Method and device for identifying signature features of atmospheric particulate matter based on scanning electron microscopy technology

Abstract

A method and device for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology is disclosed. The method uses a computer-controlled scanning electron microscope to analyze samples from a set pollution source, obtain images, composition data, and particle size parameters of the particulate matter in the pollution source samples, and construct a single-particle dataset for the pollution source samples. A single-particle emission source prediction model is constructed and trained based on a residual network model and an extreme gradient boosting model. The trained single-particle emission source prediction model is used to obtain the probability value that the target single particle belongs to the set pollution source. The single-particle emission source prediction model is interpreted using the SHAP strategy, and the key features of the single particle used by the single-particle emission source prediction model in decision-making are used as the signature features of the single particle emitted by the set pollution source. The present invention can fully mine the single-particle information emitted by different pollution sources and identify the signature features of particulate matter emitted by the pollution source.

Description

Technical Field

[0001] The present invention relates to the technical field of research on characteristics of atmospheric particulate matter emitted by pollution sources, and in particular to a method and device for identifying identification characteristics of atmospheric particulate matter based on scanning electron microscopy technology. Background Art

[0002] Atmospheric particulate matter pollution has significant impacts on climate, ecosystems, and human health, and has garnered widespread attention in recent years. The main sources of atmospheric particulate matter include natural sources such as soil dust and anthropogenic sources such as biomass combustion and coal burning. To more accurately identify the sources of atmospheric particulate matter and quantify their contributions, it is crucial to study the characteristics of particulate matter emitted by these pollution sources.

[0003] Currently, most existing research uses offline chemical analysis methods, using filter membranes to collect particulate matter and analyze its carbon components (OC, EC), water-soluble ions (such as SO42-, NO3-, Cl-, F-), and metal elements (Na, Mg, Al, Ca, Fe, etc.). However, these methods analyze the entire sample, which may obscure the subtle characteristics of individual particles emitted by different pollution sources.

[0004] Studies on atmospheric particulate matter based on electron microscopy analysis have shown that particulate matter emitted from different pollution sources differs in single-particle morphology and chemical composition. However, there is currently no method for identifying the characteristics of particulate matter emitted from pollution sources based on single-particle morphology, particle size, and chemical composition.

[0005] Therefore, how to invent a method for identifying the characteristic features of particulate matter emitted by atmospheric pollution sources, fully explore the single particle information emitted by different pollution sources, and identify the characteristic features of particulate matter emitted by pollution sources has become an urgent problem to be solved. Summary of the Invention

[0006] To this end, the present invention provides a method and device for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology. This method can fully exploit the information of single particles emitted by different pollution sources, identifying the signature characteristics of particulate matter emitted by these sources. It can also analyze the role of different signatures in the pollution source identification process, enabling further in-depth analysis of the physical and chemical characteristics of particulate matter emitted by different air pollution sources and research on the source of atmospheric particulate matter.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology, comprising:

[0008] Analyze the collected samples from the designated pollution source using a computer-controlled scanning electron microscope to obtain images, composition data, and particle size parameters of the particles in the pollution source samples, and construct a single particle dataset for the pollution source samples;

[0009] The single particle data set is randomly divided into a first data set and a second data set according to a set ratio; the first data set is used for training a single particle emission source prediction model; and the second data set is used for verifying the single particle emission source prediction model;

[0010] Constructing a single-particle emission source prediction model based on the residual network model and the extreme gradient boosting model; iteratively training the single-particle emission source prediction model using the first data set; and verifying the prediction results of the single-particle emission source prediction model using the second data set to obtain the trained single-particle emission source prediction model;

[0011] Inputting the image, composition data, and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing to obtain a probability value of the target single particle belonging to the set pollution source;

[0012] The single particle emission source prediction model is interpreted through a SHAP value strategy, and the key features of the target single particle used by the single particle emission source prediction model in decision-making are used as identifying features of the target single particle emitted by the set pollution source.

[0013] As a preferred solution of the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology, in the process of analyzing the collected samples of the set pollution sources by applying a computer-controlled scanning electron microscope, the set pollution sources include: soil dust, road dust, construction dust, coal smoke dust, biomass combustion dust and steel smelting dust.

