model learning. In the first 100 epochs, the model is pre-trained at a resolution of 1 degree, and finally the error between the model's prediction and observation results in the test set is reduced to a low level. After that, the satellite data and various auxiliary data are increased to a high resolution of 0.1 degrees, and the model is trained on high-resolution data for 50 epochs to capture more detailed features, and finally achieve seamless global atmospheric formaldehyde analysis technology.

[0065] The prediction performance of the model was evaluated on a global test set in 2022. The results showed that the model achieved good results in analyzing the spatial distribution of atmospheric formaldehyde. 2 The value is 0.58. The deviation between the predicted value of this model and the actual measured value is significantly reduced. The following is the spatiotemporal interpolation effect of the global formaldehyde distribution in a certain prediction by this model. Figure 4 The original formaldehyde tropospheric column concentration distribution map obtained by inversion of the China Environmental Monitoring Satellite on July 18, 2023, is obtained through the weighted analysis and completion method of this embodiment. Figure 5 The global coverage results for the day. Figure 6 The verification of the effect of filling the gap in formaldehyde tropospheric column concentration shows that the original observed concentration and the analyzed concentration have a good correlation, but the reconstruction result obviously has a smoother spatial coverage. After the observation results in the first half of 2023 were reconstructed, they were verified with the ground-based MAX-DOAS instrument of the Beijing Institute of Atmospheric Physics. The left side shows the verification results of the original satellite data, and the right side shows the verification results after analytical completion. After analytical completion, better consistency was achieved.

[0066] It can be seen that the neural operator model of this embodiment well captures the concentration differences of formaldehyde on land and in the ocean, in industrial areas and in forest areas, and accurately reconstructs the missing parts of the original data.

[0067] This example verifies the effectiveness and accuracy of the proposed method of the present invention in filling in satellite global formaldehyde data. With the help of prior knowledge such as auxiliary data and emission inventories, the model can accurately learn the distribution law of formaldehyde atmospheric concentration in time and space. This method avoids the reliance on supervised labels, can flexibly deal with large areas of vacancies in long time series data, and provides strong support for environmental monitoring and atmospheric chemistry research. In the future, this method can also be extended to the monitoring field of other important atmospheric components and environmental elements.

[0068] like Figure 8 As shown, the embodiment also provides a domestic satellite full-time and space continuous atmospheric formaldehyde analysis device, including a data acquisition and processing module, a model construction and training module, and an analysis and completion module, wherein the data acquisition and processing module is used to obtain formaldehyde concentration data, meteorological data, related material emission data, and human geography data obtained by inversion of domestic satellite observations, and match and align these data to construct earth space-time information data; the model construction and training module is used to perform partial random masking on the formaldehyde concentration data in the earth space-time information data and input it into a neural operator model designed according to the data type, and the random mask is completed after the neural operator calculation, and semi-supervised learning is performed based on the reconstruction error between the completed formaldehyde concentration data and the observed formaldehyde concentration data to optimize the neural operator parameters; the analysis and completion module is used to perform analysis and completion on the observed formaldehyde concentration data using the parameter-optimized neural operator model to obtain the completed formaldehyde concentration data.

[0069] It should be noted that the full-time and space-time continuous atmospheric formaldehyde analysis and completion device provided in the above embodiment should be illustrated by the division of the above-mentioned functional modules when performing full-time and space-time continuous atmospheric formaldehyde analysis and completion. The above-mentioned function allocation can be completed by different functional modules as needed, that is, the internal structure of the terminal or server is divided into different functional modules to complete all or part of the functions described above. In addition, the full-time and space-time continuous atmospheric formaldehyde analysis and completion device provided in the above embodiment and the full-time and space-time continuous atmospheric formaldehyde analysis and completion construction method embodiment belong to the same concept. The specific implementation process is detailed in the full-time and space-time continuous atmospheric formaldehyde analysis and completion method embodiment, which will not be repeated here.

[0070] Based on the same inventive concept, an embodiment further provides a computing device, including a memory and one or more processors, wherein executable code is stored in the memory, and when the one or more processors execute the executable code, the method for analyzing and completing the atmospheric formaldehyde in the domestic satellite in full time and space is implemented, specifically including the following steps:

[0071] S1, obtain the formaldehyde concentration data, meteorological data, related substance emission data, and human geography data obtained by inversion of domestic satellite observations, and align these data to construct the earth's spatiotemporal information data;

[0072] S2, the formaldehyde concentration data in the earth's spatiotemporal information data is partially randomly masked and input into the neural operator model designed according to the data type, the formaldehyde concentration data is completed by the random mask through calculation by the neural operator model, and semi-supervised learning is performed based on the reconstruction error between the completed formaldehyde concentration data and the observed formaldehyde concentration data to optimize the neural operator parameters;

[0073] S3, using the parameter-optimized neural operator model to parse and complete the observed formaldehyde concentration data to obtain the completed formaldehyde concentration data.

[0074] The computing device provided in the embodiment, in addition to the processor and memory, also includes hardware required for other services such as internal bus, network interface, memory, etc. at the hardware level. The memory is a non-volatile memory, and the processor reads the corresponding computer program from the non-volatile memory into the memory and then runs it to implement the domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion method described in S1-S3 above. Of course, in addition to the software implementation method, the present invention does not exclude other implementation methods, such as logic devices or a combination of software and hardware, etc., that is to say, the execution subject of the following processing flow is not limited to each logic unit, but can also be hardware or logic devices.