[0014] As a preferred solution for the method for identifying the identification features of atmospheric particulate matter based on scanning electron microscopy technology, in the process of inputting the image, composition data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing, and obtaining the probability value of the target single particle belonging to the set pollution source, the image features of the target single particle are extracted through the residual network model, and the source of the target single particle is preliminarily predicted based on the image features of the target single particle to obtain the preliminary probability value of the target single particle belonging to the set pollution source.

[0015] As a preferred solution of the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology, in the process of inputting the image, composition data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing, and obtaining the probability value of the target single particle belonging to the set pollution source, the preliminary probability value of the target single particle belonging to the set pollution source is merged with the particle size parameters and composition data of the target single particle, and input into the extreme gradient boosting model. The probability value of the target single particle belonging to the set pollution source is obtained through prediction by the extreme gradient boosting model.

[0016] As a preferred solution of the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology, in the process of interpreting the single particle emission source prediction model through the SHAP strategy, the expression of the SHAP value is:

[0017]

[0018] Where, φ j is the SHAP value; K is the set of all input variables; S is the subset without variable j. j >0, it means that the variable has a positive impact on the prediction; if φ j <0, it means that the variable has a negative impact on the prediction.

[0019] The present invention also provides an atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology, which adopts the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology described above, including:

[0020] A single particle data set acquisition module is used to analyze samples collected from a set pollution source using a computer-controlled scanning electron microscope to obtain images, composition data, and particle size parameters of the particles in the pollution source samples, and to construct a single particle data set for the pollution source samples;

[0021] A single particle data set division module is used to randomly divide the single particle data set into a first data set and a second data set according to a set ratio; the first data set is used for training the single particle emission source prediction model; the second data set is used for verifying the single particle emission source prediction model;

[0022] A single-particle emission source prediction model construction and training module is used to construct a single-particle emission source prediction model based on a residual network model and an extreme gradient boosting model; iteratively train the single-particle emission source prediction model using the first data set; and verify the prediction results of the single-particle emission source prediction model using the second data set to obtain the trained single-particle emission source prediction model;

[0023] A single particle pollution source probability acquisition module is used to input the image, composition data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source;

[0024] The single particle identification feature acquisition module is used to interpret the single particle emission source prediction model through the SHAP strategy, and use the key features of the target single particle used by the single particle emission source prediction model when making decisions as the identification features of the target single particle emitted by the set pollution source.

[0025] As a preferred solution for the atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology, in the single particle data set acquisition module, in the process of analyzing the collected samples of the set pollution source by applying a computer-controlled scanning electron microscope, the set pollution sources include: soil dust, road dust, construction dust, coal smoke dust, biomass combustion dust and steel smelting dust.

[0026] As a preferred solution for the atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology, in the probability acquisition module of the pollution source to which the single particle belongs, the image, composition data and particle size parameters of the target single particle are input into the trained single particle emission source prediction model for processing, and the probability value of the target single particle belonging to the set pollution source is obtained. In this process, the image features of the target single particle are extracted through the residual network model, and the source of the target single particle is preliminarily predicted based on the image features of the target single particle to obtain the preliminary probability value of the target single particle belonging to the set pollution source.

[0027] As a preferred solution of the atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology, in the probability acquisition module of the pollution source to which the single particle belongs, the image, composition data and particle size parameters of the target single particle are input into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source. In this process, the preliminary probability value of the target single particle belonging to the set pollution source is merged with the particle size parameters and composition data of the target single particle, and input into the extreme gradient boosting model. The probability value of the target single particle belonging to the set pollution source is obtained through prediction by the extreme gradient boosting model.

[0028] As a preferred solution of the atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology, in the single particle identification feature acquisition module, in the process of interpreting the single particle emission source prediction model through the SHAP strategy, the expression of the SHAP value is:

[0029]

[0030] Where, φ j is the SHAP value; K is the set of all input variables; S is the subset without variable j. j >0, it means that the variable has a positive impact on the prediction; if φ j <0, it means that the variable has a negative impact on the prediction.

[0031] The present invention has the following advantages: by using a computer-controlled scanning electron microscope to analyze samples collected from a set pollution source, images, component data and particle size parameters of particulate matter in the pollution source samples are obtained, and a single particle data set of the pollution source samples is constructed; the single particle data set is randomly divided into a first data set and a second data set according to a set ratio; the first data set is used to train a single particle emission source prediction model; the second data set is used to verify the single particle emission source prediction model; a single particle emission source prediction model is constructed based on a residual network model and an extreme gradient boosting model; the single particle emission source prediction model is iteratively trained using the first data set; the prediction results of the single particle emission source prediction model are verified using the second data set to obtain the trained single particle emission source prediction model; the image, component data and particle size parameters of the target single particle are input into the trained single particle emission source prediction model for processing to obtain a probability value that the target single particle belongs to the set pollution source; the single particle emission source prediction model is interpreted using a SHAP strategy, and the key features of the target single particle used by the single particle emission source prediction model in decision-making are used as identification features of the target single particle emitted by the set pollution source. The present invention can fully mine the single particle information emitted by different pollution sources and identify the identification characteristics of particulate matter emitted by the pollution sources; it can also analyze the role of different characteristics in the pollution source identification process, and can be used to further analyze the physical and chemical characteristics of particulate matter emitted by different atmospheric pollution sources and the source analysis of atmospheric particulate matter. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] To more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and those skilled in the art can, without inventive effort, derive other implementation drawings based on the provided drawings.

[0033] The structures, proportions, sizes, etc. illustrated in this specification are intended solely to complement the contents disclosed herein and to facilitate understanding and reading by persons skilled in the art. They are not intended to limit the conditions under which the present invention may be implemented and therefore have no substantive technical significance. Any structural modifications, changes in proportions, or adjustments in sizes, without affecting the efficacy and objectives of the present invention, shall remain within the scope of the technical contents disclosed herein.

[0034] Figure 1 A schematic flow chart of a method for identifying signature features of atmospheric particulate matter based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0035] Figure 2A schematic diagram of specific implementation steps of the method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0036] Figure 3 Schematic diagram of the SHAP analysis results of the ResNet model in the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology provided in Example 1 of the present invention (taking one typical particle from each source as an example);

[0037] Figure 4 A schematic diagram showing the ranking of the average absolute SHAP values ​​of the input variables of the XGBoost model in the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0038] Figure 5 A schematic diagram summarizing the SHAP values ​​of the top 20 important variables in biomass combustion dust in the method for identifying atmospheric particulate matter signatures based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0039] Figure 6 A schematic diagram summarizing the SHAP values ​​of the top 20 important variables in coal dust in the method for identifying the characteristics of atmospheric particulate matter based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0040] Figure 7 A schematic diagram summarizing the SHAP values ​​of the top 20 important variables in construction dust in the method for identifying atmospheric particulate matter signatures based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0041] Figure 8 A schematic diagram summarizing the SHAP values ​​of the top 20 important variables in road dust in the method for identifying atmospheric particulate matter signatures based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0042] Figure 9 A schematic diagram summarizing the SHAP values ​​of the top 20 important variables in soil dust in the method for identifying atmospheric particulate matter signatures based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0043] Figure 10 A schematic diagram summarizing the SHAP values ​​of the top 20 important variables in the steel smelting source in the method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology provided in Example 1 of the present invention;

[0044] Figure 11 Schematic diagram of the architecture of an atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0045] The following describes the implementation of the present invention using specific embodiments. Those skilled in the art will readily understand the other advantages and benefits of the present invention from the disclosure herein. It is apparent that the described embodiments are only a portion of the present invention, not all of it. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are intended to fall within the scope of protection of the present invention.

[0046] Example 1

[0047] See also Figure 1 and Figure 2 Embodiment 1 of the present invention provides a method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology, comprising:

[0048] S1. Analyze samples collected from a set pollution source using a computer-controlled scanning electron microscope to obtain images, composition data, and particle size parameters of particulate matter in the pollution source samples, and construct a single particle dataset for the pollution source samples;

[0049] S2. Randomly divide the single-particle data set into a first data set and a second data set according to a set ratio; the first data set is used for training a single-particle emission source prediction model; and the second data set is used for verifying the single-particle emission source prediction model;

[0050] S3. Construct a single-particle emission source prediction model based on the residual network model and the extreme gradient boosting model; iteratively train the single-particle emission source prediction model using the first data set; and verify the prediction results of the single-particle emission source prediction model using the second data set to obtain the trained single-particle emission source prediction model.

[0051] S4, inputting the image, composition data, and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing to obtain a probability value of the target single particle belonging to the set pollution source;

[0052] S5. The single particle emission source prediction model is interpreted using the SHAP strategy, and the key features of the target single particle used by the single particle emission source prediction model in decision-making are used as identifying features of the target single particle emitted by the set pollution source.