[0075] Based on the same inventive concept, the embodiment further provides a computer-readable storage medium on which a program is stored. When the program is executed by a processor, the above-mentioned domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion method is implemented, which specifically includes the following steps:

[0076] S1, obtain the formaldehyde concentration data, meteorological data, related substance emission data, and human geography data obtained by inversion of domestic satellite observations, and align these data to construct the earth's spatiotemporal information data;

[0077] S2, the formaldehyde concentration data in the earth's spatiotemporal information data is partially randomly masked and input into the neural operator model designed according to the data type, the formaldehyde concentration data is completed by the random mask through calculation by the neural operator model, and semi-supervised learning is performed based on the reconstruction error between the completed formaldehyde concentration data and the observed formaldehyde concentration data to optimize the neural operator parameters;

[0078] S3, using the parameter-optimized neural operator model to parse and complete the observed formaldehyde concentration data to obtain the completed formaldehyde concentration data.

[0079] In the embodiment, computer-readable media include permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules or other data.

[0080] In short, in the global formaldehyde monitoring results of domestic satellites, due to the limitations of sensor layout and cloud occlusion problems, formaldehyde concentration data at certain locations may not be available. In order to solve this problem, the above-mentioned analytical completion scheme proposed in the present invention uses a neural operator model to analyze and model the existing satellite global formaldehyde data. The neural operator can capture the trend and change law of formaldehyde concentration at unknown locations based on the periodic characteristics of existing data. Then, a model is established in combination with the emission inventory and auxiliary data related to formaldehyde to fill in the missing data and obtain a global formaldehyde gap-free distribution result. The experimental results show that the method proposed in the present invention shows high performance in filling accuracy and stability, and can effectively provide complete data on global formaldehyde column concentrations. The application of the present invention is helpful for research and decision-making in fields such as environmental protection and health risk assessment.

[0081] The specific implementation methods described above provide a detailed description of the technical solutions and beneficial effects of the present invention. It should be understood that the above is only the most preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, supplements and equivalent substitutions made within the scope of the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion method, characterized in that: The following steps are involved: Obtain formaldehyde concentration data, meteorological data, related substance emission data, and human geography data obtained from domestic satellite observation inversion, and align these data to construct earth's spatiotemporal information data; The formaldehyde concentration data in the earth's spatiotemporal information data is partially randomly masked and input into the neural operator model designed according to the data type. The formaldehyde concentration data of the random mask is completed through calculation by the neural operator model, and semi-supervised learning is performed based on the reconstruction error between the completed formaldehyde concentration data and the observed formaldehyde concentration data to optimize the neural operator parameters. The parameter-optimized neural operator model is used to analyze and complete the observed formaldehyde concentration data to obtain the completed formaldehyde concentration data.

2. The method for analyzing and supplementing the domestic satellite atmospheric formaldehyde in full space and time according to claim 1, characterized in that: The relevant substance emission data include formaldehyde emission data and emission data of other relevant species that are interrelated with the chemical transformation process of formaldehyde in the atmosphere; The human geography data includes population, leaf area index, and topographic data.

3. The method for analyzing and supplementing the domestic satellite atmospheric formaldehyde in full space and time according to claim 1, characterized in that: The meteorological data include temperature, humidity, pressure, wind speed, and potential.

4. The method for analyzing and supplementing domestic satellite atmospheric formaldehyde in full space and time according to claim 1, characterized in that: The neural operator model includes an encoder, a neural operator layer, and a decoder connected in sequence, wherein the neural operator layer adopts a spherical Fourier neural operator, an orthogonal neural operator, a Transolver, or a general neural operator transformer.

5. The method for analyzing and supplementing domestic satellite atmospheric formaldehyde in full space and time according to claim 1, characterized in that: The reconstruction error uses mean square error, L2 norm or absolute error.

6. The method for analyzing and supplementing domestic satellite atmospheric formaldehyde in full space and time according to claim 1, characterized in that: When performing semi-supervised learning on the neural operator, the earth's spatiotemporal information data is coarsely sampled at a coarse resolution grid, and the neural operator model is learned using the coarsely sampled sample data. At the same time, some random mask areas are filled with constants, and the null value areas are skipped when calculating the reconstruction error.

7. The method for analyzing and supplementing domestic satellite atmospheric formaldehyde in full space and time according to claim 6, characterized in that: When performing semi-supervised learning on the neural operator, after learning the neural operator model using coarsely sampled sample data, fine sampling of fine-resolution grids is performed on the earth's spatiotemporal information data, and the neural operator is learned using the finely sampled sample data.

8. A domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion device, characterized in that: include: The data acquisition and processing module is used to obtain formaldehyde concentration data, meteorological data, related substance emission data, and human geography data obtained by inversion of domestic satellite observations, and to match and align these data to construct earth's spatiotemporal information data; A model building and training module, which is used to perform partial random masking on the formaldehyde concentration data in the earth's spatiotemporal information data and input it into a neural operator model designed according to the data type, complete the formaldehyde concentration data of the random mask through neural operator calculation, and perform semi-supervised learning based on the reconstruction error between the completed formaldehyde concentration data and the observed formaldehyde concentration data to optimize the neural operator parameters; The parsing and completion module is used to parse and complete the observed formaldehyde concentration data using a parameter-optimized neural operator model to obtain the completed formaldehyde concentration data.

9. A computing device comprising a memory and one or more processors, wherein the memory stores executable code, characterized in that: When the one or more processors execute the executable code, they are used to implement the domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: A program is stored thereon, and when the program is executed by the processor, the domestic satellite full-time and space continuous atmospheric formaldehyde analysis and completion method described in any one of claims 1-7 is implemented.

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