[0053] In this embodiment, in step S1, in the process of analyzing the collected samples of the set pollution sources by applying a computer-controlled scanning electron microscope, the set pollution sources include: soil dust, road dust, construction dust, coal smoke dust, biomass combustion dust and steel smelting dust.

[0054] Specifically, in this example, samples from six pollution sources were collected: soil dust, road dust, construction dust, coal smoke dust, biomass combustion dust, and steel smelting dust. The pollution source samples were analyzed using scanning electron microscopy-energy dispersive spectroscopy (SEM-EDS) to construct a single particle database of typical pollution sources. Computer-controlled scanning electron microscopy-energy dispersive spectroscopy (CCSEM-EDS) was used to analyze at least 10,000 particles from each source sample, with at least 2,000 particles analyzed from each sample.

[0055] In this embodiment, in step S1, it also includes obtaining images, composition data and particle size parameters of particulate matter in the pollution source sample;

[0056] Specifically, the data collected by CCSEM-EDS includes a microscopic image of the particles, particle size parameters (such as maximum diameter, minimum diameter, average diameter, perpendicular diameter, and equivalent circular diameter), and elemental data (including carbon (C), oxygen (O), sodium (Na), magnesium (Mg), aluminum (Al), silicon (Si), phosphorus (P), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), zinc (Zn), barium (Ba), vanadium (V), chromium (Cr), cobalt (Co), selenium (Se), tin (Sn), titanium (Ti), manganese (Mn), nickel (Ni), copper (Cu), and lead (Pb). The maximum, minimum, and average diameters refer to the maximum, minimum, and average diameters of the particles calculated from multiple rotational measurements. The perpendicular diameter refers to the diameter of the particle perpendicular to the maximum diameter. The equivalent circular diameter refers to the diameter of a circle with the same area as the particle.

[0057] In this embodiment, in step S2, the single particle data set is randomly divided into a first data set and a second data set according to a set ratio; the first data set is used for training the single particle emission source prediction model; and the second data set is used for verifying the single particle emission source prediction model;

[0058] Specifically, the single-particle data sets in the source samples obtained from the analysis are divided into two sets. 80% of the single-particle data from each type of pollution source are randomly selected as the first data set, which is mainly used for training the single-particle emission source prediction model; the remaining 20% ​​of the single-particle data from each type of pollution source are the second data set, which is used to verify the accuracy of the prediction results of the single-particle emission source prediction model.

[0059] In this embodiment, in step S3, a single particle emission source prediction model is constructed based on the residual network model and the extreme gradient boosting model; the single particle emission source prediction model is iteratively trained using the first data set; the prediction results of the single particle emission source prediction model are verified using the second data set to obtain the trained single particle emission source prediction model;

[0060] Specifically, the single particle data in the first data set are input into the single particle emission source prediction model in batches, the pollution source of the single particle is predicted by the single particle emission source prediction model, the predicted result is compared with the actual pollution source, and the single particle emission source prediction model is continuously optimized.

[0061] The trained single-particle emission source prediction model is used to predict the pollution source of the single particles in the second data set. The probability value obtained by the mass or quantity of the single particle that the single particle belongs to the set pollution source is weighted, classified and summed, and the mass or quantity proportion of the atmospheric particulate matter emitted by the set pollution source is obtained.

[0062] In this embodiment, in step S4, while inputting the image, composition data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing, and obtaining the probability value that the target single particle belongs to the set pollution source, the image features of the target single particle are extracted through the residual network model, and the source of the target single particle is preliminarily predicted based on the image features of the target single particle, thereby obtaining a preliminary probability value that the target single particle belongs to the set pollution source.

[0063] Specifically, data augmentation techniques were applied to enhance the diversity of the dataset. Random rotation, flipping, and scaling of single-particle micrographs were used to mitigate the impact of variability in particle position and size on the prediction results. At the same time, image brightness, contrast, saturation, and hue were randomly adjusted to mitigate the differences in micrographs of different samples caused by differences in CCSEM-EDS analysis conditions. A ResNet model based on transfer learning was used to extract image features and predict the source of particles based on these features. The initial weight parameters of the model were obtained by training on the large ImageNet dataset. During training, the Adam optimizer was used with a learning rate of 1e-2, a step size of 7, and a decay rate of 0.1. A negative log-likelihood loss function was used to optimize model performance. Finally, the probability of each particle coming from a different source was recorded.

[0064] In this embodiment, in step S4, the image, component data and particle size parameters of the target single particle are input into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source. In this process, the preliminary probability value of the target single particle belonging to the set pollution source is merged with the particle size parameters and component data of the target single particle, and the results are input into the extreme gradient boosting model. The probability value of the target single particle belonging to the set pollution source is obtained through prediction by the extreme gradient boosting model.

[0065] Specifically, the preliminary probability value of the single particle belonging to the designated pollution source, predicted by the residual network model, is combined with the particle size parameters and composition data of the single particle as the input to the Extreme Gradient Boosting (XGBoost) model. The particle source label is used as the dependent variable. The Extreme Gradient Boosting model is trained and outputs the probability of each particle belonging to different pollution sources calculated by the Extreme Gradient Boosting model. During the Extreme Gradient Boosting model optimization process, grid search is used to select the most appropriate hyperparameters, and 5-fold cross-validation is used to evaluate the performance of the XGBoost model.

[0066] In this embodiment, in step S5, the single particle emission source prediction model is interpreted using the SHAP strategy, and the key features of the target single particle used by the single particle emission source prediction model in decision-making are used as the identifying features of the target single particle emitted by the set pollution source.

[0067] Specifically, the interpretable machine learning strategy - SHAP strategy (SHapley AdditiveexPlanation Approach) is applied to explain the model. The SHAP strategy is based on the joint game theory to distribute the total gain to each participant. In short, the difference between using variables (such as j) for model prediction and not using j for prediction is attributed to the marginal contribution of variable j. Considering the interaction effect between variables, the difference is calculated for each possible variable subset combination for each sample. For each prediction sample x i , generate predicted values For K variables, the explanatory model g can be described as a linear function of feature attribution:

[0068]

[0069] Where, φ j (g,x i ) is the SHAP value, which means that the variable j has an input of x i The impact of model g on prediction; φ0(g,x) is the base value, which represents the expected value of the prediction when considering all possible combinations of input factors.

[0070] φ j For all possible combinations of variable subsets, φ j Weighted average of values:

[0071]

[0072] Where K is the set of all input variables; S is the subset without variable j. j >0, it means that the variable has a positive impact on the prediction; if φ j <0, it means that the variable has a negative impact on the prediction.

[0073] The SHAP value represents the importance and influence of each input feature in the dataset on the prediction of an individual instance. It is worth noting that the SHAP value of each input feature is not fixed, but varies according to the specific evaluation example. A positive SHAP value indicates that the input variable increases the output result, while a negative SHAP value indicates that the input variable decreases the output result. The absolute SHAP value (|SHAP|) indicates the importance of each factor in affecting the prediction value. Therefore, a higher positive SHAP value indicates that the input variable increases the output result. , while the lower negative SHAP value indicates that the It is of higher importance.

[0074] In this example, the single-particle emission source prediction model is constructed by coupling the ResNet and XGBoost models, and the SHAP method is used to interpret the two parts separately. The key features of the image, particle size, and chemical elements of particulate matter emitted from different pollution sources are respectively mined.

[0075] like Figure 3 The following are the SHAP analysis results of the ResNet model. Each of the six types of sources is illustrated by a typical particle, and the true source label is indicated in the first column. The importance of different pollution sources to the model prediction results gradually decreases from the second column to the seventh column. The SHAP value reflects the importance of the pixel to the model's prediction of the corresponding source (the label above the small red and blue shaded image). The higher the SHAP value of the pixel, the greater the contribution of the model to classifying the particle as that specific source. In this embodiment, the image identification features of the particle are well identified (especially Figure 1 The identification features are consistent with manual experience, such as the irregular shape of soil particles, the stacked spheres of coal particles, and the shiny rod-like structure of steelmaking particles.

[0076] like Figure 4 As shown in Figure 1, it is the average absolute SHAP value ranking of the XGBoost model input variables (from large to small). RD 、 CD 、 CC 、 BB and St They are the probability value variables of particulate matter corresponding to soil dust, road dust, construction dust, coal smoke, biomass combustion and steelmaking smelting obtained by the ResNet model based on microscopic images, and D max 、D min and D avg are the maximum, minimum and average diameters of the particles obtained by multiple rotation measurements, D perpis the vertical diameter (the particle diameter perpendicular to the maximum diameter), and Dcirc is the equivalent circle diameter (the diameter of a circle with the same area as the particle). In the process of single particle emission source prediction, the image features play the most important role in identification. Ca, Al, Si and Cl are the four most critical chemical elements, and D circ The particle size is the most critical parameter.

[0077] The summary diagrams of the SHAP values ​​of the top 20 important variables in the six sources of biomass combustion dust, coal smoke dust, construction dust, road dust, soil dust and steel smelting are as follows: Figure 5-10 These key variables are the identification characteristics (variables) of different pollution sources.

[0078] The present invention can understand the identification role of different features (variables). For example, the higher the proportion of some elements in the particles, the more likely the particles will be identified as a specific source class, such as Ca mainly helps to identify construction ash, elements such as Al and Ca are essential for identifying soil particles, and P, Si and K are essential for identifying biomass combustion sources. However, although some elements are abundant in certain sources, the increase in the content of single particles may reduce the possibility of the model predicting them as their respective sources. For example, Ca in road dust and coal smoke, and Al in construction dust, because they are more abundant in other pollution sources. On the contrary, elements with lower content in specific sources, such as S in soil particles, P in construction dust, K in coal combustion particles and Ca in biomass combustion particles, also play an important identification role. Their scarcity in these sources means that higher element content reduces the possibility of particles coming from these specific sources.

[0079] In summary, the present invention uses a computer-controlled scanning electron microscope to analyze samples collected from a set pollution source, obtain images, composition data and particle size parameters of particulate matter in the pollution source samples, and construct a single particle data set of the pollution source samples; randomly divide the single particle data set into a first data set and a second data set according to a set ratio; the first data set is used to train a single particle emission source prediction model; the second data set is used to verify the single particle emission source prediction model; a single particle emission source prediction model is constructed based on a residual network model and an extreme gradient boosting model; the single particle emission source prediction model is iteratively trained using the first data set; the prediction results of the single particle emission source prediction model are verified using the second data set to obtain the trained single particle emission source prediction model; the image, composition data and particle size parameters of the target single particle are input into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source; the single particle emission source prediction model is interpreted using the SHAP strategy, and the key features of the target single particle used by the single particle emission source prediction model in decision-making are used as the identifying features of the set pollution source emitting the target single particle. The present invention can fully mine the single particle information emitted by different pollution sources and identify the identification characteristics of particulate matter emitted by the pollution sources; it can also analyze the role of different characteristics in the pollution source identification process, and can be used to further analyze the physical and chemical characteristics of particulate matter emitted by different atmospheric pollution sources and the source analysis of atmospheric particulate matter.

[0080] It should be noted that the method of the embodiments of the present disclosure can be performed by a single device, such as a computer or server. The method of the embodiments of the present disclosure can also be applied in a distributed scenario, where multiple devices cooperate to perform the method. In such a distributed scenario, one of the multiple devices may only perform one or more steps of the method of the embodiments of the present disclosure, and the multiple devices will interact with each other to complete the method.

[0081] It should be noted that the above description is limited to some embodiments of the present disclosure. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be performed in an order different from that described in the above embodiments and still achieve the desired results. Furthermore, the processes depicted in the accompanying drawings do not necessarily require the specific order or sequential order shown to achieve the desired results. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0082] Example 2

[0083] See also Figure 11 Embodiment 2 of the present invention provides an atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology, comprising:

[0084] Single particle data set acquisition module 001 is used to analyze the collected samples of the set pollution source by using a computer-controlled scanning electron microscope to obtain the image, composition data and particle size parameters of the particles in the pollution source sample, and construct a single particle data set of the pollution source sample;

[0085] The single particle data set division module 002 is used to randomly divide the single particle data set into a first data set and a second data set according to a set ratio; the first data set is used for training the single particle emission source prediction model; the second data set is used for verifying the single particle emission source prediction model;

[0086] The single particle emission source prediction model construction and training module 003 is used to construct a single particle emission source prediction model based on the residual network model and the extreme gradient boosting model; iteratively train the single particle emission source prediction model using the first data set; and verify the prediction results of the single particle emission source prediction model using the second data set to obtain the trained single particle emission source prediction model;

[0087] The single particle pollution source probability acquisition module 004 is used to input the image, composition data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source;

[0088] The single particle identification feature acquisition module 005 is used to interpret the single particle emission source prediction model through the SHAP value strategy, and use the key features of the target single particle used by the single particle emission source prediction model when making decisions as the identification features of the target single particle emitted by the set pollution source.

[0089] In this embodiment, in the single particle data set acquisition module 001, in the process of analyzing the collected samples of the set pollution sources by applying a computer-controlled scanning electron microscope, the set pollution sources include: soil dust, road dust, construction dust, coal smoke dust, biomass combustion dust and steel smelting dust.

[0090] In this embodiment, in the probability acquisition module 004 of the pollution source to which the single particle belongs, the image, composition data and particle size parameters of the target single particle are input into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source. The image features of the target single particle are extracted through the residual network model, and the source of the target single particle is preliminarily predicted based on the image features of the target single particle to obtain the preliminary probability value of the target single particle belonging to the set pollution source.

[0091] In this embodiment, in the probability acquisition module 004 of the pollution source to which the single particle belongs, the image, component data and particle size parameters of the target single particle are input into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source. In this process, the preliminary probability value of the target single particle belonging to the set pollution source is merged with the particle size parameters and component data of the target single particle, and input into the extreme gradient boosting model. The probability value of the target single particle belonging to the set pollution source is obtained through prediction by the extreme gradient boosting model.

[0092] In this embodiment, in the single particle identification feature acquisition module 005, in the process of interpreting the single particle emission source prediction model using the SHAP value strategy, the expression of the SHAP value is:

[0093]

[0094] Where, φ j is the SHAP value; K is the set of all input variables; S is the subset without variable j. j >0, it means that the variable has a positive impact on the prediction; if φ j <0, it means that the variable has a negative impact on the prediction.

[0095] It should be noted that the information interaction, execution process, etc. between the modules / units of the above-mentioned system are based on the same concept as the method embodiment in Example 1 of the present application, and the technical effects they bring are the same as those of the method embodiment of the present application. For specific contents, please refer to the description in the method embodiment shown above in the present application, and no further details will be given here.

[0096] Example 3

[0097] Embodiment 3 of the present invention provides a non-transitory computer-readable storage medium, in which the program code of the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology is stored. The program code includes instructions for executing embodiment 1 or any possible implementation method of the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology.

[0098] Computer-readable storage media can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more available media. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0099] Example 4

[0100] Embodiment 4 of the present invention provides an electronic device, including: a memory and a processor;

[0101] The processor and the memory communicate with each other through a bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology of Example 1 or any possible implementation thereof.

[0102] Specifically, the processor can be implemented by hardware or by software. When implemented by hardware, the processor can be a logic circuit, an integrated circuit, etc.; when implemented by software, the processor can be a general-purpose processor, which is implemented by reading software code stored in a memory. The memory can be integrated into the processor or located outside the processor and exist independently.

[0103] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from a website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode.

[0104] Obviously, those skilled in the art will appreciate that the various modules or steps of the present invention described above can be implemented using a general-purpose computing device, centralized on a single computing device, or distributed across a network of multiple computing devices. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. In some cases, the steps shown or described can be performed in a different order than that shown, or can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.

[0105] Although the present invention has been described in detail above using general descriptions and specific embodiments, it will be apparent to those skilled in the art that modifications and improvements may be made thereto. Therefore, such modifications and improvements, without departing from the spirit of the present invention, are intended to be within the scope of protection claimed herein.

Claims

1. A method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology, characterized in that: include: Analyze the collected samples from the designated pollution source using a computer-controlled scanning electron microscope to obtain images, composition data, and particle size parameters of the particles in the pollution source samples, and construct a single particle dataset for the pollution source samples; The single particle data set is randomly divided into a first data set and a second data set according to a set ratio; the first data set is used for training a single particle emission source prediction model; and the second data set is used for verifying the single particle emission source prediction model; Constructing a single-particle emission source prediction model based on the residual network model and the extreme gradient boosting model; iteratively training the single-particle emission source prediction model using the first data set; and verifying the prediction results of the single-particle emission source prediction model using the second data set to obtain the trained single-particle emission source prediction model; Inputting the image, composition data, and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing to obtain a probability value of the target single particle belonging to the set pollution source; The single particle emission source prediction model is interpreted through the SHAP strategy, and the key features of the target single particle used by the single particle emission source prediction model in decision-making are used as the identifying features of the target single particle emitted by the set pollution source.

2. The method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology according to claim 1 is characterized in that: In the process of analyzing the collected samples of the set pollution sources by applying a computer-controlled scanning electron microscope, the set pollution sources include: soil dust, road dust, construction dust, coal smoke dust, biomass combustion dust and steel smelting dust.

3. The method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology according to claim 2 is characterized in that: In the process of inputting the image, composition data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source, the image features of the target single particle are extracted through the residual network model, and the source of the target single particle is preliminarily predicted based on the image features of the target single particle to obtain the preliminary probability value of the target single particle belonging to the set pollution source.

4. The method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology according to claim 3 is characterized in that: In the process of inputting the image, composition data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source, the preliminary probability value of the target single particle belonging to the set pollution source is merged with the particle size parameters and composition data of the target single particle, and inputted into the extreme gradient boosting model. The probability value of the target single particle belonging to the set pollution source is obtained through prediction by the extreme gradient boosting model.

5. The method for identifying the signature characteristics of atmospheric particulate matter based on scanning electron microscopy technology according to claim 4 is characterized in that: In the process of interpreting the single particle emission source prediction model using the SHAP strategy, the expression of the SHAP value is: Where, φ j is the SHAP value; K is the set of all input variables; S is the subset without variable j; if φ j >0, it means that the variable has a positive impact on the prediction; if φ j <0, it means that the variable has a negative impact on the prediction.

6. An atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology, which adopts the atmospheric particulate matter identification feature recognition method based on scanning electron microscopy technology described in claims 1-5 above, characterized in that: include: A single particle data set acquisition module is used to analyze samples collected from a set pollution source using a computer-controlled scanning electron microscope to obtain images, composition data, and particle size parameters of the particles in the pollution source samples, and to construct a single particle data set for the pollution source samples; A single particle data set division module is used to randomly divide the single particle data set into a first data set and a second data set according to a set ratio; the first data set is used for training the single particle emission source prediction model; the second data set is used for verifying the single particle emission source prediction model; A single-particle emission source prediction model construction and training module is used to construct a single-particle emission source prediction model based on a residual network model and an extreme gradient boosting model; iteratively train the single-particle emission source prediction model using the first data set; and verify the prediction results of the single-particle emission source prediction model using the second data set to obtain the trained single-particle emission source prediction model; A single particle pollution source probability acquisition module is used to input the image, composition data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source; The single particle identification feature acquisition module is used to interpret the single particle emission source prediction model through the SHAP value strategy, and use the key features of the target single particle used by the single particle emission source prediction model in decision-making as the identification features of the target single particle emitted by the set pollution source.

7. The atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology according to claim 6 is characterized in that: In the single particle data set acquisition module, in the process of analyzing the collected samples of the set pollution sources by applying a computer-controlled scanning electron microscope, the set pollution sources include: soil dust, road dust, construction dust, coal smoke dust, biomass combustion dust and steel smelting dust.

8. The atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology according to claim 7 is characterized in that: In the single particle pollution source probability acquisition module, the image, composition data, and particle size parameters of the target single particle are input into the trained single particle emission source prediction model for processing to obtain the probability value of the target single particle belonging to the set pollution source. The residual network model is used to extract the image features of the target single particle, and the source of the target single particle is preliminarily predicted based on the image features of the target single particle to obtain a preliminary probability value of the target single particle belonging to the set pollution source.

9. The atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology according to claim 8, characterized in that: In the single particle pollution source probability acquisition module, while inputting the image, component data and particle size parameters of the target single particle into the trained single particle emission source prediction model for processing, and obtaining the probability value of the target single particle belonging to the set pollution source, the preliminary probability value of the target single particle belonging to the set pollution source is merged with the particle size parameters and component data of the target single particle, and inputted into the extreme gradient boosting model. The probability value of the target single particle belonging to the set pollution source is obtained through prediction by the extreme gradient boosting model.

10. The atmospheric particulate matter identification feature recognition device based on scanning electron microscopy technology according to claim 9, characterized in that: In the single particle identification feature acquisition module, in the process of interpreting the single particle emission source prediction model using the SHAP strategy, the expression of the SHAP value is: Where, φ j is the SHAP value; K is the set of all input variables; S is the subset without variable j; If φ j >0, it means that the variable has a positive impact on the prediction; if φ j <0, it means that the variable has a negative impact on the prediction.

Images